Let's set up a relationship that we'll explore tomorrow.
Ok, here are some metrics from a marketing campaign.
- Sales = $300,000.
- AOV = $100.
- Customers = $300,000 / $100 = 3,000.
- Ad Spend = $150,000.
- Profit Factor = 40%.
With the available metrics, we can calculate ROAS.
- ROAS = $300,000 / $150,000 = $2.00.
Oooooh ... not good.
Let's calculate profit:
- Profit = $300,000 * 0.40 - $150,000 = $120,000 - $150,000 = ($30,000).
We lost $30,000. No bueno.
The key here is to map profit to customers. We had 3,000 customers purchase.
- Profit per Customer = ($30,000) / 3,000 = ($10.00).
There it is! That is the number that matters. Not ROAS. Profit per Customer. We lost $10.00 to generate each customer who purchased from the campaign.
The secret now is to calculate how much we increase customer spend in the future as a consequence. There are two parts to this ... any new customer delivered via this campaign has a downstream value that is easy to quantify ... and any existing customer migrates to a higher segment and the incremental future value of that segment less the incremental future value of the segment the customer previously belonged to contributes to the CLV delivered by the campaign.
Let's assume the weighted average of the two audiences yields the following:
- Year 1 Profit = $18.00.
- Year 2 Profit = $11.00.
- Year 3 Profit = $7.00.
- Year 4 Profit = $5.00.
- Year 5 Profit = $3.00.
The customers who responded to the campaign deliver $44.00 of future variable profit, with $18.00 coming in the first year. This more than offsets the $10.00 you lost per customer in the campaign itself.
I mean, you invest $150,000, you lose $30,000 on the campaign, and then you make 3,000 * $18.00 = $54,000 ... your campaign pays you a 16% interest rate over the course of a year. And after that you keep producing interest.
So yes, long-term profit covers up short-term losses.
How much more long-term profit is generated? In other words, could we spend more than $150,000, get even worse short-term results, and still come out ahead over time? We'll have a different example tomorrow that answers the question, as we build out our framework for Marketing Budget Experiments.
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