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3 tripwires to accurate QSR mobile/e-club campaign measurement

The idea behind e-clubs is to help brands drive sales by turning the customer experience into something more personal. But how do you know if your e-club is working if you can't adequately measure the results. Here are three common "tripwires" to accurate measurement and how to avoid them.

November 21, 2018 by Brad Rukstales — CEO, Cogensia

E-clubs are awesome tools for building brand loyalty in the restaurant business. You simply collect a few snippets of information like your customers' email addresses and birthdays and then you can tailor communication to them by maybe offering a birthday treat or seasonal discount.

The idea behind e-clubs is to help brands drive sales by turning the customer experience into something more personal. But how do you know if your e-club is working if you can't adequately measure the results. In such cases you may end up in campaign recap meetings that sound something like this:

Director: Let's take a look at last week's e-club campaign. How did it perform?
Marketing Manager: We sent 500,000 emails with an open-rate of just under 15 percent. It's up slightly from last month.
Marketing Analyst:But, we only saw 445 transactions with that promo code.
Marketing Tech: Our servers know that's the same promo as our national ad promo. They use that code on the POS terminal because it's easier.
Marketing Analyst:Regardless, it doesn't accurately represent the impact. We need to know how many people receiving the email, then used the code.
Marketing Manager:How do you even get that info?
Marketing Analyst:We need a consistent metric on incremental impact. We can't simply lump 500,000 subscribers together and expect to get anything meaningful from their response.
Director:Okay, so we don't know how it performed. We need to figure this out.

Though that post-campaign conversation is merely a figurative one, it still highlights the three key shortcomings common in restaurant marketing measurement, including:

1. Promo code redemptions do not necessarily equal impact. Redemption shows only that a diner who received the brand's email showed it to a restaurant employee who entered it into the system. Most consumers who redeem coupons are already active with a brand, but there's no concrete evidence redemptions are incremental. Brands that develop an impact model that measures same-store sales and daily redemptions, however, get a good understanding a promotion's impact. 
2. Real-life restaurant teams may not accommodate tasks needed for measurement. Customer-facing QSR employees are — like all of us — hardwired to take the easiest path to any task, even though doing so can disrupt a brand's marketing team's efforts to improve accurate measurement. As a result, tests that include a variable and control group can be affected and produce inaccurate results.
3. Most restaurants don't have tech to match the brand's e-club/mobile app to transactions. How do you connect dollars received at your POS to promotional emails sent to potential customers? After all, friends share codes or give them away in the office or to other acquaintances. As a result, the dots aren't accurately connected and the marketing team never really realizes that. 

What's a QSR marketer to do?

First, think about what might happen in customers' lives if your brand didn't market to them at all. Design an experiment with two groups of customers including one which you will market to and the other which you will not. Then track same-store sales before and after a promotion and compare the two groups' results. With a relatively large e-club, this will tell you the overall sales impact. 

Next, consider expanding your e-club to include behavioral data. If you are fortunate enough to have track 1 data in your POS, you can append behavioral data to your e-club. You most likely can't do this yourself since this also requires external data, but you can have it done for you on a regular basis. The insights that this activity will bring will more than offset the cost of this data. 

Behavioral data helps you observe if and how actual individuals respond to a promotion and allow you to compare it with the "control group" which you did not contact. The result is an extremely valuable and honest assessment of your marketing effort's impact. 

For example, consider an e-club where 15 percent of customers receiving a given promotion end up making purchases, compared with just 10 percent in the control group. The incremental performance of the promotion is therefore 5 percent. 

But in the above scenario what happens if — as is likely — your brand gave a discount to the portion of the 15 percent who used the promotion, even though the control group's 10 percent didn't receive the discount? This highlights the critical need to include sales, discounts and margin to get a true picture of campaign impact overall. 

Granted, this kind of measurement isn't easy because true ROI determination requires changes to your everyday routine. But the value achieved is worth it when you unequivocally know the answer to that question, "How is our marketing performing?" 

Photo: iStock
 

About Brad Rukstales

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Brad Rukstales, President and Chief Executive Officer

Brad is a visionary leader who knows the transformational impact data-driven strategies can have on an organization. From a solo consultant in 2002 to accomplished business leader with over 30 technical employees, he has built Cogensia into a powerhouse marketing firm with all of the capabilities needed for companies to implement data-driven marketing. Brands such as Houlihans, Applebee’s, Red Lobster, Del Frisco’s, Morton’s, CenturyLink, and ADT have looked to Brad and Cogensia for leadership in their data-driven strategy development and 1:1 communications. Restaurants credit Cogensia with driving 3%+ revenue growth. From identity management to database, analytics, and personalization, Cogensia brings data to life for marketing and customer experience management.

Brad received his MBA from The University of Michigan and started his career as a statistician, building predictive models long before Big Data came on the scene. He also held the position of Chief Operating Officer at Infoworks, at agency analytics company.