Context-Aware Experiences: The Newest Stage in the Evolution of Customer-Facing Personalization

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 un-personalized mass messaging.

However, the aim of modern marketing efforts is not about delivering personalization per se. It is 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 grocery shopping 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 are 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.

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