We all talk about best practices to evaluate new campaigns. In general existing campaigns, most people go with the CPI/A and I find it to be very wrong; there are easy solutions that do not require a team of data scientists to stand behind you and the cost of producing these solutions will save you a lot of money in the long term.
So what is a session? Many people have different definitions, but let’s agree, a session is when a user opens your app or surfs into your site for longer than 10 seconds, while doing at least one operation such as scrolling or clicking a link.
During the session, a user will create different events, some of which will happen only once, like registration or finishing a puzzle. Other events will occur more frequently, like logging in or posting a comment. Each of these events has an effect on the user journey and ultimately drive users to achieve the goals we set for them.
We collect a lot of sessions and events each day; some come organically, some come from paid channels, and some come from CRM systems we have in the business. Each of these users will drive different actions based on their engagement, but we can agree that all of this session information and events should be collected in an easy to access the database. Some common attribution tools include Google Firebase, Adjust, and Appsflyer, but there are many more on the market, and if you don’t currently use one of these valuable tools, you should look into it.
A baseline is a minimum starting point which we will use to evaluate our campaigns. This baseline will create an easy comparison line which will help us judge our campaigns, and tell us when our campaigns are overachieving or underperforming even when we don’t have the revenue data to do so.
The baseline can be split into two types: reach campaigns, which are aimed at new users and new installs, and retargeting, which aims for a return session of a “known user” based on cookies or device ID.
For example, if I have a dating app, my campaign baseline would be for the user to finish a six-step tutorial, upload an image to their profile, and update their bio. This ideal user journey within my app would be considered a good baseline. If I find during the evaluation step that users typically achieve more or less within my app, then I can reassess my campaign from there. The easiest way to calculate how many users are reaching or surpassing my baseline is to get a percentage of each event out of the total users I reached within a certain time frame, for example, one month.
Now that I have my baseline, I can start to evaluate my dating app campaigns, comparing the performance of past campaigns to that of the current ones. We can then use this information to run more focused ads. For example, if we see that ads with models who have blonde hair perform better than ads with brunettes, we can make an effort to invest in more ads that feature blonde models. But in order to really make our campaigns most effective, we should also use segmentation on top of this.
In today’s world, it’s not enough to use CPI/A as a measure to evaluate your campaigns; the decisions need to be based on better structured KPIs using baselines that compare the past and the present. Doing so will allow you to actually understand if you are growing or declining over time, and it will ensure your ability to stay on top of the business and see when your users’ tastes are changing.
The sooner you start working on your baseline, the sooner you can properly evaluate your campaigns and optimize each euro you spend for the best performance.