AI-Based Personalization Provides 10%+ Revenue Uplift

In this case study, we delve into how ZineOne enabled the retailer to drive online conversions with personalized and relevant in-session marketing prompts by using:

Personalization based on consumer intent
A single customer view across channels and enterprise systems
Machine learning (ML) for optimized decisions and delivery of engagement

Open PDF

* Email is not necessary to download

By incentivizing visitors with relevant information in real-time, retailer increased the redemption rate of these hyper-personalized offers by more than 50%.

As a result, retailer achieved +22.6% relative lift in revenue per visitor with in-session offers.

Background

From emails to pop-ups to push notifications, today’s consumers are inundated with offers from retailers everywhere. The influx of irrelevant offers only muddies the waters for consumers, leading to lower conversion and rising cart abandonment rates. How can retailers cut through the clutter and stand out to their customers?

A top 10 U.S. retailer sought an answer to this question. With more than 1,000 brick-and-mortar locations and a growing focus on online channels, this major retailer sought to not just bring more visitors to its online storefront but to meaningfully engage them once they arrived. To support relevant, contextual customer engagement, the retailer knew that it needed a different solution — i.e., one that could take advantage of advancements in data science to deepen customer relationships, brand affinity, and loyalty in real-time.

The Challenge

The key challenge this retailer faced is also true for the retail industry as a whole — a lack of access to in-session customer data that could supplement existing stored customer data, which when combined, enabled relevant engagement in the moment. While analysis of stored customer data allows for the persona and segment creation that leads to basic personalized recommendations, it does not account for a customer’s current channel, needs, and mindset. Hence, a brand cannot meaningfully personalize a customer’s in-session experiences to prevent website or cart abandonment.

An additional layer of short-term insight, driven by machine learning-based models was needed to understand the unique goal and medium of each shopper, whether they were browsing the app, visiting a website, or embarking on a buyer journey via another channel.

In order to nudge more potential buyers to not just browse but make a purchase decision, the company enlisted ZineOne to help it achieve the following:

Deploy relevant, personalized engagement using AI-based recommendations that incorporate in-session user behavior
Integrate customer data from various enterprise systems to further enrich the customer context
Unify data into a single user view across channels
Use machine learning (ML) to analyze real-time behavior against historic data points to more accurately predict and influence in-session purchases

The Solution

ZineOne provided the retailer with a new intelligence layer that allowed it to offer the next generation of AI-driven, real-time personalization. ZineOne’s in-session marketing platform automated the deployment of interventions based on the continuous, cross-channel customer intelligence collected with its patented Customer DNA™ technology.

The platform continuously analyzed Customer DNA insights and made ML-based predictions about the customer’s current journey and the likelihood of purchase. The in-session marketing platform recommended actions to incentivize visitors with relevant information in real-time — increasing the redemption rate of these hyper-personalized offers by more than 50%.

The Result

Up to 90% accurate predictive models based on in-session user behavior
50% redemption rate for personalized offers
22.6% relative lift in revenue per visitor with in-session offers

Last Updated: August 5, 2022