A proposed store may attract strong foot traffic and still create limited growth for the wider retail network. Some customers may simply shift from an existing location rather than represent genuinely incremental demand.
Store cannibalisation analysis helps retailers determine how much demand a new location will create and how much it will transfer from nearby stores. Instead of evaluating the proposed store in isolation, retailers can measure its expected effect on total network visits, sales, market coverage, and profitability.
What Is Net-New Demand in Retail Expansion?
Net-new demand is the demand a retailer would not have captured without opening the proposed location.
It can come from:
- Customers in previously underserved areas
- Shoppers currently visiting competing brands
- Customers who find existing stores inconvenient or too far away
- New trips created by better accessibility, awareness, or local coverage
Cannibalised demand is different. It occurs when customers move their visits or spending from an existing store to the new location. The candidate store gains demand, but the retail network does not gain the same amount overall.
A proposed store’s demand can therefore be divided into four components:
Candidate store demand = transferred internal demand + competitor capture + previously unserved demand + newly generated demand
Transferred internal demand represents store cannibalisation. The remaining components contribute to network growth.
Why Gross Store Forecasts Can Overstate Expansion Potential
A standalone store forecast estimates the visits or sales a candidate location may generate. However, it does not always explain where that demand will come from.
For example, a retailer may forecast 100,000 annual visits for a proposed store. If 35,000 of those visits would otherwise have gone to existing locations, the new store creates only 65,000 net-new visits before considering other market effects.
Gross forecasts can overstate expansion potential when retailers rely only on:
- Population around the proposed site
- Fixed-radius trade areas
- Traffic volume near the location
- Demographic similarity with successful stores
- Projected store revenue
- Distance from the nearest existing location
These inputs may confirm that demand exists, but they do not show whether the retailer already captures that demand elsewhere.
Store cannibalisation analysis addresses this gap by measuring the proposed location against the wider store network.
What Data Is Needed for Store Cannibalisation Analysis?
Separating net-new demand from transferred demand requires a network-level view of existing stores, visitor origins, competitors, and local market conditions.
| Data input | What it helps measure |
| Existing store visits | Current demand across the retail network |
| Visitor-origin patterns | Where customers to each store travel from |
| Travel time and distance | How easily shoppers can substitute one store for another |
| Catchment overlap | Areas where stores compete for the same demand |
| Competitive visits | Demand that may be captured from competing brands |
| POI data | Nearby competitors, demand generators, and complementary locations |
| Demographic and consumer attributes | Whether the site reaches a different customer group |
| Historical store openings | How similar launches affected nearby stores |
Aggregated mobility data is especially useful because it shows how people actually move between areas and locations. It produces more realistic catchments than circular buffers based only on distance.
How to Conduct Store Cannibalisation Analysis
Retailers can use the following process to measure transferred demand and calculate the incremental contribution of a proposed store.
1. Establish the Existing Store Baseline
Start by measuring normal performance across every store that could be affected by the new location.
The baseline may include:
- Store visits
- Transactions
- Revenue
- Average order value
- Visitor origins
- Visit frequency
- Daypart and weekday patterns
The analysis should cover enough time to account for seasonality, promotions, holidays, and temporary changes in store performance.
Without a reliable baseline, retailers cannot distinguish ordinary fluctuations from demand transferred by the new store.
2. Build Observed Store Catchments
An observed catchment represents the areas from which a store actually attracts visitors.
Unlike fixed-radius catchments, observed catchments account for:
- Road and transport networks
- Real travel behaviour
- Competing retail destinations
- Natural and administrative boundaries
- Urban and suburban differences
- Customer willingness to travel
Retailers can use these catchments to understand which neighbourhoods, workplaces, shopping areas, or travel corridors contribute demand to each store.
3. Estimate the Candidate Store Catchment
The candidate site’s likely catchment can be modelled using comparable stores, local mobility patterns, travel times, nearby POIs, population characteristics, and competitive conditions.
The objective is not only to estimate total store demand. Retailers must also identify how much of the proposed catchment is already served by existing locations.
A candidate site that reaches a largely underserved area is more likely to generate net-new demand. A site that overlaps heavily with existing customer origins is more likely to transfer demand within the network.
4. Measure Catchment and Visitor Overlap
Compare the proposed catchment with every relevant store, not only the closest location.
A store that is farther away may still experience cannibalisation if it attracts visitors from the same residential areas, office districts, retail destinations, or transport routes.
Store cannibalisation analysis should consider:
- Shared visitor-origin areas
- Common travel corridors
- Similar store formats
- Product and service overlap
- Visit occasions
- Travel-time differences
- Existing store capacity
High geographic overlap does not automatically mean high cannibalisation. Transfer also depends on convenience, customer preferences, store quality, assortment, and the role each location plays within the network.
5. Estimate Transferred Demand
Transferred demand is the portion of candidate-store demand expected to shift from existing stores.
It can be measured using the following formula:
Cannibalisation rate = transferred demand ÷ total projected candidate demand
Suppose a candidate store is projected to generate 120,000 annual visits. If 30,000 visits are expected to transfer from existing locations, the projected cannibalisation rate is 25%.
The analysis should also identify which stores are expected to lose demand. A small decline at a highly profitable location may have a greater financial impact than a larger decline at an underperforming store.
6. Calculate Net-New Demand
Once transferred demand has been estimated, retailers can calculate the candidate store’s net-new contribution:
Net-new demand = projected candidate demand − transferred demand
Retailers should also calculate the total network impact:
Net portfolio impact = candidate-store demand − demand lost across existing stores
The portfolio calculation provides a more accurate measure of expansion value than candidate-store performance alone.
7. Test Multiple Demand Scenarios
Store cannibalisation analysis should not rely on a single forecast. Retailers should model at least three scenarios:
- Low transfer: The proposed store reaches a largely underserved catchment.
- Expected transfer: Demand shifts according to observed overlap and comparable openings.
- High transfer: Existing locations lose more demand than initially expected.
Each scenario should show the impact on visits, revenue, margin, existing-store performance, operating costs, and payback period.
When Is Store Cannibalisation Acceptable?
Store cannibalisation is not always a reason to reject a location.
A proposed store may still strengthen the network by:
- Capturing demand from competitors
- Improving coverage in a priority market
- Reducing customer travel time
- Relieving pressure on an overcrowded store
- Supporting delivery or fulfilment operations
- Protecting market share from competing expansion
- Replacing an outdated or poorly located store
The decision should be based on incremental portfolio contribution rather than a target of zero cannibalisation.
Retailers should compare the value of net-new demand with transferred sales, operating expenses, capital investment, and the effect on existing-store profitability.
Common Store Cannibalisation Analysis Mistakes
Retailers can overestimate expansion value when they:
- Count every candidate-store visit as incremental
- Analyse only the nearest store
- Use circular distance buffers as catchments
- Ignore differences between store formats
- Exclude competitor capture
- Measure revenue without considering margin
- Evaluate the candidate store separately from the network
- Use demographic similarity as a substitute for visitor behaviour
- Fail to validate forecasts after opening
Post-opening analysis is essential. Retailers should compare forecasted cannibalisation with actual changes in visits, sales, and visitor origins. These results can improve future site-selection models.
How Factori Supports Store Cannibalisation Analysis
Factori provides privacy-safe Mobility, Visit Intelligence, and POI data for retail site selection and network analysis.
Retail teams can use Factori data to:
- Build catchments from aggregated movement patterns
- Compare visitor origins across existing stores
- Measure overlap around candidate locations
- Identify nearby competitors and demand generators
- Analyse travel patterns and market accessibility
- Compare proposed sites with similar operating locations
- Monitor changes after a new store opens
Factori datasets can be accessed through APIs, bulk data, cloud environments, and the Factori platform, helping retailers integrate real-world signals into site-selection, forecasting, and network-planning workflows.
About Factori
Factori is a leading global location intelligence company that provides unmatched data insights to help businesses better understand the physical world:
Factori datasets are governed, privacy-safe, and structured to join seamlessly with your existing workflows across SQL, data warehouses, BI tools, and ML pipelines. With over 90B+ location signals collected every day across 150+ countries, Factori delivers broad market coverage and reliable location intelligence at scale. Datasets are available via APIs, raw data, the Factori platform, and MCPs to support different use cases and markets.
Conclusion
A strong candidate-store forecast does not necessarily mean strong network growth. Retailers must determine how much projected demand is transferred from existing locations and how much represents a genuinely incremental opportunity.
Store cannibalisation analysis combines observed catchments, mobility patterns, competitive context, and network-level forecasting to measure the true portfolio impact of a proposed location. This allows expansion teams to evaluate sites based on net-new demand rather than gross store potential.
FAQs
What is store cannibalisation analysis?
Store cannibalisation analysis measures how much demand a proposed or newly opened store transfers from existing locations within the same retail network.
What is the difference between net-new demand and cannibalised demand?
Net-new demand is demand the retailer would not have captured without the new store. Cannibalised demand is transferred from an existing location within the same network.
How is store cannibalisation calculated?
A basic cannibalisation rate divides the demand transferred from existing stores by the total projected demand for the proposed location.
What is an acceptable store cannibalisation rate?
There is no universal acceptable rate. It depends on incremental margin, competitor capture, market coverage, operating costs, payback period, and the strategic role of the location.
What data is required for store cannibalisation analysis?
Useful inputs include existing-store performance, aggregated mobility patterns, visitor origins, travel times, observed catchments, competitor locations, POI data, demographic attributes, and results from comparable store openings.
