Data-driven insights

This case study example shows how data-driven insights have enhanced product adoption, highlighting the power of turning analytics into action.

overview

Product Launch: AI Assistant, Ela: First Release

Problem: 3 weeks post-launch, feature usage is significantly lower than anticipated

Metric: Activation rate

Target: 15%

Actual [pre-mitigation]: 7%

Actual [post-mitigation]: 19%

Breaking down the data

GTM Activities

Launch email

  • 15% open rate -> 10,352

  • 48% click through rate -> 4,961

  • 4,961 total users clicked CTA

FEATURE USAGe

Campaign UTM platform sessions

  • 5,381 sessions

  • 62% engagement rate

  • 946 total users had a session

Peak usage

  • 3.5x increase during launch week

  • 110 sessions at peak usage

In-platform pop-up guide

  • 18% of users started guide -> 1,194

  • 70% of users continued to last step -> 833

  • 86% of last step users clicked CTA -> 719 total users

Data validation

Software experience management platform data aligned with back-end data

Activation rate

  • 7% activation rate

  • 190 unique users

Key learnings

GTM data does not align with feature usage. Total users taking action with each channel activity indicates high interest in feature - but the usage reported is extremely low.

Users are clicking to engage - but these clicks aren’t translating into usage.

1. Hypothesis: Users are struggling to find the feature

CTA buttons did not open feature directly. Users were directed to the library were they needed to identify a course supported by the feature -> open the course -> and then open the feature causing friction in UX.

2. Hypothesis: In-platform guide content not resinating with users

With a low % of users starting guide, and high % of user progression, suggests content issue with first step. Messaging and / or visual not resinating with users.

3. Hypothesis: The feature is of value to users

80% of users reported having a positive experience with the feature after interaction. Suggesting the issue is not feature capability, but education and / or user journey.

MITIGATION STRATEGY

1. Hypothesis: Users are struggling to find the feature

Mitigation:

  • Feature to be opened directly from button clicks

  • Introduce prompted interaction after relevant course completions

  • Move in-platform pop-up guides inside courses where feature is available

2. Hypothesis: In-platform guide content not resinating with users

Mitigation:

  • Gather qualitative user feedback via account managers

  • Make feature more visible in first step visual

  • Reframe first step messaging to reflect ‘tips’ covered in mid steps

3. Hypothesis: The feature is of value to users

Mitigation:

  • Gather qualitative user feedback & use cases via account managers

  • Insert more user surveys after feature interaction to validate value

results

30-days post mitigation strategy implementation:

  • 171% increase in activation rate

  • 88% increase in adoption for in-app guide

  • 67% increase in CTA clicks for in-app guide