AI-Based Personalization Increases Revenue by 10%+

AI-Based Personalization Increases Revenue by 10%+

AI-Based Personalization Increases Revenue by 10%+
AI-Based Personalization Increases Revenue by 10%+

Background


Consumers are always receiving offers from businesses, whether it be through emails, pop-up windows, or push alerts. Consumers are just made more confused by the flood of pointless offers, which lowers conversion and increases cart abandonment rates. How can merchants separate out from the competition and attract customers?

A top ten American shop wanted an answer to this query. This big retailer, which has more than 1,000 physical stores and an increasing emphasis on online channels, wanted to not only increase the number of visits to its online site but also to meaningfully engage them once they were there. The store understood that it need a different approach, one that might benefit from developments in data science to foster more meaningful, contextual customer involvement.

The Problem

Lack of access to in-session customer data that might enhance current stored customer data, which when combined, permitted appropriate engagement in the moment, was the major difficulty this retailer faced. This is also true for the retail industry as a whole. While the generation of personas and segments through the analysis of stored customer data enables the creation of basic personalised suggestions, it does not take into consideration a client’s current channel, needs, or mentality. In order to stop website or cart abandonment, a business is therefore unable to significantly personalise a customer’s in-session experiences.

The business asked ZineOne to assist it in achieving the following in order to persuade more prospective customers to do more than just explore but to actually make a purchase decision:
Utilize recommendations based on AI that take into account in-session user behaviour to implement relevant, personalised engagement.
To further improve the consumer context, integrate customer data from other company systems.
Across all channels, combine data into a single user view.
Utilize machine learning (ML) to examine present-day behaviour in comparison to past data to more precisely forecast and sway in-session purchases.

The Answer


The shop was given a new intelligence layer by ZineOne, enabling it to deliver the newest iteration of real-time, AI-driven personalisation. The deployment of interventions was automated by ZineOne’s in-session marketing platform based on ongoing, cross-channel customer intelligence gathered with its patented Customer DNATM technology.

The software provided predictions about the customer’s current journey and likelihood of purchase based on continuous analysis of Customer DNA data. The in-session marketing platform suggested steps to provide visitors with pertinent information in real-time incentives, resulting in a more than 50% increase in the redemption rate of these hyper-personalized offers.

The Outcome


50% redemption rate for personalised offers, up to 90% accurate predictive models based on user behaviour throughout the session
Revenue per visitor increased by 22.6% when in-session offers were used.


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