Retail Personalization: 4 Steps to Revenue Growth and Loyalty
“Retail Personalization” is a very abstract, impersonal-sounding term. Perhaps that’s why it’s often used with more customer-centric words like “customer personas” and “customer journey.” Smart CMOs know that retail personalization is important and want to get it right.
But how to go about it? And more importantly, is it really going to show real results and provable profits?
The stakes are high and the payoffs can be huge. For example, Japanese lifestyle retail giant Muji used a powerful Customer Data Platform (CDP) to drive a 46 percent increase in in-store purchases, but I’ll share more on that a little later.
To do retail personalization right means masterfully crafting not just one customer journey, but many different journeys, because not only do customers differ in their likes and dislikes, they often experience your brand through different channels, at different times, in different orders, taking wildly different paths and channels. Worse, the data that reflects those journeys ends up siloed because these activities are managed by separate systems (and departments) that don’t ‘talk’ to one another, making modeling consumer behavior more difficult to pull off. And how can any CMO possibly integrate all of these and plan for so many different experiences?
The answer is that you don’t have to plan for each variation. You just have to put the right ideas and technology in place, and you’ll get customer journeys and experiences that seem to work naturally, like the natural variations on themes that come up in jazz jam sessions all the time.
To cut complexity and really drive these efforts, you’ll need to embrace data analytics and personalization, powered by efficient new technology like customer data platforms (CDPs), which collect data and use AI-powered algorithms to match these behaviors to specific customers, and use this data to build profiles, segment, personalize and micro-target each customer. These CDPs are becoming increasingly crucial to every retailer’s survival in the current environment.
Retail Personalization: What Customers Really Want
The right data for a personalization-based transformation relies on customers and their behavior, and the data trails they leave as they shop. Better personalized shopping experiences are not only more effective, they’re what most consumers want. The majority of today’s customers are usually willing to trade personalized data in exchange for value, such as special discounts, delivery perks, early access, and other rewards.
A full 84% of customers say being treated like a person, not a number, is very important to winning their business and their loyalty. But what exactly do they include in “being treated like a person”?
- Contextual offers sent at the right time and in the right place
- Relevant products they might not know about
- Helpful reminders to buy things they might need
- Recognition of who they are, by name
To keep track of every detail of a customer’s interactions and to ensure up-to-date relevancy in their communications, many companies — like Muji — are using CDPs. This emerging technology takes data streams about customers and prospects, and uses sophisticated AI-assisted algorithms to piece together accurate customer profiles that can be used to micro-target individuals at the best moment, with the offer most likely to entice them.
Over time, using machine learning, the CDP uses real-world results to refine its understanding and improve targeting, which builds customer loyalty. The following four steps are critical to any retail personalization campaign, and here we illustrate them with the real-world experience of Japanese retailer Muji, which recently “got personal” with its customers in a Big Data way.
4 Steps to Personalize Your Way to Customer Loyalty
Step 1. Fill in your data gaps (and don’t forget IoT).
Blending mobile device location (a form of IoT data) into a unified customer profile — which a CDP like Arm Treasure Data is born to do — can help you understand your customers’ commuting patterns, how often they are near your store, where they are spending time, and what they are buying. And enriching your first-party data with third-party data can fill in any existing gaps in your customer journeys.
In a real-world application of this crucial step, Muji used the Treasure Data CDP to successfully merge its physical and digital shopping experiences by capturing clickstream data from its mobile app and joining it with existing web and point of sale (POS) and other customer data. This required transferring enormous amounts of data — more than 8 million web and mobile events daily. The CDP provided a dynamic, scalable way to ingest and aggregate the fast-moving data streams, combining online browsing data and in-store purchase histories to create more complete customer profiles.
Step 2: Analyze your customer data to create customer segments and a lookalike marketing strategy.
Facebook will identify key similarities between members of an original custom audience and find other users similar to them to target in a new campaign. That’s called a “lookalike” marketing strategy, and it’s part of Facebook’s power as a marketing tool.
In this step, you do something similar. (You can even use Facebook as one source of data, if you want, but the CDP lets you add many other sources to create the profiles that are unique to your business.) First, create your customer segments, which a CDP can easily do for you, with both rules-based and predictive methods. Then you can use your customer profiles to identify key similarities between your custom audiences and find users similar to them to target in new campaigns, in other words, a “lookalike” marketing strategy.
In our Muji example, Treasure Data CDP’s unique personalization algorithms and marketing segmentation syndication better connected the digital and offline customer journeys, to map entire user journeys that enabled their teams to deliver the right offers at the right times. Then Muji performed predictive data analysis so that they could determine which offers were likely to be most appealing to each customer and deliver them on the best channel.
Step 3. Send relevant coupons and offers when customers are in-store.
Many companies look for mobile browsing data for retail personalization purposes. For example, the ‘killer app’ in mobile retail is to deliver relevant offers to customers while they are shopping in-store. This requires extra complexity (such as combining databases into a unified profile), but CDPs manage it well, and the payoff is worth it.
Muji was able to correctly merge customer preferences with real-time store inventory data to execute on data-driven incentives such as personalized coupons and timely, well-organized in-app push notifications. That’s how their teams were able to recognize a 46 percent increase of in-store sales.
Step 4. Remember each shopper and anticipate their needs in the future.
And of course, each time you present a new offer or promotion, you can track which ones appealed to each shopper, and which ones made no difference. These performance results are just more new data for the CDP.
By analyzing this data, using analytics to understand why something worked well (or didn’t), and trying new approaches, you can better understand your customers, as well as which offers are working and why. With a CDP, profiles update in real time, and will always stay up-to-date. And your customers will realize that your offers demonstrate an understanding of their interests, engendering customer loyalty and improving your ability to do relationship marketing.
Technology helps you focus on each individual customer
Marketers are now in a historic position. Certainly, the pressures to perform have never been higher, nor the job more complex. But now, with the advent of CDPs, they finally have the tools to go beyond mass marketing, to personalized marketing that really does take each customer’s individual wants, desires and responses into account. And that kind of personalization is important in building customer loyalty, as customers come to feel that your company “gets them.” Smart CMOs are realizing that Big Data truly can lead to bigger profits, but only when they have the power to focus on each individual customer, in a way that scales cost-effectively and satisfies nearly everyone.