The increasing uncertainty caused by the prolonged pandemic continues to change virtually every aspect of consumers’ lives, including how they eat and shop. Retailers that best understand — and meet — consumers’ continually changing needs will win their long-term loyalty. To do this successfully however, retailers need to dust off their analytics platforms and learn how to truly personalize their merchandise assortments, pricing and promotions to each individual customer.
At the onset of the pandemic, many consumers transitioned away from making multiple supermarket visits to weekly or bi-weekly trips where they stocked up on core items needed to prepare more meals at home. Other trends included adopting the convenience of online shopping, as well as switching brands due to financial uncertainty.
Fast-forward to the first quarter of 2021: consumers’ shopping behaviors not only remain fluid, but they will likely continue to change as the pandemic enters a “post-COVID” environment. With new buying trends on the horizon, retailers are tasked with understanding how consumer shopping behavior is evolving and continuously refining assortments and adjusting promotions and pricing in a way that will consistently appeal to individual shoppers’ expectations — during every visit and in any channel.
This evolving concept is defined as “personalization at scale.” Also known as mass personalization, this optimization style requires retailers to dive deeply into their individual shopper’s needs, preferences, demographics, and buying behavior, among other categories. At the core of this process is a detailed understanding of customer data.
To unlock the power of their customer information, retailers need to adopt a foundation of new technologies and processes built on artificial intelligence (AI) and machine learning. AI-powered analytics enables users to understand individual customer’s changing needs and preferences, including categories, items, brands, channel preferences (across stores as well as online), delivery or pick-up fulfillment options, as well as meal preferences.
To gain insight into each customer interaction however, retailers should leverage cloud-enabled scoring and optimization engines that are designed to continuously segment and score customers’ changing behavior. Machine learning algorithms quickly make predictions on the relevance of specific stock keeping units (SKUs). Customer-specific data is married with the relevance of categories, items and brands among shoppers — a formula that enables companies to continually rationalize their assortment and promote the items that matter most to key customers.
Of course, in such an era of flux, personalization at scale may seem daunting — especially as many retailers are burdened with how to optimize assortment, pricing and promotions. AI-based platforms can make this task easier, identify core consumers of potential discontinued items and proactively suggest alternatives and relevant substitutes of those items for these customers. The process helps manage the shelf-level optimization while still meeting the needs of customers.
As expected, delivering successful mass personalization engagement is a challenging task. To be successful, personalization at scale requires execution through all channels (regardless of whether a customer shops in person or virtually) and takes advantage of APIs/automated execution and feedback/measurement systems to create the full feedback loop leading to continuous learning and improvement.
However, adopting technology is only one piece of the puzzle. Organizational commitment is a core pillar strategy for a company’s customer-centric transformation — and future growth. Retailers also must align process and organizational roles — from processes to performance management — as well as execute the right governance, line of business collaboration, and operating model to get the highest benefits from their personalization strategy.
Some could argue that the most successful “customer-centric” retailers are those most committed to investing in personalization at scale as a core strategy. Looking at the industry, this typically includes retailers that have strong investments loyalty programs and customer-centric strategies. Even many pure-play e-commerce retailers are increasingly leveraging their digital foundations, leadership positions and investments to deliver personalization at scale.
However, this will be a marathon, not a sprint. Retailers that best understand — and meet — each consumer’s changing needs will win their long-term loyalty. To ensure they cross the finish line, retailers need to leverage their AI platforms, and enforce procedures that support organizational cohesion, communication, collaboration and data analysis. It is these retailers that will be able to successfully execute personalization at scale, create assortments and promotions most relevant to their consumers and price products so those shoppers see value.