Turning Platform Data Into Competitive Advantage
If you are running a platform serving thousands of customers, you’re sitting on something valuable: visibility into patterns that no individual customer could ever see on their own. The question isn’t whether this data is useful, it’s how you turn it into a product that customers will actually pay for.
Your Hidden Asset
Here’s the thing about platforms: whilst your customers are heads-down running their businesses, you’re watching thousands of them simultaneously. You can see which messaging strategies convert at higher rates. Which content performs the best. Which pricing structures drive more repeat purchases. Which seasonal patterns affect different business types.
Individual businesses struggle to answer these questions. They do not have enough data. They cannot see beyond their own operations. They are guessing based on anecdotes rather than evidence. But you? You have the full picture.
Most platforms use this data internally: to improve product features, guide customer success teams, or inform roadmap decisions. That’s valuable, but it’s only half the opportunity. The real question is: which of these insights are so valuable to customers that they would pay for them?
What Customers Actually Pay For (And How To Find Out)
Not all insights are created equal. Some are interesting. Some are actionable. And some are genuinely game-changing for how customers run their businesses.
Think about the difference between reporting and insight. You could show sellers “you had 147 transactions this month” (interesting but not actionable). You could show “your page views increased 23%” (slightly better, but still so what?). Instead, focus on things like “businesses in your category who respond to enquiries within 1 hour convert at 3x the rate” or “products with video demonstrations sell at 2.4x the average.” That’s immediately actionable intelligence that changes behaviour. The difference? They are not just reporting what happened, they are revealing patterns that help customers make better decisions.
So how do you figure out which insights matter? The answer is exploratory data analysis (EDA). Before you build anything, you need to understand what questions your customers are actually trying to answer, then dig into your data to see if you can answer them better than they can themselves.
Start with customer interviews. What keeps them up at night? What decisions do they agonise over? What would they pay a consultant £500 to tell them? Then take those questions to your data team and see what patterns exist across your customer base.
You might discover that successful customers share workflow patterns that struggling customers don’t follow. Or that businesses in specific verticals have completely different success factors. Or that seemingly small behaviours (like response times or proposal length) have outsized impacts on outcomes.
The insights that customers will pay for typically have three characteristics: they are specific enough to be actionable, they are surprising enough to change behaviour, and they are validated across enough customers to feel trustworthy rather than anecdotal.
Expert help is only a call away. We are always happy to give advice, offer an impartial opinion and put you on the right track. Book a call with a member of our friendly team today.
Your Own Data: The Multi-Tenancy Challenge
Here’s something that often gets overlooked: before you can serve embedded analytics to thousands of customers, you need to solve multi-tenancy. This most commonly applies to B2B products where sensitive client data cannot sit alongside other customers’ data (think financial services, insurance, healthcare, and enterprise SaaS). Each customer has to see their own metrics, optionally benchmarked against relevant cohorts, without any exposure to another tenant’s underlying records.
This is not just a technical hurdle, it is an architectural requirement. You are effectively delivering thousands of logically isolated analytics environments on shared infrastructure, with strict data separation as a first-class constraint. Get it wrong and you have created a security and compliance nightmare; get it right and you have built the scalable foundation that makes embedded analytics possible.
Most platforms take one of two paths. The simpler route is schema-per-tenant (or table-per-tenant) with application logic enforcing access boundaries. The more advanced route is shared schemas with row-level security policies that dynamically filter data based on the authenticated tenant/user context.
Both come with trade-offs. Schema-per-tenant is easier to reason about and offers cleaner separation, but it gets painful to operate at scale and complicates cross-tenant aggregation. Row-level security tends to scale and model more cleanly, but it demands disciplined query patterns, rigorous testing, and constant vigilance to prevent leakage.
The key insight: do not wait until you are “ready for dashboards” to think about multi-tenancy. Bake it into your data model and governance from day one, or you wil end up rebuilding your analytics stack right when you want to ship customer-facing insights.
Three Models For Platform Data Products
Once you have got the infrastructure sorted and you understand which insights matter, you need to decide how to package them. There are three common models:
Benchmarking & Comparisons: “How do I compare to similar businesses?” This is often the easiest place to start because it requires relatively simple aggregations. Stripe Atlas does this brilliantly, showing startups how their metrics stack up against their cohort. The value is immediate: customers can self-assess and identify improvement areas without hiring a consultant.
Best Practice Insights: “What do successful users do differently?” This is where you start identifying patterns that drive outcomes. Shopify surfaces high-converting product page layouts. Intercom shows which support response patterns correlate with customer satisfaction. These insights reduce time-to-success for newer customers whilst giving established customers optimisation opportunities.
Predictive Recommendations: “What should I do next?” This is the most valuable and most difficult to execute well. You are not just showing patterns, you are using them to make specific recommendations. HubSpot suggesting optimal email send times based on industry patterns. Shopify recommending which products to feature based on seasonal trends in your category. Stripe identifying customers at risk of payment failure before it happens. These drive platform usage because they’re directly actionable.
Most successful platforms start with benchmarking, layer in best practices once they have sufficient data, then add predictive elements as their ML capabilities mature.
Monetisation Strategies That Actually Work
Let’s talk about money. There are basically three approaches to monetising platform data products, and the right one depends on your business model and customer base.
Free tier as a hook: Basic benchmarking is free because it drives adoption and engagement. You want customers logging in regularly to check how they are performing. This creates habit formation and makes your platform stickier. Our client Ablefy does this well: basic seller insights are available to everyone, which increases platform value even for non-paying customers.
Premium tier for serious users: Advanced insights and recommendations live behind a paywall. This is where you show the patterns that actually change behaviour. “Businesses like yours who do X see Y% better outcomes.” These insights are specific enough and valuable enough that customers will pay for them.
Enterprise for at-scale customers: Custom analytics, white-labelling, and API access. Larger customers often want to integrate your insights into their own systems or resell them to their customers. This is typically negotiated pricing rather than standardised tiers.
The mistake most platforms make is trying to monetise too early. If your insights are not genuinely changing customer behaviour, nobody will pay for them regardless of how you price them. Better to start free, prove value through usage and customer feedback, then introduce pricing once you’ve identified which features drive meaningful outcomes.
Building Yourself vs. Partnering
Here is the uncomfortable truth: building data products is genuinely difficult. It requires infrastructure engineering to handle multi-tenancy, data science to identify meaningful patterns, product design to make insights comprehensible, and iteration to figure out what customers actually find valuable.
Many platforms underestimate this complexity. They assume their existing data team can just “add some dashboards” or their product team can design embedded analytics in a sprint. Then six months later they are still debugging query performance whilst customer adoption languishes.
The question of whether to build in-house vs. partner depends on a few factors. If you are early stage (pre-Series B), you probably do not have the resources to build this properly whilst also building your core product. If you are already at scale (Series C+), you likely need the control and customisation that comes from in-house development. The middle ground is often where partnerships make most sense, you have got product-market fit and the budget to invest, but not yet the scale to justify a separate team building data products.
The key is avoiding the trap where you are just building dashboards instead of insights. Customers do not want more charts, they already have Google Analytics and Stripe dashboards and a dozen other reporting tools. They want answers to specific questions that help them make better decisions.
Another classic error is over-engineering before validating demand. Do not spend six months building a comprehensive analytics platform before you know if customers want it. Start with one insight for one customer segment. Measure adoption. Iterate. Then expand.
Finally, don’t ignore the “so what?” question. Every insight you surface should have a clear answer to “so what should I do about this?” Showing someone their conversion rate is 3.2% is meaningless unless you also show them that similar businesses average 4.7% and here’s what the top performers do differently.
Where To Start
If you are convinced this is worth pursuing, here’s the practical path forward:
First, identify which customer questions have the highest value. Talk to your most successful customers. What drove their success? What do they wish they’d known earlier? What would have saved them months of trial and error?
Then, take those questions to your data and see what patterns exist. Can you answer them with confidence? Are the patterns strong enough to be actionable? Do they hold across different customer segments or are they specific to certain contexts?
Start with an MVP. Pick one insight that matters to one segment. Build the simplest possible version, even if it’s just a weekly email with a single stat and context. Measure whether it changes behaviour. If it does, build more. If it does not, figure out why and iterate.
The platforms that win with data products are not the ones with the most sophisticated ML or the prettiest dashboards. They are the ones that give customers intelligence they cannot get anywhere else, intelligence that makes them more successful and, by extension, more reliant on the platform.
Of course if you need some impartial advice on how to build data products, why not get in touch with the friendly team here at 173tech?
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