StorePlannix
AI-Powered Retail Expansion Planning & New Site Selection
A bad store location does not announce itself the day you sign the lease. It is felt 12 months later when the revenue curve does not reach the forecast, in a ramp that stalled, a cannibalization ratio that quietly eroded 2 stores closest to it. By then, most brands have already spent millions in capital expenditure.
The difference between an expansion that builds the network and the one that breaks it always comes down to what was known or what was unknown.
StorePlannix is built for new store planning in retail. For every site in your expansion pipeline, it delivers a predicted monthly revenue, a ramp-up trajectory, a quantified cannibalization and lift impact on your existing network, a feature-by-feature attribution of what is driving the prediction, and a category mix recommendation tied to that specific catchment. This is retail location planning powered by AI, household-level consumer intelligence, and the live performance data of your existing store network.
What StorePlannix Does?
Every location site in an expansion pipeline carries risk. StorePlannix converts that risk into a number.
It takes into account each planned location, layers in the catchment data around it — consumer demographics, household income bands, spending behavior by category, footfall proxies, competition density, and points of interest, and runs it through a trained revenue prediction model. The model has been evaluated across 25 algorithm variants and validated on held-out sites before any prediction is produced.
The output is not a heatmap or a directional score. It is a site-level monthly revenue forecast, a 24-month ramp-up curve, a store-by-store cannibalization impact on your existing portfolio, a ranked list of the variables driving each prediction, and a category mix plan the opening team can act on immediately.
A Closer Look at Each Module
Predicted Revenue & Ramp Up Trajectory
The first question any retail network expansion decision needs to answer is also the most direct. What will this store make, and how fast will it get there?
StorePlannix provides a month-by-month revenue forecast for the first 24 months of operations. Each site is classified into a ramp-up cluster — Fast Ramp, Medium Ramp, or Slow Ramp, all based on how comparable stores in similar catchments have historically built revenue from opening to maturity.
Fast Ramp sites reach their predicted steady-state revenue within three months. These are typically established urban markets with high footfall density and clear consumer demand signals. Medium Ramp sites take longer, between six and twenty-four months, because they sit in markets where brand awareness needs time to build or where competition dynamics slow the initial share capture. Knowing which cluster a site belongs to before opening allows the finance team to plan the cash flow curve accurately and the operations team to calibrate the opening investment accordingly.
The ramp-up view also accepts a seasonality adjustment. Selecting an opening month recalibrates the monthly trajectory to reflect the demand cycle of that market, so the first-year revenue projection is tied to the actual calendar the store will open into.
Site A is a Medium Ramp location in a fast-developing suburb, with a maturity horizon of 22 months and a predicted steady-state of ₹3.5 crore per month. Site B is a Fast Ramp location in a high-density urban corridor that hits its ₹3.1 crore steady-state by month three, generating ₹37.1 crore in the first 12 months. Two different catchment profiles, two very different expansion stories, both visible before any commitment is made.
Cannibalization & Network Lift Analysis
Cannibalization is one of the most underestimated risks in retail network expansion. StorePlannix quantifies it precisely.
For every planned site, the cannibalization module maps all nearby stores in your existing portfolio, calculates the distance and catchment overlap, and produces a predicted revenue change — positive (lift) or negative (cannibalization) for each neighbor. The net impact on the new site’s contribution to the overall portfolio is then surfaced as a single number.
A cannibalization ratio of 30% on a site means that 30% of its predicted revenue is likely to come at the direct expense of existing stores, not from new consumer demand. That changes the expansion decision materially.
Site C is planned for a mid-sized township node, with a predicted monthly standalone revenue of ₹2.09 crore. Its cannibalization ratio is 27%. The nearest existing store, 1.1 km away, is projected to absorb a ₹46.5 lakh revenue decline, a 4.7% drop from a store currently doing ₹9.88 crore monthly. Net portfolio contribution for Site C drops to ₹1.52 crore, 27% below the standalone prediction. Without cannibalization analysis, this opening looks materially better than it is.
Contrast that with Site D, planned in an underpenetrated Tier 2 market. Zero cannibalization. Its single nearby network neighbor is projected to lift by 5%, adding ₹86.6 lakhs in incremental network revenue. Net portfolio contribution: ₹2.41 crore — higher than the standalone prediction. The new site creates genuine demand in a market the network was not yet serving.
Feature Attribution
Predicting revenue at a selected location is one thing. Knowing which specific variables are responsible for that prediction at that site is what makes the output genuinely actionable.
StorePlannix uses SHAP (Shapley Additive Explanations) to calculate the exact contribution of every variable to the predicted revenue of each site. Variables are separated into two categories: controllable factors, which the retailer can adjust, such as SKU range, staff count, brand promoter deployment, and structural factors fixed by the catchment, like household income, consumer spending behavior, footfall density, competition profile, and points of interest.
In a premium urban location in a high-income western suburb, the highest-impact variable is SKU range, which significantly increases the predicted monthly revenue above the model baseline. Total staff deployment shows a negative SHAP contribution at this specific site, a signal that the current staffing assumption may be pulling the prediction down and deserves review before the opening plan is finalized. Catchment variables, including the concentration of high-income households, contribute positively, confirming the strength of structural demand independent of the retailer’s operational decisions. Every variable’s direction and magnitude are visible, site by site.
Category Mix Recommendations
Opening a new store without knowing which product categories the catchment is most likely to buy is one of the most avoidable planning gaps in new store expansion. StorePlannix closes with a site-specific category mix recommendation built from predicted revenue, the catchment’s consumer profile, and the performance benchmarks of comparable stores in similar markets.
For each site in the pipeline, the category mix module produces a ranked view of which categories are expected to drive disproportionate sales, which should be adequately represented, and which carry lower priority given the local demand profile. This becomes the first structured input into the pre-opening assortment plan, replacing the standard practice of defaulting every new store to a network-wide template.
The category mix output reflects the consumer reality of each catchment rather than a single national standard. High-income urban catchments index strongly toward Hearables, premium Smartphones, and advanced Computing categories. Emerging city markets index differently around Air Conditioners, Washing Machines, and refrigerators, which drive a larger share of projected revenue. A site in a Tier 2 city with a predicted monthly revenue of ₹2.48 crore gets a category mix recommendation calibrated to that specific market, not averaged across hundreds of stores in fundamentally different catchments.
Who is this built for?
The Bottom Line
Knowing which sites to open, in what order, with what assortment, and with a clear view of what they will cost the stores already running is the real advantage in retail network expansion planning. StorePlannix provides a revenue-predicted, cannibalization-adjusted, category-calibrated decision set for each location.
If you want to understand the performance story behind your existing store network, like why a strong location is producing weak sales, or which stores are running below their catchment potential, StorePlannix works alongside StorePerformix by Kentrix. And for assortment intelligence across your live store base, StoreSKUmix benchmarks every store’s SKU range against top performers in its archetype and surfaces a ranked action plan for expansion, rationalization, or brand rebalancing. Together, the three platforms cover the full retail location lifecycle, from new store site selection to performance management and inventory planning.
FAQ
New store planning in retail is the process of evaluating site locations, projecting their revenue potential, assessing their impact on the existing network, and building the operational and assortment plan for opening. Most approaches rely on manual catchment analysis or market benchmarks that don't account for each site's specific consumer profile.
StorePlannix replaces this with a machine learning model trained on your own network's data, validated on held-out sites, and surfaced through a five-module dashboard covering revenue prediction, ramp-up trajectory, cannibalization impact, feature attribution, and category mix.
Traditional retail location analysis typically relies on trade area mapping and generic demographic overlays. It rarely accounts for the specific combination of consumer spending behavior, category demand, footfall quality, and competitive overlap that determines how a particular site will actually perform.
StorePlannix builds its predictions from the actual performance of your own store network. It also quantifies cannibalization, something most location tools ignore entirely, and produces a category mix recommendation for the opening assortment rather than leaving that decision to convention.