Mars Ask SRM
An AI-powered Commercial Decision Intelligence Platform transforming weeks of sales revenue analysis into minutes of evidence-backed decision support.
Project Overview
Sales Revenue Management (SRM) is one of the most data-intensive functions inside a consumer goods organization. Pricing, promotional effectiveness, sales performance, pack formats, product mix, and market dynamics constantly change, making timely decision-making critical.
Traditional analytics workflows require analysts to identify potential causes, gather data from multiple sources, write SQL queries, create charts, validate statistical significance, prepare reports, and communicate recommendations. This manual workflow often delays decisions and creates heavy dependency on technical teams.
"Ask SRM reimagines this process as an AI-powered Commercial Decision Intelligence Platform. Instead of asking users to navigate dashboards, it allows them to ask natural-language business questions while an orchestration of AI agents investigates, validates, visualizes, and explains the underlying business drivers with complete transparency."
Business Context
The commercial organization at MARS relies on numerous business metrics, including Net Sales Value (NSV), Gross Sales Value (GSV), promotional effectiveness, pricing, sales volume, brand performance, and product segmentation.
When these metrics fluctuate unexpectedly, business leaders need immediate answers:
- Why did Net Sales Value (NSV) decline?
- Which brands contributed most?
- Was pricing responsible or were promotions successful?
- Which customer segments were affected, and what should we do next?
Answering these questions traditionally required multiple analysts working across BI tools, spreadsheets, SQL environments, and presentation software. The challenge was not data availability—it was making that data understandable, explainable, and actionable for decision-makers.
Problem Statement
Commercial decision-making suffered from four key challenges:
Fragmented Analytics
Business insights were spread across dashboards, SQL queries, Excel reports, presentations, and internal documents.
Technical Dependency
Commercial users relied heavily on analytics teams to write queries, validate hypotheses, and build visualizations.
Slow Investigations
Even straightforward questions often required hours of manual exploration across multiple tools before arriving at a reliable answer.
Lack of Trust in AI
Enterprise users needed confidence in AI-generated insights. Without visibility into reasoning, underlying queries, or evidence, recommendations were difficult to trust.
Primary Objectives
Accelerate Investigations
Reduce time-to-insight and accelerate commercial investigations through AI-assisted data retrieval.
Enable Self-Service
Reduce dependence on BI specialists and enable self-service analytics using natural language.
Actionable Decisions
Convert analytics into actionable business recommendations and automatically generate presentation-ready outputs.
Ask SRM Conversational Workspace
Select a commercial query from the buttons below to trigger the 4-step AI agent orchestration.
Select a preset commercial question above to launch Ask SRM.
My Design Responsibilities
As the Product Designer, I was responsible for designing a conversational analytics experience that balanced AI automation with enterprise transparency:
End-to-End UX
Conversation Design
Data Visualization
Multi-Agent UX
Experience & Business Outcomes
Primary User Personas
Revenue Managers
Monitor pricing, revenue, promotions, and overall portfolio profitability margins.
Sales Leaders
Track commercial performance across brands, countries, and major retailers.
Category Managers
Understand brand movement, product mix variations, and pack format performance.
Pricing Analysts
Investigate pricing impact and recommend price realization strategies.
Key Research Insights
We identified several recurring behavioral patterns from research sessions with MARS commercial planners:
- Users think in questions, not reports: "Why did NSV decline?" over "Open dry food sales report."
- Trust requires explainability: Users want to validate AI outputs rather than blindly accept black-box advice.
- Layered outputs are mandatory: Executives value concise narratives, while analysts require technical SQL, Python code, and statistical significance validation.
UX Principles
1. Question-First
Start investigations with natural language queries rather than navigating charts.
2. Explainability
Make reasoning steps, code, and source references fully inspectable.
3. Storytelling
Convert complex data transformations into cohesive, executive-ready presentations.
End-to-End User Journey
The platform translates analysis from a sequence of dashboard clicks into a structured progression:
SRM Ecosystem IA Hierarchy
Ask SRM is structured into a persistent workspace layer connected to existing commercial modules:
360 Cockpit
Global commercial health dashboards and brand performance overviews.
Promo Effectiveness
Analysis of trade spend, discount depths, and incremental volume ROI.
Pricing Simulator
Scenario testing for list-price updates and pack format mix margin changes.
Multi-Agent Intelligence Architecture
Instead of relying on a single LLM, Ask SRM distributes analysis across specialized sub-agents:
SQL Agent
Translates natural language questions into optimized database queries, ensuring insights remain grounded in enterprise databases.
EDA Agent
Performs exploratory data analysis to identify anomalies, trends, distributions, and relationship correlations.
Visualization Agent
Automatically determines and generates the most effective chart type (bar, trend, bridge) to support findings.
Insight Agent
Synthesizes data outputs into concise, decision-ready business narratives explaining what, why, and what next.
Three Levels of Progressive Disclosure
To support diverse user roles (executives, analysts, developers), AI responses reveal detail progressively:
Level 1: Executive Summary
One-paragraph summary explaining the business outcome, drivers, and recommendations.
Level 2: Supporting Insights
Key business drivers, interactive charts, and contribution waterfall diagrams.
Level 3: Technical Validation
Direct access to generated SQL code, Python transformations, and statistical p-value validation tests.
AI-Powered Root Cause Factors
When commercial teams investigate declining sales value (NSV or GSV), the platform decomposes the variance into six contributing factors:
Exposing reasoning to build trust
Commercial decisions influence millions of dollars in revenue. Ask SRM was designed to encourage trust through continuous human validation loops: rating AI responses, regenerating logs, refining parameters, and tracing evidence back to source tables.
Standardized Commercial Components
Maintaining consistency across SRM modules required reusable UI assets: KPI cards, filter bars, AI conversation panels, recommendation checklists, and investigation timeline logs.
Design Challenges Mapped
Balancing Simplicity with Depth
Exposing highly technical statistical validations (ANOVA, regression correlation) without overwhelming category managers.
Reducing Dashboard Fatigue
Integrating conversational shortcuts directly into existing dashboards, avoiding the need for another standalone tool.
Key Learnings & Summary
This project demonstrated that successful enterprise AI experiences are not defined by conversational interfaces alone—they are defined by how effectively they support decisions. Exposing explainability, code correctness, and source attribution proved far more impactful than simply increasing model parameters.