Before a potential customer books a trip, applies for a loan or rents a car, he or she may be exposed to 10 or more touchpoints from a brand. How does the marketer know which ads are most effective? What are the synergies driving conversions? How does the effectiveness of marketing spend across multiple channels change over time?
Particularly when your product is a considered purchase rather than a spur-of-the-moment buy, it’s an oversimplification to assign all the impact for conversion to the final AdWord or the first banner ad the prospect sees. Touchpoint timing and sequencing are important.
If your goal is to encourage prospects to schedule a test drive for a new car, how and when should you follow up your 10 pm TV commercial? When will the prospect be in the right frame of mind and have enough information to pull the trigger and book the appointment? Which device will they have in their hand?
Attribution modeling can help you answer these and many other key questions. Attribution modeling is a method designed to measure the financial return on multi-channel marketing activities. The insights from this technique enable you to course correct campaigns and adjust activities to optimize conversion.
Traditional attribution modeling uses less sophisticated — and less powerful — approaches to determine the conversion driver, like first touch or last touch. This ignores interactions between channels, may not include offline behavior and can potentially produce biased recommendations.
What marketers need today is the predictive power that comes from identifying true cause and effect, and captures the multi-layered interrelationships between channels. Classical models ignore these relationships.
A much more sophisticated attribution modeling approach uses advanced analytics, operating in real time at the individual customer level. It delivers a far deeper level of insight that can drive customer intervention strategies to maximize conversion. For example, it can help you understand:
- Which marketing channel is most effective immediately after direct mail
- Which channel is most effective on weekends
- When to retarget with banner ads
This type of algorithmic approach determines “credit” for conversion across touchpoints by using granular, time-stamped data from millions of customers exposed to billions of ads, because the time decay rate of a banner ad, an email and a YouTube ad are different.
For example, a piece of direct mail can hang around in the memory for much longer than the few seconds of the YouTube ad that a viewer can’t skip. Direct mail is making a comeback because, despite its high cost, it can be effective in the right circumstances. For example, if you are selling a family season pass to a theme park for $500, direct mail to the right audience sequenced with digital media could be effective.
By combining marketing touchpoints with other vital elements of the purchase path, like customer credit checks, app activity and inventory availability, you can bring more science to bear on managing your marketing spend. This requires a flexible data model that can handle unstructured contact history. Overlaid with algorithms and a tool to integrate it, this information provides a rich, real-time and actionable view of the customer funnel.
As digital channels continue to proliferate, marketers face the challenge of selectively investing in channels and customers that bring the highest return on investment. An advanced approach to multi-touch attribution can help you understand the impact of all marketing and media, from aggregated channel views to user-level interactions. The result: Improved marketing ROI.