Retail loyalty has been around since the 1700s, when American retailers gave customers 'coins' that they could redeem in-store during a future visit. These were later replaced with stamps and, at the beginning of the last century, box-top coupon cutting was introduced. Later came points, frequent flier miles, and other brand or retailer-specific programmes. In this piece, Steve Grout, director of loyalty, Collinson, explains that although the goal in loyalty has always remained the same, there has been a clear evolution in the mechanisms that deliver it. For retailers who understand that loyalty is a strategy, not a tactic, there is a plethora of options they can implement to improve levels of personalisation and interaction with their most valuable customers.

One point that consistently comes up when we research how customers feel about loyalty is that communication could improve – after all, it’s a fundamental ingredient of any relationship. Our research found that only 37% of UK shoppers receive relevant information and offers from a retailer after they have made a purchase. It seems many retailers are guilty of not adequately tailoring their outreach; yet it is easy to improve communication with their most valued customers by introducing technological solutions.

Collecting data is the starting point

Machine learning and automation of marketing and loyalty communications are particularly useful for the efficient delivery of individualised customer experiences. Increasingly, we see brands using the customer data acquired in-store and online to help with targeted promotional communications and offers. These are not only more relevant to each individual consumer, but are also delivered at a time they are most likely to engage with them.

Steve Grout, Director of Loyalty, Collinson

Although machine learning has been available to retail brands for a while now, it is surprising to see how few customers feel they are getting a personal service. Retailers are still struggling to effectively implement the technology that will help them to discover what a customer’s behavioural data is telling them. This could be about their preferred times to shop, whether it’s online, in-store, or both, what motivates them to purchase and which products they like or could be buying next.

Personalisation adds value

Making manual decisions in an attempt to deliver ‘personalised’ rewards across a diverse customer base can naturally lead to irrelevant rewards, not to mention how time-consuming and costly this process can be. No two customers should be treated the same; and their loyalty incentives should reflect this. Taking an algorithmic approach to a much wider data set on what a retailer knows about each customer’s habits and interests is simply more accurate and efficient. It also enables cost-effective personalisation that evolves over time, as feedback and additional shopping data produced by customers is pulled into the system.

This, in turn, helps customers feel as though their favoured brands understand them and want to add value to their experiences, rather than appearing at unwanted times with irrelevant sales content. If retailers successfully offer their customers this tailored, one-to-one experience, they are more likely to drive engagement and longstanding devotion to their brand.

Sharing the love outside sales periods

Over half of UK consumers (58%) say they enjoy the thrill of finding a bargain during sales periods, but a good deal is not enough to keep them devoted to a brand. It is important to realise that while sales periods may be great for acquiring new customers, the follow-up is just as important. Unfortunately, retailers are not maximising the acquisition of loyal customers, as only a third (36%) of UK consumers we surveyed were encouraged to register as a member, rather than check out as a guest when they made a purchase online. This is a trick that’s being missed – whether you are in a sales season or not.

The savviest retailers out there are already using advanced analytics to mine and analyse the data gathered from their customers during the increased sale season footfall; M&S, Sainsbury’s, and Amazon are prime examples of brands we think are doing, or starting to do, this well. They then use this insight to make increasingly accurate predictions about the types of items specific customers might be interested in or will consider purchasing next. As a result, brands will be able to deliver the personalised marketing experiences that will help them to drive longer-lasting customer loyalty.

Raising the bar

Each time a customer interacts with a brand and has a good experience, a new standard is set. Machine learning represents a fantastic chance to ensure that retailers keep meeting, and indeed raising, that bar with individual interactions and shopping experiences that make customers feel recognised and valued. Ultimately, the goal for every retailer should be to explore how they can use machine learning and individualisation to build on the successes of busy sales periods, providing each customer with better rewards, content, and customer experiences that will keep them coming back year after year.This content was originally published in RetailTechNews.