Unleashing the Value of Customer Intent and Intelligent Interventions

credit cards on table

The Opportunity

For a Fortune 500 retail company with over $300 billion in annual global revenues, there exists an opportunity to focus on the characterization of customer purchase intent in the context of the completion of online shopping sessions.



Advancements in Understanding Shopper Data

The impact of predicting the probability of a person abandoning a session creates numerous positive impacts in the form of building testable hypotheses as well as creating “Intelligent Interventions”.

Intelligent Interventions is defined as a system of understanding the driving forces behind customer behavior that lead to the generation of actionable hypotheses, which in turn can be used to design timely interventions throughout the customer journeys.

Utilizing Intelligent Interventions is the key to ultimately enhance customers’ experience and create additional value for the organization. Potential outcomes include tailored hyper-personalized actions for the customers in order to alter the outcome for each session.

To build this roadmap, Theory+Practice set to answer the following key questions:

● How to estimate the probability of a customer completing a session?

● How to identify the drivers of the decision to complete or abandon a session?

● What is the appropriate intervention for each particular case?


The What

To answer these questions Theory+Practice implemented a Risk Quantification model and Driver Identification model. The outcome of these models identified the key drivers behind customer behavior determining session conversion rates. These models enabled the building of testable hypotheses for the next phases of the project.


The Solution

Theory+Practice simplified the process into three distinct stages. This approach created an actionable path to create value for the business.

1. Identification Stage – which consisted of the development of models that can quantify abandonment risk for each session and identify drivers behind the prediction.

2. Experimentation Stage – where the insights from the identification stage were used to build and test hypotheses of relevant interventions that would reduce the number of abandoned sessions in the form of A/B testing on the digital platform.

3. Decisioning Stage – which focused on the optimization of interventions for customers based on the experimentation result to further refine when a particular intervention would be selected.


Actionable Insights

Theory+Practice’s implementation of Intelligent Interventions gave rise to multiple areas of opportunity to increase value and conversion. Outcomes of engagement revealed:

● An individual is more likely to complete a shopping session when there are a significant number of diverse items added to their cart.

● A recommendation model that suggests complementary items instead of substitutes would increase the probability of conversion. These complimentary items have a higher chance of being added to a cart. Leading to a more diverse basket and presumably a higher conversion rate.

● A recommendation system that pre-populates the routine shoppers’ baskets with their frequently purchased items, could potentially improve the conversion rates.

● Customers view more expensive items several times before they complete their purchase.

These insights offer a clear way to improve customer experience through the generation of testable hypotheses that are used during customer sessions to improve conversion rate

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