Enhancing CPG Reporting

Role: Design Lead • Timeline: 1 year • Stakeholders: Customer Success, Customer Experience, Sales Solutions Engineer

Consumer Packaged Goods (CPG) companies use Vividly to manage their finances, promotions, and plan for the future. Vividly’s reporting limitations frustrated users, forcing them to export data to third party analysis tools multiple times a day to make informed planning decisions.

Stakeholders started with a solution: Let users organize their customers and products into nested trees and use that in the Promotion Analytics feature. Stakeholders focused solely on the data implementation, not the value delivered to users. I re-framed the work to emphasize what nested data unlocks for users:

Promotion Analytics Reports: Improve analysis with big picture trends, granular metrics, and improved filtering. This reduces the need to export to third party software.

Data Management Users can match their Vividly setup with their external Enterprise Resource Planning (ERP) software.

Problem - Analysis Limitations

Sales and Finance users at CPG companies struggle to understand their promotion performance which ultimately assists with future planning decisions. Vividly's limited analysis tools made understanding performance a cumbersome process:

ERPs organize customers and products in logical nested categories, which Vividly doesn't support.
In Vividly, users tag items with categories, but tags don’t support nested relationships.
- Example: a soda is tagged with "beverages" and "carbonated” but the tags exist at the same level, without a nested relationship.
Workaround: Users export data to third party BI tools to do necessary analysis, adding friction to their workflows.
These limitations make Vividly vulnerable to competitors filling this analysis gap

Solution

Support customer organizational models with enhanced customer and product data management. Improve reports with better data analysis tools, such as value aggregation, differences, and improved filtering.

A promotion analytics report showing product performance per month. Each month includes Actual, Expected, and Difference values — Promotion Analytics

Success Metrics

Easily understandable promotion performance, business trends, and ROI.
Improved data foundations that allow Vividly to support large-scale datasets.
Improved NPS and customer sentiment on analysis reports and data management.
Reduce exports by 50% per month.

Discovery & Validation

I ran user interviews with internal experts and users at Health Ade, Bob's Red Mill, and Second Nature Brands. Synthesizing responses defined the problem landscape:

Quick identification is key

Users need to analyze high level promotion performance and view detailed information during their daily analysis.

Unnecessary exports

Users export data to BI tools to make up for Vividly's limitations. Exports should remain to support companies with complex reporting and workflows that rely on Vividly data.

Scale and organizational needs vary

Data management must support any organizational and operational strategy. CPG company data is complex, containing one or more brands going to hundreds of distributors and customers.

Reporting Gaps

Users are extremely frustrated with data analysis limitations, manual filtering, and the inability to save multiple report configurations. To address these gaps, I reviewed discovery sessions where users demonstrated how they used Vividly data to generate detailed reports, including:

year over year comparisons
in-depth sales analysis - "shipment dollars by channel"
Created hierarchy layers on top of Vividly data.

Users work hard to make up for Vividly’s shortcomings, further emphasizing the need to support saving reports. These findings changed the perception of the problem and I designed a simple and effective solution.

Exploration

Build for Scale

Use the design system to build familiar interfaces. Test with real world data to ensure the new screens support the scale and complexity.

Report Customization

Give users a way to save custom reports to reduce manual tasks.

Flexible Hierarchy structures

Allow users to create hierarchical relationships that match their ERP.

Information Architecture

Only show contextually relevant information or interactions.

Solution Development & Final Design

Enabling advanced analysis in Promotion Analytics required an improved backend, data management UI, and enhanced reporting tools.

Data Management Platform

The customer and product hierarchies management feature lets users manage their data and match their ERP.
Using common file management patterns reduces training time.
Backend improvements prepares Vividly for ERP API integration, enabling scale.
Reduces information overload by prioritizing customer and product information.

Screenshot of a product hierarchy management tool. On the left is a product tree view. on the right is the contents of a folder — Product management screen

Detailed Analysis

Users can quickly create bespoke reports and analyze data thanks to aggregated metrics and hierarchical filtering. These reduce the need to export, keeping users in the platform and closing a risky feature gap.

Handling Inactive Customers or Products

To handle inactive items (such as deprecated or seasonal):

Users can move items out of the main “active” hierarchy
Users can view reports with active, inactive, or all items, providing accurate analysis.
Next: Automate item status with a date range.

Ward at Health Ade liked this approach, confirming that matches their external deprecation workflows.

Saving Multiple Reports

The Views feature allows users to save multiple filter configurations to quickly reference helpful reports. Users can export an individual view as a standalone report.

Promotion analytics report showing a list of additional saved views — Views Interface

Challenges

Alignment is important, and unfortunately communication breakdowns marred this project. We faced pressure from stakeholders worried that MVPs are incomplete and destined to fail. They pushed for a fully-formed release, suggesting we use an old proof of concept.

Stakeholder hesitance to iteration caused scope creep
Packed calendars meant resistance to meetings
Asynchronous updates snowballed into misalignment

Aligning stakeholders took work, but presenting a phased plan resolved the blockers. Stakeholders appreciated that the plan addressed high impact areas, allowing for improvement over time. The plan appropriately scoped the work and formalized team check-ins to prevent future misalignment.

Impact

Strong foundations helped design a user centric solution that reduces cognitive load, enabling powerful analysis.

Summary

Users reacted positively to the new features.
Data improvements helped existing features and unlocked new workflows such as demand planning.
Familiar design patterns reduced learning curves, allowing focus on analysis and content organization best practices.
Uploading hierarchy data generated significant debate, pausing implementation.
Manual hierarchy creation takes approximately one hour. Users found this acceptable with the guarantee of API integration (expected in a future release).
Large datasets showed minor performance impact.