Black Pearl Group Limited (BPG) Earnings Call Transcript & Summary

Good morning, and welcome to Black Pear's Q2 Financial Year 2027 Trading Update. From the company today, we have the Founder and CEO, Nick Lissette; the company's CFO, Karen Cargill; and the company's CTO, Sam Daish. Before I hand over to Nick to go through the presentation up on your screen, [Operator Instructions] present. But with that, Nick, I'll hand it over to you. Thanks.

Thank you very much, Simon, and thank you, everyone, for joining us for our FY '27 Q2 update. Well, Q2 was a massive quarter. Black Pearl met its EBITDAF guidance. We, in fact, closed the gap to profitability by 34% in a single quarter. We established our first strategic partnership to underpin our partnership and platform growth strategy, and the Pearl Engine increased its net score outputs 7.7x with the release of our latest model, RTSA 2. Now 2 years ago, I was famously quoted saying that SaaS as we know it is dead. Now of course, there will always be value in SaaS tools for a long time to come. But the technology advantage, which was historically protected by development costs, scarce engineering, and integration complexity can and is getting eroded by AI. That is why we believe the durable value is moving underneath the application to proprietary data to specialized intelligence and infrastructure embedded into how other businesses generate revenue. This is why Data as a Service and Platform as a Service are so important for Black Pearl's future. Accordingly, over the first half of FY '27, the Group's focus has been on revenue quality. It has been on distribution economics over direct sales and R&D in the Pearl Engine over application level. This is what we believe secures longevity of the company. And our goal moving forward is growth of data and platform revenue from a position of profitability without sacrificing our technical advantage. And this is what we've delivered on in Q2. Today, we update the market with firm guidance on profitability. We met our EBITDAF guidance in Q2. In a single quarter, we closed 1/3 of the gap to profitability. We brought our sales, our marketing, our development and customer success teams together across 3 ventures and pointed them directly at data and platform. This has enabled us to take around $9.4 million in annualized costs out of our cost base. That's roughly 27%. Those changes landed during Q2. So Q2 only shows part of those savings. And from Q3, the lower cost base will be applied in full. Now we carefully picked our time to execute this. Our Q2 is the U.S. summer holidays, similar to, I guess, Christmas and New Year's in Australia and New Zealand. And it's when buying decisions slow and new customers are harder and more costly to acquire. The group opted not to spend into this and instead invested in launching its partnership and platform strategy. So yes, accordingly, our growth has temporarily slowed because we chose to slow it. Could have we kept growing while doing that? Yes. But you have to fund growth through equity. And it's a capital-intensive game, and the market for this has changed. Software, again, has lost something like $2 trillion in valuation since September of last year. Capital is super dilutive and raising to buy growth is not, in the Board's view, in the shareholders' interest. So our sequence is very deliberate. First, we get to EBITDAF profitability, and then we focused on ramping up growth again from a position of strength on better unit metrics. If we want to accelerate our growth beyond what self-funded profits do, then we'll be doing that on our terms. Now of course, there's another option. We could have continued growing revenue through Q2, but that would have been at the expense of research and development and furthering our technical advantage. And we believe that kind of quarterly capitalism would have been the worst kind of short-termism. We've been proved correct because it's that technical advantage that we invested in that has created our first partnership opportunity and launched our data intelligence onto our first platform with Snowflake. Now to tell you more, I'm going to hand you over to our Chief Technology Officer, Sam Daish.

Hey, everyone. It's great to be talking with you again. Black Pearl continues to be at the forefront of AI for sales and marketing and the advances in general frontier AI are making our Pearl Engine more valuable, not less. So last quarter, we launched the Go-To-Market Bench, our open benchmark for AI systems that do real go-to-market work. And we're committed to updating it every quarter. This is the first update. So as Nick said, it was a heck of a quarter, and it was a heck of a quarter in AI as well. So a new generation of frontier AI arrived this quarter. Anthropic's Claude Fable 5.1 and others is much stronger at reasoning and agentic work. Reasoning is thinking a problem through and agentic work is planning and then carrying that work out across a whole number of systems. And the jump in capability, it's among the largest lift since LLMs became generally available. So a real significant step forward. And you can see this in our leader board. So at its best, Fable 5.1 scored over 47,000 and it beat our previous system, RTSA 1, our June system. And when used with our data layer, it blew away the other results. So the previous best frontier model scored 4,000, Fable 5.1 scored 47,000. So incredible. Now as I've discussed before, our technology strategy is to build on the advancements in frontier models and apply that to our very specific market of sales and marketing. So we take those general horizontal frontier models and build our vertically specific systems with them. As they get better, we get better. And this is the strategy that we are executing day in, day out is the strategy we executed last quarter. And you can see this right here in the results. So our system went from being about 26,000 points better in June than the leading frontier to over 205,000 points with RTSA. So the improvement is phenomenal. So our lead grew from 22,000 to 158,000, and that's the chart that you can see there on the left. And you can see the kind of quantum shift there. And this is our vertical AI strategy. It's AI built for one domain, in our case, sales and marketing, and built to keep improving. I just want to make that point really clear. It is built to keep improving. The frontier labs are spending billions making their models better. That's going to continue. Meta and Google have started to catch up to OpenAI and Anthropic. And those billions flow through to Black Pearl, that billions of investment flow through to us. Every generation of frontier AI gives RTSA more capability to put to work in our specific market. And we test these models on GTM Bench, and we bring the best of it into RTSA. And then we share those results with you every quarter so that you can know that it's working. So we're not building the best general purpose AI systems. That's not what we are about. That's not what we aim to do. We're built for sales and marketing. RTSA 2.0 is the first on the measure mark on the bench by a wide margin. And our customers experience this advantage. They grow their revenue faster with less waste. And, as Nick said, that's what sits behind the quality of opportunities that are now in play for us. Back to you, Nick.

Thanks, Sam. One of the reasons -- in fact, the core reason we actually released benchmarking wasn't to appease you guys. It was actually because we wanted third-party data providers to try our models. And I'm pleased to say that is exactly what happened, and that's what resulted in the U.S. distribution agreement that supports our path to profitability and velocity of growth beyond that. Now for people in the data industry, our benchmarking highlights something of keen interest. And it wasn't the fact that our models outperformed foundational models by now 201x. It was the fact that when you put good quality third-party sales and marketing data into our models, we make it hundreds of times -- hundreds of percent more likely in finding a product for your -- sorry, a buyer for your product or service. So we take third-party data, we make it way more valuable, way more likely for it resulting in a sale. Now this solves a big problem for third-party data providers. If you ask any data provider, the #1 reason for churn, they're going to tell you that it's because the customers complain that they're getting poor financial outcomes or bad outcomes from bad data. Now we heard this years ago as well. In fact, this is what propelled us to accelerate the growth of our models. And now with our models, we know at Black Pearl that it's not so much about bad data. It's more that the wrong data gets used in the wrong way because clients don't know how to take broadly useful data sets and make them specifically useful for them. The example I give is like crude oil. Crude oil is super valuable, but you do not want to put that directly into your car without refinement, and data is just like that. In fact, if you put unrefined data into your marketing engine, you risk burning your whole marketing budget. And that is an outcome worse than 0. And if that happens, the provider of those data sets risk losing those customers. So again, because of that benchmarking, one of America's leading sales and marketing data providers now works with us, to use our models to ensure their customers get maximum use from the data. Let me show you an actual real-life example of this. So the file on the left, very hard to see, but it's an actual intent data file. It's a real, genuine intent data file, right? And intent data is a data set that implies a business or an individual has interest in a particular topic. This particular topic is people that are showing interest in the keywords coastal retreats. Now as you can see, and it is very common when you purchase an intent data set is delivered typically as a flat file like a CSV with the sensitive information being hashed. So accordingly, at the best, it is exceptionally difficult or more likely impossible for the average business to take this data set and find out what part is specifically useful for them. This is where our models shine. So if we jump and hopefully, the video works, we're trying something new here today, we took this data file and we put it into our Platform as a Service. And this is actually shown you in collapsed time because it does take a few minutes to set it up, connecting Claude via MCP, that's an API for AI through to our RTSA 2 models. It's showing our models on the background, taking that data file, building it up or expanding it out using our 31 billion data points, and then actually using our model analysis to find what the composition -- genuine composition of that audience is, and how that relates best to the specific client. So if we jump through to the next slide, this is the actual outputs. And this is why getting -- taking broad data sets and making them specifically useful is critical for success. So this customer was trying to push out book now offers for high-end West Coast, coastal retreats that they had a network. And it's a tough area to live in because you're competing against Air B2B, I forget the name of it. Help me out, Sam, you're laughing at me.

Airbnb.

Got it. You're competing against Airbnb, you're competing against Expedias, like that is an expensive game. That's why you're looking for a competitive advantage with intent data. Now when we analyze this data, we found something amazing. Out of that list, only 34% of those people, and I think it was 270,000 individuals on that CSV, only 34% of them were actually interested in West Coast coastal retreats. Now you see that big red mark there, right? That is people that were interested in retreating from the coast. In fact, the largest cohort was that. So again, this is the problem about intent generated from keywords is that, technically, yes, that is coast retreats, but it's not coastal retreats that's useful for if you're trying to book West Coast coastal retreats. That data set is actually useful if you had a removal company from the Coast inland. Secondly, our technology is able to identify at what stage of the buying journey you're at. Again, a very unique output. And we could see that only, I think, 35% of the 34% of people interested in coastal retreats were actually at buy-now stage. So I'm going to frame this in the negative and then the positive. If you've taken that raw data set and put that into Meta or put that into Google and said, "Put buy now offers in front of these people because they've got intent in coastal retreats," you would have burned $0.90 on the dollar. That is your marketing budget gone. And that customer gone. With our technology, you can focus your marketing budget on that elite 10% that are both interested in your specific proposition and ready to buy today. That is the power of our data. And because of our technology, the data provider is now confident in extending their TAM, their target addressable market downstream to small- to medium-sized businesses and has partnered with us to service those clients. Now this isn't just any old data provider. We are hoping to be able to name it soon and going through all the -- jumping through all the hoops there. But this is one of America's leading intent data providers servicing Fortune 500 companies, household names that you know, down to upper ends of SMEs. But during their standard go-to-market, they regularly engage with prospective customers that fall below the minimum commercial and qualification thresholds. These people, they'll decline today because they feel that the clients don't have the resources or budgets to get proper utility from unprocessed data sets for exactly the reasons you've just seen. Now by adding our data analysis offering on the top of their existing Data as a Service offering, clients can now extract maximum value from those partners' data sets. So the partnership deal was great because the partner can now start making money from clients they never could have before. But for Black Pearl Group, this means a whole new channel of customers with near immediate CAC payback. Contracted recurring revenue with a minimum deal sizes being around USD 15,000 per month up to USD 40,000 per month. It is a revenue share agreement where Black Pearl gets up to 80% of the revenue. We manage the clients. We bill the clients. We have the customer relationship. They have 70 deals in backlog and forward projected velocity of between 1.5 to 3 new opportunities per month. Obviously, very exciting, but I point out this is the first partnership under our partnerships and platform strategy, and it's one shape of several. In this case, the data provider hands us customers that it can't serve itself. There are other examples we're actively working through where the partner will keep the customers and embed our data intelligence inside their own product. So thousands of users are getting better outcomes, but they won't be seeing our name. So different structures, similar economics in the sense that we're not paying to acquire customers like we have been in the past because the partner carries the distribution and our data gets better with every customer it touches. So very exciting for us. The partnership and platform strategy has enabled us to optimize our cost base accordingly. And we're doing this without sacrificing R&D or mid- to long-term growth. So to talk to you more about that, I'm going to hand you over to Karen Cargill.

Thanks, Nick. So Q2 was the quarter we changed the shape of this company's cost base. This chart is the quarterly cost base from Q4 FY '26, which was our peak. In total, we have taken around 27% off compared to Q4 FY '26. That's around $9.4 million annualized savings. These changes landed during the quarter. So the Q2 and half year interim results only reflect part of the saving. The lower cost base implies -- sorry, applies in full from Q3. The box on the right shows where these cost savings came from, 2 structural changes and both are permanent. The larger is our go-to-market model, a lower cost route to market through partnerships and self-service. And the second is -- sorry, acquisition integration. Duplicate roles across the 3 ventures were removed rather than redeployed. Importantly, we've done this without cutting our ability to grow. We've kept the capacity to service the partnership pipeline. And none of this has come out of the Pearl Engine. We protected R&D and Sam's slide earlier shows the result from the go-to-market bench. What I'd underline is that none of these costs we've removed depend on revenue growth to hold. These are decisions already executed, not targets. A lower cost base combined with partner revenue that carries high margin and a low cost to serve is what puts profitability within reach. Earlier, Nick talked about how our focus this half has been on distribution economics over direct sales. This slide is what that means in numbers because taking $9.4 million of annualized cost out of the company only works if we change the way we grow. On the left is how we grew previously. Every DAS dollar started life as a SaaS sale. It took 4 months of acquisition cost to win the customer and at a 30% cost of sales margin, that's 5.7 months of revenue before we have recovered what we've spent. Then the revenue ramps is over about 90 days and sits on 45-day payment terms. So that's another 4.5 months before the cash is fully in, all of it funded upfront by us. That is a model that consumes cash in order to grow. It is also why significant sales and marketing investment was not optional. We were buying the only go-to-market channel we had. On the right is what has changed. Partnerships bring us customers. The partner is paid through a revenue share, so there's no front -- upfront acquisition cost and payback is immediate. Self-service lowers the acquisition cost on the SaaS we sell directly and recycles the cash faster. Centralization reduces fixed costs out and the shift -- it shifts the investment to DaaS and PaaS where the higher margin is. And pricing. Some customers are simply better suited to flexible plans and have been moved to variable consumption pricing. It benefits how they use the platform, and it means that revenue moves with usage. The result is a business that recycles cash faster, carries less fixed cost and has its revenue weighted towards SaaS and PaaS. And with that, I'll hand back to Nick.

Thank you, Karen. All right. Let me close out on the position from which we grow from. We went into FY '27 with a go-to-market that was still heavily weighted towards our applications, a high cost base and sales cycles, which had increasingly long cash payback periods. We come out of the first half of FY '27 with a substantially lower cost base, a model, a lead that's widened 7.7x, and U.S. distribution partner bringing us customers we don't have to pay for to acquire. Guidance has been met, 1/3 of the gap to profitability closed and a firm date in the calendar for profitability. We've quietened the noise that's been -- that we've created for ourselves by driving revenue from 3 separate applications, moving to self-service and unifying the Group's energy and resources on where we believe the deeper, defensible value lies, proprietary data, specialized intelligence, and infrastructure embedded into how other businesses generate revenue. Now our goal is to grow that data and platform revenue from a position of strength, which we control. That is what we did in Q2. Thank you for your continued support. Ad astra, and over to you for Q&A, Simon.

Great. Thanks very much, Nick. We've had a number of questions that have come through, and we'll do our best to group them up. And if we don't get to any, we'll commit to coming back on e-mail. First question, when does management expect Black Pearl to become sustainably cash flow positive? And what level of revenue is required to achieve this without additional capital?

Yes. So as we've projected out, Q1 FY '28. So for people not operating on the New Zealand financial year, that is between April and June next year. Second half of that question. So yes, that's the time frame that we've put out for that. And by the way, with us, EBITDA is a reasonable proxy for cash as well. So I think you asked about cash, so rough similar.

Thanks for that, Nick. A few questions just on the partner program. Maybe first of all, who is the partner? And how should we think about this in terms of being a repeatable program?

Yes. So we're working very hard to -- with that partner on a suitable disclosure. They're a substantial partner. So there are some intricacies there we're working through, but expect them to disclose the name, very repeatable. What's been super interesting for us, and we're talking about the fact that it's like, really, what we're experiencing at the moment is technology-led distribution opportunities, and there is actually several influx at the moment, paper work out for another 1 last week. So very, very repeatable, not just at those big third-party data providers that anyone that has multiple customers that rely on data to drive outcomes, that's a lot of businesses for sales and marketing, everything from CRMs, the outbound e-mail agents to martech tools, all those sorts of things, there other kind of partners that we're engaging with, some very exciting.

Great. And just on the U.S. partnership, when do you expect -- I think you mentioned about working hard on the agreement, but when do you expect it to be signed and how many of the 70-plus referred companies are contracted and paying as of today?

None today. So we're expecting of having the ultimate agreement executed this week. And then we move to immediately looking to move over as many of those existing opportunities as we can. One of the things that should be pointed out as well is that we didn't take all the costs out last quarter that we had initially anticipated we may. So we left additional cost to ensure servicing of customers if we get a substantial volume out of the gate.

Great. And maybe one for Karen. The second quarter revenue was down on the first quarter, the first quarterly decline. How much of that was churn versus the pullback in marketing spend? And should we expect revenue growth again in Q3?

That was mainly due to the pullback in the marketing spend, not the churn. In Q3, yes, hopefully, we will be getting a lot more revenue than we had done in Q2 with all these partnership deals coming through.

Perfect. And a question just in terms of has the gap between ARR and subscription revenue narrowed?