CR and CPL: what's the cost of an estimation error?
A stream of our marketing funnel might be designed according to a model of acquiring leads through a third party with the aim of converting them in the short term with specific actions.
In this context, we pay someone to have a sing-up somewhere on our website, for example to the newsletter. We do not trade views or clicks, but a customer contact to work on in the future, like an e-mail address or a phone number. Legally.
The number of contacts we then convert to a purchase over the total number of contacts acquired determines the Conversion Rate (CR).
The CPL, divided by the CR, determines the CAC. In this analysis, we want to investigate which parameter between the CPL and the CR is impacting the CAC more heavily.
CR and CPL initial estimation
Let’s assume we have an agreement with a partner to run a campaign with a CPL of €0,8 for each user who signs up to the newsletter. Hence, for every newsletter sing-up coming from the marketing campaign, we pay the partner €0,8.
Our assumption is to be able to convert, over time, the leads acquired with a rate of 2%. This means that, every 100 sign-ups coming from the campaign, we will covert 2 contacts into a purchase.
As a consequence, the CAC expected by the campaign is €40.
|Cost per Lead||€0,8|
|Cost of Customer Acquisition||€40|
Now, what happens to the CAC in case the CR is lower or greater than 2%? Clearly, for greater CR the CAC lowers and vice versa.
Cristal clear. But what we aim at is to get a clearer view of how the CAC is sensitive to the CR and CPL.
CR and CPL impact over CAC
Let’s then build up a comprehensive picture of how the CAC changes when the CR and CPL change.
And let’s show the change as a deviation from the initial case set up above. Doing so, we can compare different campaign performance with the reference campaign.
The analysis was run varying the CR and CPL with a 25% step in order to be able to investigate the marginal impact of each factor at the same variation rate.
If we’re running a campaign at a certain CPL, a different CR than expected has significant impact on the customer profitability. To see the impact, we just need to move along the graph’s vertical axis.
But, what we want to highlight is that the CPL has more significant marginal impact over the CAC than the CR has.
So, if we are evaluating alternative campaigns to improve our performances, we should prefer campaigns with lower CPL at the same expected CR rather than campaigns at the same CPL from which we expect a higher CR.