Building your own attribution models can increase your cash flow and ROI

Research indicates that using different multi-touch attribution (MTA) methods will produce different budget allocations and resolve into statistically different ROI performance. If this is true, you can improve your ROI simply by customizing the attribution lens a marketer uses for LTV.

The best way to create attribution models is to get programmatic access to your ad and site/app interactions digitally. By mapping out the entire customer journey event by event, data scientists use this information to test and attribute LTV to different parts of that journey.

Once a custom attribution model is built, you can test its efficacy by setting up a series of A/B tests that pit a previous attribution model against the new attribution model. Such a test would require the same budget allocation discipline for both A and B when running campaigns. Over time you’ll be able to determine if there is a significant differnece in performance in cash flow and ROI when using different attribution models.

Most marketers will start off with a simple tool that uses some kind of built in attribution logic. The simple marketer will rely on the tool to have the attribution logic and focus purely on optimizing ROI based on the campaigns they’re running. This can help a marketer at first glance, but the limitations of cookie-cutter models will never perfectly match the specific requirements your business may have.

As a result, serious marketers who spend ad dollars at scale will employ data scientists to customize and iterate on attribution models (as well as their campaigns) by testing new methods for performance improvements in ROI. By customizing your MTA model and realizing performance improvements in ad campaigns, you can quickly justify the amount of work that goes into hiring a data guru.