Omnichannel Allocation
AI Matching for Products, Stores, and Sales Associates

Rebuild store allocation around data-driven matching — across every store, SKU, and associate in your network, not just across sales channels.

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3 Core ElementsProducts, channels, and associates matched in one loop

7 Business ModulesDiagnosis, ordering, in-season adjustment, and review

AI Diagnosis in SecondsA four-step engine scans structural gaps in seconds

Typical Decision Flow

Business Question

How should the first allocation for 50 Shanghai direct stores be planned for next season?

System Processing

The engine analyzes sell-through, local customer preferences, size curves, and merchandising plans to generate store-level allocation models.

Final Output

SKU-level first-allocation recommendations that balance headquarters' breadth goals with each store's expected productivity.

Product Positioning

Omnichannel Allocation is built for multi-store retail. It identifies each store's role and customer demand, matches the right product mix to the right store, and assigns the right associate to carry the selling strategy forward.

Not blanket allocation — precise product-store-associate matching

Identify Stores

Store and Customer RecognitionIdentify the merchandise direction each store can best support, based on location, customer base, price band, and shopping preferences.

Allocate Goods

Product-to-Store MatchingRecommend the product mix each store should carry, based on store positioning, merchandise structure, and sales targets.

Assign Associates

Associate-to-Assortment MatchingMatch sales associates to the right merchandise using their category strengths, selling style, and average order value track record.

Store profilesProduct matchingAssociate matchingAssortment optimizationStrategy executionPerformance feedback

Matching EngineUnifies product, store, and associate labels into a continuously improving decision engine.

Closed-Loop OptimizationDiagnosis, recommendation, execution, and review make allocation more precise over time.

Real-Time FeedbackSales, sell-through, average order value, and associate performance feed back into strategy calibration and model iteration.

Who It Is For

Store Managers

Single-store issues are hard to isolate, and actions lag behind.

Receive anomaly alerts and action recommendations so more time goes into store execution.

Regional Supervisors

Managing across stores is hard, and problems take too long to locate.

See regional rankings and store health in one view, then focus coaching where it matters most.

Retail Executives

Company-wide data lags, and decisions lack quantified evidence.

Monitor sales changes and quantified impact to support faster resource deployment decisions.

Core Capabilities

Turn allocation into a repeatable decision workflow across profiles, diagnosis, execution, and review.

Profile Foundation

Build standardized labels for products, stores, and associates so every object is clearly understood.

Product tagsStore tagsAssociate tags

Standardize how products, stores, and associates are described, so every downstream diagnosis starts from one shared definition.

Store Diagnosis

Analyze store productivity, regional ranking, health, and conversion funnels to find weak spots faster.

Store outputRegional rankingStore health

Build the diagnostic workflow around store performance breakdown and execution standards, so regional supervisors pin down coaching priorities faster.

AI Recommendations

Surface growth opportunities and churn risk from member health, activity trends, and average order value — plus market share.

Member healthActivity trendsAOV analysis

Help brands see conversion efficiency clearly when customer structure shifts or campaigns are reviewed, reducing member churn.

AI Review

Convert operating data into conclusions, then turn conclusions into prioritized actions with impact estimates.

Driver analysisImpact sizingAction dispatch

From anomaly detection to cross-diagnosis to closed-loop action, improve the speed and efficiency of end-to-end operating decisions.

Product Form

Multi-Device Touchpoints

Deliver conclusions and action lists through enterprise IM, PC dashboards, and command-center screens.

Enterprise IM (WeCom, DingTalk)PC dashboardCommand center

Intelligent Decision Hub

Automatically runs anomaly detection, three-way cross diagnosis, and loss quantification, then generates action recommendations.

Anomaly monitoringException diagnosisAction recommendations

Industry Data Foundation

Connect product, store, and associate data while accumulating industry know-how and standard metric definitions.

Product dataStore dataAssociate data

Key Mechanisms

Proactive Exception Sensing

Monitor core operating metrics around the clock — no one has to open a report — and trigger diagnosis the moment an exception appears.

Three-Way Cross Diagnosis

Break down silos between product, store, and customer data and cross-analyze them to pinpoint the true root cause.

Quantified Impact

Attach an estimated financial impact (loss or upside) to each conclusion so teams can prioritize actions.

Closed-Loop Action Delivery

Turn conclusions into replenishment and inter-store transfer tasks, then route them to accountable owners or business systems for closed-loop execution.

Typical Business Scenarios

Coordinate allocation and adjustment decisions across stores, regions, and customer touchpoints.

Pre-season planning and assortment matching

Pre-Season Planning & Assortment Matching

Customer Challenges: The assortment set at headquarters often misses each store's local customer preferences, causing structural mismatch and overstock.

Decision: AI gives explainable structural recommendations for pre-season buying and assortment, aligning headquarters' strategy with each store's local demand.

In-Season Anomaly Scan & Tuning

Customer Challenges: Stockouts and overstock happen at the same time across different stores.

Decision: A four-step analysis engine completes the scan in seconds and returns SKU-level transfer and action recommendations, recalibrating operations quickly.

Associate-to-Assortment Matching

Customer Challenges: Associates skilled in high-ticket or specific scenarios are randomly assigned to merchandise they cannot sell, dragging down productivity and conversion.

Decision: Based on merchandise structure and store tags, the system recommends the associates best suited to each assignment, with targeted talking points.

Strategy Execution Review

Customer Challenges: Teams often cannot tell whether a selling strategy was executed or improved conversion.

Decision: The system tracks execution, validates outcomes, and feeds successful practices back into future recommendations.

Business Value

Maximize resource use through product matching and inventory orchestration from a network-wide view.

Efficiency

Break inventory boundaries across online, offline, and regional channels to achieve optimal network-wide allocation and sharing.

Loss Reduction

Accurate initial allocation and in-season transfers put the right products in the stores most likely to sell them, accelerating turnover and reducing overstock depreciation.

Growth Loop

Match VIP customers with associates who fit their style and build better campaign conversion.