What Keeps Mid-Sized Retailers Up at Night (or Should)?
Mid-sized retailers have their hands full. On the one hand, they have access to more data than ever before, but squeezing out the value for relevant customer messaging and personalized product recommendations looks like a daunting, expensive task. On the other hand, they need to fight to avoid losing market share from approaching behemoths like Amazon that are increasingly committed to 1:1 communications that resonate personally with each individual customer. Retailers that cannot keep up risk having their customers stolen away by the more personal experience customers can find elsewhere.
Retailers seeking to dominate retail markets, like Amazon, are spending a fortune on sophisticated big data analytics so they can personalize customer experience and recommendations down to each individual. Now, everybody has to compete at that level. It’s not a matter of “if,” it’s only a matter of “how.” Fortunately, mid-sized retailers have a way.
New technologies, while expensive at first, tend to become more accessible over time. Before long, someone comes up with a way to make it easier and more affordable, often by zeroing in on the things that are really important and effective, instead of trying to do too much with a laundry list of features. The essential kind of predictive customer analytics –that retailers need to message according to customer lifecycle stage and make individual product recommendations — is becoming available as a much more affordable, automated service. This means retailers of practically any size can use it, not only to reduce customer churn, but increase revenue and customer loyalty.
The secret to making this affordable and automated is two-fold.
One is to focus the analytic process on what’s really important to know about each customer – Who is ready to buy? When will they buy? What will they spend? What products are they most likely to want? And who is falling off-pattern or ready to defect?
The second is to leverage advances in data science that constrain the source data requirements only to what is essential, without compromising predictive accuracy and effectiveness. Those advances make it is possible to automate and vastly simplify the effort.
This is a big deal — a way to get immediately usable predictive metrics about loyalty, risk, and value of each customer, and product recommendations ranked by likelihood to buy, WITHOUT having to source or integrate gobs of customer data, model and analyze the data, or learn and maintain enigmatic tools or platforms. A big deal by any standards. It may sound impossible, but it’s not, and it evens the playing field. Any mid-size retailer can do it, and most small ones can afford it as well.
Like it or not, the big retailers are already doing it. The answer is not to sit back and worry, wondering if you’ll be able to keep up or how long before it really starts to bite. Now, practical, sensible, affordable personalization techniques are allowing retailers to take back the initiative and draw customers back where they’ve traditionally always found a more customized experience. Amazon should start watching out for you.
Peter Moloney is CEO of Loyalty Builders, whose Marketing Lift Service offers a simple, cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.