The pandemic has catapulted the retail industry into an unprecedented level of digital growth, which will help accelerate intelligent and automated personalization in the industry. This next generation of data-driven hyper-personalization will optimize customer experiences, reduce churn and operational efficiencies, says Rob Saker, global lead for retail and manufacturing, Databricks.
Retailers have spent years trying to figure out how to optimize the omnichannel customer experience — they understand that personalization is the key to attracting and retaining customers and converting sales. However, for better or worse, the pandemic has catapulted the retail industry into an unprecedented level of digital growth. In the first ten weeks of quarantine alone, the percentage of ecommerce relative to total retail grew at the same rate as it had in the previous ten years.
With a surge of new customers flooding digital channels, almost every retailer from grocery to apparel now has no choice but to compete more aggressively – and primarily – online. This dramatic shift in the traditional retail model will serve as a forcing function for the next wave of consumer personalization: intelligent and automated customization. Here are three ways to up-level your customer experience with the next generation of data-driven hyper-personalization.
Learn More: Why B2B Personalization Fails – and How to Change That With the Help of Well-Informed AIOpens a new window
Expand Your Dataset To Build Deeper Customer Engagement
Most retailers collect and utilize basic information on their customers, such as geolocation, demographics, and purchase history. And while this kind of data is important, it is only scratching the surface. With so much more activity happening online and customers engaging with brands almost entirely virtually, there is a whole new set of data points data scientists can track and analyze to better understand customers.
 With an expanded set of consumer behavior data, brands can advance their marketing algorithms to more accurately and intelligently curate the customer experience and optimize their internal operations. For instance, ButcherBoxOpens a new window leverages order history and behavior data to make recommendations for customers that are likely to churn and predict consumer demand, which enables them to optimize their supply chain. Going forward, they are looking to leverage alternative data sets like data from meal planning apps to further optimize personalization in the future.
We often think about the data that we can acquire to enrich our understanding of consumers, but what if we enabled our customers to provide this information themselves? Apparel retailers already extract useful information from their own apparel photos into hundreds or thousands of “features†(i.e., data points) that can be used to generate insights. Taking it one step further, leading retailers are looking to allow consumers to upload photos of themselves and their own outfits that they like, which enables the retailer to “match the style,†or even show how the outfit looks on the shopper. Not only does this deliver on the promise of deep personalization in ways that even exceeds the in-store experience, but it also helps retailers to monitor which trends and fashions are relevant to their most valuable customers, which can then inform future buying and inventory needs.
Optimize Conversion Rates by Predicting What Consumers Want
The next wave of personalization in retail will be all about anticipating what consumers are looking for when they visit a store or website. Are they looking for a birthday gift? Do they need groceries to make dinner? Do they need to return or exchange something?
 The key to higher conversion on personalization is about satisfying the personalization value equation. Consumers are willing to give up information to brands that provide recommendations that are relevant, take precautions to protect their data, and are broadly viewed as trustworthy. Timeliness is key. If you combine mobile app data that shows the activity of consumers near your business with historical sales data, you can predict when customers have a propensity to buy. From there, retailers can start to target those precise moments in personalized engagements. For instance, if a grocery retailer knows that a parent drops his or her child off at soccer practice every Tuesday evening at 5:30, they could send that customer an alert such as, “would you like us to prepare your grocery list and a prepared dinner for pickup at 6:00pm?â€
In addition to relevancy and timeliness, trust is central to any consumer engagement program. The better a brand performs over time in delivering a great consumer experience, the higher their trust multiplier. Consumers expect the protection of their data and full transparency around how you are using their information, and the consequences for violating that trust are extreme. While relevance and timeliness will build trust over time, a failure to protect data can erode it overnight. And not only will you lose the trust of your current customer base, but you will likely lose out on the opportunity to attract any potential customers who they have influenced.
In the near term, predictive analytics in retail is about providing a consistent and relevant experience each time to make every encounter with a brand easier for the consumer – if you can save a person even just five or ten minutes while shopping, that goes a long way in building brand loyalty and converting site visits to purchases. CVS Health, for instance, serves eight million customers per day, so even a small increase in personalization can have a huge impact on sales. By applying advanced analytics and machine algorithms to its customer data, CVS Health can measure the probabilityOpens a new window of a customer buying a particular product or send a reminder to a patient to fill or pick up their medication.
Explore New Purchasing Models To Retain Customers and Minimize Churn
Many brands today take an episodic approach to personalization, meaning they focus on each standalone visit or purchase rather than the entire relationship with that consumer. Some brands have challenged that thinking and created new subscription-based revenue models. These brands look at the entire relationship with a customer from the first encounter through today. They are better able to understand what that customer prefers and to develop more predictable revenue streams.
Companies like Rent the Runway, Stitch Fix and Trunk Club disrupted the apparel industry, and subscription boxes gained a lot of tracking over the last decade. Between 2014 and 2017, approximately 15% of online shoppersOpens a new window had signed up for subscription box services, and by late 2019, that rate had grown to over 50% with registrationsOpens a new window . In food, as the pandemic has many people avoiding in-person stores, the appeal of doorstep-delivery promotes even further growth in the subscription market. We have seen major growth in farm-to-table and humanely sourced food. The subscription model represents an opportunity to reach new customers and secure recurring revenue streams. In turn, it helps to reduce marketing costs as brands develop long-term relationships with customers.
Learn More: 4 Ways Data Drives Truly Amazing Customer Experience
Delivering on the Promise of Hyper-Personalization
In ecommerce and direct to consumer industries, companies must focus on understanding their consumers, driving greater personalization, and looking to prevent churn. Many companies saw a surge of subscribers during COVID, and if they can retain these customers, they will accelerate out of COVID and quarantine at a faster growth rate. This cannot be a back-burner initiative. Everything we are learning about consumer personalization needs to be in place as soon as possible if retailers are going to capitalize on holiday shopping and avoid the slowing growth rate in digital commerce. As retail data teams adopt a modern data architecture with new data sources and apply machine learning to these challenges, we can expect to see a renaissance and rebirth of retail coming out of the tumultuous 2020.