It’s time for retailers to drop their expectations – of loyalty, that is. Too often, rigidly focusing on the best, most loyal customers leads to ignoring others who are worth pursuing. Winning in retail today requires keeping an eye on all customers, even those not living up to loyalty expectations.
Winning in this retail environment requires keeping an eye on all customers, even if they are not living up to loyalty expectations. There needs to be an approach that balances both loyalty and engagement.
Loyalty vs. Engagement
Engagement is a gradient of loyalty: people that aren’t active can still be loyal. They may only shop once a year, but still be seasonally predictable. Solely relying on the marketing cornerstone RFM (recency, frequency, monetary) lets too many customers slip through the cracks.
Don’t just forget about the one-time buyers who purchased a year ago – they may well be worth engaging. Instead of a 12-month cutoff, more retailers are starting to consider 36 months. To make this a smart investment, brands need to know what is going on with customers on their terms, in their world. And that means outreach. Like a “haven’t seen you in a while – how come?” survey to fill out for a gift card. Or keeping a first-purchase anniversary program in place for multi-year buyers/reactivated buyers. They may not be currently active, but continue to treat them like they are, or will be soon.
Start looking at customers that haven’t bought in the last 24-36 months and work to reactivate them. Let customers know about any changes in the brand. Keep them engaged.
Don’t Preach to the Choir
To optimize return-on-ad-spend (ROAS), brands have blasted away for years at every sure thing they could find, ignoring good buyers with potential life changes. But as CEOs’ frustration with static top lines has grown, so has marketers’ receptiveness to new ideas, lifecycle recommendations and new program testing.
Brands are starting to shift their focus from acquisition, looking more at re-purchase and retention. They are learning that being customer-centric should be a data-fueled philosophy. They are taking a closer look at what it costs to acquire a new customer and comparing that to the costs of engaging and retaining them. Also, they are looking at the way customers were retained to help determine the value of the customer.
Value of a Customer
The timing to reactivate or write-off varies by retailer. Cost, quality and nature of the product (e.g., t-shirt vs. high quality shoes) are all variables in the customer relationship, timing, and the value of that customer.
Let’s say a T-shirt company has a 12-month period of retention, which may be reasonable. Armed with advanced data analytics, marketers can see when in that calendar year customers were acquired, what actions they took, and what they bought. What might have been impossible or prohibitively expensive to learn just five years ago, retail marketers can now regularly predict and execute. They can afford to go deeper in the file for a lot more intelligence and base marketing decisions around that intelligence – they don’t need to make assumptions and they don’t need to break the bank to do it. These greater capabilities yield more informed decisions that deliver a greater final product.
Getting Answers to the Right Questions
These concepts always made sense, but now that we have more to work with, it’s worth it. We can see how valuable customers are now vs. when they lapsed. For example, let’s say Jane was acquired through Google and her first purchase came via the retailer’s website. That’s a very different route than John, who came in-store, made a purchase and supplied his email address. How should they/could they be treated differently? Is John more valuable, based on his engagement? Brands can make these assumptions, but now they have the data to back it up.
The message and cadence of that first acquisition can determine the customer’s likelihood of coming back. Did they get a message because of an action they took or because they missed out on something? Did it feel personal or was it another mass blast to the database?
Successfully reactivating customers, whether newly reactivated or a long time gone, requires that brands gain more insight into average lag time at various lifecycle stages, and try to stay ahead of those time frames with relevant communications.
Advanced analytics, stored in one place, can answer all these questions. It can let retailers decide to reactivate or write-off based on customers’ behavior instead of trying to fit customers into a rigid mandate. Those days are over — there is too much intelligence that can be accessed, understood and acted on. There is too much revenue to be lost by giving up on customers at the wrong time for the wrong reasons, especially when those reasons are not backed up with facts.