Lecture 4 Trade Area Delimitation and Lecture 4 Trade Area Delimitation and AnalysisAnalysis

Trade Area Conceptualization (1):

Refers to the spatial extent (or distribution) of customers around an individual stores or a network of stores.

can be viewed as a contiguous area (or polygon) around a store (supply point) that contains the majority of the customers or potential customers (demand points).

also known as market area or customer catchment area.

Trade Area Conceptualization (2): Trade Area Conceptualization (2):

Also viewed as the way of mapping the confines of interaction between a set of store locations and the customers that patronize them.

Interaction can be measured in different ways:

number of customers,number of transactionsdollar value of transactions

It has a spatial dimension and geographical boundaries, though boundaries are not always clear

Trade Area Conceptualization (3): Trade Area Conceptualization (3): Trade Areas vary in size and shape. Factors that affect trade area size and shape

are: Store size (attractiveness) Settlement patterns (residential density) Transportation network Barriers to movement Presence of competitors (which provide

alternative locations and intervening opportunities)

Can be used to provide information for trade area analysis

characteristics of consumers/customers screen development potential assess existing stores performance,

Can be conceptualized and defined in different ways.

Who are concerned with Who are concerned with trade area trade area delimitation/analysis?delimitation/analysis?

Who are concerned with trade Who are concerned with trade area delimitation/analysis?area delimitation/analysis?

Retailers/ commercial service providers

Commercial property developers

Real estate department of retail chains

Leasing companies

Location analysts working for the above

Marketing firms who do advertisement for businesses

Educators who train students in the profession of marketing geography, retail geography, and business geography

Three approaches to trade area Three approaches to trade area delimitation:delimitation:

Spatial Monopoly (Deterministic)

Market Penetration (Probabilistic)

Dispersed Market (Customer profiling)

Deterministic approach has the Deterministic approach has the following characteristics:following characteristics:Makes a clear-cut assumption about the

spatial dimension of the trade area

Trade areas are polygons, each has definite boundaries; they do not overlap

Assumes all customers come from this area; (those living outside are excluded from consideration)

Probabilistic approach has the Probabilistic approach has the following characteristics:following characteristics:

Makes no clear-cut assumption about the spatial dimension of the traded areas

Trade areas are not polygons, with no definite boundaries; they overlap

Assign persons (households, CT etc.) to stores partially, with the assumption that people do not always go to the closer store

Treat trade areas as the surface of probabilities: primary (60%) and secondary (60-80%) etc.

Dispersed Market (also known as Dispersed Market (also known as Customer Profiling) has the Customer Profiling) has the following characteristics:following characteristics:The supplier is often highly specialized.

(e.g., specializing one or two lines of imported furniture, selling a narrow selection of books, or serving a widely scattered ethnic group.)

There is no obvious spatial concentration of customers; customers are widely dispersed.

Distance decay relationship is weak

Trade area is defined through customer profiling (i.e., age, income, ethnicity and life style.)

Two types of data for trade area Two types of data for trade area analysis:analysis:

Secondary data : the most commonly used are census data ◦ less expensive; and need less effort to acquire

◦ can be used to identify potential customers, but many of these potential customers do not necessarily patronize the store. So, the demographic profiles produced are not real customer profile report.

Primary data: compiled by retailers. ◦ collected at POS (either based on credit card transactions or

by sales associates asking postal codes and phone numbers)

◦ Through customer data analysis, retailers develop a customer profile consisting of demographic, social and economic attributes.

◦ They can also use this profile to search for suitable sites in new markets.

User defined trade areaUser defined trade area Also called “rules of thumb”. It is hand-drawn

around a given store, from which the analyst believes the majority of customers are attracted.

Relies on the level of experience and expertise of the person who defines the trade area. It assumes that the person has knowledge of customer base and how far they travel.

It is highly subjective, not scientific.

Quality can be improved, if limited customer spotting data are available and used as reference.

Usually used to define trade area for a single store

There are two types of such trade areas:◦ Unconstrained trade area that do not follow

census geographies (but may follow physical barriers)

◦ Boundary constrained

User Defined Trade AreaUser Defined Trade Area

Free-hand Census tract confined

DA confined FSA confined

Circular trade areaCircular trade area

The easiest, quickest and least expensive method

Trade area defined as a circle using pre-defined radius (usually walking distance or driving distance)

Assuming the transport surface is uniform, and the store is equally accessible from all directions

Competition is not a major factorAdjacent trade areas may overlap or not

overlap, depending on distances between stores and pre-defined radius.

Circular Trade AreaCircular Trade Area

Percentage of CustomersPercentage of Customers Percentage of customers uses

customer data. The analytical tools is the “customer

spotting” map. This simply is a map of distribution of

customers around a given store. Boundaries are drawn to include the

CTs/DAs that contain a given percentage of customers.

Usually, distance is used to select the closest 60% and 80% of the customers to the store location.

Travel Distance/travel timeTravel Distance/travel time This method uses travel distance or driving time to

define trade area. can be 5km or 10km. Can also be 20 minutes or 30

minutes. Distance and travel time are influenced by the

characteristics of the road network (such as speed limit, one-way street, number of lanes, road capacity, etc.)

The map may look like a spider’s web The trade area is irregular shaped

Market PenetrationMarket Penetration

Divides the area into grids (200x200m, 500x500m, or using DA)

Place the same grid over the customer spotting map Count the number of spotted customers in each cell Divide the number of customers in each cell by the

cell’s total population The ratio or percentage is regarded as a measure of

market penetration If sales are known from the customer data, the

number of customers can be translated into sales, and sales can be divided by total disposable income in the cell to develop a ratio.

Outward from the store location, the number of cells is counted until 60% or 80% of the customers or sales are reached. These cells form the primary and secondary trade areas.

With this method, there may be some holes which have no data or no customers; or some outliers which have a significant number of customers. It is the analyst’s decision to include them or exclude them.

Thiessen PolygonThiessen Polygona geometric procedure for

delimiting theoretical trade areas for a network of stores

 assumes the stores are similar in

size and sell similar products for similar price; consumers purchase products from the closest store.

most suitable for delimiting trade areas of chain stores.

Thiessen polygonThiessen polygon

Thiessen polygonThiessen polygon

Reilley’s LawReilley’s Law

Reilley’s LawReilley’s Law

A B A-B B-A

Sherway Garden Yorkdale 3.38 cm (6.8km)

Sherway Eaton 3.10cm (6.7km)

Yorkdale Fairview 2.25cm (4.7km)

Yorkdale Eaton 2.05cm (4.4km)

Fairwview Eaton 3.06cm (6.3km)

Fairview Scarborough 1.7cm (3.2km)

Scarborough Eaton 3.44cm (7.6km)

Reilley’s LawReilley’s Law

Statistically-Calculated Probabilistic Statistically-Calculated Probabilistic Method;Method;The Huff Model (1)The Huff Model (1) Huff model is useful in the following ways:

◦ Generate customer volume estimate for existing stores

◦ Generate customer volume estimate for proposed new stores

◦ Answer such strategic questions: What would happen to my trade area if my store

expand by 50%?

What would happen to my trade area if one of my stores is close?

What would happen to my trade area if an existing competitor were to leave the market?

What if a competitor introduces a new store in the market?

◦ Map the probability surface◦ Estimate sales potential

Statistically-Calculated Probabilistic Statistically-Calculated Probabilistic Method;Method;The Huff Model (2)The Huff Model (2)Huff model requires the following data: A list of stores (shopping centers), their

locations and attributes (attractiveness)

A list of building block areas (CT or DA) with demographic and social economic data (market size and purchasing power)

A matrix of distance, driving time, travel costs between each building block and each store

A sample data set (for calibrating parameters/weights) .

Statistically-Calculated Probabilistic Statistically-Calculated Probabilistic Method;Method;The Huff Model (3)The Huff Model (3) The challenge is to estimate the parameters.

There are two ways to estimate them:

1. To make an ‘educated guess”. This is used when sample data are not available. It depends on the experience and knowledge of the local market. Usually several guesses are made for experiment to find out which one generates better results.

2. To statistically estimate or calibrate the model. Often, it includes using a number of different non-linear models. This requires the use of sample data. Several parameters are experimented, and a measure of goodness of fit is produced. Calculations are undertaken to estimate the direction and amounts each of the parameters should change to improve the fit. Each change is then entered into the model, and the model is re-run until the best values that give rise to the best fit to the sample data are found.

St. James Town exampleSt. James Town example

St. James Town exampleSt. James Town example

Sales potential estimateSales potential estimate

S. P. = No. of HH * average HH income* % of income spent on consumer

goods* probability

Example:

S.P. in building 1 at supermarket A:=567 * $26,700 * 0.3 * 0.93=$4.22 million

Comparison of Thiessen, Reiley’s and Comparison of Thiessen, Reiley’s and HuffHuff

Factors Thiessen Reiley’s Huff

Quality of transport system

Yes(d-time; travel

time reflect quality of transport system)

Yes(d=time)

Attractiveness Yes Yes

Types of goods Yes (λ)

Competition Yes Yes Yes

Transport barriers Yes (d=time) Yes

Accuracy of sale estimate

Low Low High

Comments Good for chain stores (similar size, identical goods and price);

No major barriers;Simple to use

Good for different sized stores;

Consider barriers;Relatively simple

to use

Good for different sized stores;

Consider barriers;Complicated to

use