Your data has a price. Here is how to find it.

Ask a retailer what their first-party data is worth and the answer usually starts with volume. Millions of loyalty members, years of transactions, billions of page views. Impressive numbers, and commercially meaningless. A database is not an asset until someone will pay to use it, and nobody pays for rows. They pay for outcomes.

That is why the valuation has to start at the other end of the market. Not "what do we hold?" but "what will advertisers in this region pay to reach the people this data describes?" The difference between those two questions is the difference between a business case a board approves and a slide that dies in the first finance review.

Start with demand, work backwards

A demand-first valuation prices each channel the way an advertiser buys it. Onsite placements are priced against the inventory you can genuinely sell without damaging the shopping experience, which is a fraction of total traffic once transaction pages, utility pages and suppression rules are removed. CRM media is priced on the audience that has actually opted in, not the total base. Offsite activation is priced on the members you can match to the buying systems agencies already use. In-store screens are priced on footfall that actually passes them.

Each channel then carries the assumptions this market supports. Regional fill rates, not imported ones. Pricing that reflects what a media agency in this region will sign, not what a US benchmark says they should. The result is three scenarios, conservative, base and aggressive, with every figure traceable to a formula and a benchmark.

Nobody pays for rows. They pay for outcomes.

The valuation is the business case

Done properly, the valuation does three jobs at once. It sets the revenue ambition, so the board knows what success looks like in year one and year three. It sizes the investment the opportunity genuinely requires, which is usually less than the technology pitch suggests. And it sequences the build, because the channels that need no new infrastructure should earn first and fund what follows.

It also does a fourth job that is rarely discussed: it tells you if the opportunity is not there yet. A valuation that cannot clear the cost of the operation is a finding worth paying for, because it arrives before the investment rather than after it.

Before data earns money, it needs a price. Finding that price is a discipline, not a guess, and it is where every serious data monetisation journey starts.