Overview
Flipkart Commerce Cloud Pricing Service
Product Documentation v1.0 Built By Retailers, For Retailers
Confidential — For Publisher and Partner Use Only © 2026 Flipkart Commerce Cloud
About This Document
This document is the product reference for Flipkart Commerce Cloud (FCC) Pricing Service. It covers the Dynamic Pricer and Rule Engine (Formula UI, Optimiser, and Custom-File rule types), the Price Optimiser, approval workflows and guardrails, the 24-report analytics suite, and integration guidance for publishers and their commercial and technical teams.
Audience: Network publishers, commercial and pricing teams, category managers, integration engineers, and e-commerce product managers.
Prerequisites: Familiarity with basic e-commerce pricing concepts (price rules, margins, discounts) is helpful but not required.
Table of Contents
- Introduction
- How the Platform Works
- Dynamic Pricer & Rule Engine
- Price Optimiser
- Approval Workflows, Guardrails & Reporting
- Onboarding Checklist
- Glossary
1. Introduction
Overview
In e-commerce, pricing is the single most powerful lever — but most retailers are managing it reactively. Price changes are triggered by gut feel, delayed competitor data, or manual category-by-category decisions. Errors creep in. Margins erode. Competitor moves go unnoticed for hours. And when you have hundreds of thousands of SKUs across multiple channels and geographies, the idea of manually pricing each one isn't just inefficient — it's impossible.
FCC Pricing Service is built on the same pricing intelligence engine that powers Flipkart — one of the highest-volume commerce platforms in the world — with 15+ years of pricing experience and a proven track record across 40+ enterprise clients. It gives publishers a centralised, automated pricing platform that spans the full spectrum from rule-based strategies to ML-driven optimisation: competitive price matching, margin-floor guardrails, category-level pricing rules, configurable approval workflows, and a 24-report analytics suite — all through a self-serve interface that puts pricing teams in control without engineering tickets.
Critically, FCC Pricing Service is not just a rule engine — it is a complete pricing ecosystem. Competitor intelligence feeds in automatically from the CI layer. Inventory signals, sales history, and cost data inform every rule. The Optimiser selects the mathematically best price for a configured business objective. And every price recommendation can be reviewed and approved before it goes live, giving pricing managers confidence without losing speed.
Expected Business Impact
| Outcome | Expected Lift |
|---|---|
| Revenue and margin uplift from automated pricing strategies | +2–5% margin improvement |
| Reduction in manual pricing effort through automation | 40–60% operational cost savings |
| Reduction in pricing errors from automated guardrails | Near-zero manual pricing mistakes |
| Time-to-reprice on competitor price changes | From hours/days to near-same-day |
Who This Platform Is For
| Persona | Description |
|---|---|
| CXOs / Business Heads | Leadership seeking a step-change in bottomline (margin) and topline (revenue) through centralised, reliable pricing automation — with visibility into pricing performance at a portfolio level |
| Category Managers / Pricing Practice | Day-to-day users who configure pricing strategies, define rules across categories, and review recommendations before they go live |
| Business Finance Teams | Stakeholders who need margin guardrails, approval workflows, and audit trails to ensure pricing decisions are commercially sound |
| Integration Engineers | Technical teams integrating the Reprice Push API and data feed outputs into the client's pricing management and e-commerce infrastructure |
2. How the Platform Works
FCC Pricing Manager is a complete pricing ecosystem — not just a rule engine. It ingests the client's product catalogue, cost and margin data, inventory signals, and live competitor prices from the CI layer, and uses these inputs to continuously generate commercially sound price recommendations across the entire catalogue.
The platform covers three core capabilities that work together: the Dynamic Pricer and Rule Engine applies configured pricing strategies to generate SKU-level price recommendations; the Price Optimiser uses ML to autonomously select the price that best achieves a defined business objective; and Approval Workflows and Guardrails ensure every recommendation is commercially vetted before it reaches the storefront. All capabilities are accessible through a self-serve console with Role-Based Access Control and a full audit trail.
Key functional concepts:
Rulebase with conflict resolution. Pricing strategies are configured as an ordered set of rules. Each rule defines which products it applies to (via Product Filters) and what action to take. When multiple rules apply to the same product, rule ordering resolves the conflict — every product gets exactly one repricing rule per run.
Three rule types. Formula UI rules are built directly in the self-serve interface. Optimiser rules delegate the price decision to an ML model. Custom-File rules handle complex pricing grids uploaded as a file — for use cases like gross margin lookup tables or channel-specific price matrices.
Guardrails on every rule. Price floors and ceilings — sourced from cost/margin data, competitor prices, MRP, or brand contract files — can be attached to any rule to prevent it from ever generating a commercially unsafe price. Price round-off logic can also be applied to ensure the final price meets commercial formatting requirements.
3. Dynamic Pricer & Rule Engine
Status: ✅ Live
Overview
The Dynamic Pricer is the core repricing engine of FCC Pricing Manager. It evaluates every product in the client's catalogue against a configured rulebase, determines the applicable pricing strategy for each product, and generates a price recommendation that can be pushed to the client's platform. The engine is designed to handle the full complexity of enterprise pricing — thousands of rules, millions of SKUs, multiple channels, and pricing strategies that combine catalog data, live inventory signals, and competitor intelligence in a single formula.
How It Works
Every pricing run follows three stages:
1. Match — the engine scans the rulebase against the product catalogue. Product Filters on each rule define which products it applies to. Filters can use static catalog attributes (brand, category, vertical, supplier), dynamic data (inventory age, inventory count, rate of sale, competitor price change), and store/channel selectors. Every product is annotated with the set of rules whose filters it satisfies.
2. Resolve — where multiple rules apply to the same product, the rule ordering resolves the conflict. The rule that appears first in the configured order wins. Once a product is matched to a rule, it is removed from the scan for subsequent rules, ensuring every product gets exactly one repricing rule.
3. Execute — the assigned rule's Action is applied. Actions are constructed from: an output selection (price, discount, or price change), parameter selection (data fields from the catalog, inventory, or CI feed), UI inputs (numerical values, percentage operators), and combinational logic (how multiple action components are aggregated). The output is a new price recommendation per SKU.
Rule Types
| Rule Type | Description | Best For |
|---|---|---|
| Formula UI Repricing | Pricing formulas configured entirely in the self-serve UI using catalog, inventory, and competitor price data points | Competitive matching, cost-plus pricing, category discounts, event pricing |
| Optimiser Repricing | ML model selects the price that optimises a configured target (GMV, Margin, or Units) within defined constraints | High-volume SKUs where the optimal price is non-obvious; demand-sensitive categories |
| Custom-File Logic | Complex pricing formulas derived from a client-uploaded matrix file (e.g., a gross margin lookup table) | GM-based pricing, channel-specific price grids, complex wholesale pricing structures |
Execution Modes
| Mode | Description |
|---|---|
| Real-time | Rule output is sent directly to upstream systems for immediate price application |
| Simulation | Rules execute against the catalogue to preview price changes without publishing — used for testing and impact analysis |
| Scheduled | Rules execute on a configured recurring schedule with a minimum granularity of one hour |
| Ad-hoc / One-time | A single on-demand execution triggered by a user |
Business Use Cases
| Use Case | Example |
|---|---|
| Competitive price matching | "If competitor price < our price on any matched SKU, reprice to 2% below competitor" |
| Inventory clearance / Markdown pricing | "If inventory age > 60 days, reduce price by 5% from current selling price" |
| Margin-based pricing | Custom-file rule using a GM matrix to compute the markdown discount for each product's gross margin tier |
| Event / campaign pricing | Scheduled rule runs for a sale event window with dedicated price targets and guardrails |
| New product launch | Optimiser rule targeting units-maximisation for the first 30 days after launch |
4. Price Optimiser
Status: ✅ Live
Overview
The Price Optimiser is FCC's ML-driven pricing capability — the evolution beyond rule-based strategies. Where the Dynamic Pricer applies a human-configured formula, the Optimiser determines the best price autonomously: it selects the price that maximises a configurable business objective (GMV, Margin, or Units Sold) within defined guardrail constraints, informed by historical price-demand patterns and a demand forecasting model. For high-volume categories where the relationship between price and demand is non-trivial, the Optimiser consistently outperforms static formulas.
How It Works
- For each SKU in scope, the Optimiser retrieves historical price, sales, and conversion data.
- A Forecasting Module predicts demand at a range of price points for each SKU.
- An Objective Function (configured by the pricing team — GMV, Margin, or Units) selects the price that best achieves the target given the demand forecast.
- Guardrail Constraints (margin floor, competitor price anchor, MRP ceiling, brand contract limits) are applied to ensure the optimal price remains within commercial bounds.
- The output is a price recommendation per SKU. Depending on configuration, this is either auto-applied or routed through an Approval Workflow for manual review.
Configuration Options
| Parameter | Description | Configurable |
|---|---|---|
| Objective Function | Target metric for optimisation — GMV, Margin, or Units Sold | Yes |
| Execution Style | Conservative, Moderate, or Risky — controls how aggressively the Optimiser moves away from the current price | Yes |
| Guardrail Constraints | Minimum margin floor, competitor price anchor, MRP ceiling | Yes |
| Coverage Scope | Full catalogue or a defined product subset via Product Filters | Yes |
| Approval Workflow | Whether Optimiser outputs require manual review before publishing | Yes |
Business Use Cases
| Use Case | Example |
|---|---|
| Peak demand capture | Optimise prices upward during a demand spike to maximise revenue per unit without breaching MRP |
| Clearance / end-of-season markdown | Optimise prices downward on aged inventory to minimise holding cost and clear stock by a target date |
| Category margin management | Set a category-wide margin target; let the Optimiser distribute price adjustments across SKUs to collectively hit it |
| New product pricing | Target units-maximisation for the first 30 days post-launch to drive early adoption and demand signal generation |
5. Approval Workflows, Guardrails & Reporting
Status: ✅ Live
Overview
FCC Pricing Manager embeds commercial governance directly into the pricing loop. Every rule can be configured to require manual review of its outputs before they are published. Guardrails cap price movements at defined thresholds to prevent commercially unsafe prices from ever reaching the storefront. And a comprehensive 24-report analytics suite — covering competitive positioning, pricing automation performance, and business outcomes — gives pricing leadership complete visibility into how the platform is performing.
Approval Workflow
Any rule can be configured to require manual approval — triggered always, or only when a recommended price breaches a defined threshold. Category managers see a dedicated review screen presenting each SKU awaiting decision, with current price, recommended price, competitor context, and rule rationale.
Available actions per SKU:
| Action | Description |
|---|---|
| Accept | Approves the price recommendation for publication. Available at single-SKU or bulk level. |
| Reject | Rejects the recommendation. Requires selection of a predefined reason code (e.g., "Cost higher than recommended price", "Incorrect competitor match", "Other"). |
| Suspend | Temporarily suppresses the SKU from repricing for a configured period — "next 3 runs", "end of day", "one week". If a new recommendation is generated during the suspension period that matches the suspended price, it is auto-rejected. |
All actions are captured in a full audit trail with user identity, timestamp, and action detail. Role-Based Access Control governs which users see which SKUs on the approval screen (filtered by assigned categories and roles).
Guardrails
| Guardrail Type | Source | Example |
|---|---|---|
| Competitive price ceiling | CI feed — competitor price | "Never price more than 5% above Amazon on matched SKUs" |
| Cost floor | Client cost/margin feed (DFF/MFF) | "Never price below cost plus 10% margin" |
| MRP ceiling | Client catalog MRP | "Never price above the product's Maximum Retail Price" |
| Brand contract floor | Custom-file uploaded by user | "Minimum Advertised Price for Nike SKUs = $89" |
Reporting — 24 Report Types
FCC Pricing Manager's analytics suite spans three reporting dimensions, all filterable by Date, Competitor, Business Unit, Category, Brand, KVI flag, Event, and Rule:
Competitive Intelligence Reports
| Report | Description |
|---|---|
| Competitiveness Trend | Client pricing position vs. competitors over time |
| Competitiveness by BU | Competitive analysis at Business Unit level |
| Competitiveness by Supercategory | Competitive breakdown at L1 category level |
| Price Index Trend | Client price-to-competitor price ratio over time |
| Price Index by Category | Price index at L2 category level |
| Brand Level Competitiveness | Competitive positioning aggregated by brand |
| Product Level Competitiveness | SKU-level price comparison with matched competitor prices |
| Product Match % Trend | % of catalogue with at least one matched competitor product, over time |
| Product Match Absolute Trend | Absolute count of matched products over time |
| Product Match % by BU/Category | Match coverage breakdown by product hierarchy |
| Product Match Absolute by Category | Absolute match counts by category level |
| Price Freshness Trend | Recency of competitor price data — % of matched products refreshed within threshold |
Dynamic Pricer Performance Reports
| Report | Description |
|---|---|
| GMV on Automation Trend | GMV attributable to products under automated pricing rules, over time |
| Verticals on Automation | Automation coverage by product vertical |
| Rules Pending for Approval | Pipeline view of price recommendations awaiting category manager review |
| Price Application Trend | Rate of successful price updates published to the client's platform |
| Guardrail Hit Trend | Frequency and category distribution of guardrail breaches by rule |
Business Outcome Reports
| Report | Description |
|---|---|
| GMV Trend | Overall GMV performance |
| RPI (Revenue Per Impression) Trend | Revenue efficiency across the priced catalogue |
| Units Sold Trend | Volume performance over time |
| Conversion Trend | Conversion rate for products under automated pricing |
| Average Margin % Trend | Margin contribution vs. targets |
| Impressions Trend | Catalogue visibility |
| Category Level Change | Price change analysis at category level |
| Brand / Product Level Change | Price change analysis at brand and SKU level |
6. Onboarding Checklist
Overview
The following are some of the high-level milestones of onboarding FCC Pricing Service:
- Catalogue and data scope definition — agree on the initial category scope, and establish the Master Feature Feed (MFF) and Dynamic Feature Feed (DFF) from the client to FCC
- Rulebase and guardrail configuration — design the initial set of pricing rules, configure Product Filters, and set margin floor guardrails for all active rules
- RBAC and workflow setup — define Pricing Manager, Category Manager, and Admin roles; configure approval workflow requirements per rule
- Technical integration — integrate the Reprice Push API into the client's pricing or e-commerce platform and validate end-to-end data flow
- Simulation and validation — run initial rules in Simulation Mode to review price change distribution and guardrail behaviour before going live
- Go live and performance review — enable live pricing rules, monitor Automation Coverage and Competitive Price Index, and establish an ongoing pricing performance review cadence
Glossary
| Term | Definition |
|---|---|
| FCC | Flipkart Commerce Cloud |
| CI | Competitive Intelligence — competitor price data scraped and matched by the CI layer, fed into the Pricing Engine as a live data source |
| DFF | Dynamic Feature Feed — real-time or near-real-time product data from the client (inventory, age, rate of sale) used as dynamic Product Filters in rules |
| Dynamic Pricer | The core repricing engine that applies configured rules to generate new price recommendations for every product in scope |
| Formula UI | Rule type where the pricing formula is configured directly in the self-serve UI using parameterised data inputs |
| Guardrail | A price threshold (floor or ceiling) applied to a rule to prevent it from generating a commercially unsafe price |
| Inference Engine | The backend component that runs the three-phase rule execution cycle: Match, Resolve, and Execute |
| KVI | Key Value Item — hero or price-sensitive products flagged for priority pricing treatment and elevated monitoring |
| MFF | Master Feature Feed — static product catalog data from the client (brand, category, cost, MRP) used as static Product Filters in rules |
| MRP | Maximum Retail Price — the maximum permissible selling price for a product |
| Optimiser | The ML-driven rule type that selects the price maximising a configured business objective (GMV, Margin, or Units) |
| Price Round-off | Logic applied after rule execution to round the calculated price to a commercially appropriate figure (e.g., ₹1,799 instead of ₹1,782) |
| Product Filter | Rule configuration that defines which products a rule applies to, using static and dynamic data attributes |
| RBAC | Role-Based Access Control — the permission system that governs access to the pricing console and approval screens |
| Rulebase | The ordered set of active pricing rules configured in the Dynamic Pricer |
| Simulation Mode | An execution style where rule outputs are computed but not published — used for previewing the impact of a rule before go-live |
© 2026 Flipkart Commerce Cloud — Confidential. For Publisher and Partner Use Only.