Verizon Plan Recommender

Overview

An AI-powered tool that leverages real-time usage data to help customers choose the right plan to meet their evolving wireless needs.

Design Director: Robert Dalton
UX Design: Sam Szerlip, Matt Zabita
Content Strategy: Lindsay Jawor
Research: Claire Kearney Volpe
Product: Gregg Scavron, Alberta Tsolu, Megan Gottfried
AI Engine: Sachin Chorey, Raynor McFarlane, Stephanie Bowers

Goals

Increase plan take rate
Increase ARPU
Increase digital self-serve usage rates
Improve NPS
Improve retention rates

Challenges

Customers don’t see a need to change their wireless data plans.
Changing a plan is confusing, so customers rely heavily on rep assistance to mitigate risk.
Customers want to use self-serve digital channels but don’t feel informed enough to make confident decisions.

Design solution

Results

+13%

Increase in plan take rate (all channels)

+10%

Increase in digital self-serve adoption rate

+57%

Increase of customers selecting the recommended plan

+9%

Increase in ARPU for customers who took our recommendations

-6bps

Reduction in care call-in rates

+66bps

Higher retention rate for customers who use our recommendations

Process

Prior to the design team’s involvement, the plan recommendation experience looked like this…

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… And was manually managed and updated using a spreadsheet.

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We created an AI powered recommendation engine (SOI System of Insights), that improved accuracy and removed manual intervention.

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Experience principles uncovered through customer research.

1. Simple

Recommendations must be easy to understand using humanized language.

2. Transparent

Recommendations must show the reason for the recommendation.

3. Personal

Recommendations must be based on customers’ individual usage.

4. Empowering

Recommendations must help customers become more informed users.

5. Positive

Recommendations must be in the best interest of the customer - not a pushy upsell.

6. Valuable

Recommendations must clearly show the tangible benefit to the customer.

Recommendation touchpoints.

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A modular recommendation framework provides a flexible and scalable design system.

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Plan Recommender experience components.

Experience component touchpoint mapping.

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Final recommendation modal design examples.

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