ElectroMart

Company Overview

ElectroMart is a regional consumer electronics retailer operating 15 stores across Maharashtra and Gujarat, along with a growing e-commerce presence. With over 5,000 SKUs ranging from smartphones to home appliances, they serve both urban and semi-urban customers looking for trusted electronics purchases.

The Challenge

ElectroMart was facing a perfect storm of challenges that threatened their market position:

Price Competition from Online Giants

Amazon and Flipkart were undercutting their prices on popular items like smartphones and laptops. By the time ElectroMart's category managers noticed competitive price drops, they had already lost 2-3 days of sales. Their manual price monitoring process couldn't keep up with the velocity of online price changes.

₹2Cr Dead Stock Problem

Fast-changing technology meant last year's models became obsolete quickly. ElectroMart was sitting on ₹2Cr worth of slow-moving inventory — TVs with older panel technology, smartphones from previous generations, and accessories for discontinued models.

Frequent Stockouts on Best Sellers

Ironically, while dead stock accumulated, popular items frequently went out of stock. The buying team relied on gut feel and historical averages, missing demand spikes during festivals and new product launches.

The Solution

ElectroMart implemented Decisio's AI platform with a focus on three key capabilities:

1. Astra: Dynamic Competitive Pricing

Astra began monitoring competitor prices across Amazon, Flipkart, and Croma, matched to ElectroMart's catalog. Within days, it identified 340 products where ElectroMart was overpriced versus competitors, and surprisingly, 180 products where they were underpriced and leaving margin on the table.

• Real-time price tracking for 2,000+ matched competitor products
• Automated price recommendations within guardrails (min 8% margin)
• Category manager approval workflow for changes > ₹500

2. Nova: Inventory Optimization

Nova analyzed 18 months of sales data to build demand models for each SKU at each store location. It identified slow-movers early and recommended markdown schedules to clear inventory before it became obsolete.

• Store-level demand forecasting with 89% accuracy
• Automatic reorder point calculations based on lead time
• Markdown timing recommendations for aging inventory

3. Orion: Demand Forecasting

Orion's forecasting incorporated festival calendars, new product launch dates, and local events to predict demand spikes 30-60 days in advance, giving the buying team time to prepare.

Implementation Timeline

Results After 6 Months

The transformation was measurable within the first quarter:

Pricing Impact

• 3.2% margin improvement by optimizing prices on 2,000+ SKUs
• Response time to competitive price changes dropped from 3 days to 2 hours
• Identified ₹12L/month in revenue from products that were underpriced

Inventory Impact

• 35% reduction in stockouts on high-velocity items
• Dead stock reduced from ₹2Cr to ₹80L through optimized markdowns
• Inventory turns improved from 4.2x to 5.8x annually

Operational Efficiency

• Category managers spend 60% less time on price monitoring
• Buying decisions now backed by data, not just gut feel
• Store managers get proactive alerts for low-stock situations

Key Learnings

• Start with high-velocity SKUs: Quick wins build confidence and demonstrate ROI
• Keep humans in the loop: Category managers approved all major changes initially
• Trust the data: Some AI recommendations felt counterintuitive but proved correct
• Iterate on guardrails: Adjusted margin floors and price change limits based on results