3 Ways to Use Big Data for Revenue Attribution
Do you know what drives your customers' behavior? Most organizations have implemented some type of attribution, such as last click, double counting, fractional or arbitrary rules. But each of these traditional types of attribution miss the mark. Worse, they grow increasingly misleading due to the complexity of new and ever-growing marketing channels and order channels. The growth of mobile commerce has even blurred the line between the two, and is now widely considered both a marketing and an order channel. Understanding the impact of each revenue driver is key to making the right marketing decisions.
In a complex and shifting landscape, where is a marketer to start? Good question. Instead, here are some places you shouldn't finish:
- Don't stop the analysis at the matchback stage. This is where double counting is introduced and where probably 80 percent of direct marketers get stuck;
- Don't make up rules on how to attribute revenue (7-day, last click, last touch); and
- Don't disconnect your attribution from your targeting engine.
The next step is to assemble several types of data and resolve to analyze them:
- Behavioral data
- Offline matchback data
- Online matchback data
- Contact history (search, display, catalog, DM, email, affiliate, etc.)
- Demographic/overlay data
Yes, this is Big Data. With the new tools available today, marketers are finally able to harness the power of their data through the use of predictive analytics, allowing them to optimize their marketing spend and attribute revenue to the right place. Knowledge is power, and every website click, page view, ad impression purchased/served, email sent and order taken—be it in-store or online, via mobile shopping or through a call center—can now help marketers improve every step of the decision-making process.
Through the use of powerful and scalable predictive analytics solutions, top marketers have learned a few things: