Use case
What has a customer been worth, and does the cheap source bring the good ones?
Not a forecast. This is what each customer has actually paid you, net of what came back, laid beside what it cost to acquire them. It is the report that settles whether the source with the lowest cost per lead is the one worth more spend.
Customer value by acquisition source
saved report · Sales / Customers
By source
| Acquired via | Customers | Net revenue | Avg per customer | Orders each | Cost to acquire |
|---|---|---|---|---|---|
| Referral | 96 | $198,400 | $2,067 | 2.4 | $44 |
| Organic | 47 | $112,500 | $2,394 | 2.1 | — |
| Webinar | 61 | $96,300 | $1,579 | 1.6 | $518 |
| Paid search | 84 | $142,800 | $1,700 | 1.3 | $814 |
| Unknown | 8 | $12,400 | $1,550 | 1.1 | — |
| All sources | 296 | $562,400 | $1,900 | 1.8 | $394 |
applied — a cancellation is a separate negative row, not a flag — these figures are net of it, which is why they sit below the gross totals shown elsewhere
Every figure above is invented. We build these reports for real businesses and do not put their numbers, or their names, on a public page.
Why it is not one query
The rules that decide the number.
We do not predict it
There is no model here estimating what a customer will be worth. Everything on this page is money that has already moved. If a forecast is what you need, that is a real piece of work and we would rather say so than dress up an average as a prediction.
Gross is not net
In Keap a cancellation is a separate negative row, not a flag on the original order. A total that sums order amounts counts refunded money as revenue, and the gap between gross and net is usually where the argument starts.
One customer, several records
The same person signs up twice, or a household buys under two names. Whether those are one customer or two changes every number in the table, so it is a rule the business sets rather than something inferred from an email match.
The new ones drag it down
A customer who bought last week has had no time to be worth anything. Averaging them in makes a growing month look like a declining one, which is why the cohort being averaged is a parameter you can see.
Cost and revenue rarely meet
Acquisition cost sits with the campaign and revenue sits with the order. Putting them on the same row is the whole job, and it is the only place the two can be divided without somebody copying numbers between two exports.
Averages hide the shape
A handful of large customers will carry a mean a long way from anything typical. The count sits beside every average here for that reason, and a detail sheet behind it shows the distribution rather than asking you to trust one figure.
Related questions
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