In comparing the moats and share price, the market mostly didn’t discriminate. Companies with multiple strong moats were downgraded alongside companies with limited moats.

The share price of most tech companies took a massive hit in February 2026 (the start of the SaaSpocalypse). But, as the first article in this series observed, the sell-off was mostly down to sentiment rather than recent performance.

The second article in this series looked at the durability of ASX tech companies through the lens of the 8 AI Moats Framework. Each company was scored for how strong its moats were against the moats of data, workflow, regulatory, distribution, ecosystem, network, physical, and scale.

This article brings the two data sets together to provide another lens to try to make sense of this massive revaluation of technology companies.

Plotting Moats and Share Price Change

The chart below plots every ASX-listed tech company on two axes:

Figure 1: 187 ASX-listed tech companies. X-axis = number of moats scored 2+ (from the Article 2 scorecard). Y-axis = three-month price change through the February 2026 selloff. Bubble size proportional to market cap.

The chart also divides the companies into four quadrants:

• Top-left: Valuation Up, Limited Moats

• Top-right: Valuation Up, Multiple Moats

• Bottom-right: Valuation Down, Multiple Moats

• Bottom-left: Valuation Down, Limited Moats

Let’s zoom in on each.

Top-left: Valuation Up, Limited Moats

In the top-left quadrant are companies where the valuation bucked the trend and increased over the SaaSpocalypse even though these companies have limited moats.

A few are probably genuine exceptions that the AI Moats Framework underrates. You don’t necessarily need multiple moats to have a strong, durable company in the future. Having one moat that you’re strong in can carry you a long way and allow you to build more.

The cohort is also heavy on micro-caps which can be more driven by company-specific news rather than a broad AI thesis.

Special Mention: There are two companies that shot up in price so much they had to be clipped from the chart: EIQ at +228%, BCC at +129%.

Top-right: Valuation Up, Multiple Moats

The top-right quadrant is the smallest. Only four companies sit in the window: Technology One, Codan, Infomedia, and RPMGlobal. NEXTDC (-0.7%, 6 moats at 2+) sits just below the line.

Bottom-right: Valuation Down, Multiple Moats

The bottom-right quadrant is the interesting one. 24 companies that were sold off despite carrying multiple strong moats. These companies are the large, household names of the ASX with a total market cap that is greater than every other quadrant combined.

Seeing all of these with multiple moats sold off, alongside companies with less, reinforces the view that SaaSpocalypse is a broad repricing of technology companies.

Some will turn out to have been mispriced. Over time we will learn which deserved to be repriced and which genuinely faced a threat to durability and future success. This does hint at opportunity being found in a more nuanced analysis per company.

Bottom-left: Valuation Down, Limited Moats

The bottom-left quadrant is the most intuitive and probably least surprising. 125 of the 187 companies had limited moats and were sold off.

This is most of the long tail of micro-cap ASX tech. These are generally smaller companies that are building their way to long-run durability and size.

Key Insights and Questions

Durability against AI didn’t matter in the SaaSpocalypse. Almost everything was repriced. This lines up with our findings in an earlier article that showed the disconnect between recent performance and share price movement.

Outside of that, this analysis raised more questions than it answered:

1. Are all moats created equal? Perhaps the number of moats is not the greatest predictor of future performance. Maybe some moats are going to be stronger than others up against AI. While I’m hesitant to draw too many conclusions, beyond SaaSpocalypse being a mass repricing, this does bring me to my next question:

2. What is it about the companies with limited moats but increased share prices? The companies with a limited number of moats and increased share prices over the SaaSpocalypse may have some interesting insights for us around market views on AI. Do they share a set of moats? What’s driving the price? With their smaller size it will likely be easier to pinpoint what is driving price changes and therefore how the market is thinking about AI. A topic for a later post in the series.

3. Is out-performance against AI to be found in smaller companies for tech investors? The chart would seem to suggest it’s an interesting line of inquiry.

Next in the Series

As you can tell, we still have more questions about the SaaSpocalypse to explore.

Now that we’ve broadly covered valuations and moats, the next few articles will look at more specific insights and relationships in the data.

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Notes:

• Drawing the line: I’ve used “four or more moats at 2+” as the moat-stacking threshold and “zero” as the price threshold. Different cutoffs would shuffle a few names — NEXTDC and 4DMedical are the most obvious — but the four-quadrant pattern is robust to the choice. The point is the relationship between the views, not the borders between the quadrants.

• Number of companies: Keen regular readers will note that in the last posts we had 201 companies. Only 187 were used here because 187 of the 201 scored companies have both three-month return data and moat scores. Excluded names were missing one of these, usually the return data due to being thinly traded or for other reasons.

I evaluated 201 ASX-listed tech companies for their durability in the face of AI.

I used a framework proposed by legendary venture capitalist, Gokul Rajaram, that describes 8 Moats technology companies need if they want to survive and thrive in the age of AI. I like to call it the “8 AI Moats” for short.

The small number of companies with strong moats and therefore durability have multiple moats stacked together. Unsurprisingly, these are mostly the companies with the biggest market caps.

Most companies fall at the other end of the spectrum, relying on a relatively weak workflow moat. The moat most at risk of being bulldozed by AI.

This second article in my series on AI and ASX Tech looks at the AI moats of listed companies. It lays out the 8 AI Moats Framework, shows how companies were scored, and explores some of the insights from scoring the 201 companies.

Whether you are an investor, founder or technology executive, it will hopefully help you compare against the rest of the market.

The contents of this article are:

1. The 8 AI Moats Framework

2. How Companies Were Scored: A Selected Sample

3. Frequency of Moats on the ASX

4. Six Durability Archetypes

5. The Moat Barbell Curve

6. Key Insights the 8 AI Moats Give for ASX Tech

The 8 AI Moats Framework

The 8 AI Moats Framework is the best framework I’ve come across for thinking about the durability of technology companies in the face of AI.

I came across the 8 AI Moats while listening to venture capitalist Gokul Rajaram. Gokul is on the front line of wrestling with AI through his venture investments in companies like Airtable, Vercel and Figma. On top of that, Gokul faced strategic challenges directly as a technology executive at Google, Facebook and Block. Gokul’s background, my own experience and some thoughtful consideration are why I’ve adopted this framework for this analysis and in my own businesses.

The 8 AI Moats

The 8 AI Moats, in no particular order, are:

• Data: Proprietary, hard-to-replicate data accumulated through normal operations. Not third-party data the company licences.

• Workflow: How embedded the product is in the customer’s day-to-day. Switching is painful or risky.

• Regulatory: Licences, clearances, accreditations or compliance regimes a competitor would need to replicate.

• Distribution: Owned or privileged paths to the customer that a competitor can’t easily duplicate.

• Ecosystem: Third-party developers, integrations, or partners that depend on the platform.

• Network: Each new user makes the product more valuable to other users.

• Physical: Hardware, infrastructure, or physical inventory required to deliver the product.

• Scale: Cost or feature advantage that compounds with size.

You can see that while it shares similarities with a comprehensive and rigorously researched strategic framework like the 7 Powers by Helmer, it isn’t a replacement for it. The 8 AI Moats is more specifically targeted at technology companies and thinking about the AI challenge (and opportunity).

Scoring and Methodology

To score the listed companies each AI Moat was converted to a scale of:

· 0 (not present),

· 1 (weak / emerging),

· 2 (moderate, meaningful but not dominant),

· 3 (strong / entrenched).

For each AI Moat we developed some guidance around what each score on the scale looked like to make sure we had some consistency.

Then I had Claude run through all the companies and score each of them. In reviewing the research, most scores were sensible, so I left them as is for consistency rather than reviewing and rescoring each company in detail. There are some specific moats that you or I might score slightly differently, but directionally, the AI-driven research produced a sensible result.

How Companies Were Scored: A Selected Sample

To show how the moats were scored, a table of a sample of recognisable companies is shown below. The selected companies are spread across B2B SaaS, marketplaces, HealthTech, consumer, hardware, and data centre infrastructure.

Figure 1: Moat fingerprints for twelve recognisable ASX tech companies. Source: Terem Capital moat scoring, April 2026.

A few things are worth drawing out.

WiseTech, Xero and Pro Medicus all land in the top tier with slightly different shapes. WTC is the most complete profile in the dataset, scoring 3 on every dimension except physical. XRO stacks data, workflow, ecosystem, network and scale but has no meaningful physical moat. PME combines data, workflow, regulatory and distribution at the top of the scale, with softer network and ecosystem profiles. Three of the most admired ASX tech businesses and three genuinely different moat architectures.

The marketplaces are data-heavy, not just network-heavy. SEEK and CAR both score 3 on the network moat, which tracks intuitively. Less intuitively, they also score 3 on data and on distribution. The decades of candidate behaviour data SEEK holds, and the vehicle pricing data CAR holds, are moat ingredients that outlast any single marketplace mechanic. The fingerprint makes that visible in a way a one-line “marketplace” label doesn’t.

NEXTDC is the inverse of a typical SaaS profile. No data moat at all, workflow only moderate, no regulatory lock-in. But 3s across distribution, ecosystem, network, physical and scale. The moat is the building, the fibre, the interconnect and the cross-connect ecosystem. This is a useful calibration of the framework: “no data moat” can sit alongside “genuinely hard to displace” without contradiction.

Codan is the only company that scores 2 or above on all eight dimensions. Not WiseTech. Not Xero. CDA is a hardware company that sells into defence, mining and security markets — businesses that combine proprietary data, certified hardware, regulated channels, a global distribution network, and a partner ecosystem. It is a useful reminder that the “full-stack moat” company is rare, and when it shows up it doesn’t always wear the usual SaaS uniform.

HUB24, PEXA and Computershare illustrate the workflow-plus pattern. Each has a workflow moat score at 3, plus reinforcing moats in regulatory, distribution or scale. Each one sits inside financial infrastructure (wealth platforms, property settlement, corporate registry) where workflows are strangled by compliance, and where replacing the incumbent costs years and regulatory approval. This is where workflow-lock-in is at its most AI-resistant.

Frequency of Moats on the ASX

The chart below, Figure 2, shows how often each moat appears. This tells us how common or rare moats are across the ASX’s tech companies.

Figure 2: Distribution of Terem moat scores (0–3) across 201 ASX-listed tech companies for each of the eight dimensions.

· Data and workflow are the most common meaningful moats. Both dimensions sit at 75 of 201 companies scoring 2 or above, or about 37%. Almost two thirds of the companies scored didn’t have these as meaningful moats. This is particularly interesting given how important proprietary data is viewed by the tech industry.

· Regulatory, network and ecosystem moats are concentrated. Only 34 companies score 2 or above on regulatory, and those 34 are heavily concentrated in health, government and financial services. Networked platforms are even rarer with 26 companies scoring 2+. Ecosystem moats are rarer still, at 27 companies. If you think of regulation, networks and ecosystems as the structural moats that would hold AI off a vertical, they exist in concentrated pockets rather than across the tech sector more broadly.

· Physical is the rarest moat on the ASX tech index. Just 11 of 201 companies (5.5%) score 2 or above, and five of those are clustered in data centres or regulated hardware (NXT, CDA, MAQ, MP1, DUG). The rest of the index is effectively pure software, which by definition gives AI a clean field to compete on.

The shape of this chart matters for the AI-era question. The two dimensions AI preserves most strongly, physical and regulatory, are also the two rarest. The dimensions most common in ASX tech, workflow and data, sit on opposite sides of the AI substitution line: workflow is under pressure, data is elevated.

Six Durability Archetypes

Having scored every company, a few clusters emerge from the 201 companies. I’ve called the clusters Durability Archetypes, each with a different defensibility logic in an AI world.

Table 2: Durability Archetypes across 201 ASX-listed tech companies.

1. Stacked Compounders (20 companies, median market cap $3.4bn). Five or more of the eight dimensions scored at 2 or above. This is where the reinforcing-moat pattern lives. The cohort includes the ASX tech flagships like Xero alongside mid-caps like Catapult. The median market cap here is more than 450 times the median of the moat-thin cohort. That gap is the clearest single signal in the dataset.

2. Regulated Specialists (17 companies, median market cap $171m). A regulatory moat at 2 or above, combined with a meaningful data or workflow moat. Clustered in health, financial services, and government. These are the companies where an AI model can’t skip the regulator, even if it could replicate the product.[C2]

3. Workflow SaaS (27 companies, median market cap $60m). The bread-and-butter of ASX software: meaningful workflow embedding plus proprietary data, but without the regulatory, physical or ecosystem layers that move a business into the Stacked Compounder group. This is the archetype most directly exposed to AI substitution pressure on workflows, offset by the data asset. AI could be an opportunity or a value destroyer for them.

4. Networked Platforms (11 companies, median market cap $72m). A network effects moat at 2 or above, without the full Stacked Compounder profile. These are primarily marketplaces and payment networks. These are the companies that might be most exposed to the marketplace-disintermediation thesis, depending on the network without the reinforcing data, regulatory or workflow moats of the larger platforms.

5. Physical Infrastructure (6 companies, median market cap $253m). A physical moat at 2 or above, but not enough other dimensions to land in the Stacked Compounders. These businesses own infrastructure or hardware that AI can’t replicate.[C3]

6. Limited Durability (120 companies, median market cap $6m). The long tail mostly has no moat scoring above 2, with a few exceptions, as well as a relatively weak data or workflow moat. Mostly micro-cap software and, surprisingly, health. These are the companies most exposed to AI substitution and least able to resist it.

The Moat Barbell Curve

The ASX tech sector looks more like a barbell rather than gentle continuum. Eighty companies, or 40% of the index, have zero dimensions scored at 2 or above. Twenty have five or more and the middle is thin.

You can see this in the chart below, Figure 3. The left panel is how many companies sit in each moat score bucket. The right panel is the median market cap of each moat score bucket.

Figure 3: Left: count of companies by number of moats scored 2 or above. Right: median market cap per bucket on a log scale.

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More moats comes with step changes in market cap rather than linear increases. The median market cap climbs gently from $7m at zero moats to $67m at two moats to $100m at three moats. Then it jumps an order of magnitude to $365m at four, and another order of magnitude to $3.5bn at six or more. The market rewards stacked moats.

It’s worth acknowledging that bigger companies have had more time to build moats, and scale itself buys some of the inputs that score as moats. It’s hard to untangle causation (did the moat come first or the did the size enable the moat?). But the stacking pattern is still useful because it tells us where the durable businesses sit, and it shows how few of them there might be.

Key Insights the 8 AI Moats Give for ASX Tech

Four observations fall out of the scoring:

1. ASX tech is a moat barbell. Twenty Stacked Compounders sit on top of a 120-company long tail with no meaningful moat. The companies with a small number of strong moats in the middle is sparsely populated. For investors this is an expectations-setting exercise. The tech sector seems to be a “small number of strong durability companies with a long tail facing existential challenges” rather than “two hundred tech companies with moderate moats.”

2. Workflow and data are the most common moats. They’re the two most common dimensions at 2+ (both at 75 companies), and they’re the two dimensions where you most often see a 3 (14 companies at data, 12 at workflow). Most of the other moats (i.e. regulatory, network, physical, ecosystem) might be structural features of the vertical the company is in or come about as the company has scaled over-time.

3. Physical is rare: the physical moat, the one most difficult for the latest wave of AI technology to challenge, is also the rarest.

4. AI could propel most companies or destroy them. If all you have is a workflow moat then you also have an opportunity with AI. You have customers you can ship more to, faster. You have customers you can improve workflows for immediately. “AI-native” competitors have catching up to do while you have the opportunity to win. But, if you don’t move and don’t move quickly it’s significantly more likely that you’ll lose customers (relative to 2 years ago).

5. How we build durability may have changed (or be completely the same). Xero didn’t start with a stack of moats. They started with a workflow that they’ve used to build moats in the other areas. This might be harder to do now as companies will need another moat to succeed in the face of AI. Then again, it’s always been the case that extra moats help so perhaps nothing has changed, we’re just being more sharply reminded of business fundamentals again.

Next in the Series

The next article takes the moat scorecard from this article and puts it alongside the market’s verdict from the February 2026 SaaSpocalypse. The early analysis I’ve done has some interesting insights into where investor sentiment aligns with and diverges from a considered view of durability.

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The “SaaSpocalypse”, as it is known, has wiped billions in shareholder value across the globe.

But what’s really going on here?

I analysed the financials, share price and AI durability of every ASX-listed tech company to find out.

This first article shows that one-year revenue growth explains just 0.1% of three-month price movements.

Companies growing revenue at 20% were hit harder than companies whose revenue was declining. The SaaSpocalypse was overwhelmingly a sentiment event, not a fundamentals event.

Recent Performance Didn’t Cause the Selloff

The most important finding in our dataset is this: the relationship between revenue growth and share price performance during the selloff is essentially zero. We regressed three-month price changes against one-year revenue growth for 180 companies with available data. The result: R² = 0.001. Revenue growth explains 0.1% of the variance in returns.

Key Finding: R² = 0.001. Revenue growth explains just 0.1% of the February 2026 tech selloff. The other 99.9% was sentiment.

Figure 1: Each dot is an ASX tech company. The near-flat regression line shows revenue growth had virtually no predictive power over the selloff.

This means the market was not responding to business deterioration. Companies growing revenue at 20% per year were punished just as severely as companies with declining revenue. The selloff was a wholesale repricing of AI existential risk across the sector.

The Growth Hump of Pain

The pattern becomes even more counterintuitive when we group companies by revenue growth rate. Companies growing 10-30% were hit hardest, averaging -26.5% over three months. Companies with declining revenue fared better (-14.3%). Even companies growing above 30% averaged -20.0%.

The worst place to be in the SaaSpocalypse was growing 10-30 percent. Not struggling. Growing.

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Figure 2: Average three-month returns by revenue growth bucket. The 10–30% growth cohort was punished most severely.

Table 1: SaaSpocalypse Impact by Revenue Growth Rate

Source: Koyfin, Terem Capital analysis. 180 ASX-listed tech companies.

The explanation is straightforward: companies growing 10-30% carry high valuation multiples (the market was paying for their growth) but sit squarely in the AI substitution zone. These are the mid-cap SaaS companies, like Megaport (-42.6%), Zip (-47.7%), ReadyTech (-54.4%), SiteMinder (-52.8%), where the market is asking: can an AI agent do what this software does?

Declining companies had already been de-rated. Hyper-growth companies (>100%) were either so early-stage that AI risk felt abstract, or in hardware and HealthTech subsectors where AI is a tailwind rather than a threat. It was the middle of the growth curve that absorbed maximum pain.

The Market’s AI Fear Rankings

To isolate pure AI sentiment from fundamental performance, we computed a sentiment residual for each company: the gap between its actual three-month return and the return predicted by its revenue growth. Aggregating these residuals by subsector reveals the market’s implicit ranking of AI vulnerability.

Figure 3: Average sentiment residual by subsector. Negative values indicate the subsector was sold off beyond what its revenue performance justified.

EdTech stands out as the subsector the market fears most from AI, averaging -25.6% beyond what revenue justified. AI tutoring and content generation directly threatens every EdTech business model. Cybersecurity (-14.5%) and Data Centres (-13.0%) follow. The Data Centre result is notable: despite 21.2% average revenue growth, the market applied a significant AI fear premium, perhaps questioning whether ASX-listed operators will capture the AI compute boom or be bypassed by hyperscalers.

On the resilient side, IoT/Hardware (+10.7%) and HealthTech (+9.1%) earned a resilience premium. The market is implicitly saying: companies with physical products or regulated medical data are harder for AI to displace.

Key Finding: The market’s implicit AI vulnerability ranking: EdTech > Cybersecurity > Data Centres > IT Services > Fintech (most feared) through to IoT/Hardware and HealthTech (most resilient).

Four Quadrants of AI Sentiment

Plotting three-month returns against three-year returns for 174 companies with both data points reveals four distinct cohorts:

Figure 4: 174 ASX tech companies mapped by three-year and three-month price performance. Bubble size proportional to market cap.

Q1: Selloff Victims (42 companies)

Long-term winners hit by the selloff. 26 of these 42 are also growing revenue above 10%. These are the purest AI sentiment victims. Names include Pro Medicus (-43.3% 3M, +31.9% revenue), Zip (-47.7% 3M, +23.5% revenue), Life360 (-38.6% 3M, +31.8% revenue), and Megaport (-42.6% 3M, +16.3% revenue).

Q2: Double Trouble (82 companies)

Down over both periods. The surprise inclusion is WiseTech (-41.4% 3M, -41.4% 3Y) at $9.0 billion. SEEK (-38.7%), Xero (-30.6%), and SiteMinder (-52.8%) also fall here.

Q3: AI-Proof Winners (16 companies)

Positive over both periods: Technology One (+1.3% 3M, +87.1% 3Y), Codan (+10.0% 3M, +488.6% 3Y), NEXTDC (-0.7% 3M, +15.9% 3Y), 4DMedical (+21.5% 3M, +955.6% 3Y). Disproportionately in IoT/Hardware, HealthTech, and deeply vertical B2B SaaS. Their average regulatory moat score (1.0) is notably higher than the sector average (0.6).

Q4: Turnaround Candidates (15 companies)

Down over three years but stable or recovering recently. The most notable is Appen ($267M), which rose 65% in three months after declining 43% over three years. The market may be reassessing its AI training data business.

AI Sentiment or Just Overpricing

Social media will tell you that the repricing was about how AI is going to eat software.

Sentiment can be hard to pin down. However, there could be something else at play.

Much of ASX SaaS was overvalued long before “AI.” Years of near-zero rates inflated multiples that revenue growth could never justify. AI gave the market a narrative that made the repricing feel rational rather than embarrassing. Investors get to save face with an “unforeseeable” change in the market. The selloff was real; the question is how much of it was AI-specific fear and how much was gravity finally reasserting itself on valuations that had drifted free of fundamentals.

The next article in this series tests that question. We scored every ASX tech company on eight dimensions of AI durability and correlated them with selloff performance. The results challenge conventional wisdom about what makes a tech company defensible.

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Methodology and Data Notes

All figures in AUD millions unless stated. Price changes reflect market data. EV/EBITDA and EV/Sales are last-twelve-months figures. The sentiment residual is calculated as actual three-month return minus the return predicted by a linear regression of three-month returns on one-year revenue growth (R² = 0.001, slope = 0.003, intercept = -0.179). Quadrant analysis uses thresholds of 0% for three-year returns and -10% for three-month returns.

Carma’s managed to build up a sizeable revenue base and seems set to grow it further this financial year. It’s burning cash and will need to keep going for a few more years to get to the scale it needs.

It’s clearly found product/market fit in Australia and an operational cadence. Carma’s also shown strong operational execution to do this over a relatively short time frame.

If Carma can replicate just some of the success of Carvana then there is a substantial business and plenty of upside. But, anything other than fairly rapid growth from here could be challenging.

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About Carma

Company was founded in 2021 by Lachlan MacGregor and Yosuke Hall to disrupt used car sales in Australia by “replicating the online, full-stack model that we see achieving success in the U.S. and globally”. In other words, to replicate the success of Carvana’s model, challenging the dominance of incumbent carsales.com.au.

The “full-stack model” is about owning the entire end-to-end journey for buying a used car online. Find the car you want, hit buy, have it delivered or picked up without any of the negotiation hassles, pick-up coordination issues or post-purchase worries.

Carma came out of stealth in 2021 with a massive seed round of A$28m from Tiger Global. Since then it has raised A$133m from investors like Regal Funds Management, General Catalyst and Five V.

Interestingly, A$30m of this was a Convertible Note in the 6 months before the IPO, rather than an equity investment.

Carma listed on the ASX in early November 2025 at $2.70 per share, raising $100m with a market capitalisation of $370m. The share price has since fallen to $0.90 (as of 25 March 2026).

A back-of-the-napkin estimate says the last priced round, the Series A, would have been at about a $190m-$375m valuation. So the listing price represented as high as a 48% premium on the Series A valuation but today’s market capitalisation of ~$123m is possibly below the valuation Series A investors invested at.

Business Model

In practice the “the full-stack business model”, driven by an online experience, is about how Carma handles every step of the journey in buying or selling a used car.

The “full-stack” line is also in comparison to the typical digital classifieds model where the website, like carsales.com.au in its most popular form, only takes care of the listing and puts responsibility of everything else on the buyer and seller.

Carma’s prospectus contains a great diagram that illustrates their business model well:

The implications of this model are worth considering, relative to the online classified model. The full-stack model is taking risks on:

1. Pricing of the car when purchasing: if they pay too much they lose money on the sale, if they pay too little then people won’t want to sell to them. Doing this at scale requires some strong systems and pricing models, particularly if a car needs reconditioning.

2. Carrying of stock: the beauty of the online classified model is that you have no stock. The full-stack model means holding inventory that you may or may not sell. You also need somewhere to keep it.

3. Reconditioning the car: fixing or touching up the car to increase the sale price or make it sale worthy isn’t necessarily difficult on its own but requires strong systems at scale. Not only that, it requires a good network of skilled suppliers who can perform the reconditioning work. Balancing outsourced, in-house employment of these skills at scale isn’t an easy task.

4. Delivery to the customer: the end delivery to a customer requires additional effort and coordination. Though this is a fairly solvable area, relative to the risks and additional costs listed above.

Each of these has been solved by others, particularly Carvana. Solving them opens up the potential of this opportunity. However, they represent important considerations as Carma grows, beyond those of an online marketplace.

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Market

Geographically Bound

The market for the car marketplaces has proven to be geographically bound. By looking at the various mature players globally, like carsales, cars.com and autotrader, they have all ended up with geographic boundaries defining their subsidiary marketplaces.

Here is a slide from Car Group on how they address different markets with different businesses:

If you look at Carma’s directly comparable business, Carvana, it has remained focused on the US. While the US is a bigger market, the infrastructure, market intelligence and networks that a Carma style business model needs to be successful point to Carma needing to be Australia focused in the short-to-medium term. This isn’t necessarily a bad thing, Carvana produces significantly more revenue in the US than its peers.

Carma is even a little more geographically bound, it is currently only operating in Sydney. But for our purposes here, we will consider the total market as Australia.

The Australian Market

In 2025, 1.21m new cars were sold in Australia (source: Federal Chamber of Automotive Industries) and 2.32m used cars were sold (source: Annual Automotive Industry Report). Passenger cars and SUVs make up the majority of sales, followed by utes.

Used Car Sales in 2025 from the Annual Automotive Industry Report

Sales are highly fragmented. No single dealer group holds more than ~6% and many individuals sell their cars via carsales.com.au.

Competition

Online Classifieds: Carsales.com.au

Carsales is the giant in the room, with a dominant market share of used cars listed and sold. There are others like Gumtree and Drive.com.au but they have a small share relative to Carsales.

Carsales market share is a little difficult to ascertain, but they do publish that they have “78% share of time against competitors”.

Carma uses carsales.com.au and other classifieds to advertise, arguing that Carsales is an advertising channel for them. But, Carsales is also trying to provide a more seamless purchase experience akin to what Carma uses.

Carsales is on a slightly different strategy to Carma. Carsales is focused on “making buying and selling vehicles a great, seamless digital experience”. However, the trouble with a pure digital experience is that it isn’t seamless. Users feel hassled and don’t get the car sold seamlessly. Yes, Carsales does provide for instant trades but they aren’t investing in the full-stack experience. For more on this, read my deep dive analysis of Carsales strategy.

Dealerships

Carma also competes directly with dealerships selling and buying new and used cars. Most sell new and used.

Dealers have advantages in that they are physically present in many locations already, they provide (or purport to provide) a higher level of service, and they may have exclusive arrangements with brands people desire.

Dealers also have disadvantages. Research conducted on consumers by Frost & Sullivan found buying from physical dealerships (both new and used) highlights that pressure to sign immediately (32%), long wait times (30%) and lack of product knowledge (28%) are among the most common complaints at dealerships.

Listed automotive groups Eagers, Autosports and Peter Warren provide some insights into the market because they need to publish information.

Here is a chart of the growth rate of each business by half year for total vehicles sold:

Full Stack, Global Peers

It’s tempting to consider Carma’s global peers like Carvana, AUTO1 and Constellation Automotive as potential competitors as they may want to enter the Australian market. However, given the way the digital car marketplaces have played out it is much more likely that they will look to acquire Carma, particularly given the additional on-the-ground infrastructure the Carma model requires.

Customer Value Proposition

The real job-to-be-done for customers is to buy or sell a car. When buying second hand, reliability and quality are cited as the most important purchase criteria (according to Frost & Sullivan). Carma attempts to deliver on this in a frictionless online buying experience that transitions into physical reality in a low stress way.

Product

Carma’s core product is the end-to-end used car transaction — both buying from consumers and selling to them — delivered through a seamless digital interface backed by physical operations.

On the sell side, customers receive an instant online offer for their car, with Carma handling pick-up logistics. On the buy side, customers browse an online inventory of inspected and reconditioned vehicles, with home delivery and a return window (akin to Carvana’s 7-day policy) reducing the typical anxiety of buying used. This return policy is a meaningful product differentiator — it shifts the trust burden from the buyer to Carma, which in turn places pressure on the quality of Carma’s reconditioning and inspection process.

The technology platform underpinning these experiences — pricing algorithms, inventory management, reconditioning workflows — is where Carma is investing meaningfully (including the $2.7m of capitalised software development noted in their financials). This platform is a key source of potential competitive advantage as it scales, but remains largely unproven at higher volumes.

Go-to-Market

Carma’s go-to-market is heavily centered around the physical locations of its facilities. These are currently around Sydney with plans to expand to Newcastle and Wollongong next. These facilities are needed to buy, verify, refurbish and sell the cars.

Their primary go-to-market channels are social media, sponsorships, partnerships, outdoor advertising and, interestingly, classifieds platforms (including carsales, drive.com.au and gumtree). There is this little line in the Prospectus section Key Risks for Carma, the business depends heavily on advertising and classifieds platforms to attract and retain customers.”

They also put little “carma” logos on some of the cars they sell.

Over time they will likely expand into less facility heavy modes of delivering their service like Carvana has, but for now this is how Carma is operating.

Finances

Carma is making substantial losses, with quite thin gross margins in pursuit of a large volume of sales. Revenue was flat over FY24/FY25 but is expected to jump over FY26 and the half-year results support this.

The key points of interest with their financials are:

1. Their cost of sale doesn’t appear to include the direct classified costs for a car sold.

2. Their occupancy expenses drop significantly in FY26 even though the number of locations they are occupying is growing. This is largely due to the lease being amortised as a long-term lease.

3. They appear to be capitalising a few operational parts of their business:

   1. $2.7m of software development

   2. $2.6m for “reconditioning” and upgrading their St Peters facility.

New facility costs

It will be interesting to understand the costs involved in setting up a new facility as they continue to report financials over time. This is a key ingredient in the costs to scale and grow.

Valuation

At $0.90 per share, Carma trades at a market capitalisation of approximately $123m. Against FY26 revenue of ~$127m, that implies a price-to-sales multiple of roughly 1x. But, the revenue multiple alone isn’t that useful in valuing the business.

Carma needs to grow revenue more than 4x from here to reach its ~$550m breakeven threshold based on current margins. If it can’t get there or takes longer then generating profits is going to prove challenging.

There are three scenarios that help us think about valuation:

1. Bull Case: 47% Growth Continues to Breakeven

2. Base Case: Moderate Growth (15% annually)

3. Bear Case: Flat Revenue, Restructure Required

It’s also useful to compare to Carvana, so we will include that as well.

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Bull Case: 47% Growth to Breakeven (~FY30)

This scenario models the Prospectus trajectory: 47% compound revenue growth from the FY26 base of $127m. At that rate, Carma crosses the ~$550m breakeven threshold in FY30, approximately four years away.

This will almost certainly require an additional cash injection beyond Carma’s current cash at bank.

By the end of FY30 (in 4 years) Carma would be at about $593m revenue with a 5% EBITDA margin. Carma would generate ~$30m EBITDA. At a 15x multiple (a reasonable premium for a profitable growth business), that implies an enterprise value of ~$450m which is roughly 3.5x the current market cap. The upside is real, but it is entirely contingent on sustained 47% revenue growth across four years and an additional capital raise at acceptable dilution.

Base Case: Modest Growth (15% Annually)

This scenario looks at how more modest growth might impact the outcomes of the business.

There are a variety of reasons why growth might be more modest. For instance, Carma has had a flat period in very recent history (FY24/FY25) or competition might become more intense.

At a more modest 15% growth, which is somewhat more generous than the flat FY24/FY25, it will take until approximately FY37, which is eleven years from today, to reach breakeven without any cost restructuring.

Even with cost restructuring, the gross margins make it challenging to achieve an EBITDA number meaningful enough to get to a $123m valuation.

Bear Case: Flat Revenue, Restructure Required

In this scenario, revenue stalls at $127m after FY26, like it did over FY24/FY25, and Carma is forced to restructure to survive.

At 7.3% gross margins on $127m, gross profit is approximately $9.3m. It is important to note here that a 10% EBITDA margin on $127m revenue ($12.7m) is mathematically impossible at 7.3% gross margins.

EBITDA can never exceed gross profit regardless of how aggressively costs are cut. The maximum EBITDA achievable in a flat-revenue scenario is bounded by the $9.3m gross profit ceiling.

If management cuts OPEX beyond $3-5m at these numbers, then this would likely mean a reduction in facilities which would hurt revenue.

In a restructured bear case producing ~$4–5m EBITDA from the current revenue, at a 10x EBITDA multiple, the implied enterprise value is ~$40–50m. This is not only difficult to achieve but below the current valuation.

Carvana Comparison

Carvana is the most instructive comparable. After near-bankruptcy in 2022, it restructured its debt and by FY2024 was generating approximately $1bn+ in EBITDA on ~$18bn revenue (an EBITDA margin of roughly 5–6%). Its market capitalisation now sits in the $45–55bn range, implying an EV/EBITDA multiple of approximately 40–50x, reflecting market leadership and a growth premium. On a price-to-sales basis, Carvana trades at roughly 2.5–3x revenue.

Applying Carvana’s 2.5x revenue multiple to Carma’s current $127m revenue would suggest an enterprise value of ~$318m which is 2.5x the current market cap. But this comparison is premature: Carvana earned its multiple by achieving profitability and demonstrating the model at scale. Carma is at least four years and a capital raise away from being in that position, and in a market roughly 140x smaller.

Key Insights

1. There is clearly demand for a service like Carma, overseas businesses like Carvana have proven it. The revenue growth for Carma is further validation of this product/market fit.

2. Growth in the company’s valuation relies on the substantial growth story coming true.

3. Carvana at scale makes for a good comparison and illustrates the potential of where Carma can go, including with add-ons like finance, there is still a ways to go, potentially dilutive funding rounds and competition to beat to get there.

This looks like the beginnings of a build-and-buy (mainly buy) rollup of older “AI” or AI-like companies on the ASX.

It’s an all-scrip deal valued at A$13m, but the A$31m investors put into Complexica is going to need a lot of growth out of the combined group to see returns.

I’ll give a brief background on the companies involved and share the terms of the deal itself. Then, I’ll look at how it’s going to improve the financial performance of the buyer and how the acquisition fits their strategy (or not).

Company Backgrounds

Prophecy is an ASX-listed company with two distinct offerings, one in IT/security log analysis and another in contact centre analytics. Prophecy has customers like QBE, Fidelity, Novartis and UPS.

Prophecy did A$22m in FY25 with EBITDA losses of $5m. This is essentially flat compared to FY24; the prior year's results are $22.6m in revenue and $4.6m in losses.

Prophecy is currently trading at A$0.26 with a market cap of A$19.18m (10 September 2025).

Complexica provides supply chain optimisation through software, algorithms and AI to customers like Metcash, CUB, Endevour Group and Asahi. They seem to have a niche in beverages, and they’ve been doing AI since before it was cool (they won an AI award in 2018).

Complexica did A$13m revenue with $2.3m EBITDA in FY25, up slightly from FY24, where revenue was $12.5m and EBITDA was $2.0m. Of note is that they’ve grown their recurring revenue to 58% of revenue for 2024.

Complexica was bootstrapped for 7 years then raised ~$31m across 2021-2023 from MA Financial, Microequities Asset Management, Flinders Ports, Perennial Partners, and Acorn Capital.

The Deal

The deal is A$12.35m in Total Consideration. This implies an EBITDA multiple of 5.3x and a revenue multiple of 0.95x. The Total Consideration is mostly shares issued upfront with a small option component.

Let’s look at each component.

The Upfront Equity is ~$11.778m in shares (45.3m shares at $0.26 per share).

The Options are ~$0.57m (2.2m zero exercise price options at $0.26 per share). The Options don’t appear to have any other terms attached to them. Keep in mind that with options, these will be worth more or less depending on the performance of the business.

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Strategic & Financial Impact

Strategy

The commercial strategy here seems to be for Complexica to add layers of decision-making intelligence to Prophecy’s data:

The challenge with this is that Complexica sells and services supply chain customers and problems. These are very different to contact centres and different again to IT teams.

At a corporate level, it seems that Complexica’s $31m worth of investors needed an IPO exit, the outcome their investment targetted. Prophecy needed something to pull their share price fortunes out of a persistent, gradual decline and looked to M&A.

More generally, there is a combination of factors that say M&A will become a more significant part of their strategy. The CEO is a former Constellation Software employee, the businesses have a history of executing M&A, and there is a focus on future M&A in their announcement of the merger.

Financials

The merged group will have a combined revenue of $35m and EBITDA of losses of $2.3m to start with, based on the FY25 financials of each business.

Management says they will take $1.7m of costs out of Prophecy. This would bring the losses down to $0.6m, which gets down to breakeven with the planned Complexica cost reductions. This is a little different to management’s “incremental, annualised run rate” EBITDA of $1.8m. It also doesn’t allow for transaction costs or redundancy costs that are likely being normalised out of these numbers.

Neither business appears to be growing substantially, but both have growing recurring revenue streams.

Final Thoughts

• AI build and buy: are we seeing the beginnings of an AI-focused rollup on the ASX? It’s an interesting strategy. There are plenty of compounding success stories on the ASX that have executed well on rollups. The slight difference is that the others went for a common customer, whereas this seems to be going for a common technology around AI. The Constellation Software link adds to the intrigue.

• Can’t make sense of the cross-sell strategy: Complexica is doing supply chain optimisation, while Prophecy’s solutions are in IT and call centres. I can’t see how decision software for chain supply optimisation at Asahi can easily be repurposed for the IT team at Services Australia. Compare this to Catapult’s acquisition of Perch where they are selling an add-on to customers who have already shown take-up of Perch. Maybe I’m missing something.

• Broad language loses me: On first read through, I was really struggling with the overly broad phrases like “AI pure play” and “leading provider of AI software”. But buried deep in the announcement on slide 18, and a look at the websites shows there is specialisation in supply chain optimisation. Being more specific and more specialised like this is a more compelling value proposition in what is a very competitive marketplace.

• Merged EBITDA discrepancies: the fact that the planned cost out matches the FY25 EBITDA of both businesses tells me that my merged EBITDA calculation of -$2.3m is probably somewhat accurate.

• Further cost out: given the businesses have slower growth, and given the maturity of the businesses, it might be sensible to produce higher profits by taking more costs out.

Catapult is an experienced acquirer with 5 acquisitions over the years. So it’s no surprise that the deal stacks up.

The Perch acquisition represents what looks like a no-brainer revenue expansion opportunity with a relatively insignificant dilution cost.

I’ll give a brief background on the companies involved and share the terms of the deal itself. Then, I’ll look at how it’s going to improve the financial performance of the buyer and how the acquisition fits their strategy (or not).

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Company Backgrounds

Catapult is a global leader in sports performance technology, providing a combination of hardware and software to professional and semi-professional teams across the world and across sporting codes. I’ve previously done a deep dive into Catapult here.

Catapult did US$116m in FY25, up 16.5% from $100m in FY24. EBITDA was $18m in FY25, up from $9m in FY24. The revenue is from subscriptions to their software and devices.

Perch provides strength training analytics software and devices to professional sports teams. Perch has a list of customers that includes St George (NRL), Miami Heat (NBA), and the Seattle Seahawks (NFL).

Perch was founded in 2016 and went through MIT’s delta v accelerator. Over 2021/2022, Perch raised just over US$8m from investors and grew the business to US$2.5m in Annual Recurring Revenue (ARR).

Catapult says Perch is doing slightly better than 31 on the Rule of 40. Given Perch’s headcount was 10+ people, this likely means revenue growth was somewhere between 31-40%.

The Deal

The deal is US$28m in Total Consideration. This implies a revenue multiple of 7.2 or 11.2 depending on whether you include the performance component or not. The Total Consideration is broken down into Upfront Cash, Upfront Equity, Tranched Shares and Performance-based Shares.

Let’s look at each component.

The Upfront Cash is US$3m.

The Upfront Equity is US$6m, with half locked up for 6-months and the other half locked up for 12 months.

Tranched Shares of US$9m will be issued to Perch shareholders in 2 tranches, with US$4.5m being issued after 6 months (in December 2025) and another US$4.5m tranche after 12 months in June 2026.

Performance-based Shares of up to US$10m in shares tied to achieving ACV (Annual Contract Value) growth objectives between June 2027 and May 2028.

Strategic & Financial Impact

Catapult’s revenue and profitability will barely be impacted, initially, by this transaction. $2.5m in revenue is only 2% of Catapult’s total revenue of $116m. Although it will make their revenue growth look better, contributing to ~17% of revenue growth in the coming year if Catapult continues its current organic growth rate.

The real opportunity is the expansion revenue opportunity. If Catapult can sell Perch to its existing customers, then there is a much higher revenue growth opportunity.

Selling Perch to existing Catapult customers is likely a straightforward proposition because of Perch’s “couple of hundred clients”, mostly in American football, almost all of whom are Catapult clients. It’s just a case of selling Perch to Catapult’s customers in other sports codes.

Final Thoughts

• Venture Investor Returns: Perch received US$8m in venture capital which often, but not always, carries terms that see the investors paid back first. So, there is a good chance that the $9m upfront in cash and equity goes straight to the investors or $8m to the investors with the left spread amongst the founders. Given the numbers so closely align, there is a good chance that the transaction was designed with this payback in mind to make it easier to get approval from Perch’s backers.

• Small acquisition price with high upside: At today’s US$1bn market cap for Catapult, Perch’s $28m mostly equity purchase price is less than 3%. This is a small price to pay for something that appears certain to have a strong impact on revenue growth.

Audinate’s acquisition of Iris Studio is interesting because:

1. AI Product Acquisition: this is an acquisition of an early-stage AI product with little to no revenue by a public company that is looking to use the startup’s technology to deliver better products and ultimately grow revenue.

2. Mostly upfront: 71% of the transaction is cash up-front (compared to last week’s acquisition deep dive that had almost nothing up-front).

I’ll give a brief background on the companies involved and share the terms of the deal itself. Then, I’ll look at how it’s going to improve the financial performance of the buyer and how the acquisition fits their strategy (or not).

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Company Backgrounds

Audinate replaces traditional audio and video connections with a computer network. There are over 4,000 products and 600 manufacturers using Audinate’s technology. They appear to be a leading global player in professional audio and video.

Audinate did $62m in revenue in FY25, down from $91m in FY24. The revenue is a combination of software (43%) and hardware (55%). They are sitting on $109m in cash. EBITDA went from a $20m profit in FY24 to essentially breaking even in FY25.

Iris Studio is a startup, still in beta, with 6 employees (inclusive of the 2 founders) according to LinkedIn and “about a dozen” according to an investor call. Iris provides software that automatically controls cameras for professional video situations like live events, broadcasts and recordings.

There isn’t anything public about Iris’s early investors, revenues or earnings. But there are cool pictures of their technology in action:

The Deal

The deal is US$28m Total Consideration. Iris is certainly loss-making and has minimal revenue (nothing is published, meaning it is immaterial) so this is likely in the realm of a 25-28x revenue multiple. The Total Consideration is broken down into Upfront Cash, Performance Payments and Vested Audinate Shares.

Let’s look at each component.

The Upfront Cash is US$20m.

Performance Payments of US$4m tied to achieving certain revenue goals and “service-based” objectives (most likely product milestones) over 3 years. The payment will be paid after the three years is up.

Vested Audinate Shares of US$4m in equal tranches at each anniversary, over 3 years. This means that $1.33m in Audinate shares are released every July for the next three years. These are tied to the “service-based” objectives for, it seems, individual people.

Financial Impact

The expected revenue from Iris isn’t clear and isn’t shared publicly. Given Iris’s website says it’s still in beta, it is understandable why they aren’t making revenue commitments. It’s hard to know what revenue will be from new launches accurately, and it likely isn’t material in FY26.

The acquisition reduces cash by ~A$28m to A$72.8m.

This acquisition comes with additional operational costs as it needs further investment. Audinate will “invest in go-to-market and product development” in FY26 (from their FY25 Investor Presentation). Iris related costs are bundled in with their overall forecast of an FY26 cost increase of 25%, which equates to an A$13m increase in costs.

Audinate is intending to generate positive cashflow from Iris in the next 3 to 4 years. That implies it’s a revenue, growth play more than an expectation of directly returning the capital invested.

Strategic Rationale

The acquisition is in what I call a Product Acquisition. Audinate isn’t buying a business. Audinate is buying a product, with product/market fit that they can sell and grow.

Iris is currently integrated with 14 camera brands like PTZOptics, Marshall and Telycam, across 1.5m devices. Iris has exclusivity agreements with some of those brands for as long as 5 years.

Iris’s initial use case, where they have found fit, is in the remote production of live events. Audinate probably sees it can expand to other use cases given the broader situations Audinate caters to.

Cost of Development Case

One way to justify a Product Acquisition like this, with immaterial revenue is to consider the alternative that Audinate has, which is to develop it themselves.

It took Iris ~2.5 years of 6-12 engineers to develop their product so the total development cost of somewhere in the range of US$4m to $7.5m. This might seem high but with strong AI/Machine Learning engineers in the team, it’s not unreasonable to have a salary bill of US$1.5m to $3m per year. Once you factor in some overheads of doing it in a larger organisation like Audinate then you are likely at a $5m-$10m price tag to develop the product.

Then you need to allow for risks like misreading the market, developing the wrong product, internal distractions, time-to-market and more.

In this context, if you can get the product you want by paying slightly more than your planned development costs then you are probably ahead.

Final Thoughts

• Development Cost Multiple: The implied development cost multiple, using my back of the napkin math, is somewhere between 3-7x. On the lower end, it is fairly reasonable. On the higher end, you could say there might be a premium being paid to get in market with a forward-looking AI-based product.

• New go-to-market channel: the riskiest part of this transaction and the area that I’d be thinking most about how to deal with is that Iris sells directly to end-customers, while Audinate sells to manufacturers. Launching a new product to customers you know well is risky enough. Launching a new product to a new set of customers adds extra risk to the equation. I would want to experiment around this a bit to see where we have the best chance of success and double down there (probably looking at our existing customers). This is easy to say at a distance, harder in practice.

• Nice pay day: There is a good chance the two founders just made $8m each and the employees are getting a nice pay day of a few hundred thousand each. Not bad for 2 years of work.

• Overall performance: Audinate broke even in FY25 and must have some confidence in revenue returning over FY26 given their desire to increase operational expenses. I’m not close enough to stock levels and movements to know how the chaos of tariffs while drive revenues back to where they were in 2024 and thus bring Audinate back to profitability or whether larger losses are ahead. That being said, they’re sitting on a pile of cash so they can go for it if they want.

I came across XPON’s acquisition of Alpha Digital which is fascinating on multiple fronts:

1. AI-enabled agency roll-up? AI-enabled roll-ups of services companies are all the rage in private equity and VC-land. With XPON being publicly listed we get first row tickets to one possibly* playing out in front of us. (I say possibly because I’m not 100% sure they are all-in on this)

2. Nothing Upfront: 93% of the transaction is deferred consideration in the form of a vendor loan and deferred payments. Even the upfront component is a deferred stock payment.

I’ll give a brief background on the companies involved and share the terms of the deal itself. Then, I’ll look at how it’s going to improve the financial performance of the buyer and how the acquisition fits their strategy (or not).

Company Backgrounds

XPON calls itself a “marketing technology company” that on closer look seems to specialise in customer data and virtual reality content.

XPON’s ~$8-10m in annual revenue is driven by licenses to their own software, reselling Google products, and related professional services. XPON is loss making.

Alpha Digital is a digital marketing agency that provides a fairly comprehensive digital marketing service offering from performance marketing (i.e. Google Ads), to social media, conversion rate optimisation and email marketing.

Alpha Digital generated $4.6m in revenue with $0.7m EBITDA. Most of the revenue (about $4m it seems) is Alpha Digital’s managed service offering that is said to be recurring. It’s unclear if this includes or excludes purchasing ads on behalf of clients. Given their EBITDA it’s unlikely it is included.

The Deal

$2.611m Total Consideration which gives an implied multiple of 3.73x EBITDA or 1.76x Revenue. The Total Consideration is broken down into an Upfront Share Issue, Deferred Payments and Performance Payments.

Let’s look at each component.

The Upfront Share Issue is $180k in XPON shares which are subject to a 12 month escrow (meaning the Alpha Founder can’t sell for 12 months).

Deferred Payments make up the bulk of the consideration of $1.54m paid over 36 months with 8% interest. These are done through a Vendor Loan (the Alpha founder is loaning XPON his shares in exchange for payments over time).

Performance Payments of $891k in equal performance payments of $445.5k over 2 years provided that the EBITDA targets are hit. The EBITDA targets are $800k in FY26 and $1.1m in FY27.

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Financial Impact

XPON's acquisition strategy reveals both opportunity and risk. On the surface, the deal makes sense - they're acquiring a profitable, cash-generating business with blue-chip clients like Target and Kmart that can immediately access XPON's AI-powered Wondaris customer data platform.

The strategic thesis centres on cross-selling: XPON can upsell its AI and data tools to Alpha's established client base, while Alpha can provide managed services to XPON's existing customers. This "land and expand" approach could significantly increase revenue per customer.

However, the strategy also exposes some underlying challenges:

The agency vs technology tension: Selling AI platform software requires different capabilities to selling managed advertising services. Alpha Digital's team are likely skilled practitioners, not technology salespeople. The value proposition, sales cycles, and customer conversations are fundamentally different.

Market positioning questions: XPON talks about "consolidating digital marketing agencies" but this suggests a pivot from their core AI platform business. This scattered focus might indicate there are issues with their software as standalone business and so they are instead pursuing easier, but less valuable services revenue through acquisitions.

Execution risk: The deal structure places significant performance pressure on Alpha Digital, requiring EBITDA growth from $700k to $1.1m within two years - a 57% increase. This ambitious target might strain the team or require investment that erodes margins.

The acquisition does represent XPON's first step in a stated M&A strategy targeting "tech-enabled digital marketing" businesses. If successful, this could become a repeatable playbook for rolling up agencies while cross-selling their AI platform. If it works and works well, it could be a services to AI transformation success story.

Final Thoughts

When you strip away the complexity, this deal needs to be evaluated on whether it accelerates XPON's path to profitability and provides sustainable competitive advantages.

The math on value creation:

• Alpha Digital's $700k EBITDA minus ~$123k interest costs leaves ~$577k net contribution

• If performance targets are hit, total payments could reach $891k over two years ($445k annually)

• This leaves Alpha contributing perhaps $250-300k net to group EBITDA annually

• As a percentage of XPON's current ~$8m revenue base, this moves the needle but isn't transformational

The strategic verdict: XPON appears to be hedging their bets. Rather than purely scaling their AI platform business, they're diversifying into profitable services that can fund their technology development. This pragmatic approach reduces risk but potentially dilutes focus.

The 3.73x EBITDA multiple is reasonable for a profitable digital agency with recurring revenue, especially given the deferred payment structure that reduces XPON's immediate cash outlay. However, the real test will be whether XPON can actually execute the cross-selling strategy and justify the performance earn-outs.

For XPON shareholders, this represents a sensible first step in building a more diversified, profitable business - provided they can resist the temptation to keep acquiring rather than integrating and optimising what they've bought.

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Carsales.com.au, now known as Car Group, started out in Australia and has grown this foundation into the dominant Australian marketplace for cars, boats, farm machinery, and more. Carsales is miles ahead of its nearest competitors.

Car Group’s success doesn’t stop there. The group has been able to take this model globally by acquiring leading digital vehicle sales marketplaces across multiple countries, including Australia, Korea, Chile, and the United States.

It’s a great Australian success story, but it might be about to face its biggest test yet in the form of a massive US disruptor called Carvana.

Let’s get into this deep dive on the business formerly known as Carsales.com.au.

What we’ll cover:

1. About Car Group

2. Business Model

3. Customer Value Proposition

4. Market

5. Competition

6. Go-to-Market

7. Finances

I’ll finish, like usual, with some key insights.

About Car Group (formerly Carsales.com.au)

Car Group (ASX: CAR) was founded as Carsales.com.au in 1997 by Greg Roebuck and Wal Pisciotta in Melbourne, Australia with what was at the time just a small idea: moving print classified advertisements for motor vehicles onto the internet.

Early Acquisitions and IPO

Prior to its IPO, in October 2005, Carsales began to build its acquisition muscle, acquiring ACP Magazines' online classified businesses, including Carpoint.com.au, Boatpoint.com.au, Bikepoint.com.au, Ihub.com.au, and statistics company Equipment Research Group. In exchange, ACP’s parent, which would go on to become Nine Entertainment, took a 41% stake in Carsales.

This transaction was followed by the acquisition of Red Book in 2007, a provider of new and used car specification and value information.

In 2009, Carsales listed on the Australian Securities Exchange. The company raised up to A$248.7 million ($205 million) in the nation's biggest initial public offering that year, with a market value of A$812 million.

In a rare misstep, Carsales made an acquisition of Quicksales, a general classified and auctions site, in 2010 that was shut down in 2018.

Nine Entertainment sold out of Carsales in 2011.

International Expansion through M&A

This didn’t stop them, however. A few years later, starting in March 2013 and occurring like clockwork every 2-3 years, Carsales made acquisitions for whole or partial stakes in international marketplaces.

From 2013 to 2023 Carsales purchased stakes in iCar Asia, Webmotors in Brazil, SK Encar in Korea, Soloautos of Mexico, Chileautos of Chile were purchased, and Trader Interactive in the United States. The remaining 51% stake was acquired in 2022. Some of those stakes were increased and some became complete acquisitions.

This transformed Carsales into Car Group, the owner of leading global vehicle marketplaces.

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Business Model

Car Group’s business model is fundamentally a marketplace. But Car Group has gone beyond taking a cut of the transaction to monetising the data and audience they have.

Car Group sells access to its audience and data to dealers, car manufacturers, insurers, and others.

The metrics that matter are:

1. Active listings

2. Page views and unique users

3. Leads delivered to dealers

Customer Value Proposition

Car Group’s value proposition is “to make buying and selling [vehicles] a great, seamless digital experience”.

This makes sense. A great, seamless digital experience is what will retain and grow their audience, which allows them to monetise.

For the most part Car Group’s properties give people the ability to easily list their car or boat, search, and browse. The dealer experience is of a high standard as well.

However, the focus on digital, rather than the complete experience, misses the necessary physical interactions. The hassles of messaging to negotiate a sale and the need to interact with strangers in person to get the car sold creates some friction.

You can argue this is somewhat countered by the ability Car Group provides to sell immediately but this seems somewhat more of a hassle than just going to a dealer.

Market

The market for Car Group is any transaction for a boat, car, farm machinery, campervan or other vehicle in the countries they operate in.

In Australia, beyond cars, they sell farm equipment, tyres, and construction vehicles. In the US they do RVs, motorcycles, and commercial trucks.

This doesn’t have to be a consumer selling directly, it can be a consumer listing through a broker or a dealer selling a car. It can also be businesses buying or selling vehicles or equipment.

Geographically Bound

The interesting structural characteristic, just judging by how Carsales has evolved, seems to be that digital vehicle sales are geographically bound.

That is, unlike Airbnb, which scaled the marketplace for hotels globally, Carsales (and some of its competitors we’ll look at later) has had enough time to demonstrate that vehicle sales seems to end up with winners in each country or region. In a way, the reasons for this are beside the point because this is just how it is.

So it’s through the lens of geography, mainly by country, that we need to think about Car Group’s market.

United States: A Different Entry to the Norm

Car Group has entered the United States, giving them access to one of the largest markets on the planet but they have entered differently to what has worked historically for them.

In every other market, Car Group has done well by acquiring the leading car marketplace. From there they’ve been able to use this dominant position to expand to other types of vehicles. But in the United States, Car Group has entered through Trader Interactive which doesn’t sell cars. It sells RVs, Trucks and other vehicles.

There is merit in getting into the US market, given how geographically bound this business seems to be. But, it appears to be somewhat of a compromise. This compromise was likely necessary because of the market cap (unaffordability) of the dominant US players.

It’s an interesting play to watch, to see if this variation on the model will work.

Australia: Is There Room for Continued, Meaningful Growth?

Given the 41% contribution Australia makes to Car Group’s revenue, the Australian market needs consideration.

Carsales is dominant with 78% ‘share of time against category competitors’, according to independent Ipsos research. This is ~8.6x more than the nearest competitors Drive and CarsGuide.

The key question in the Australian market is whether there is room for continued, meaningful growth. Australia will continue to play a key role in Car Group’s performance over the short to medium term.

A sensible starting point here would be to expect Car Group to track the vehicle sales market generally. So any future growth prospects are essentially derived from your views on the market for cars, boats and other vehicles in Australia.

Going deeper on this market is beyond the scope of this deep dive.

Global: Acquisitions a Must for More Market Share

It seems the only way to access more growth is to access more of the global market for vehicle transactions, which means acquiring leading marketplaces in other countries. It’s noticeable that they don’t have representation in Europe, Africa, India, or China.

Any further analysis of market share potential would need to look at the likelihood of acquiring dominant players in each of these regions.

Competition

Car Group is almost equal to its global peers, Cars.com and Auto Trader, in terms of revenue. It’s a strong position to keep acquiring market leaders in other countries and to continue growing profitably. Right?

Maybe not. Carvana has just switched on its profitability, and it can’t be ignored.

While Car Group and its peers have around $720m in revenue each, Carvana did $13.7bn around the same period.

Now, you could argue that Car Group is focused on “#1 digital marketplaces for vehicles around the world” (their words in their Annual Report) while Carvana is in the market for second-hand cars. But, if you look closer, both are going after “making buying and selling a car a great experience” (Car Group’s Purpose).

Having sold a car and a boat recently, there is a strong argument for the end-to-end experience that Carvana provides winning.

While Car Group is focused on “building a seamless digital experience” Carvana is focused on the entire experience of buying and selling a car.

Here is a slide from a recent Carvana investor report that shows the innovation Carvana is bringing to the entire experience with car vending machines and car deliveries.

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Go-to-Market

Audience

Car Group is reaching its audience through, for the most part, fairly typical channels for a digital marketplace.

• SEO: They have a strong SEO score, with plenty of backlinks and content, however they surprisingly didn’t rank as well as expected versus their competition for terms that people wanting to buy a car were likely to search for (e.g. “what to look for when buying a car” featured others above them including competitors, the same with “buy a second hand toyota”).

• Search Engine Advertising (SEM): They were one of the top links for many relevant paid search terms sampled.

• Socials: They have active and strong social accounts

• Other Media: They run ads on streaming services, do press, and pay for other media.

What was interesting to discover was the shift and investment the Car Group is making in Youtube and the growth it is seeing in video content. Ben Tyers, Head of Content at Carsales, says, "More car buyers are turning to video to research and compare, and our YouTube channel growth is reflective of that.”

Finally, given their dominant position, word of mouth and brand awareness likely plays a major role in their audience.

Advertisers & Dealers

For advertisers and dealers that pay to be on one of the website or advertise through it Car Group appears to have a fairly typical business development organisation.

Finances

You can see the general trend of Car Group with compounding growth year on year. Behind this is strong free cash flow.

What’s interesting about Car Group’s financials is that, except for 2023, Operating Income and Net Income were largely the same. The difference in 2023 is a an upwards revaluation of Car Groups interest in Trader Interactive (they refer to it as a “net gain on step acquisition of associates” in their financial statements).

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Key Insights

What can we learn from this

1. Marketplaces may not be so globally scalable: In theory, and often in pitch decks, a marketplace is globally scalable. But Car Group seems to show the opposite is the practical reality. They didn’t expand to Korea or South America by growing into these markets; they acquired. This says each of their marketplaces is geographically bound and they need to acquire to enter a new country. This isn’t necessarily an issue, but it’s an interesting insight in considering marketplaces more generally.

2. End-to-end customer experience: It’s one thing to be the market leader in one slice of the customer experience, but you need to be aware of how someone else might focus on the end-to-end customer experience and disrupt you. The battle between Carvana and the marketplaces will be interesting to watch.

3. Year on year compounding is a beautiful thing: Slowly but surely Car Group improved revenue and operating income each year. By small increases initially and then, eventually, the improvements unlocked significant growth and the ability to acquire.

That’s right, we’ve been tracking promising ASX tech companies for 5 years now.

We use our Mopoke Cloud Index as a framework to identify technology companies with high potential that are worth deep-diving into.

What companies are we looking for?

For the Index, we look at the 180+ technology companies listed on the ASX and then select a subset with the following characteristics:

1. ☁️ Software or cloud-based products: this may seem like a moot point, but much of what is defined as ‘technology’ on the ASX isn’t necessarily software, but can be things like hardware or technology services. We want to focus solely on software products with high gross margins and scalability.

2. 🖥 Clear Product Market Fit (PMF): the company has built a software product or service that sells - typically because it is cheaper, faster or better than existing solutions. The real test of PMF is unit economics which we’ll be digging into in another post, but PMF can be time-consuming to assess and few companies will tell investors that they don't have product market fit. To keep things simple, we define PMF as having greater than $10m of expected revenue in the next twelve months (NTM).

3. 💵 <$10bn market cap: the best investment opportunities are likely under-followed by brokers and institutions, and the law of large numbers is difficult to escape. We want to increase our chances of finding companies that could grow to be 10x bigger in the next few years.

4. 📈 Revenues growing >20% annually: technology investing is mainly focused on growth, so we want to find companies with momentum (normalizing for any COVID-related disruptions).

Why we do this?

Australia’s public technology companies provide a window into what’s happening in Australia and globally - many of our businesses play in global markets. It helps us see what’s working, where opportunities are and, hopefully, we might find a good investment.

Mopoke Cloud Index 2025

Insights

In putting together the index there are a few observations worth sharing:

• 15 less companies: In 2024 there were 46 companies that met our performance criteria, this year in 2025 there are only 31. That’s a loss of 15 companies. Some were acquired, like TASK, and some grew beyond our $10bn market cap, like Pro Medicus, but many didn’t meet the performance criteria.

• Less categories: the loss of companies meant it wasn’t worth breaking the companies into as many categories. We dropped Sales & Marketing and Games, both are industries that anecdotally seem to have been under pressure.

• Emerging categories: We introduced two categories: Physical Environment and Data Centre. With AI, Data Centers and related services are gaining more and more momentum. Physical Environment is an interesting one to emerge, the meeting point of software and hardware working together to digitally transform physical industries.

• Only 2 ‘pure’ vertical market SaaS - as an area we invest in, it was interesting to observe that only two pure-play Vertical Market SaaS companies, Siteminder and Gentrack made the criteria.

• Cost of living? Temple & Webster unexpectedly made the list given the general sense of cost of living pressure.

What next

Subscribe to get deep dive analysis on the companies in the index, insights around trends shaping the index, insights into tech companies and more.

With reports that the healthcare industry needs structural changes, we thought it would be enlightening to do a Sector Research post analysing healthcare companies listed on the ASX to identify emerging trends, market dynamics, and investment opportunities within the industry.

By examining financial metrics including market capitalization, growth rates, valuation multiples, and profitability indicators across different healthcare sub-sectors, we provide insights into where the Australian healthcare market is today and where it might be heading.

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Introduction

The Australian healthcare sector represents a meaningful component of the national economy (9.9%). With an aging population, increasing chronic disease burden, and technological advancements transforming healthcare delivery, understanding the financial performance and market positioning of healthcare companies provides valuable insights into broader industry trends.

This analysis examines data from over 100 ASX-listed healthcare companies spanning multiple sub-sectors including pharmaceuticals, medical devices, healthcare software, diagnostic services, and aged care.

The beauty of using data from public companies is we get a real view of what is happening on the ground.

Methodology

Our analysis utilizes financial data from ASX-listed healthcare companies, including:

• Market capitalization

• Revenue growth rates (1-year trailing twelve months)

• EBITDA growth and margins

• Valuation metrics (EV/Sales, P/E ratios)

• 5-year CAGR total returns

Companies were categorized by healthcare sub-sector to enable cross-segment comparison and identification of sector-specific trends.

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Key Findings

1. Market Capitalization Distribution

The Australian healthcare market shows significant concentration, with a few large players dominating the sector. The top five companies by market cap are:

1. CSL Limited (Plasma Therapeutics): $79.3 billion

2. ResMed Inc. (Sleep & Respiratory): $34.1 billion

3. Cochlear Limited (Hearing Implants): $21.0 billion

4. Sonic Healthcare Limited (Pathology Services): $16.7 billion

5. EBOS Group Limited (Healthcare Distribution): $8.1 billion

These five companies account for approximately 75% of the total market capitalization across the analyzed healthcare stocks, indicating high market concentration.

2. Sub-Sector Performance Analysis

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High-Growth Sub-Sectors

Several healthcare sub-sectors demonstrate strong revenue growth:

• Radiopharmaceuticals: Companies like Telix Pharmaceuticals (55.9% revenue growth) are showing exceptional performance, driven by increasing adoption of targeted radiation products for cancer diagnosis and treatment.

• Healthcare Software: Pro Medicus (29.9% revenue growth) exemplifies the strong performance in this sub-sector, reflecting increasing digitization of healthcare services.

• Aged Care: Providers like Regis Healthcare (24.8% revenue growth) show solid growth as Australia's aging population drives demand.

Profitability Leaders

Sub-sectors with the strongest EBITDA margins include:

• Healthcare Software: Pro Medicus demonstrates an impressive 72.6% EBITDA/Revenue ratio, highlighting the scalability and high margins of software business models in healthcare.

• Sleep & Respiratory: ResMed maintains strong profitability with 34.8% EBITDA margins, benefiting from recurring revenue streams through device sales and consumables.

• Hearing Implants: Cochlear shows solid profitability at 17.3% EBITDA margin, leveraging its market leadership position.

3. Valuation Insights

Valuation metrics reveal significant variation across healthcare sub-sectors:

• Healthcare Software commands high multiples with Pro Medicus trading at 101.1x EV/Sales and 190.1x forward P/E, reflecting investor optimism about high-margin, scalable business models with recurring revenue.

• Radiopharmaceuticals companies like Telix trade at 7.1x EV/Sales, indicating investor confidence in the growth potential of precision medicine.

• Traditional healthcare providers (hospitals, pathology, aged care) typically trade at lower multiples (1-3x EV/Sales), reflecting their more mature, capital-intensive business models.

4. Long-Term Returns Analysis

The 5-year CAGR total returns show divergent performance across companies:

• Top performers: Telix Pharmaceuticals (84.5%), Pro Medicus (68.8%), and Cryosite (65.4%) have delivered exceptional returns, indicating strong market validation of their business models.

• Underperformers: Several companies in the medical cannabis and early-stage biotech spaces show negative 5-year CAGRs, highlighting the risks in these segments.

5. Emerging Healthcare Trends

The financial data reveals several key trends shaping the Australian healthcare landscape:

a) Precision Medicine Acceleration

Companies focused on personalized and targeted therapies are showing strong growth and attracting premium valuations. Radiopharmaceuticals (Telix), genetic testing, and advanced diagnostics companies are gaining traction as healthcare moves toward more precise, patient-specific approaches.

b) Digital Health Transformation

Healthcare software and digital health companies like Pro Medicus demonstrate the market's recognition of technology's transformative potential in healthcare. Their high growth rates and premium valuations reflect expectations that technology will continue reshaping healthcare delivery models.

c) Consolidation in Traditional Healthcare

Established segments like pathology (Sonic Healthcare), imaging diagnostics, and hospital operators show lower growth rates but maintain significant market share. These sectors appear ripe for consolidation as scale becomes increasingly important for profitability.

d) Emerging Therapies Face Challenges

The data shows mixed results for companies developing novel therapeutics. While some demonstrate promising growth, many smaller biotechnology companies focused on areas like medical cannabis, psychedelics, and early-stage drug development show negative returns, highlighting the challenges of commercializing innovative therapies.

Limitations

Some of the limitations of this research are:

1. Concentration: concentration of market capitalisation in a small number of companies leads to a few insights that would ideally have information from more companies (e.g. there are software companies trading at lower multiples but Pro Medicus’s well-deserved dominance somewhat obscures this in this research)

2. Company stage: breaking out early-stage biotech companies may change the analysis but we didn’t segment the list by company stage.

Conclusion

The Australian healthcare sector presents a diverse landscape with varying growth, profitability, and valuation characteristics across sub-sectors. The financial data indicates a market that rewards companies demonstrating technological differentiation, scalable business models, and alignment with demographic trends like the aging population.

The most promising segments appear to be those at the intersection of healthcare and technology, particularly in precision medicine, diagnostics, and healthcare software. These companies show superior growth rates, stronger profitability, and command premium valuations from investors.

Meanwhile, traditional healthcare providers continue to provide essential services but face margin pressures and consolidation forces. The pharmaceutical and biotechnology spaces show significant variation in performance, with companies developing novel, targeted therapies outperforming more traditional approaches.

For investors and industry participants, these trends suggest focusing on companies that leverage technology to improve healthcare outcomes, increase efficiency, or enable precision medicine approaches. The Australian healthcare sector's future appears increasingly driven by innovation, digitization, and personalization, with financial markets already recognizing and rewarding companies aligned with these trends.

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As an elite amateur athlete*, it’s an analysis that combines my love of high-performance sports with my passion for technology companies.

* probably not true

Catapult has combined hardware, software and science to create a solution that most of us can wrap our heads around because we all understand sports (even if we aren’t big fans). Catapult helps teams and athletes improve their performance.

Summary

Catapult has built a global leader in sports performance, with a solution that generally works across most sports as well as specialist solutions for everything including soccer, basketball, and hockey. It can be used by professionals at the top of their game, as well as amateurs and school teams.

They’ve managed to grow revenue almost every year over the past 6 years, with a bit of a blip due to COVID (the fact that they managed to hold their revenue is impressive).

Now they are at a bit of a turning point where strong profitability and growth is insight thanks to a huge improvement in gross margin and their dominant industry position.

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Let’s dive in with the usual format:

1. About Catapult

2. Business Model

3. Market

4. Competition

5. Value Proposition & Product

6. Go-to-Market

7. Finances

8. Key Insights

About Catapult Sports

The company was founded in 2006 by Shaun Holthouse and Igor van de Griendt.

Catapult’s technology came out of work the founders were doing at a cooperative research centre (a collaboration between researchers, government and industry). They’ve also had additional collaborations with researchers, such as a positioning system developed with Australia’s CSIRO.

Holthouse led the company as CEO through it’s listing in 2014, through to 2017 when he handed over the reigns to Joe Powell. Powell ran Catapult from 2017 to February 2019, where the CFO also moved on. The current CEO, Will Lopes, joined later in November 2019 and has led the company since.

Catapult raised $10m from Disruptive Investment Group and Aura in the lead up to IPO. Then, the IPO in 2014 raised $12m. Another fundraise of note is the ~$100m raised to help fund acquisitions in 2016.

They’ve acquired 4 companies over the years:

• GPSSports was acquired in 2014 to consolidate.

• XOS Digital in 2016 to combine a coaching video system with Catapult’s customer data and gain more market access.

• PlayerTek was acquired in 2016 to go into tracking devices for amatuer athletes (consumer market).

Business Model

Catapult has used direct sales and M&A to build a strong base of sports and teams that see Catapult’s technology as the standard. M&A is/was needed because many sports settle on a technology as their standard, making it hard for competitors (even if better) to break in.

From there Catapult has successfully been able to continue to develop its products. In particular, focusing on speciality features for different sports. The success of this aspect of their business model is seen in their increasing annual contract value (ACV or revenue per customer).

Market

The market for Catapult is primarily professional and semi-professional sports teams and organisations. This segments in two ways, by sport and by geography.

Each sports code has differences that need to be taken into account in the product and marketing. Sports codes will also look to what others, usually the best in the code, are doing. Each geographical region will look to what others they know locally are doing and likely need some level of local support.

For this analysis, we will only cover a sample of the sports codes that Catapult pursues and the regions it operates. This will give a sense of the market size but not an exhaustive view.

By Sport

If we look at some of the most popular sports:

• Soccer is one of the most popular sports globally. According to Fifa there are 4,400 professional football teams around the world.

• Cricket: India has ~38 domestic teams, 10 Indian Premier League (IPL) teams. England has 18 professional county clubs. Australia has ~32 teams across the Sheffield Shield, BBL and WBBL.

• Hockey: 32 North American teams, many field hockey teams, and competitions in Europe.

• Basketball: The NBA has 30 men’s teams, the NCAA (American college basketball) has 68 teams, and Europe has 38+ men's teams. Smaller countries, like Australia have their own leagues with more teams (Australia has 9 teams for example).

Most countries also have professional teams in each of these sports. With ~195 countries, according to the United Nations, across just the sports above that is another ~780 teams for country teams.

So our initial, not overly comprehensive or exhaustive list is indicative of 10,000+ professional or semi-professional sports teams from these sports alone.

By Geographical Region

Another way to think about Catapult’s market is by geographical region. Each region tends to have preferred sports.

In North America, there are over 120 professional sports teams. College sports teams also take sports quite seriously in North America, where there are over 600 schools and over 600,000 student-athletes. Division I, the most serious, has 350+ schools and 190,000 student athletes.

In Europe, the major sport is soccer with 1130 clubs (each may have multiple teams) in 33 different countries. Outside of soccer, it is a little more fragmented by country. For example, in rugby France, Ireland and England have ~16, ~4, and ~14 respectively with many more semi-professional teams. There are many more smaller sports like this.

Looking at geographical regions, we see a market of professional teams of over ~2,000 in Europe and North America. With many more countries to consider, you can guestimate that the market is in the thousands if not low tens of thousands once you include semi-professional teams.

Competition

The competitive landscape for sports performance technology has a few dimensions to it. One dimension is the type of value proposition which generally fits into video analytics, wearable trackers and combined solutions. The other dimension is the sport focus of the company, with some focusing on a single sport and others as genuine multi-sport solutions.

Multi-Sport, Combined Solutions

Hudl and Catapult are out competing head to head in the multi-sport, combined solutions (analytics, trackers and more in one platform).

Hudl’s offering largely seems to match Catapult in terms of breadth. Catapult will win some teams or association, Hudl will win others.

Single Sport Specialists

They are up against some very specialised, but mostly much smaller video analytics solutions that tend to focus on a single sport. For example, Zone14 gives soccer teams a 360-degree camera to record matches and then runs analytics through the video, using image recognition technology to identify and track players rather than wearables.

There are also the specialised wearables, like playmaker and STATSports for soccer. These companies have wearables for specific sports or specific issues in sports.

General Solutions

Outside of the solutions focused on sports, you also need to consider the competitive impact of more general solutions. There are general video editing and playback tools like Adobe and VLC. There are general wearables that can track sports related metrics like Fitbit, Apple Watch and Garmin.

These general solutions can take market share, particularly in the semi-pro teams where money is more of an issue. There are different discussions online of people using VLC instead of Hudl, for example, due to price.

Special Mentions

Champion Data, needs a special mention because it is well known in Australian sports. It could be considered a competitor however, at present, they are strong partners of Catapult, with Catapult supplying Champion Data and the AFL with Catapult’s technology.

The other special mention is for Genius Sports. Genius has compelling size and strong video analytics technology but it appears to be going down a different strategic path with much of its focus on fan engagement and betting. Given we’re focused on Catapult here, I didn’t explore this strategy beyond considering what this means for how Genius’s video analytics competes with Catapult.

Value Proposition and Product

Catapult helps teams and athletes improve their performance.

To improve performance you need real data. But sports teams want to play the sport they’re good at, so Catapult removes the pain of collecting data and making it available for review.

Then this allows them to give sports teams what they really want, the ability to analyse, plan and improve performance. This is delivered primarily through two solutions, monitoring devices combined with video analysis software. This comes together as a sports performance solution.

Go-to-Market

Catapult’s go-to-market is largely a direct go-to-market motion with what seems to be a strong referral base.

There is some leverage gained through partnerships with associations and leagues, where Catapult will approach the league itself to roll the product out to the various teams within a league. An example of this is the partnership with the Indonesian soccer league to roll Catapult out across their teams and help improve the overall professionalism of the league.

There isn’t currently a “self-service” option. The Buy Now still connects you with a sales person.

Finances

The key points of interest in Catapult’s finances are:

1. Consistent Revenue Growth

2. Strong, Growing Gross Margin

Consistent Revenue Growth

If you exclude COVID, which impacted sports substantially, then Catapult has shown an ability to consistently post revenue growth.

Gross Margin

Gross Margin is strong and has grown every year. Strong, growing gross margin combined with growing revenue means strong profits are easy to make happen.

There is a small level of scepticism over this gross margin though because Catapult also reports “Contribution Margin” without too much explanation as to what the difference is between Contribution Margin and Gross Margin for their business. It could be that their gross margin is much lower at around 45% up from 33% in the prior financial year. If this is the case, the growth here still points to the ability for Catapult to product stronger profits in the future, especially with the revenue growth they have.

Key Insights

There are some key insights we can take away from Catapult:

1. M&A helps: M&A has helped Catapult establish beachheads in different sports in different regions as well as provide innovation to existing customers. There is even an example where it didn’t work (prosumer) but overall this strategy has helped propel them.

2. Direct sales: going direct and finding points of leverage, like partnerships with leagues and associations, works to drive revenue. But, it’s also come at a substantial cost with Catapult not turning a profit in a while.

3. Sport focus: choosing which sports to focus on seems like a key strategic question given competitive pressure from Hudl as well as specialist providers.

Go to Terem Capital's website

The ideal businesses we want to acquire at Terem Capital:

💹 Profitable Growth: $1m-$5m in net profit (or direct line of sight to) with sustainable revenue growth

💡 Independent: the business can run without the founders or new capital

💻 Technology-driven: your income can only be made with tech (like software, tech services, and tech-enabled services)

🏢 B2B: your customers are businesses, government and other organisations

We like founder-owned businesses, venture-backed businesses and spinouts. We're also comfortable with special situations.

If you have a business that you think we might like, then please get in touch.

EnergyOne has managed to grow revenue every year for almost a decade. They’ve grown profits every year, except this year when they posted a reduced profit to invest in the globalisation of the business.

But hey, if you’re profitable and coming from this history, you’ve earned the right to invest like this.

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Summary

EnergyOne provides software solutions for energy trading. This could be for an energy retailer but it could also be for one of the many organisations involved in the energy supply chain, like energy generators, pipelines, and financial traders.

EnergyOne has built itself on organic growth and M&A. 

Let’s dive in.

About EnergyOne

EnergyOne was founded in 1996 and became an independent energy retailer selling to small to medium-sized businesses as well as residential customers. 

EnergyOne listed in 2006, driven primarily by Ian Ferrier (of Ferrier Hodgson, the insolvency firm, fame) and Vaughan Busby (a former Ferrier Hodgson Director). 

The IPO prospectus touts their “sophisticated IT system”, a sign of things to come because the company would eventually exit energy retail in 2008 to focus on providing software solutions to organisations that trade energy.

The exit from retail came after a volatile year in energy when wholesale prices were between 732% and 1000% higher compared to prior year prices. The Chair’s 2007 annual report letter finishes with we would “like to resume retailing electricity to consumers.” 

A year later, volatility in the retail energy market continued, and the company pivoted completely to software, winning its first customer for its software and acquiring an energy trading and risk management solution. 

Since then, primarily under the leadership of the current CEO Shaun Ankers, EnergyOne has acquired 7 more companies while managing to post growth in revenue and profit each year.

Business Model

EnergyOne’s business model centres around its suite of software and services for organisations trading energy. Their suite covers the full spectrum of organisations that trade energy large businesses, small traders, electricity generators, gas pipelines, energy retailers, and wholesalers.

They find customers through a traditional, direct go-to-market strategy combining sales, trade shows and events. Market access and local market insights appear to be a key aspect of the model (e.g. access to various pipelines a business may need to trade on). 

Market

EnergyOne’s market can be defined broadly by any organisation engaged in the trading of energy however, in practical terms, it is more tightly constrained to organisations trading energy in the markets EnergyOne has access and localisation for. 

The diagram below helps bring these constraints to life:

And this market map helps show all the types of organisations that might trade in energy:

Sizing the Market

With that context, we can now broadly size the market.

It would be too time consuming, for the purposes of this publication, to get an estimate of the number of organisations that trade energy because of the diversity of organisations involved. So, we will use energy utilities as a proxy, knowing that this represents just a portion of the total number of firms that trade energy.

Globally, there are 8,000 energy utilities (from our analysis of Gentrack).

This then narrows to their accessible market of Europe and Australia. In Europe, there are 4,600+ electric utilities. In Australia,  there are between 43 and 114 energy providers (according to the Australian Energy Regulator and an energy comparison site respectively). 

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Competition

EnergyOne’s competition comes in two forms, energy-focused trading platforms and broader platforms. Broader platforms here includes commodity trading platforms, financial services firms and capital markets software.

Customer Value Proposition

EnergyOne has a wide suite of offerings that target a variety of different customers, which makes it inappropriate to apply one broad customer value proposition. 

These tables provide the best way to understand EnergyOne’s customers and what problems they solve for them:

Products & Solutions

The products and solutions of EnergyOne are interrelated but service a different customer segment or customer problem:

• Energy Balancing, Scheduling & Nomination: solutions for the logistics of energy

• Energy Trading & Risk Management: solutions for buying and selling energy

• Gas Transmission: solutions for managing gas

• Market Analytics: solutions to provide insights on the market more wholistically

• Outsourced Trading: a service, utilising EnergyOne’s solutions, to run trading operations on behalf of a customer.

Go-to-Market

EnergyOne’s go-to-market appears to be a fairly vanilla, direct enterprise sales model supported by your usual activities like trade show booths and social media. 

There wasn’t much detail beyond this to provide deeper analysis here. What we can say is that this model appears to be working given their continued revenue growth.

Finances

Compounding Profits and Revenue

For every year, except the most recent year, EnergyOne has managed to grow profits and revenue. But even in the most recent financial year EnergyOne grew revenue and was profitable.

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The recent year’s lower profits are attributed to investments in global growth. They’ve got the profitability to make these calls. 

Key Insights

There are some key insights to take away from EnergyOne:

1. Energy markets, while similar on the surface, are highly localised. 

2. You can grow revenue and profits.

3. Industry focus and an industry-wide view of solutions has made for a differentiated offering. 

4. You need services wrapped around software solutions for medium-to-large or non-technical customers (this is a running theme on mopoke at the moment).

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It’s one of just five technology companies we track on the ASX tracks that has grown revenue, profit and free cash flow over the last 3 years.

Summary

Cosol provides asset management technology and operations solutions to larger organisations with significant assets. 

Through acquisitions and organic growth, Cosol has built a holistic and somewhat differentiated solution offering that is a combination of professional services, managed services and software. 

Cosol looks set to keep growing and acquiring. 

In this deep dive, we’ll follow the usual format and look at:

1. About & History

2. Business Model

3. Market

4. Competition

5. Customer Value Proposition

6. Product / Solution

7. Go-to-Market

8. Finances

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About Cosol

Cosol was founded in 1999 by Brad Skeggs and Mark Cooper. They were later joined by Ron Fredericksen in 2002. The three led Cosol through two decades of private ownership.

The goal was to help organisations maintain their assets.

Cosol was then listed on the ASX in January 2020, with Brad and the team stepping back and Geoff Lewis, Steve Johnson, and Scott McGowan stepping forward to lead Cosol as an ASX-listed company. 

Geoff Lewis has had previous success building listed IT services organisations, having founded and listed ASG Group. ASG Group was ultimately acquired for $350 million. 

Scott McGowan is the current CEO and his appointment says something notable about their culture. He worked with Cosol early in his career then left for stints elsewhere in executive and leadership roles, only to return to Cosol. 

Since listing Cosol has consistently posted revenue and profit growth while making a string of accretive and strategically valuable acquisitions.

Business Model

Cosol wins business through its partners and sales team. Many of their new, material contracts are attributable to one of their technology partners like IBM Maximo, SAP and Hitachi. 

From there they sell these types of offerings:

1. Advisory & Professional Services: consulting projects for clients

2. Product & Product-led Services: licensing IP and consulting work resulting as a direct result of IP. 

3. Managed Services: managed support, hosting services and related services.

The margin on the Product & Product-led services being 37% margin says that this is mostly consulting revenue. Gross margins where you are purely licensing your own IP, such as in pure SaaS, are usually above 80%. 

Cosol typically works with larger organisations with engagement budgets in the hundreds of thousands of dollars. 

In a business like this, you often see high customer concentration with a few clients accounting for large segments of revenue. However, Cosol has a relatively diverse customer base for a business model like this with the largest 2 customers accounting for only 15% of revenue between them for the year ended 30 June 2024.. 

Market

Cosol thinks about their market as Public Infrastructure, Utilities, Natural Resources, Government & Defence, and “Other”. Organisations with assets that represent a significant part of their cost base and assets that, if managed well, can unlock opportunity.  

Geographically Cosol focuses primarily on Australia and the United States, but they’ve also picked up customers in a smattering of customers across the globe.

Cosol has a large market to pursue. In the United States for just two of their target markets, they have 500-1400 possible customers and each customer is worth hundreds of thousands or potentially millions.

Competition

Competition for enterprise asset management technology comes from partners to the software platforms as well as the global systems integrators and consulting firms.

In technology, organisations tend to go in one of two directions:

1. They will often go with a specialist partner the vendor recommends or that has specialist expertise in the software the organisation needs to install. Or,

2. They will go with a global systems integrator as it is seen as the less risky option. 

Cosol brings what seems to be a unique edge in that it has the specialist skills in the software and the industry expertise that you may only find in a large consulting partner. This helps reduce risk for organisations.

Customer Value Proposition

There are a few value propositions that Cosol offers its customers, but their strategic focus is on the “Enterprise Asset Management as a Service” offering. 

Enterprise Asset Management as a Service is the combination of their various offerings, that helps clients with significant asset management needs think strategically about their asset management technology, then implement, support, extend and the technology. 

Cosol looks to be extending beyond technology and into advising on the performance of the assets themselves.

Solutions

Cosol has a suite of interlinked solutions centered around asset management in larger organisations. They can be summed up as:

1. Asset Management as a Service: outsource complete end-to-end asset management operations. Mostly focusing on the software used to manage the assets.

2. Application Managed Support:

3. Implementation Services: implementation of asset management software (e.g. SAP, IBM Maximo, Hitachi).

4. Data Migration: software and services to help migrate data from one asset management system to another.

5. Advisory & Consulting: advisory and consulting around asset management software and operations.

Cosol has a solution roadmap based around acquisitions, investment and organic growth to continue to build out complementary solutions around enterprise asset management. 

Go-to-Market

Cosol’s recent major engagement wins are an even spread with their partners:

In addition to the partnerships you can see that they also pursue:

1. Content: downloadable material on various aspects of asset management technology can be found on their website. This is often combined with ads. They also rank reasonably well on google for various searches around enterprise asset management services.

2. Events: you can see they attend and sponsor various trade shows and conferences.

3. Direct sales: salespeople networking and directly contacting potential customers.

There is limited information around the specifics of their go-to-markets in their public reports so most of the above is gleaned from reviewing their website and social media.

Finances

Cosol is in a healthy position overall. A strong history of growing overall revenue and profits, combined with a healthy cash balance.

Here are Cosol’s key financials: 

Key Insights

There are a few insights from Cosol:

1. Acquisitions, when done right, can be a driver of success.

2. In enterprise, partnering with large and established solutions provides a path to market that gradually allows the development of your own intellectual property.

3. Patience. Cosol didn’t start being able to provide “asset management as a service”, it gradually built towards it over 6 years.

There is a competitor listed on the ASX, called Urbanise. So, I just had to dig into Urbanise this week. 

Summary

Urbanise is facing an existential moment. 

$50m+ has been invested over more than a decade producing a $2.3m loss, $12.6m in revenue, and a 1.9% revenue decline from last year.

Urbanise has $1.9m in cash and is burning up to $0.2m per month. It’s going to try and out grow its burn (get revenue to outpace costs), despite missing forecasts most years historically and the recent revenue decline.

Urbanise has been through a number of iterations in the software it sells, the customer, its leadership, and the market(s) it plays in. But it looks to have settled on strata and facilities management. 

We’ll follow the usual format for these deep dives and look at:

1. About & History

2. Customer Value Proposition

3. Market

4. Product / Solution

5. Business Model

6. Competition

7. Go-to-Market

8. Finances

About Urbanise

Urbanise was initially called Majitek and it was founded in 2001 by Rob Cumming and Steve Outtrim. Steve Outtrim had seen prior success listing Sausage Software during the dot com boom. 

Urbanise was not initially focused on where it has landed today. Under the name Majitek, the company raised $7.5m from Cisco to focus on “[enabling] any device or system connected to a network to be [accessed] on a pay-per-use basis” as well as “innovative services for the real estate industry and others, including transportation, smart-grid, and safety and security”.

As Urbanise the company listed on the ASX and raised ~$20m at $0.50 per share in late 2014. In the prospectus the strategy shifts to cloud-based solutions for the facility management industry. 

When Urbanise was listed it had just under $4m revenue and was posting just over $1m in profit. Since listing Urbanise has repeatedly posted losses (despite constant regular cost programs) and missed revenue forecasts (from 2014 to 2017 they kept forecasting ~$9m revenue the following year but didn’t get there until 2019).

In total Urbanise has raised over $50m and its current enterprise value sits around $26m.

CEO Tenure

There has been relatively high CEO turnover, with 3 of the 4 CEOs over the last decade being in the role for less than 3 years. The average ASX200 CEO tenure is over five years. 

It is unclear who the CEO was prior to listing, but over 2001 to 2014 it seems to have been the founders, Rob Cumming and/or Steve Outtrim, followed by Bernie Devince (although there could have been others).

The founders and original leaders now appear to have little to no involvement in the day-to-day of the company.

Customer Value Proposition

Urbanise has three primary customer value propositions:

1. Strata Management

2. Facilities Management

3. Strata and Facilities Management

Within each of these there are different types of strata and facilities. For example, a school runs its facilities in a different way to an aged care provider, who runs this differently again to a high rise block of units.

The Strata software targets strata and property managers:

The Facilities (or FM) software targets facilities managers:

The combined value proposition lets Urbanise expand contract value with most larger strata customers but is unlikely to be relevant for facilities managers without strata needs or smaller strata managers that don’t have need software to manage their facilities.

Market

The sweet spot for Urbanise is strata managers that need facilities management as well. This is a subset of all strata managers. 

Urbanise can also tackle each market separately with it’s different software offerings. 

Then they target these markets in multiple regions across the world:

1. AU/NZ

2. Middle East

3. Parts of Asia

Going after multiple regions imposes some costs on the business. The compliance and finance features, for example, will need to be developed and updated in line with local regulation for each region they target.

It was challenging getting strata and facilities management market data for each region with the exception of AU/NZ strata managers (there are ~372,000 of them). If we loosely extrapolate the AU/NZ data then the number of strata managers across the regions Urbanise targets is measured in the millions.

Urbanise estimates its market opportunity to be up to $355m.

Product / Solution

Urbanise’s primary product lines are the Strata and Facilities Management products. Within Strata there are add-on products for managing a strata community. 

The products:

1. Urbanise Strata

2. Urbanise FM

3. Strataware

4. My Strata Community

Urbanise’s products appear to be for organisations that want a more customised solution. The Colliers contract, for example, was $500,000 of license fees and Urbanise incurred “costs to develop their [Colliers’] platform” which means Urbanise does development for some of their customers. There is also $1.2m in bad debts related to customers in the Middle East. Compare this to, say, Microsoft Teams where you can configure the software but Microsoft won’t do development just for you. 

Business Model

Urbanise follows your typical Software-as-a-Service business model, that mostly sells into larger organisations:

Competition

Depending on how you look at it, Urbanise either competes in a market of strata that requires facilities management or it competes in strata management, facilities management and property/facilities service providers. 

Urbanise’s website and reports point towards the company’s view as the latter.

In this case, Urbanise is competing on multiple fronts:

1. General ERP: big, broad systems that do it all (Urbanise provides custom solutions)

2. Property/Facilities Service Providers: software solutions for providers to property and facilities managers (Urbanise states this is a target for them, e.g. 60% of Facilties revenue comes from outsourcers)

3. Strata/Property Management Specialists: software for strata and property. Many solutions cover beyond strata, and help with property management as well (Urbanise doesn’t cover property management).

4. Facilities Management Specialists: software that specialises in facilities management (Urbanise sells its facilities management product as a standalone offering). 

Go-to-Market

Urbanise’s go-to-market is fairly typical of a software company that sells into businesses, particularly larger ones.

The Partnerships seem mostly built around Strata and include Ace Strata Franchise and Stratavote.

Finances

Cash is a problem

Urbanise had a cash balance of $1.9m as at 30 June 2024. Urbanise has a history of material losses through most of its life over the last decade as a public company. 

The loss for the 12 months to June 2024 was $3.46m with $2.3m in cash out over the period. This translates to ~$194,000 in cash burn per month. This gives Urbanise 9.8 months runway without any drastic changes or growth. 

There is or was a cost reduction program in place that is referenced as being initiated in October 2023. It’s unclear what the status of this is. 

Bad debts

Urbanise has late receipts of $1.2m and debtors of $3.9m. This is somewhat unusual, in particular the late receipts, for a software-as-a-service company where software is usually billed in advance.  This may be linked to the custom development done around their solution for some customers.

Neither Growth or Profits

Urbanise is producing losses and no revenue growth to show for it. This leaves Urbanise in a precarious position for potential investors as well as for itself financially. The revenue growth isn’t enough to out pace costs soon enough (within their runway of 9 months) so they face a brick wall unless they cut costs to get to profit.

The report argues they will bridge the cash gap through revenue growth, but Urbanise has a history of not meeting revenue forecasts so its not clear if this will be hit.

Key Insights

What insights or lessons can we draw from Urbanise:

1. Low cash, losses is problematic: the low cash balance in relation to expenses puts the company in a tough spot. It needs to grow fast quickly, which has not happened historically or it needs to cut costs to give it more time. 

2. Coming down market might be hard: Urbanise looks like it is geared towards larger customers and contracts. It is hard to come down market from here because sales incentives, team incentives, product dynamics, setup costs and more all constrain you so that smaller contracts are harder to fulfil. This isn’t necessarily a bad thing, rather a constraint to be aware of. 

3. Split focus competitively: Urbanise is head on with a variety of competitors, all competing with different angles. Can Urbanise keep competing with a split focus? Or does Urbanise need to use its unique position across strata and facilities to focus on something more integrated?

Some companies grow 100x bigger than others.

There are 151 tech companies listed on the ASX with $108bn market cap. The power law is just as real here as it is in Silicon Valley.

💠 WiseTech, Xero, and NEXTDC = $57bn

💠 47x other B2B software companies = $24bn

💠 Some big telcos and data companies = $10bn

💠 Then a long tail of up and comers = $17bn

Shout out to Mopoke founder Andy Crebar for contributing this one.

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SEEK’s Venture Investments

There has been some discussion about SEEK’s venture fund “taking a hit” so we decided to dig into this.

Overall SEEK is killing it in VC.

The power law strikes again. A few of their early investments have seen massive gains in valuation.

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Last week we outlined the research methodology, the industry data set we will use (ANSIC) and the industry attributes we think matter.

Today’s post is about doing a data collection test for a small sample of industry domains to test our method before we analyse all the industry domains.

Industry Attributes That Matter

For reference, the industry attributes we initially set out to look at are in the table below: 

As you’ll see later, our data collection test has already meant an update to the industry attributes we will collect and analyse in future.

Data Collection Test

For the Data Collection Test, we selected a small sample of two industries (Mining and Energy & Utilities) to make sure that the data we’re collecting and the way we are collecting it will scale and provide the insights we want. 

This means that before we do an exhaustive analysis of every industry domain we can get a sense of what might need to change in our methodology, data collection approach or industry attributes considered.

Industry Financials

We need to collect financial information on the industries. This financial information, like market size and growth, was straightforward to collect from the Australian Bureau of Statistics (ABS). 

The ABS data doesn’t get down to the finest grain industry domain available but, if we can get the “subdivision” industry code. We can at least use this for an initial filter of which industry subdivisions might be more or less interesting.

It was relatively straightforward to build the following from ABS data:

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Other Industry Attributes

Once we moved onto the fields that required professional judgement it became more difficult.

Regulation was challenging because we could not find quantitative data points that could be comparable across industries. 

We were hopeful we might find measures like “there are 389 regulations that are specific to the Coal Mining industry subdivision” or “the cost of regulation to this industry is $5bn.”

The Productivity Commission and Prime Minister’s Office have both released different reports and guidance, including guidance on Measuring the Cost of Regulation. However, we were still unable to find a quantifiable measure for a single industry or across industries.

So we needed to shift Regulation to a professional judgement based on analysing Productivity Commission reports, relevant legislation, and relevant industry association reports. This will then inform categorising an industry’s regulatory burden as Light, Moderate, or Heavy.

Local Service, Physical Distance, Customer Type, and Buying Cycle were determined based on experience and understanding of the industries rather than any specific analysis. We’ll probably need to re-evaluate this and provide a stronger framework for making the determination in the future but, for now, we want to consider what insights we can gain.

Initial Insights

In doing this Data Collection Test a few insights start to emerge:

1. Waste Collection looks, in relative terms, highly competitive with low growth, a high number of firms, and low profit margins. 

2. Gas, Water Supply, Coal Mining, as well as Oil and Gas Extraction all have a low number of firms. But, Gas and Water Supply, with lower profits and lower growth, seems likely to be less dynamic relative to the Mining subdivisions. 

These broadly match our subjective understanding but will be worth analysing further in the future. It’s this next step - of taking this high level industry comparison - a step further that we will do in the next part of this research. 

We want to see what, if anything, makes any of these industries and the companies in these industries have a more natural advantage by being in Australia. Then, we want to see if we can relate this back to the Industry Attributes.

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We have a hypothesis that this comes down to the industry or domain (a subsection of an industry). We want to test this hypothesis with some data.

We want to find out which industries give a company a more natural advantage and defense against global competition. 

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Rather than just make statements based on anecdata we want to take a robust and rigorous approach to this analysis. 

The method we’re going to follow will be:

1. Identify Industry Attributes

2. Design Industry Data Collection Method

3. Build Industry Data Set

4. Analyse Data

5. Test Attributes Against Industries/Companies 

6. Review Results

This body of research requires some time, so we will publish in stages as we work through it. We’d love to hear your thoughts as we go because we know there are talented people who have done similar work in the Mopoke community.

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Today’s post will focus on Identifying Industry Attributes and the Design of the Industry Data Collection Method.

Identify Industry Attributes

For the Industry Attributes, we will consider what makes an industry or economic activity more naturally defensible against international competition.

Industry attributes that are barriers or dynamics will make it difficult for another player to enter the market. We want to think about the incentives, like factors that affect market entry or indicators of a lack of opportunity.

The attributes that we believe are likely to be of interest, in no particular order, are:

• Market Size ($): Bigger markets are more attractive and therefore more likely to invite competition. With a larger market, it is easier to justify the cost of entry.

• Competition (Number of Firms): Existing competition may have a bearing on how defensive an industry might be, possibly in relation to market size. We’ll use the number of firms as a proxy for competition.

• Market Growth: A growing market is likely to be more attractive to international competition. 

• Regulation: the extent to which local regulation requires unique changes to products or services in order to be viable. 

• Local Service: Some industries, due to regulation or other reasons, require or heavily prefer local service providers.

• Physical Distance: where products or services in an industry are required to travel or be sold across larger physical distances there are likely to be barriers to entry or leverage of existing capability in other regions. 

• Customer Type: the size and nature of the type of customer may have a bearing on international competition.

• Buying Cycle: where buying cycles take longer it may be harder for out-of-region competition to get a foothold. 

We are not conducting a more general assessment of the attractiveness of an industry. That is, we aren’t doing a something like a PESTEL or Porter’s Five Forces analysis to determine whether an industry itself is attractive. We are looking at defensiveness or natural advantages for companies that focus locally (regardless of whether the industry itself is attractive). 

We suspect there may be some overlap but it isn’t something we will cover.

We are also going to focus on Australia and, to a lesser extent, New Zealand.

Design Industry Data Collection Method

If we want to build a useful data set then we are going to have to 

Define An Industry List

To do this we need to start with a comprehensive view of Australian industry. 

This is provided to us by the Australian Bureau of Statistics and Statistics New Zealand who co-developed the Australian and New Zealand Standard Industrial Classification (ANZSIC). 

ANZSIC provides a way to categorize businesses in Australia and New Zealand as well as the world so that statistics can easily be compared. It is based on and compatible with the International Standard Industrial Classification of All Economic Activities (ISIC) developed by the United Nations and the North American Industry Classification System (NAICS) used in the United States, Canada and Mexico.

The only other available alternative in Australia and New Zealand with some level of rigor behind it is the Australian Tax Office’s Business Industry Codes but these are just derived from ANZSIC, so we will use ANZSIC. 

ANZSIC will help us categorize and analyze businesses by their economic activities. ANZSIC classifies businesses by:

1. 19 Divisions: broad macroeconomic activities (e.g. “Agriculture, Forestry and Fishing”)

2. 86 Subdivisions: further refinement of the broad macroeconomic activities (e.g. “Agriculture”, “Forestry” and “Fishing”)

3. 214 Groups: more specific economic activities (e.g. “Fruit and Tree Nut Growing”)

4. 508 Classes: most specific economic activities (e.g. “Olive Growing”)

Attribute Data

We will collect the attribute data as per the table below:

For some of the attributes we will use professional judgement to classify the industries:

• Physical Distance: Short (< 10km), Moderate (10km - 100km), Large (100km+)

• Customer Type: Small (0-19 employees), Medium (20-199), Large (200+), Government (government-owned or controlled agencies). 

• Local Service: Yes, No, Optional

• Buying Cycle: < 1 month, 3 months, 1 year, > 1 year

Next Steps

We are going to collect data on these attributes for a sample of industry domains to see what sort of insights we can gain.

If you want to follow along then please subscribe. If you want to share your thoughts, please hit reply.

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So, today’s analysis is a deep dive into ReadyTech because of its ability to compound revenue and profits as a listed software company.

We’ll look at the markets they’re in, the business model, competition, go-to-market, and finances. In short, we’ll cover it all.

Let’s dive in.

Summary

ReadyTech’s long-term, compounding performance is driven by the unique combination of the type of customers ReadyTech serves and ReadyTech’s ability to acquire, connect, and cross-sell.

ReadyTech serves government and government-like customers directly and in a connected way across community stakeholders. This type of customer takes time to win due to procurement processes and the relatively slower speed at which these types of organisations make decisions. But, once you’re in with this type of customer, they’re often eager to do more with you (if you deliver) because it makes their lives easier.

ReadyTech has used M&A to get more access to this type of customer faster and add solutions to cross-sell into their existing customer base. 

It’s a compounding combination that serves them well. 

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About ReadyTech

ReadyTech was founded in 1997 by Marc Washbourne and Ken Sheppard. Sheppard handed over the reins to Washbourne early on in the business. ReadyTech was bootstrapped for five years.

“We were self-funded, so we had to be profitable from day one,” Washbourne tells Forbes. “We bootstrapped the company for five years before we crossed the chasm and achieved scale. It was tough. What got us through was dogged determination and the patience that came from having a long-term view.”  

The determination paid off. Pemba Capital invested in June 2016, then upped its share to 32% prior to listing on the ASX in April 2019.

ReadyTech has made over 15 acquisitions over its lifetime, including Avaxa for $2.2m, Open Windows for $14.3m, PhoenixHris for $3.3m and IT Vision for $23m.

Business Model

ReadyTech’s business model focuses on providing a range of software-based solutions for a specific community industry, primarily vocational education, recruitment, and employment. 

I use the word community because they focus on the different people in the community within a wider industry. 

It’s unique and interesting in the sense that ReadyTech has acquired and built solutions not just for one specific customer but for all the various customers within part of the community industry. 

The community industries they focus on are:

• Education & Work Pathways

• Workforce Solutions

• Government & Justice

For example, they have solutions for registered training organisations to manage students, including tracking and assessing students. They have a solution for helping a student get employed, trained, and supported on the job. The same solution also helps the training organisation and employer manage funding and the administration of this process.

ReadyTech has mastered M&A to help them add solutions for the communities they currently serve and add communities they can target. 

Market

ReadyTech is pursuing different but related markets in each of the community industries they serve. ReadyTech estimates its total addressable market as $970m+, divided as follows:

Collectively, ReadyTech’s markets share these attributes:

1. Heavily Regulated: each of the markets ReadyTech operates in is heavily regulated and consists primarily of government or quasi-government organisations.

2. Similar buying cycle: For the most part, the types of organisations they deal with across the marketsshare a similar buying cycle. Universities procure in a similar way to TAFEs, which is comparativeagain to councils, courts, and tribunals.

3. Multiple Stakeholders: ReadyTech’s solutions or combination of solutions allows them to solve for the challenges across a value chain in a community, and not necessarily for just one stakeholder. In many of these situations, this is necessary. Yet, it’s notable for ReadyTech because many organisations often try to avoid this.

Competition

This analysis of competition looks at ReadyTech broadly rather than any specific ReadyTech solution because ReadyTech has such a wide range of solutions. overing each solution in detail is beyond the scope of this analysis.

ReadyTech is competing against generalists like Salesforce, SAP, and Microsoft Dynamics, as well as specialists who provide solutions for specific problems that ReadyTech’s target customers might have.

Go-to-market

ReadyTech acquires new customers and new contracts through:

1. Direct Sales: selling into new accounts with a sales force. 

2. Existing Install Base: upsell and cross-sell, as well as additional revenue through increased usage. This matters to these regulated industries and larger organisations.

3. M&A: they acquire new companies to gain access to more contracts that they can further upsell and cross-sell into.

Finances

With ReadyTech’s use of M&A to drive growth in mind, their balance sheet and free cash flow matter because they give them the ability to make further acquisitions. These acquisitions add to EBITDA and growth.

The table below shows how ReadyTech’s available cash and net cash flow could be used to finance an acquisition over the next year: