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Boosting Recognition with a Button Switch

A simple change, but a big impact!

Summary

Increase employee recognition on Culture Cloud by O.C. Tanner.

Role and Team

  • Product Designer ✋
  • Product Manager
  • UX Researcher
  • Engineering Manager
  • 3-4 Engineers

Skills

  • Data analysis
  • Research
  • Cross-functional collaboration
  • User flow diagramming

Outcomes and Impact

  • 5% increase in recognition activity
  • 200% boost in users returning to give more recognition
  • 90% increased completion rate for repeat recognitions

Overview

O.C. Tanner empowers companies to cultivate employee appreciation through peer-to-peer recognition tools. This case study explores how a seemingly minor design change significantly increased recognition activity on our platform.

The Problem

Users were abandoning the “Give Flow”—the process of giving recognition—at a high rate. We needed to understand why and identify areas for improvement.

Users and Audience

This project targeted internal stakeholders (product triad and leadership team) and ultimately benefitted the platform’s core users – our clients’ employees.

Roles and responsibilities

As part of a product triad (product manager, engineer, designer), I collaborated with the product manager on user research and data analysis. This included analyzing user behavior, mapping the “Give Flow,” and identifying drop-off points.

Scope and constraints

Initial approaches involved creating data artifacts that weren’t ultimately used as we refined our understanding of the problem. Obtaining accurate data was challenging due to a branching path in the “Give Flow.” We overcame this by building an aggregated data view outside our existing analytics solution.

Process

We collaborated with the UXR team to track user progress through the “Give Flow” using our analytics tool (Pendo) and observed user sessions with Fullstory. This revealed a high drop-off rate at the flow’s end. We hypothesized a button swap could improve engagement.

Results

  • 5% increase in recognition: This seemingly minor change led to a significant increase in recognition activity.
  • 200% boost in returning users: Users who completed recognition were much more likely to return and initiate another instance.
  • Increased completion rate: Those who started a second recognition were 90% more likely to complete it compared to the initial flow’s completion rate.

Lessons Learned

While analysis and design were straightforward, delays hampered implementation. This highlighted the need for streamlining our process to push out such minor, impactful changes more frequently.