The research and data businesses that attract the highest quality of acquirer attention — and the strongest multiples — are rarely the ones with the biggest top line. They're the ones whose revenue is genuinely durable, whose client relationships would survive the founder leaving, and whose data or methodology would take a well-resourced competitor years to replicate.
Most owners in this sector know they have a good business. Fewer have done the specific analysis that separates a good business from one that performs well in a sale process. The gap between those two positions is real, and it's worth closing well in advance of any process starting.
The UK research and insights industry generates over £7 billion annually and has seen consistent M&A activity as both trade buyers and private equity seek businesses with defensible revenue bases and proprietary data assets. The businesses that achieve premium outcomes in that market share a small number of characteristics. Here's what they actually look like in practice.
Recurring revenue quality beats recurring revenue volume
Almost every research and data business will describe some portion of its revenue as recurring. The question that matters in a sale process isn't how much of it is recurring — it's whether it actually is.
True recurring revenue, in the way sophisticated buyers think about it, has three qualities. Renewal rates are high — above 85% gross is a starting point, and the best businesses are demonstrably above 90%. The revenue base is sticky because switching genuinely costs the client something meaningful — time, data migration, workflow disruption, relationship rebuilding — not just because inertia hasn't yet been overcome. And the subscription price has demonstrated the ability to grow over time, because the value delivered is clear enough that clients accept increases rather than using renewal as a renegotiation moment.
The metric that separates genuinely recurring businesses from ones that happen to have contracts is net revenue retention. An NRR above 100% — meaning existing customers are spending more each year, even before new client growth is counted — tells an entirely different story about client relationship quality than a business with 80% gross retention and heavy new client dependency just to stay flat. According to benchmarking data from SaaS Capital, the top quartile of subscription-based B2B businesses consistently achieves NRR of 110% or above. That's the standard against which buyers in this sector are measuring what they look at.
The best research and data businesses have built something that integrates into how their clients make decisions — not just how they fill a budget line. That integration is what creates real stickiness, and it's what serious buyers pay a premium for.
The practical test: if you doubled your prices tomorrow, which clients would leave and which would stay? The clients who'd stay are your real recurring revenue. Understanding that split — honestly — is where the analysis has to start.
Client concentration is the risk that gets priced in hardest
Client concentration is the most common value-discount factor in research and data business transactions, and the one that owners most frequently underestimate until they're in a process.
The standard benchmark used by most buyers is that no single client should represent more than 20–25% of revenue going into a transaction, and that the top five clients combined should ideally represent less than 50%. When those thresholds are breached, buyers price it in — not because the revenue isn't real, but because the risk of a single departure materially altering the economics of the business they've just acquired is too significant to ignore.
The more interesting question, and the one worth having proactively with any potential buyer, is how the concentration got to where it is and what the trajectory looks like. A business that was heavily concentrated on two or three clients three years ago and has since built meaningful diversification across ten or fifteen is telling a positive story about market penetration and commercial capability. A business that has been concentrated for years with no clear path to diversification is a different proposition entirely.
Founders who address concentration proactively — who can show what they've done to reduce it, what their new client pipeline looks like, and what the concentration numbers will be in twelve months if current trends continue — have a substantially more productive conversation in a data room than those who wait for the question to surface.
A real data moat is rare — and immediately obvious when it exists
The concept of a data moat is genuinely powerful when it's genuine. Proprietary data that can't be replicated — because it's been gathered over many years, because it represents a methodology that's genuinely distinctive, because it's derived from relationships or access that are structurally difficult to build — creates a defensible competitive position that buyers price into a multiple in a way that few other factors do.
The problem is that many research businesses claim a data moat that's more porous than it appears under diligence. If a well-funded competitor could replicate the dataset in two years, that's not a moat. If the methodology is similar to three others operating in the same space, the differentiation story requires significantly more work than "our data is proprietary."
The businesses that genuinely have a data moat can articulate it specifically. Not "we have twenty years of longitudinal data" but "we have twenty years of panel data from 4,000 respondents in a sector where regulatory constraints make that panel impossible to reconstruct from scratch — we've been quoted in Parliamentary submissions and our data set is referenced in three competitor research reports." That specificity is immediately compelling to a buyer doing diligence. Vague claims about proprietary methodology are not.
If you can describe your data moat in two clear sentences that would make a competitor uncomfortable to read, you probably have one. If it takes a paragraph of caveats to explain what makes your data different, the moat needs more work.
What the management meeting reveals about the business you're actually selling
The management presentation is where sale processes are won or lost in the research and data sector, and the dynamics are different here than in many other business types.
Buyers know that in knowledge-based businesses, the quality of the leadership team is frequently the most important variable in whether the business performs post-acquisition. They're not just evaluating the team's competence — they're assessing whether the business will continue to function when the founder steps back from day-to-day involvement. Can the commercial director explain the client relationships they own? Can the head of research articulate the methodology in a way that would survive the founder's absence? Is there a management team here, or is there a founder with a supporting cast?
The businesses that perform best in this environment are the ones where the preparation for a management presentation reflects genuine depth in the team rather than scripted answers. Where the finance director can speak to the margin structure without referring to the founder. Where the client director can describe the top five client relationships with the specificity that comes from genuine ownership of those relationships.
That kind of team depth doesn't appear overnight. It's built over two to three years before a process, through deliberate investment in leadership capability and genuine delegation of commercial responsibility. The businesses that have done that work are in a fundamentally stronger position when it matters.
Positioning a research business for the outcome it deserves
The gap between what a research or data business is worth and what it actually gets sold for is real and recurring. It's not usually a function of the quality of the business — it's a function of how well positioned the business is when a process starts, and how well that positioning is communicated to the people who ultimately determine the outcome.
At Eranos, we work with research and data businesses across the full preparation-to-exit journey. We understand this sector — the commercial structures, the valuation dynamics, and the specific questions that matter in a diligence process. If you're thinking about your next five years and want to understand how your business would look to an acquirer today, we're worth a conversation.
Published by Eranos ·