Data as a Differentiator for a Successful Insurtech Exit

This piece is by Natalie Cramp partner at JMAN Group.

Last year, private equity (PE) firms showed substantial interest in the insurtech market, particularly in terms of opportunities to consolidate market share and leapfrog competitors through advanced technology solutions. That focus will continue this year, with eyes on innovative business models that prioritise speed, efficiency and customer centricity, as well as those that apply AI to improve predictive analytics, fraud prevention and address emerging risks, like climate change. To that tune, leading indicators suggest even more PE insurtech investment in 2025, building on the record levels achieved last year. 

For insurtechs eyeing a successful exit, particularly when engaging with sophisticated PE firms, this increasingly requires a robust approach to data. Put bluntly, the era of simply showcasing impressive top-line growth is over. Today, data reigns supreme. It’s the bedrock upon which compelling value stories are built, the lens through which operational efficiency and scalability are scrutinised, and ultimately, the key to unlocking those coveted higher valuation multiples. A robust data strategy, coupled with the ability to extract meaningful insights, is no longer a ‘nice-to-have’ but a fundamental requirement for securing a lucrative exit in today’s competitive landscape.

So, what exactly are these discerning investors looking for in the data of a prospective insurtech acquisition? The foundation, without a doubt, remains the ARR bridge, or what we often refer to internally as the ‘revenue snowball’. This isn’t just about presenting a static ARR figure; it’s about demonstrating how that recurring revenue has evolved over time.

Investors will dissect this data from every angle – group-wide, segmented by product, customer cohort, and geography. They want to see the trajectory, understand the drivers of growth and churn, and identify any potential vulnerabilities. Therefore, your ARR bridge needs to be more than just a spreadsheet; it needs to be a dynamic, drillable, and rigorously stress-tested view that can withstand the intense scrutiny of due diligence.

Beyond the ARR bridge, several other key insights are paramount. Sales pipeline reporting provides a crucial forward-looking perspective. Investors want to see a healthy, well-managed pipeline with clearly defined stages, and understanding of cost of sales, realistic conversion rates, and accurate forecasting. This demonstrates the predictability and sustainability of future revenue growth. In addition, being able to defend your pricing model with a data-driven approach is important. Similarly, classic FP&A reports remain essential, offering a historical view of financial performance, profitability trends, and cost management.

However, the truly forward-thinking insurtech firms are now leveraging product usage insights to a greater extent than ever before. Understanding how customers are interacting with the platform, identifying power users, and tracking feature adoption provides invaluable insights into customer stickiness, potential for upselling, and overall product value.

Looking ahead, the role of data in shaping insurtech valuations will only intensify. We anticipate that the level of scrutiny and the expectation for data maturity and insightful analysis will continue to rise. Gone are the days of presenting raw data; investors will increasingly demand actionable insights and a clear understanding of the ‘why’ behind the numbers. When it comes to performance and trends; just saying profitability has grown by X% year on year is now not enough – it needs to be evidenced by granular data and solid analytics. This comes in the form of a data cube which will be with you long after acquisition, giving them the confidence that you will have the tools to manage the next stage of growth.

Investors want to know what’s working now and how your company can scale post-acquisition. By providing the context behind the metrics, it makes it easier to showcase opportunities for further growth, with potential investors being able to leverage these data “assets” to underpin their investment cases. With higher investor expectations, those who fail to do so risk undermining their valuation potential or, worse still, failing to secure the deal.

Furthermore, I believe that companies will need to start demonstrating how they are leveraging data to capitalise on the value that AI can bring. This could range from using AI-powered analytics to identify at-risk customers to employing machine learning to optimise pricing strategies or enabling your business in your customer product and optimising your internal operations. However, this advanced application of AI is only possible when the fundamental metrics and insights are already firmly in place.

So, how can insurtech firms proactively use data to build a compelling value story that resonates with potential acquirers? It boils down to demonstrating operational efficiency, scalability, and long-term viability.

Data can paint a vivid picture of operational efficiency. By tracking metrics like customer acquisition cost (CAC), lifetime value (LTV), and the ratio between them, companies can demonstrate a sustainable and profitable customer acquisition engine. Analysing support ticket volumes, resolution times and customer satisfaction scores can highlight the efficiency of customer support operations. Even granular data on engineering productivity and release cycles can showcase the efficiency of product development.

Demonstrating scalability requires showcasing the ability to handle significant growth without a disproportionate increase in costs. Data on infrastructure utilisation, the cost of serving an additional customer and the automation of key processes can provide compelling evidence of scalability. What’s more, tracking cohort performance over time can illustrate the long-term value and potential of the customer base.

Finally, long-term viability is underpinned by data that demonstrates customer loyalty, product stickiness and the ability to adapt to market changes. High customer retention rates, low churn, and positive net promoter scores (NPS) are crucial indicators of a healthy and sustainable business. Data on cross-sell and upsell success demonstrates the potential for future revenue growth within the existing customer base.

In a saturated insurtech market, differentiation is paramount. For the forward-thinking insurtech, a well-executed data strategy and the ability to present that data in a compelling equity narrative can set a company apart from its competitors. Supporting that with a roadmap that shows how you are and plan to utilise AI as a key lever only enhances your position. As the insurance landscape continues to evolve, insurtechs that leverage data effectively will command stronger valuations and unlock greater exit opportunities.