StoreSKUmix

AI-Powered Assortment Planning for Store Networks

Organisations running physical networks such as retail chains, dark stores, FMCG outlets, or pharma distributor points typically face the same inventory and portfolio problem. Some locations carry too many SKUs or products that barely move. Others miss out on the ones that would sell or be utilised quickly. In most cases, no one knows exactly which is which, category by category, location by location. 

StoreSKUMix solves this with AI-powered assortment and portfolio planning that brings precision to every location, every category, and every brand decision across your network. It looks at every location in your network and tells you exactly what assortment or product portfolio it should be carrying, benchmarked against comparable locations that are already succeeding. It does not just say “add more” or “cut down”. It tells you which categories to expand, which to optimise, and which brands are over-concentrated. All of this using AI-ML models and data.

What StoreSKUmix Does?

It groups every store into an archetype based on catchment affluence, store size, competition intensity and category focus. Then it compares each store’s SKU range against the top performers within its own archetype.

The gap between what a store currently stocks and what its peer benchmark suggests becomes the starting point for every assortment decision, whether it is expansion, reduction, or brand rebalancing.

The result is a dashboard that a merchandising head, a category manager, and a store operations lead can all use to move in the same direction.

A Closer Look at Each Module & What It Highlights

Executive Summary

Before anyone dives into individual stores or categories, leadership needs a single view that answers one question. Where does the network stand right now? The Executive Summary is built for that first ten-minute read.

It shows total stores analysed, how many are top performers setting the benchmark, how many need action, average sales per SKU versus the benchmark, and the total SKU gap across the network.

📌 From the Dashboard

Across a 317-store network, 81 stores emerge as top performers setting the benchmark for their archetypes. 296 stores need action — 29 for expansion, 8 for optimisation, and the rest for rebalancing or monitoring. Average sales per SKU sits at ₹5.34 lakhs against a benchmark of ₹8.06 lakhs, an 83% productivity gap. The total unaddressed SKU gap comes to 13,623.

The Key Findings panel adds further context. 277 category-archetype combinations carry high brand concentration risk, and 5,962 category-store combinations are ready for range expansion.

Store Deep Dive

Pick any store from the dropdown and see its archetype, current SKU count, benchmark, overall gap, and a category-by-category breakdown. Each category gets its own card, colour-coded for under-stocked, over-stocked, or well-aligned. This is where most assortment conversations actually begin, because assortment decisions are made category by category, not at the store level.

📌 From the Dashboard

A large-format store carries 865 SKUs against a benchmark of 605 — a 33% excess overall. On the surface, the call seems obvious: cut SKUs. But drilling into the category cards tells a different story. The excess is concentrated in low-velocity categories, while high-potential categories like Hearables — where the benchmark is 145 SKUs but the store carries only 68 — are actually under-stocked. Two opposite problems in one store, invisible at the aggregate level.

Strategic Insights

Strategic Insights converts everything the dashboard knows about a store into a single, structured strategic brief. Select any store and receive a complete picture — its performance classification, the breakdown of what is driving or holding back its sales, the top quick-win opportunities ranked by impact, and a clear recommendation on whether to expand, optimise, rebalance, or maintain.

📌 From the Dashboard

For a store flagged as a high-priority expansion candidate, the module produces a clean brief — significantly under-ranged with a +192 SKU gap, productivity strong at ₹5.23 lakhs per SKU, and statistical confidence high. The recommendation is to expand range, with Hearables, Smart Phones, and Mobile Computing named as the top three categories to prioritise.

Store Archetypes

Comparing a flagship premium store in a metro micromarket to a large-format store in a Tier 2 suburb is meaningless — their customers, footfalls, and competitive environments are all different. StoreSKUmix groups stores into four archetypes — Flagship Premium, Flagship Premium Atypical, Large Format, and Large Format Atypical. Within each, benchmarks are set by the top 25% of performers, so every store is measured only against peers that share its market reality.

Flagship Premium
₹5.74LAvg Sales/SKU
672Avg SKU Range
Flagship Premium Atypical
₹7.62LAvg Sales/SKU
814Avg SKU Range
Large Format
₹5.20LAvg Sales/SKU
262Locations
Large Format Atypical
Top 25%Set Benchmark
PeerBenchmarked
📌 From the Dashboard

Flagship Premium stores average ₹5.74 lakhs per SKU across a 672-SKU range. Their atypical counterparts, fewer in number but operating in distinctive catchments achieve ₹7.62 lakhs per SKU on an 814-SKU range, a 33% productivity lead. Meanwhile, Large Format stores, which make up the bulk of the network at 262 locations, average ₹5.20 lakhs per SKU. These are not minor differences. They are the difference between benchmarking a store against the right peer and mis-managing it against the wrong one.

Range Gap Analysis

Range Gap Analysis sorts every store into three buckets — under-ranged, well-aligned, or over-ranged — by comparing its current SKU count to its archetype benchmark.

What makes this module actionable is that it does not stop at flagging gaps. Every store is given a specific recommendation — SELECTIVE EXPANSION for stores where the gap is genuine and the productivity supports adding SKUs, OPTIMIZE & REDUCE for stores carrying excess SKUs that are diluting productivity, MONITOR for borderline cases, and DATA QUALITY REVIEW for stores showing anomalies that need verification before any action is taken.

📌 From the Dashboard

Of the 317-store network, 129 stores are under-ranged, 95 are over-ranged, and 93 are well-aligned. Take a store carrying 865 SKUs against a benchmark of 605 — a 259-SKU excess, 33% over-ranged. The recommendation is OPTIMIZE & REDUCE — a clear call for the merchandising team to review the slow-moving tail and rationalise SKUs contributing minimally to sales.

Category Insights

Individual store data is important. But network-wide patterns only become visible when you zoom out to the category level.

Category Insights aggregates performance across every category and cross-references it against every archetype. It shows which categories are driving the bulk of network sales, which are systematically under-represented against benchmarks, which are carrying excess SKUs network-wide, and how the category mix shifts between premium and large-format stores.

📌 Dashboard Example

Smartphones account for over 20% of sales across all archetypes, making them non-negotiable on any expansion list. At the other end, categories like Apple Audio, DSLR Cameras, and Gaming Consoles show 100% expansion gaps — they are under-represented almost everywhere in the network. Meanwhile, categories like Monitors, Water Purifiers, and iPad Accessories show 60–70% excess, suggesting a case for network-wide rationalisation rather than store-by-store tweaks.

Brand Portfolio

The Brand Portfolio module analyses brand concentration across every category-archetype combination and classifies each as Balanced, High Concentration, or Fragmented. High Concentration flags vendor dependency risk. Fragmented flags the opposite — too many brands diluting focus and weakening partnerships.

📌 Dashboard Example

Out of 368 category-archetype combinations, 277 show high brand concentration, 84 are balanced, and 7 are fragmented. In Air Purifiers within the Flagship Premium archetype, a single brand holds 73.7% share — the category is almost entirely dependent on one vendor. In Air Conditioners, the opposite is true: 23 brands compete with the top holding only 15.9% share, a clear case for rationalisation.

Action Plan

The Action Plan puts all analysis into an executable, prioritised roadmap. Every store is scored and sorted into High, Medium, or Low priority based on the size of its gap, the strength of its productivity, and the statistical confidence behind the recommendation.

📌 Dashboard Example

Across the network, 37 stores are flagged High Priority, 133 Medium, and 144 Low Priority or already well-aligned. A New Delhi store in the High Priority list gets a specific brief — expand range by 192 SKUs, prioritise Hearables where the category gap alone is +82 SKUs, and maintain the current productivity of ₹5.23 lakhs per SKU. The action is named, the quantity measurable, and the rationale removes any ambiguity on why this store is near the top of the list.

Who This is Built For?

Merchandising and Category Heads
Use it to make assortment decisions backed by data, knowing exactly which SKUs to add, which to cut, and in which stores.

Store Operations Leads
Use it to understand why two stores in similar markets perform differently and what assortment changes will close the gap.

CXOs and Strategy Teams
Use it to see the total network-level assortment opportunity, rationalise SKU complexity, and rebalance brand exposure before it becomes a business risk.

How Quickly Can You See Results?

StoreSKUmix works with your existing store sales, SKU master, and brand data. There is no lengthy implementation or custom modelling required. Most retail networks are up and running within a few weeks, with the first round of archetype benchmarks, category gaps, and ranked action plans ready in the first month itself.

1
Connect your existing data
StoreSKUmix works with your existing store sales, SKU master, and brand data. No custom modelling required.
2
Live within a few weeks
Most retail networks are up and running within a few weeks of onboarding.
3
First results in month one
The first round of archetype benchmarks, category gaps, and ranked action plans ready in the first month itself.

The Bottom Line

Knowing exactly which SKUs to add, drop, or rebalance and which stores to prioritise is the real advantage. StoreSKUmix turns the most complex question in retail merchandising into clear, actionable steps.

If you want to go a level deeper and understand the complete performance story behind each of your existing stores, you can check out StorePerformix by Kentrix, a performance benchmarking dashboard that gives you a holistic picture of your store performance.

Explore StorePerformix →