USE CASES · CANNIBALISATION

CANNIBALISATION ANALYSIS · NETWORK DECISIONS

Cannibalisation, measured before it happens

Know how much of a new site’s revenue would come from your own network, which existing sites overlap today, and what the network actually keeps.

OVERLAP BOARD

NETWORK OF 86 SITES · OVERLAP REVIEW

Schematic map of two overlapping site catchments sharing 38 percent of demand, plus a projected new site with 7 percent cannibalisation

Healthy catchment

Overlap zone

New site, projected

PAIRS · ON ONE GRAVITY MODEL

O·01 · PAIR

Act

Arcade × Mill St

38% SHARED CATCHMENT · −9% EACH

O·02 · NEW

Acceptable

Riverside, projected

PROJECTED CANNIBALISATION 7%

O·03 · PAIR

Monitor

Parkside pair

11% SHARED · STABLE

WHY OVERLAP GOES UNNOTICED

Revenue you take from yourself never shows up as theft

When a new site takes sales from a sister site, the network total barely moves. The damage hides inside two mediocre P&Ls instead of one honest number.

01°

01°

The new site always looks fine

Openings are judged on their own revenue. Nobody asks how much of it simply walked over from the store down the road.

02°

02°

The victim gets blamed, not the cause

The older site’s decline is explained by management, staff or weather. The real cause opened 800 metres away, a year earlier.

03°

03°

Totals hide the bleeding

Network revenue can grow while pair after pair of sites quietly split the same customers. Averages absorb the loss.

04°

04°

Nobody owns the trade-off

Expansion wants the new site, operations defends the old one, and no shared number says what the network actually gains.

HOW OVERLAP IS HANDLED TODAY

Openings judged on their own P&L

Overlap noticed a year later, if ever

Anecdotes about stolen customers

No agreed threshold for acceptable overlap

Expansion and operations arguing past each other

WITH CANNIBALISATION ANALYSIS

Every pair of sites checked for shared catchment

New sites carry a projected cannibalisation number

Net network gain decides, not gross revenue

Agreed thresholds by format and market

One number both teams argue from

DETECT · PROJECT · DECIDE

From gut suspicion to a net-gain number

One analysis workflow. Today’s overlaps detected across every pair of sites, tomorrow’s projected for every candidate, and decisions made on net network gain. The thresholds stay agreed, the arguments stay short.

DETECT · TODAY’S OVERLAP

Find the pairs eating each other

Catchments computed for every site on one model

Shared-catchment pairs ranked by severity

Trends watched as your network and rivals shift

OVERLAP RANKING · TOP PAIRS

Arcade × Mill St

Station pair

Parkside pair

PROJECT · EVERY CANDIDATE

Price the overlap before you build

Projected cannibalisation for each candidate site

Net gain after overlap, not gross forecast

Alternatives tested a street further out

Projections run on Network Impact Modelling, the same gravity logic that models client absorption after closures.

CANDIDATE · RIVERSIDE

Projected cannibalisation

7%

Net network gain

+11%

Nearest own site

22 MIN

ACCEPTABLE · WITHIN THRESHOLD

DECIDE · ON NET GAIN

Approve on what the network keeps

Thresholds agreed per format and market

Openings, relocations and closures use one basis

The overlap review repeats as the network grows

REVIEW · Q3

Arcade × Mill St

GO

Station pair

PLANNED

Parkside pair

MONITOR

NET GAIN POSITIVE · NETWORK HEALTHY

WHAT DRIVES OVERLAP

Why two nearby sites can thrive, and two distant ones collide

Distance is a poor predictor of cannibalisation. Two sites a kilometre apart can serve different worlds, while two on the same commuter line share half their customers. What matters is how demand actually flows.

Overlap is computed from gravity-modelled catchments calibrated with trusted location data and your own transactions, so shared customers are estimated from real behaviour, not radius circles. Every pair and every candidate reads from the same basis.

SCORE COMPOSITION

What moves an overlap estimate, by market type

Dense urban

Suburban

SHARED FLOW

Customers whose routines touch both sites

88

84

DISTANCE DECAY

How quickly willingness to travel drops

90

38

BARRIERS

Rivers, rails and roads that split demand

62

90

FORMAT SIMILARITY

How interchangeable the two offers are

80

52

ABSORPTION

What each site could absorb from the other

66

72

MARKET HEADROOM

How much new demand the area still has

70

55

Dense urban: shared flow decides

Suburban: distance decay decides

SAMPLE WEIGHTS · CALIBRATED ON YOUR TRANSACTIONS

THE PAYBACK

What measured overlap changes

EXPANSION QUALITY

New sites add revenue the network keeps

Candidates carry a projected overlap number, so growth is judged on net gain and the pipeline stops funding its own decline.

NETWORK HEALTH

Colliding pairs get fixed, not explained

Detected overlaps become consolidations, relocations or format changes instead of two underperforming P&Ls.

ALIGNMENT

Expansion and operations share one number

With agreed thresholds, the opening debate becomes a comparison against a standard instead of a turf war.

BEYOND RADIUS CIRCLES

A radius on a map is not a catchment

RADIUS RULES

Good for a quick look, wrong at the edges

A fixed no-closer-than rule ignores barriers, flows and format. It blocks good sites and waves through bad ones.

P&L REVIEWS

Good at finding victims, a year late

Revenue reviews spot the damage after it happened. By then the lease is signed and the loss is structural.

GROSS FORECASTS

Good headline numbers, silent on the source

A forecast that ignores your own nearby sites cannot say how much revenue is simply moving between them.

FIELD ANECDOTES

Good stories, impossible to settle

That store steals our customers may well be true. Without a shared model it is just the loudest voice in the room.

CLEVERMAPS

Overlap as a number, not an argument

Shared catchments detected, projected and held against agreed thresholds on one gravity model of your network. Part of the Performance solution.

Cannibalisation analysis is part of the Performance solution, built on trusted location data and shared decision logic.

MEASURED IN THE REAL WORLD

Proven on dense networks

FEATURED CASE STUDY

The data science behind Dr. Max parcel locker expansion

Dr. Max expanded its parcel locker network with overlap checked before every batch, so new boxes added pickups instead of splitting them with boxes already running.

PHARMACY RETAIL · OOH NETWORK

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Read the story →

Estimating how much revenue a site takes from your own other sites rather than from the market. Catchments are gravity-modelled from real behaviour, every pair of sites gets a shared-catchment estimate, and every candidate site gets a projected cannibalisation number before it is approved.
50.1033, 14.4458 · DĚLNICKÁ HQ

30-MINUTE WORKING SESSION

Bring the pair you argue about

In 30 minutes we will compute the shared catchment of any two of your sites, or project the overlap of a candidate you are weighing, on your own data.

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for the people who decide

CONTACT

CleverMaps, a.s.

Vídeňská 101/119

619 00 Brno, Czech Republic

© 2026 CleverMaps, a.s. · VAT CZ03728277

SOC 2 TYPE II · ISO 27001 · EU DATA RESIDENCY

CleverMaps footer visual for location intelligence and GeoDecision workflows

The GeoDecision platform
for the people who decide

CONTACT

CleverMaps, a.s.

Vídeňská 101/119

619 00 Brno, Czech Republic

© 2026 CleverMaps, a.s. · VAT CZ03728277

SOC 2 TYPE II · ISO 27001 · EU DATA RESIDENCY

CleverMaps footer visual for location intelligence and GeoDecision workflows

The GeoDecision platform
for the people who decide

CONTACT

CleverMaps, a.s.

Vídeňská 101/119

619 00 Brno, Czech Republic

© 2026 CleverMaps, a.s. · VAT CZ03728277

SOC 2 TYPE II · ISO 27001 · EU DATA RESIDENCY