Woo them for life: Retailer lessons for the digital age

On a recent Saturday afternoon, having taken the dog for a long, muddy walk, I indulged in some me time by preparing a cup of hot chocolate and making myself comfortable with the newspaper in a quiet corner of the house. Half a page in, the phone in the next room rang. I got up to answer it and was greeted with the recorded message, “Because you were recently in an accident, you are entitled to compensation of $4,326. Press 5 to speak to one of our agents.” But I hadn’t been involved in an accident recently, and no way would I do business with anyone ignoring my registration with the National Do Not Call Registry.

The following Monday morning, mail in the form of a heavy brochure from a broadband provider had invaded my letterbox. The mailing—equivalent to half a tree in paper—implored me to switch to the provider’s services—for the 79th time. What are the chances that having ignored its offer 78 times previously I would give in and sign up with the provider now?

Enough with the unwanted offers

We’ve all been there, and presumably such irrelevant, untimely and impersonal offers annoy us all. Dare I say, some retailers can be just as bad as the ambulance chasers and the telecommunications providers. The more devices we have, the more we seem to be bombarded by offers from retailers—from those we once browsed online to those whom we frequent and remain loyal to. Just because these intrusions are cheap and easy, doesn’t make them right.

Busy, hard-working consumers appreciate the convenience of being served a proposition that shows that the retailer knows them, respects them, excites them and grows with them. I would even trade my personal data for such good service.

Data. That’s the crux of the matter.

In my job today working for a technology vendor, and previously in the consumer packaged goods (CPG) industry, I’ve never met a retailer who didn’t have lots of customer data: basket data, browsing data, call center records, payment details, surveys, transaction logs (t-logs), third-party market data and in many cases, loyalty card data. Social media too has heaps of data.

However, I do meet many retailers who look at all this data in silos—if they look at it at all—and therefore have a fragmented, channel-by-channel, brand-by-brand view of the customer. As a result, segmentation is one-dimensional, high level and doesn’t tell the retailer what customers really prefer, are willing to pay for or even what puts them off. Without that information, the obvious tactic becomes spray-and-pray marketing, which generally produces a very low response rate. All the more damaging to retailers is the unsubscribe response, which means that it has lost the customer—perhaps forever.

Positive customer experiences through predictive analytics

How do retailers overcome this result? By allowing the data to do the hard work for them. If you’re a retailer, let the data talk to you. The data is there for you to harvest, analyze and convert into the driver of great customer experiences that can generate profits for your organization. By getting to know customers through behavioral segmentation, utilizing data from all your touch points and understanding how to take advantage of up-sell or cross-sell opportunities to each microsegment through granular affinity analysis, you set yourself on the right track. In addition, learning customers’ price sensitivity and inclination to respond to certain tactics should steady your course in the right direction.

With this insight, you can predict what each microsegment or each individual customer will most likely respond to positively, and consistently execute the next-best action through any system of coordinated engagement. That action means that if a customer is on your website, you can capture what they are browsing. Moreover, you can relate it to the fact that they recently returned some items, sent a query email to your customer service team and put a few items in your mobile app’s basket before abandoning them. You can then use predictive analytics to define an optimal action. For example, the action can send a banner or a text message from whichever channel the customer has shown a preference for and that includes an offering the customer sees as absolutely spot on, relevant, timely and personal.

Apply data to predicting customer intelligence

If you get this action right, millions of other shoppers, including me, will be your satisfied, loyal and big-spending customers forever. Learn more about IBM solutions for predictive customer intelligence and solutions for retail.

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