Introduction

Network strategy

Location planning

Omnichannel analysis

Spatial modelling

Our whole business is about location planning. As trusted

advisors we help our customers decide how many stores,

who to acquire, where to open, which format and how to

optimise home delivery and click & collect operations.

Team of 30 location

specialists to work

collaboratively with your

business

We have led in-house location planning teams for major

grocery retailers.

We are experts in spatial modelling and analytics, building

forecasting tools, web development and systems.

We create innovative new datasets bespoke for local

markets.

Growing to a global

company

Europe and Asia Pacific are our two key focus areas.

We have offices in London, Leeds, Warsaw, Dortmund,

Shanghai, Tokyo and soon to be Melbourne

COMPANY INTRODUCTION

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2. MODELSpatial Models and

Analytics that drive market intelligence, network

recommendations and revenue forecasts

1. DATAWide range datasets providing a detailed

view of both Demand and Supply in retail

markets

3. TOOLIntegration with GIS

tools providing access to the data and the models as required

We offer a one-stop shop:

● We assemble, process and integrate

datasets from a wide range of

sources…

● These datasets then feed into our

world-leading spatial models that

replicate the real-world interactions

between demand and supply in retail

market…

● Optionally we provide access to both

the data inputs and the model outputs

through GIS platforms.

OUR CORE SERVICES

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HOW DID WE GET HERE?

Clients Key Events Team

2012 Sainsbury’s

Whole Foods

Foundation 1

2013 ASDA, Boots

Waitrose

ASDA project transformative, enables growth.

Build key datasets.

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2014 Post Office,

Camelot, Barclays

New multi-year deals giving confidence.

Take office space. Evolve data offer.

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2015 Amazon, Swinton,

Savills

Growth in ‘adjacent’ spaces.

Invest in capacity and recurring revenue growth.

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2016 M&S, TRG, EE Growing & diversifying the client list.

Exploring innovative global DAAS solutions.

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2017 adidas

Dominos

Growth in international markets, Shanghai & Tokyo office

open. Leeds office opens in the UK.

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2018 Costa, Dr Martens Development of data & analysis in Asia Pacific. Warsaw office

opens. Drive for global penetration

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2019 Starbucks Melbourne office opens 30+

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Sarah Hitchcock, OperationsBrought together analytical, property and

customer insight at Sainsbury’s and Boots.

Led a 40 strong network planning

department at Sainsbury’s.

LEADERSHIP TEAM

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Blair Freebairn, CEOBuilt forecasting models for retailers,

telecoms and banking over the last 20 years

- including 10 of the 15 largest UK retailers.

Head of European analytics and global data

at MapInfo.

Simon Dixon, TechnicalDelivered solutions and user interfaces

for many blue chip retailers.

Previously worked as Technical

Manager at Sainsbury’s.

Samantha Colebatch, AustraliaShaped the space expansion strategy of

major retailers in the UK and Australia,

across multiple formats and regions.

Formerly Head of Network and Investment Planning at Sainsbury’s.

Neil Farricker, ModellingBuilt network and customer

segmentation models across all

industries over the last 10 years.

Overhauled modelling and processes

whilst at the Co-op.

Ben Purple, Asia PacificHas over 20 years experience in Network

Planning, and a passion for understanding

customer behaviour. Led location planning

teams for Tesco in the UK, China, Korea and

Malaysia.

PARTNERSHIPS AND AWARDS

2013

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DATA & SERVICES WE PROVIDE

DATA & SERVICES

GEODATA SERVICES

● Location data (store, POI, demographic) underpins

all we do at Geolytix.

● In the majority of cases we build our own datasets

as off the shelf products are either not available or

are out of date.

● We hold demographic data across all of Europe and

the majority of Asia Pacific.

● We have experience in collating and mapping retail

locations across the world and often in challenging

markets and environments.

● Defining a reliable store location dataset often

requires layering multiple data sources together.

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As investing in stores and online is expensive, our

services are designed to enable retailers to make

better decisions where location matters.

Our core services cover:

● Network Strategy – How many stores should I

have?

● Location Planning – Where should the stores

be?

● Omnichannel Retail – How will online impact

offline?

● Spatial Modelling – What will the revenue of a

new store be? How much will it impact my

current store sales?

GEODATA

Where people live, work, study and shop:

● We create novel datasets often using Open Data as inputs, to help our clients with network strategy and location based decisions.

● Our Geodata suite covers retail data (Retail Points & Retail Places) as well as demographic data and boundaries.

● Geodata underpins our analysis, and is used internally by a number of retailers.

Central Dublin Retail Places 9

RETAIL PLACES

● More than 21k scored, profiled and named polygons detailing where people shop in the UK, covering all places

with 3 or more shops.

● The Retail Place type determines:

● How far people will travel to it e.g. a city centre has a larger catchment than a town centre.

● The level of trading intensity – for some of the most attractive retail places a weight is given to increase

the level of trading e.g. Regional shopping centres (Meadowhall, Westfield, Bluewater) are increased by

~2.5.

● There are 23 different types of retail places – some examples are shown here:

Type # Definition

City Centre 39 City centre with between 200,000 and 300,000

population and between 70 and 5,000 retailers

Large Town Centre 127 Town centre with between 30,000 and 100,000 population and

between 70 and 5,000 retailers

Regional SC 12 A large shopping centre that attracts customers from other parts of

the country

Leisure Park 138 An area with leisure facilities e.g. Bowling Alleys and Cinemas

along with restaurants

Rail Station 102 Retail Place within a train station

Large Parade 2,041 Areas with between 30,000 and 300,000 population and between 8

and 19 retailers10

DEMOG DATA

● We have access to

and utilise global

demographic data

in all our analysis -

Working with the

latest available

datasets allows us

to generate up to

date and insightful

analysis for our

clients.

● In support of our

Open Data

initiatives we have

recently released

fully processed

2011 European

census data for free

on our website.

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Demographic mapping across Europe, easily

identify pockets of affluence and key

demographic groups.

We have generated Affluence & Lifestage Indices

across Europe, useful for regional comparisons. The

data is here shown in our partner ESRI’s ArcGIS

software

SERVICES - OVERVIEW

1. WHERE DO CONSUMERS

LIVE, WORK & PLAY?2. WHERE DO CONSUMERS

SHOP/CONSUME?

Analyse Market Demand,

where are my consumers

and how much do they

spend?

Model flows of residential demand into

Retail Places

Add in the contribution of inbound

tourist spend where relevant

3. WHAT IS THE POTENTIAL?

Predictive Models can be built to

quantify the sales potential of

new stores for use in P&L reviews

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Why is it important?

Overview

● The key data source to understand the potential spend in an area.

● Helps us understand the drivers of buying particular goods.

● It is the key building block to an accurate gravity model.

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● National and city level market potential (spend €) can

be forecast, enabling cities to be ranked by opportunity.

● This market potential can be further disaggregated to

the smallest available geography in each city based on

the size and profile of each demographic zone.

● Data related to the density, affluence and life-stage of

consumers is utilised in the process.

DEMAND

A demand surface we recently built for a client in Hong Kong

Example Catchment flow map - Spend by UK Output

Area (Demographic zones) to store

CATCHMENT CREATION

● Where customer data is available we have experience in profiling the data to identify the typical consumer types for the retailer.

● Using the same customer data we have worked with retailers to create ‘natural’ primary and secondary catchments that match what a skilled analyst would draw based on visualising store spatial sales distributions, enabling the catchments to be closer to those defined in a new store assessment process.

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Customer data can be profiled and mapped to understand a retailers likely consumer mix (Affluence and Lifestage) as well as how they interact with the brand (both online and offline)

PREDICTIVE MODELS

● One of our core strengths is the ability to create bespoke

models that forecast the expected turnover in a given

location.

● The models are always built based on a detailed

understanding of the specific market and business, and the

key drivers of successful expansion.

● The type of model (typically gravity, regression, analogue, or

scorecard) will be determined based on which is most

appropriate and likely to be the most beneficial and

predictive for the individual customer.

● If there are different formats being opened, with different key

drivers, we may employ multiple models.

● The forecast turnovers allow for macro site factors such as

the demographics of a catchment(s), number of workers

nearby, competition, adjacencies and the quality of the Retail

Place it is located in.

Predictive models can be

applied to retail store

networks to identify

which locations offer the

greatest opportunity.

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GRAVITY MODEL – GROCERY EXAMPLE

● For many of our clients we create a gravity model which is calibrated to achieve the best possible accuracy.

● Sales are driven by the surrounding residential population and customers completing a ‘main’ or ‘convenience’ shopping mission, which makes gravity modelling a robust approach for forecasting sales.

● In the example opposite the model calculates ASDA have a 55% share of the weekly grocery spend of the focus (purple) demand zone.

● ASDA have a stronger share of this zone compared to Morrisons and Tesco as the store is more attractive (represented by the size of the circle) and is more accessible to the residents of the zone.

● Despite Tesco being of equal overall attractiveness it represents a weaker share of the zone due to distance decay.

55%

Aggregating the

flow of spend from

each geographical

zone to each

supermarket

provides a sales

potential for that

store.

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RETAIL RANKINGS

● One output of the modelling process is the ability

to rank Retail Place by total spend.

● In the UK rankings are provided for all Retail

Places where the comparison revenue is greater

than £1m per annum.

● The rankings are produced by aggregating the

forecast sales for the stores within the retail place.

● The top 25 centres for comparison spend are

shown opposite.

● Five regional shopping centres make it in to the

top 25.

OTHER MODELS WE UTILISE

● Analogue – Often beneficial in achieving high model accuracy. We create automated analogue selection tools

that can be adjusted by the Network Planner to incorporate their knowledge of micro pitch factors.

● Regression – Amongst other applications, we use regression models to forecast linear relationships.

● Scorecard – For CBD locations we typically use a scorecard approach and base the catchment on a radius

around the store to count workers as well as the residential population. Our Retail Places dataset is used to

benchmark the retail strength of the location. Where available footfall data (or proxies) are also be incorporated.

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