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By leveraging the ZineOne’s in-session marketing, the Retailer was able to deliver real-time offers and incentives to only those site visitors who were undecided about a purchase. The result was — improved margins and significant increase in topline revenue growth.
One of the issues that’s eluded most retailers is the conversion of casual browsers into purchasers. Even though more consumers are online today, the average conversion rates range from 2%-4%. Most marketers turn to offers and promotions to solve this problem. But offering the same promotions sitewide erodes margins for all visitors. The retailer wanted to determine which visitors to target for offers and promotions.
To better understand the right visitors to target, the retailer partnered with ZineOne. In particular, the retailer leveraged ZineOne’s RevPredict dashboard. Powered by its in-session Early Purchase Prediction (EPP) model, the dashboard reveals the predicted purchase outcomes early on in a visitor’s session. The platform then uses these predictive insights to determine the best action to engage each visitor in real-time.
By leveraging in-session intent prediction, the Retailer was able to identify influenceable visitors and send personalized, real-time engagement for them. These visitors are revenue opportunities that would have been lost had the retailer not proactively acted to nudge them in their customer journey at the right time. As a result, the retailer achieved a 20% average lift in revenue per visitor.
The Retailer deployed in-session marketing to readily identify influenceable visitors. It then activated personalized interactions to motivate them to make a purchase while they were still on the site. The in-session intent analysis took into account multiple factors, including the visitor’s past purchase patterns, preferences, number of times they have viewed a product in the past, as well as their current behavior on the site. Based on these and other information, the EPP model inside the platform calculated a visitor’s purchase propensity and identified those within an influenceable range.
After identifying on-the-fence visitors, the EPP models quickly determined the best action to show each visitor after considering their historical purchase decisions and preferences. For example, is the visitor price conscious? Do they need social proof for product confidence? Is convenience the most important factor for them? Additionally, the platform considered data such as the location, time, in-store inventory, or any external events that might impact the purchase decision.
At this stage, the retailer had the opportunity to influence the buying behavior across a range of possible personalized engagement such as the following:
In summary, the U.S. retailer was able to create differentiated experiences for every customer’s unique needs, particularly those who would have bounced off the website without making a purchase. The retailer achieved a 20% average lift in revenue per visitor by offering them a variety of engagements that are timely, helpful, and relevant.
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