Case study · Retail

Intelligent Item Markdown for Retail

We replaced ad-hoc, store-by-store markdowns with a corporate-controlled optimization engine on GCP that delivers consistent regional pricing, clears slow-moving stock faster, and gives merchandisers real-time visibility into performance.

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E-Commerce & RetailGoogle Cloud Platform (GCP)Managed data warehouseServerless computeSales and inventory data pipelineBatch and streaming ingestion

Impact

Results at a glance

30-40%

Excess inventory reduction

90%+

Pricing consistency

50%

Faster markdown decisions

15-25%

Sales performance uplift

40%

Operational efficiency gain

Background

The situation

A large-scale retail enterprise operating hundreds of physical stores across multiple regions managed a broad mix of FMCG, seasonal merchandise, and promotional inventory. Pricing was decentralized, so individual store managers applied markdowns on their own judgment, producing inconsistent prices for identical products from one location to the next. With no unified framework, demand shifts and promotions were handled manually, slow-moving stock lingered and inflated holding costs, and corporate had little real-time insight into store-level pricing or compliance. The resulting price inconsistency confused shoppers and steadily eroded trust in the brand.

The objective

Consolidate markdown decisions into a single corporate-governed engine that enforces one pricing strategy across every region. Turn inventory age, demand, and seasonality signals into dynamic markdown recommendations, and give merchandisers real-time visibility to act on them quickly.

Our approach

How we delivered

Rebuild pricing infrastructure on GCP

We re-platformed the fragmented pricing setup into a centralized pricing ecosystem on Google Cloud Platform, establishing unified markdown rule management as the single source of truth across all stores and regions.

Engineer the sales and inventory data pipeline

We built a data engineering pipeline to ingest and normalize sales, inventory, and promotional feeds from every location, creating the clean, consolidated dataset the optimization engine needs to reason over item-level performance.

Build the centralized markdown optimization engine

We implemented a corporate-controlled engine that enforces standardized pricing strategy and generates dynamic markdown recommendations from inventory age, demand trends, and seasonal factors, ensuring promotional campaigns execute consistently across regions.

Deliver a real-time intelligence dashboard

We shipped a web-based analytics dashboard giving merchandisers real-time pricing and inventory visibility, intelligent markdown recommendations, product performance insight across sales velocity, stock movement, and profitability, plus proactive slow-moving inventory alerts.

Drive adoption through change management

We supported rollout with hands-on training, documented standard operating procedures, and ongoing enablement so store and corporate teams could confidently move from manual markdowns to engine-guided decisions.

Architecture

The technical solution

A block-level view of the system we designed and delivered — data and control flowing across each stage.

Source Data

Store Sales Feeds

POS transactions by region

Inventory & Promotions

Stock levels, age, campaigns

GCP Data Pipeline

Ingestion & Normalization

Batch + streaming

Data Warehouse

Unified item-level dataset

Optimization

Markdown Optimization Engine

Corporate-controlled rules

Demand & Seasonality Models

Inventory age, demand trends

Consumption

Merchandiser Dashboard

Recommendations & alerts

Corporate Oversight

Real-time compliance view

Sales and inventory data flow from stores through a GCP pipeline into a centralized markdown optimization engine that feeds the merchandiser dashboard.

The interface

What the users see

A wireframe of the experience we shipped — laid out for the people who use it every day.

Merchandiser Analytics Dashboard

Excess Inventory

Pricing Consistency

Sales Uplift

Slow-Moving Alerts

Real-time markdown intelligence for merchandisers across regions.

The pipeline

From raw data to decisions teams can act on

A governed pipeline moves data from ingestion through modeling to activation — accurate, compliant, and fast enough to support real-time decisions.

How it works

AI that earns its place in production

We take models from data through training to deployment and monitoring, with governance and guardrails built in — not a pilot that never ships.

The results

Outcomes delivered

Excess inventory dropped by 30 to 40 percent as timely, data-driven markdowns cleared slow-moving stock and cut holding costs.
Pricing consistency reached over 90 percent, giving shoppers dependable prices for identical products across every region.
Markdown decision speed improved by 50 percent, letting merchandisers respond to demand and seasonal shifts far sooner.
Sales performance lifted by 15 to 25 percent through better-timed, optimally priced promotions.
Operational efficiency rose by 40 percent while real-time dashboards gave corporate leadership the oversight they previously lacked.

Under the hood

Technology stack

The platforms, frameworks, and standards behind the solution.

Cloud Platform

Google Cloud Platform (GCP) Managed data warehouse Serverless compute

Data Engineering

Sales and inventory data pipeline Batch and streaming ingestion Data normalization and modeling

Optimization Engine

Centralized markdown optimization engine Demand and seasonality models Unified markdown rule management

Analytics Dashboard

Web-based analytics dashboard Real-time pricing and inventory visibility Slow-moving inventory alerts

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