Go Big or Go Personal With Smart Analytics
Retail is intensely competitive. You either need to go really big or find a niche where your service, quality, convenience or personal touch are more appealing to some people than going to Amazon.com, Wal-Mart, Home Depot or some other take-your-pick competitor with crushing economies of scale.
However, there’s a new problem with this. In their quest for ever-more growth, those behemoths mentioned above spend loads of money on marketing and improving the customer experience both online and offline. Why? They need more customers. If you're a midsized retailer, they need your customers. They want your share, and they don’t want to share. They want it all. They may not have the flexibility to match your quality or personal service, but they can invest in marketing technology that makes them seem personal.
I had exactly this conversation recently with an executive who has been the head of e-commerce merchandising for several retailers in the $2 billion to $7 billion range, but it applies to retailers of all sizes. Consumers not only appreciate, but have come to expect more personalized interactions to win their loyalty.
This executive wasn't simply talking about the Amazon-famous “customers who like this also liked that” general suggestions. He was talking about the Amazon-ilk’s recent push to make the best personal product recommendations that are most relevant to each individual customer from among all available products, and the right offers and messages appropriate to each customer’s lifecycle stage, at the right time for any one customer or in a variable offer campaign to millions of customers at once. If customers get that from Amazon, guess where they will shop first?
The behemoths have led the way by 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 with that. It’s not a matter of “if,” it’s only a matter of “how” there's a way.
Fortunately, 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. This is the case now for the kind of predictive customer analytics needed not only to reduce customer churn, but increase revenue and customer loyalty.
The secret is two-fold: One is to focus on what’s really important to know about each customer. It’s not their “persona," sentiment or how they're similar to other customers. Those are nice things to know, but only to the extent you can translate that into more purchasing. What’s really important is who is ready to buy? When will they buy? What will they spend? What products are they most likely to want? Who is falling off pattern or ready to defect? You can act on that information.
The second is to leverage advances in data science that constrain the data requirements only to what's essential without compromising predictive accuracy and effectiveness. That makes it possible to automate and vastly simplifies the effort.
This is a big deal. A way to get accurate, immediately usable predictions about future purchases, loyalty, risk and value for 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. It may sound impossible, but it’s not, and it evens the playing field. Any midsize 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 always found a more customized appeal. Amazon should start watching out for you.
Peter Moloney is CEO of Loyalty Builders, which offers a simple, cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.
