Automating the In-the-Moment Customer Journey with ML
In ZineOne’s Thought Leadership Interview series, we sit down with experts from across industries to discuss the latest movements and technologies transforming the customer experience.
For years, retailers relied solely on top of funnel insight to drive value for customers. When a new customer landed on a website, the retailer would utilize ‘wisdom of the crowd personalization’ to serve up the most popular items based on previous customer experiences. When paired with data points such as a customer’s current location, the top of the funnel provided key inputs from the start to engage a customer.
Today, retailers are utilizing machine learning (ML) to extend value from the top of the funnel to the mid-funnel. In these interactions, the retailer has gained clues on the shopper’s intent based on the areas of the site they have engaged with and can utilize ML to automate intervention in-the-moment to create more compelling customer experiences.
By utilizing in-session data to tailor the customer experience, retailers can create more compelling and personal experiences at every stage of the customer journey. Nikhil Chandurkar, the former Director of Product Management at Kohl’s, recently sat down with Debjani Deb, CEO and Co-founder of ZineOne, to discuss how to create a differentiated experience during each shopping journey to convert on-the-fence shoppers and improve retention. In the discussion, he covered:
- Utilizing in-session machine learning (ML) to personalize a visitor’s journey in real time
- The most valuable in-session consumer behaviors retailers must be aware of
- The changing landscape of customer retention
Creating Differentiated Experiences that Personalize a Visitor’s Journey in Real Time
Technological shifts and the increased proliferation of customer-specific data has empowered retailers to tailor experiences based on each customer’s historical behavior. However, this oftentimes results in retailers assuming common patterns apply in every situation for a given customer. Yet, every customer has different—and constantly changing—needs, and so retailers much be able to recognize these nuances to personalize the shopping journey.
For example, one session may revolve around urgency: the customer is in a hurry to make a decision and wants their product delivered as fast as possible. In another session, safety may be their top consideration: the customer is buying a car seat for their child, and so is interested in reviews, ratings, and safety specifications. By leveraging historical data and purchasing pattern alone, retailers cannot recognize customer intention and react in-the-moment to amplify value.
At Kohl’s, dynamic real-time offers are utilized to understand customer intent, identify the on-the-fence shopper, and serve up the right incentive based on their current shopping journey. This allows Kohl’s to embed more personalized insight into the specific customer journey to provide more meaningful offers and increase conversion.
The Most Valuable In-Session Consumer Behaviors
In order to utilize predictive capacity to deploy relevant experiences in the moment, retailers should be aware of two key areas of in-session consumer behavior: is the customer focused on price or is the customer focused on the specific product?
By proactively determining whether a customer is price-sensitive or product-sensitive, retailers can amplify different aspects of the product (e.g. reviews, rating) or price (e.g. free shipping, BOGO) to help the customer make a decision. Two key insights used here are the historical assessment of the customer, which includes propensity scores, and real-time data that illustrates the customer’s unique journey in-the-moment.
It’s important to keep in mind that real-time signals are not only based on the current shopper’s journey, but are also based on contextual factors such as weather or location. These signals can be used by retailers to “get more personal with personalization” and deliver differentiated experiences.
The Changing Landscape of Customer Retention
Today’s customers have more choices than ever before, and traditional retailers are competing with behemoths like Amazon and Walmart to attract and retain customers. As such, more and more retailers like Kohl’s are paying equal parts attention to customer retention as they do to customer acquisition.
Traditionally, marketing created lifecycle segments that were manually curated based on a data pool, and then launched campaigns against those customer audiences. However, manual processes do not offer a feedback loop, and so customers would fall through the cracks. With the advent of automation and ML, lifecycle segments were automatically managed. For example, when a shopper transitions from a new customer to a retained customer, the system automatically shifts them to the next phase of engagement. In the event that a shopper isn’t moving on the correct path to a retained customer, triggers or signals programmed into the ML model are sent out that empower the retailer to act with the right incentive before a customer is lost. This approach prioritizes long-term loyalty and future transactions, rather than solely focusing on the current transaction.
By utilizing ML to determine the state of an individual customer, retailers are increasingly moving toward a “campaign-less” world—a world where in-session actions, rather than mass-deployed campaigns, determine action. This campaign-less world becomes an omni-channel experience where a customers immediate context is the most powerful factor for determining their wants and driving the highest ROI for retailers.
Interested in learning more? Check out the full interview with Nikhil Chandurkar to discover how to create differentiated experiences for your customers.