What lifetime value tells you
Lifetime value is the total value a scanner brings over the whole relationship, not just the first purchase. It decides how much you can afford to spend acquiring a scan in the first place.Lifetime value is a forecast built from your history, not a fact. State the inputs and the window whenever you quote it.
How to compute lifetime value
Lifetime value = average order value multiplied by purchases per year multiplied by average years as a customer. All three inputs are estimates from your own data.Illustrative example numbers, not real-world data: an average order value of 25 currency units, 4 purchases per year, and an average relationship of 2 years gives 25 x 4 x 2 = 200 lifetime value. That is the ceiling for acquisition cost if the relationship is to pay for itself.
How to collect the data
Track repeat purchases by scanner cohort, estimate the average relationship length from your own history, and refresh the inputs as behaviour changes.Keep the raw counts, not just the final percentage. Raw counts let you recompute the metric if you later fix the definition, and they make it possible to spot a denominator problem, such as a week where exposures were estimated instead of counted. Store the counts in one place with the date range, the placements included, and the name of the person who pulled them. That small discipline turns a spreadsheet into an audit trail.
Reading the result
Never judge lifetime value as a single number. Break it down by cohort, channel, and offer type. A campaign that looks average overall may be strong in one segment and weak in another, and the fix is completely different in each case. Compare the same placement against its own past, and compare different placements only when their audiences and contexts are genuinely similar. A short trend over four or more comparable periods is worth far more than a single snapshot, because it separates a real shift from weekly noise.Also check the relationship between lifetime value and the metrics around it. If scans rise while conversions fall, the new scanners may be less interested visitors, or the landing page may have changed. If cost rises while lifetime value stays flat, you are paying more for the same outcome. Metrics make sense in groups, not in isolation, and the habit of reading two or three together prevents most misreadings.