Here’s what’s easy: Measuring the effect of individual engagements like Web page views, email opens, paid and organic search clicks, call center interactions, Facebook likes, Twitter follows, tweets, retweets, referrals, etc.
Here’s what’s hard: Understanding the combined effect of your promotions across all those channels.
Many marketers turn to online attribution methods to assign credit for all or part of an individual order across multiple online channels. Digital marketing guru Avinash Kaushik points out the strengths of weaknesses of various methods in his blog, Occam’s Razor in “Multichannel Attribution: Definitions, Models and a Reality Check” and concludes that none are perfect and many are far from it.
But online attribution models look to give credit to an individual tactic rather than measuring the combined effects of your entire promotion mix. Here’s a different approach to getting a holistic view of your entire promotion mix. It’s similar to the methodology I discussed in the post “Use Market Research to Tie Brand Awareness and Purchase Intent to Sales,” and like that methodology, it’s not something you’re going to be able to do overnight. It’s an iterative process that will take some time.
Start by assigning a point value to every consumer touch and every consumer action to create an engagement score for each customer. This process will be different for every marketer and will vary according to your customer base and your promotion mix. For illustration’s sake, consider the arbitrary assignments in the table in the media player, at right.
Next, perform this preliminary analysis:
- Rank your customers on sales volume for different time periods
—previous month, quarter, year, etc.
- Rank your customers on their engagement score for the same periods
- Examine the correlation between sales and engagement
—How much is each point of engagement worth in sales $$$?
After you’ve done this preliminary scoring, do your best to isolate customers who were not exposed to specific elements of the promotion mix into control groups, i.e., they didn’t engage on Facebook or they didn’t receive email. Compare their revenue against the rest of the file to see how well you’ve weighted that particular element. With several iterations of this process over time, you will be able to place a dollar value on each point of engagement and plan your promotion mix accordingly.
How you assign your point values may seem arbitrary at first, but you will need to work through this iteratively, looking at control cells wherever you can isolate them. For a more scientific approach, run a regression analysis on the customer file with revenue as the dependent variable and the number and types of touches as the independent variables. The more complete your customer contact data is, the lower your p value and the more descriptive the regression will be in identifying the contribution of each element.
As with any methodology, this one is only as good as the data you’re able to put into it, but don’t be discouraged if your data is not perfect or complete. Even in an imperfect world, this exercise will get you closer to a holistic view of customer engagement.