Are Retailers’ Personalization Expectations Unrealistic?

According to a new report from RSR Research, retailers have identified personalization as their top opportunity for growth of their digital channels. And with good reason — personalization has proven to drive engagement and sales.

Seventy-three percent of consumers prefer to do business with brands that use personal information to make their shopping experiences more relevant, and 78 percent are more likely to be a repeat customer if a retailer provides them with targeted, personalized offers. Just look at Amazon.com. According to McKinsey, the online retailer’s recommendations engine accounts for roughly 30 percent of its annual sales of approximately $107 billion.

However, failing to provide personalized, contextual experiences will have the opposite effect. A study from the CMO Council found that more than half of U.S. and Canadian consumers consider ending their loyalties to retailers that do not give tailored, relevant offers.

While retailers understand the benefits and are certain that personalization will be a core tenet of their marketing strategy, there are many different meanings for what personalization really is and, ultimately, how to implement it. RSR suggests this could lead to unrealistic expectations.

What Does Personalization Mean Today?
The concept of retail marketing personalization has been around since the 1990s. Unfortunately, many early approaches overpromised and underdelivered (for myriad reasons), ultimately giving personalization a bad name amongst the retail community.

The Holy Grail of retail marketing has always been one-to-one personalization, but who has had the time to look at every customer or prospect uniquely, much less formulate and execute a plan of attack for each person? That vision has become even more challenging with consumers bouncing from device to device and channel to channel, making our connections to them more difficult to follow and manage.

Therefore, I’d agree with RSR’s analysts that retailers’ expectations for personalization are unrealistic — if they think they can achieve true one-to-one real-time personalization by using the same methods they've been using for the past several years. Instead, they need to consider new, advanced approaches, including the following:

• Use data for action, not just insights. A combination of proprietary and third-party data will give retailers maximum value if they use the learnings for action and to gauge outcomes. Data has the power to illuminate the motivations behind a consumer’s buying journey and, with the right technology solutions in place, makes it easier for retailers to improve customer experience and engagement across all channels.

• Leverage artificial intelligence (AI). I’m not talking about some crazy sci-fi stuff here; I’m talking about really sophisticated math that can be incredibly powerful when used in retail. Using AI, retailers can test each data point they have on a shopper in aggregate with other people who have some matching data points. AI can try relevant combinations of weights among these data points until it adjusts and finds the right combination to deliver a contextually relevant marketing message that will ultimately drive a purchase.

• Combine location with historical context. Many retailers are using location-based offers, beacons and similar technologies to make recommendations to customers based on where they are physically. But what happens when you measure customer activity of that week compared to what they were doing and buying every week before that, compared with contextual social information — e.g., whose birthday it is, travel plans, etc. — as part of a location-based offer? Combining the first two practices allows retailers to gain historical perspective on purchase behavior and move beyond recommendations based simply on past purchases.

The importance of retail personalization cannot be underestimated. However, in order to realize its full potential, retailers will need to adopt new and novel approaches.

Craig Alberino is co-founder and CEO of Grey Jean Technologies, an AI-powered personalization company that provides retailers with predictions of consumer behavior.