Context-Aware Experiences: The Newest Stage in the Evolution of Customer-Facing Personalization
Leading retailers are interested in personalization efforts that drive the “right” customer behaviors. In marketing, the right messages, delivered at the right time to the right consumer and featuring easy-to-act-upon content result in more revenue at a better margin than non-personalized mass messaging.
However, the aim of modern marketing efforts isn't about delivering personalization per se. It's about achieving specific outcomes through augmenting the array of data used to perform personalization and the immediacy and variety of mechanisms to engage the consumer. Put simply, retailers must prioritize context-aware experiences that consider the consumer’s current mindset and mission.
Examples of Context-Aware Experiences: Grocery Shopping App
Imagine a customer searching for “milk” on a grocer's app. The results will vary significantly based on the context.
- Context 1: “On-the-Go Shopper Near the Store.” A customer is physically near a grocery store around 7 p.m. They're searching for “milk” on their mobile app. Here, the app should prioritize quick-buy options like single-serve milk cartons or ready-to-drink flavored milk, potentially paired with nearest store pickup availability.
- Context 2: “At Home With Dietary Preferences.” The customer is at home early in the morning and has dietary restrictions/preferences (e.g., vegan or lactose intolerance) either saved in their profile or determined based on purchase history. Ideally, the app should highlight plant-based alternatives like oat or almond milk and suggest bundles (e.g., granola plus oat milk).
- Context 3: “Frequent Shopper With Seasonal Behavior.” The customer historically purchases eggnog during the holiday season and searches for “milk” in late November. Around this time, the app should suggest seasonal products like holiday-flavored milk or milk-rich recipes, with discounts to entice repeat purchases.
Of course, no human merchandiser could set rules for this and the hundreds of similar contexts. However, personalization engines leveraging technology like artificial intelligence, machine learning and real-time analytics can process vast amounts of data and adjust their algorithms accordingly to maximize outcomes.
Helping Customers Achieve Their Goals
Retailers need to understand in real time what a shopper is trying to achieve specifically, their objective, and the context. While delivering context-aware experiences can be straightforward when customers interact directly with in-store employees, large retailers serving millions of customers must replicate the expertise of their best store colleagues in digital channels. For these retailers, ensuring personalized, context-rich experiences at scale often requires sophisticated digital solutions that mimic the personalized guidance customers receive in a one-on-one in-store interaction.
As such, data must be at the heart of context-aware experiences. It enables brands to make customers feel at ease and understood. The more data a retailer feeds into its personalization engine, the better it can optimize for key business goals, increasing revenue, improving margins or managing inventory to reduce stock-outs and excess inventory that may require markdowns. Achieving an optimal balance between preventing stock-outs and minimizing the need for markdowns can be challenging, often requiring careful trade-offs.
In this effort, retailers leverage various data sources, such as geolocation, current basket content, time of day, season, weather, and search and purchase history. Additional factors, including allergens, dietary preferences, price sensitivity, product inventory, and SKU-level margin provide even deeper insights for personalization.
Much of this data is constantly changing, and it's paramount that retailers absorb this wide range of fluctuating signals, process them in real time, and deliver context-aware experiences to customers through the most convenient and effective channels that help them achieve their goals.
Delivering Context-Aware Experiences is Difficult
The complexity, scale and variety of customer data continue to expand. Similarly, the number of channels available to retailers to deliver context-aware experiences is increasing. Retailers require top-tier software engineering and data processing capabilities, yet some of the most prominent retailers fall short in these areas. It is, therefore, advisable to supplement skills with partners specializing in services such as automated marketing, the Internet of Things and computer vision, among others. Only with these right behaviors can you expect to be able to encourage your customers to follow suit.
Martin Ryan is vice president of retail, Europe, EPAM Systems, Inc., a digital transformation services and product engineering company.
Martin Ryan, Vice President of Retail, Europe, EPAM Systems
Martin leads EPAM’s Retail industry client portfolio. He has over 30 years of experience leading strategy consulting and digital transformation service providers. With a technical background, he delivers advisory services for retailers and brands on their technology strategies, software selection and operating model, covering all aspects of retail, food service, eCommerce and D2C business models and operations.
