Business Insights
Designing the Learning Layer for a Data-Heavy Product Feature
Quick Take
I designed a structured, in-product learning layer for a complex analytics feature that helps managers interpret sales performance, forecasts, and labor models — transforming a dense dashboard into a habit users return to as part of daily decision-making.
The goal wasn’t just comprehension. It was confidence, continuity, and repeat use.
The Product Feature
Business Insights is a data hub within a workforce management platform — surfacing sales trends, labor %, wage variance, budgets, forecasts, and labor models that inform smarter scheduling and operational decisions.
It’s the engine behind answering questions like:
Can we afford this schedule?
Where are we over or under budget?
What’s driving variance — and what should change next?
The Problem
Despite its power, the feature was under-adopted.
Users struggled to:
Understand what the data actually meant
Maintain accurate budgets and forecasts
Navigate a dense, multi-surface dashboard
Connect insights to real scheduling decisions
As a result, one of the platform’s most sophisticated features wasn’t becoming part of everyday workflows.
What I Discovered
Through user analysis and friction mapping, several patterns emerged:
Users didn’t know where to start
Core concepts didn’t reinforce one another
Budgets and forecasts became “set and forget”
Navigation increased cognitive load
Complex logic required scaffolding — not static documentation
The challenge wasn’t data availability.
It was sensemaking.
The Strategy
Design a habit-forming learning layer inside the feature itself — one that reduced cognitive load while reinforcing mental models over time.
The strategy centered on:
Foundations → Complexity: Build confidence before depth
Micro-steps, not macro-lessons: Teach one concept at a time
Concept repetition across workflows: Reinforce learning through use
Explaining why actions matter: Not just how
Cognitive load reduction: At every decision point
Learning needed to feel like guidance — not instruction.
What I Built
A system of 13 guided, in-product walk-thrus, including:
Business Insights Tour
Budget Setup and Management
Forecast Configuration
Sales Adjustments
Custom Metrics (Create / Edit / Delete)
Labor Models (Build / Edit / Iterate)
Each flow was designed to:
Teach a discrete skill
Reinforce a mental model
Encourage a repeat behavior
Together, they formed a cohesive learning layer — not isolated tutorials.
The Behavior Shift
The learning experience was intentionally designed to support these habits:
Checking insights before scheduling
Monitoring variance regularly
Keeping budgets and forecasts current
Revisiting and refining labor models
Tying decisions directly to data
Treating Business Insights as part of daily workflow
The goal was not mastery in one sitting — but ongoing fluency.
The Impact
Early signals showed:
Higher revisit rates
More accurate budget and forecast setups
Increased variance monitoring
Clearer mental models of how data connects
Fewer navigation-related issues
Even without sharing internal metrics, the pattern was clear:
When learning was embedded into the workflow, the feature became stickier.
My Role
I led the end-to-end learning and experience design, including:
User research and friction analysis
Content and learning architecture
Sequencing and mental model development
Microcopy and UX writing
In-product walk-thru build
Cross-functional collaboration with Product, Support, and Education
The result was a learning layer that didn’t explain the feature —
it made the feature usable.
Details have been generalized to respect confidentiality while preserving the structure and impact of the work.