Leading Tax Filing Company
Recommendation Engine for Tax Forms
Problem
The client needed a solution to minimize drop-offs and enhance user experiences caused by missing forms and documents during the tax filing process.
Solution
An advanced ML model was developed to identify patterns in the required documents for users, predict their personas, and offer persona-specific recommendations to guide them effectively.
Outcome
User engagement in the tax filing process increased significantly, leading to a notable reduction in drop-offs during submissions.



Subtitle
Enhancing Online Tax Filing with a Persona-Based Approach
The client, a tax filing company faced challenges with its online tax filing process due to its complexity and varying document requirements based on user profiles. Missing forms led to poor user experience, causing delays and high drop-off rates. To address this, a persona-based approach was implemented, segmenting users into over 34 distinct profiles using past data such as demographics and activity patterns. A supervised machine learning model predicted user personas and provided personalized document recommendations to guide users.



The implementation of the predictive solution led to significant improvements in both consultant engagement and sales performance. Within six months, consultant engagement increased by over 28%, which helped drive stronger relationships and productivity. Additionally, the sales performance saw an uplift of more than 12%, indicating a direct correlation between early intervention and improved revenue.
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