The Feature

Analytics is the platform’s reporting engine — where managers build, filter, group, and export custom reports to understand labor trends, compliance needs, sales patterns, and operational performance.

For many managers, Analytics is the tool they rely on to explain why something happened and to make data-driven decisions going forward. But because reporting involves multiple high-friction actions and configurable steps, users often need guidance in the moment — not before or after.

Analytics was powerful — but only if users knew how to use it, when to use it, and what each step meant.

The Problem

Before this redesign, Analytics learning content existed — but it lived outside the workflow, in formats that didn’t reflect how people actually learn to analyze data.

Even motivated users struggled because:

• Help lived outside the product surface

• Learning was reactive, not embedded

• A long, linear course didn’t match task-based behavior

• The Help Menu functioned as a one-time destination

• Key actions (Create, Filter, Group, Export) weren’t supported at the moment of use

The issue wasn’t lack of content — it was access, timing, and relevance.

Discovery

Through audits, usage analysis, and friction-point mapping, I identified several core insights:

1. Users only sought help when stuck

2. Entry points required leaving the workflow

3. Learning behavior was task-driven, not sequential

4. There was no mechanism for return engagement

5. Learning needed to live inside the product experience

This reframed the goal: build a learning ecosystem, not a one-off tutorial.

Strategy

I designed a multi-touch, in-product learning system grounded in three pillars:

1. Contextual Launchers
Lightweight entry points that surface at high-friction moments, inviting learning at the moment of action.

2. Short, Task-Based Walk-Thrus
Focused micro-guides for the most common reporting actions:

• Create

• Filter

• Group

• Export

Each flow was intentionally scoped to match real user intent.

3. A Returnable Help Menu
Repositioned as a learning hub — designed for discovery and repeat use, not troubleshooting alone.

Together, these created low-friction pathways into learning that felt native to daily product use.

What I Built

A complete in-product microlearning ecosystem, including:

• Contextual launchers embedded near reporting actions

• Task-based walk-thrus optimized for speed and clarity

• A redesigned Help Menu positioned as learning infrastructure

• Trigger logic to balance visibility and non-intrusiveness

• Microcopy and UX language that encouraged autonomy

• Full instrumentation to measure engagement and return behavior

Learning was treated as a product feature, not supporting documentation.

Behavior Shift

Post-launch, usage patterns shifted meaningfully:

• Passive → Active: Users chose when to engage with help

• One-Time → Repeat: Launcher usage per user increased

• Long-Form → Contextual: Microlearning outperformed the full course

These behaviors directly supported feature adoption.

Results

• ~2× increase in daily engagement with learning content

• Significant growth in repeat launcher and Help Menu usage

• Higher completion rates for task-based flows

• Clear shift from reactive to self-initiated learning

TL;DR: When learning meets the workflow, users choose it — and they come back.

My Role

I owned the end-to-end learning system design, including:

• Discovery and friction analysis

• Learning architecture and microlearning strategy

• UX writing for in-product guidance

• Build, trigger logic, and segmentation

• Instrumentation and impact analysis

The outcome was a learning system that didn’t just support Analytics — it amplified its adoption.