Retail CMOs Feel the Heat, Look to Data
The insights, control, decision making and results made possible by data analysis are truly impressive. Think of where a business would be if employees just performed duties without recording sales, payments or production activities, and couldn't monitor the analysis of these variables in income statements and reports. Good data, if accurate, is gold. Unfortunately, many marketing teams are still way behind the curve on making better use of their data. For many retail marketers, getting data smart isn’t just good business; it’s quickly becoming a matter of survival.
“Among the top 30 retailers with an enterprise-level chief marketing officer, just under half have seen their marketing leader depart within the past 12 months.” – Russell Reynolds Group, May 2016
Recent studies show that retail CMOs are under fire more than ever and are suffering extraordinarily high turnover as a result. The reason? They need to add data, optimization and digital mastery to their positioning and branding skills. They need to close the gap with CIOs and even CFOs. It’s not optional anymore.
It's hard to find a marketer today who doesn't leverage data to do his or her work, or who doesn't believe in data-driven marketing. Many marketers explain how they use their email program and Google Analytics to measure campaign response and web activity. Some say they’ve profiled customers into more than one segment. This is all a great start, but it’s so rudimentary given the data available to most marketers and the enormous potential of that data.
The most common complaint among retail marketers is that the company has all kinds of customer data, but cannot figure out how to use that data to improve marketing efforts.
One reason used to avoid or delay smarter use of data is the concern that opportunities exposed by the data are less critical than the creative appeal, positioning and brand experience reflected in the campaign.
Another excuse also comes up regularly: “We want to leverage our data more, but we’re too busy to deal with that now.”
All of this — despite the lip service — has shown that too many marketers are still uneasy with data, or don’t appreciate how it can be used. Data seems so antithetical to traditional marketing sensitivities for creativity, artistry, psychology, identity and emotion. There's no doubt the mastery those elements make for good marketers, but data brings undeniable facts into the mix. It’s not that data is more important; it’s that it is important.
Learning to Trust Data
Many point out that data can be deceptive as well. This is where it gets interesting. Incorrectly recorded data (i.e., data that doesn’t accurately reflect the facts it was meant to record) sabotages the accuracy of any analysis. Data consumers can also misinterpret what the data or its analysis actually represents, often misguided by presentation. You have to work to make sure you understand what you're looking at. There’s no way around that.
Finally, data analysis itself compromises the underlying facts it relies on. But what does that mean?
Analysis compiles and relates data, summarizing the underlying facts. However, the “average” customer, as reported by the analysis, doesn't look like most individual customers. The profile of a loyal customer is an abstract fact that may be true mathematically, but few, if any, real customers match the profile exactly. And that’s OK. That profile will still be extremely helpful in identifying loyal customers. It will not get it right all the time, but it will improve your odds. It’s like timing your commute to avoid traffic based on your analysis of observed facts. You won't be able to avoid traffic all the time, but you know how to improve your chances.
Upping the Odds for Customer Response
Retail marketers don't necessarily need to analyze the data themselves. There are experts, systems and cloud services that can do that. However, marketers still must ask the right questions and leverage the results if they want to improve campaign performance. Good questions may include the following:
- Which customers will be ready to buy during the campaign period?
- Which customers will spend the most, or at least more than the campaign costs per customer?
- Which customers will be interested in a certain product promotion, or what products will be best to recommend to each customer?
These are abstract facts that can be obtained from the transactional data most retailers already have.
The answers obtained per customer will not be true for each customer; it’s just a mathematically derived abstract fact about patterns found in the data. Such facts will improve your targeting accuracy, however. The more accurately the analysis predicts each customer’s probability to respond to a campaign, the more you can leverage it to impress customers with a personalized user experience, increasing response rates.
Of course, the creative appeal of the communication makes a difference, too. However, experience shows that whether the creative appeal is strong or weak, leveraging facts from the data will consistently deliver significantly more revenue. It’s a compelling truth that deserves a spot high on the priority list of retail marketers looking to effectively survive and compete in today’s increasingly personalized world.
Peter Moloney is CEO of Loyalty Builders, which offers a cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.