Ad Campaign Optimization: Customer Value Prediction

This is part 4 of our series on Ad Campaign Optimization, previous segments are available in our archives.

When optimizing your ad campaigns, it is important to know exactly what a customer is worth to you so that you know you are not paying more to acquire them than the benefit they provide you. The better you know a customer’s value, the more tightly you can optimize your advertising spend.

Earlier this week, because I did not have user data, I created a value metric based on CPC and bounce rate:

Value = CPC * (1 – Bounce Rate)

 

While this captures the relative value of one campaign against another, it does not tell us if the price we are paying to acquire traffic is more or less than the expected revenue from those users. If you optimize your advertising using this metric, you risk losing money on every user you acquire!

Ideally, we would choose a value function that relates the revenue generated by a customer to the cost of acquiring the customer:

 

Customer Value = Revenue – Cost

One measurement of revenue is the customer’s Lifetime Value, or LTV, which is the total revenue we expect to make from this user after acquiring them. Cost, as we’ve discussed earlier, can be represented by the CPC. By subtracting the CPC from the LTV, the metric is simply how much more money we earn from customers than we paid to get them to click. We can still use this metric to compare campaigns, detect anomalies and do optimizations but now we are sure that, as long as the value metric is positive, we are making more money from the campaign than it costs.

In some businesses, like e-commerce, the LTV might simply be the total amount the user purchases after visiting the website from the ad click. In others, such as subscription businesses, it might be the value of a 12 month subscription. Whatever your business, the more effectively you can measure LTV the better you can formulate a value metric and optimization your advertising.

As with everything in life, no metric is perfect. A user might click on a few different ads before buying something, making calculation of the cost hard to do. You should take into account that whatever measures you are using are not perfect.

Tomorrow we’ll talk about how to take everything we’ve covered this week and automate it so that you don’t have to spend all day everyday on optimization.

Is Ad Campaign Optimization a problem you face in your business? Outlier is a product designed to help! Outlier monitors your business data and notifies you when unexpected changes occur, allowing you to know immediately when ad campaigns are underperforming or overperforming. If you’re interested in seeing a demo, schedule a time to talk to us.

Quote of the Day: “Try not to become a man of success. Rather become a man of value.” ― Albert Einstein