Site Evaluation and Sales Estimation Modelling For

Site Evaluation and Sales Estimation Modelling For

Retailers and Banks

Tony Lea, Ph.D.

Senior Vice President

Environics Analytics, Toronto

Presented at ESRI Business Conference Chicago, April 19, 2005 … in conjunction with David Huff

The Environics Analytics ViewIntegration

Clean DataRemove OutliersFind Gaps

Enhance Fill in MissingAppend External

GeocodeUrbanRuralStreet-basedLat/LongLink to areas

Create a DataMart

Add Spatial Layer

AnalyticsTrade Area DefinitionArea Profiling Area Scoring/RankingPostal Walk Scoring/RankingCustomer ProfilingCustomer Origin Studies Penetration Analysis Custom Segmentation Churn AnalysisShare of WalletLifetime Value Untapped Potential Analysis Product Correlations Merchandise MixFacility MixStore/Branch Scoring/RankingStore/Branch ClusteringSite Evaluation/Selection Models Network OptimizationSales Territory OptimizationPropensity ModelsPredictive ModelsMedia Scoring/Ranking

DataInternal (Client)

- Transactions- Account

Records- Loyalty

Programs- Credit Card

Info- Custom

Surveys- Product

Registration- Custom

Segmentation

External (EAG)- Demographics- Clusters- Location Files- Survey Data- Street Files- Boundary Files

EXECUTION

3

Environics Analytics Group Site Evaluation Modelling Experience

Team members have built high quality site evaluation models and/or network optimization models for a wide variety of industries in these sectors:

▲ Banking▲ Retail department stores▲ Shopping centers▲ Specialty retailers▲ Convenience retailers▲ Restaurants (fast food and other) ▲ Automotive dealerships▲ Medical offices and clinics▲ And several other types of “offices”

4

Environics Analytics Approach▲ Not “off-the shelf” software or “black-box” modeling

▲ Custom designed and built statistical/mathematical models▲ EA works with customers to identify:

- what you have, what you need, and how to improve quality of your data holdings

- how your existing systems are working (or not)- what you can change and what you must change (within

budgets)- what are the appropriate data/variables to use (in your

models)- what are the appropriate modeling techniques for your

situation

- what are the best tools to create the applications you need

▲ Based on all the above EA will build models, software and data systems (either on our own or with other vendors) to give you the customized solutions needed

5

Types of Site Models We Have Built▲ Simple trade area based models▲ Rule of thumb based models▲ Vacuum models----------------------------------------------------------------------------------------▲ Site screening models▲ Simple analog models▲ Database analog models▲ Discriminant function models▲ CHAID models▲ Regression models (observations – own branches)▲ Regression models (observations – flows)▲ Huff type models▲ Nakanishi-Cooper Spatial Interaction Models (SIM)▲ Multinomial logit SIM models▲ Full nonlinear optimization SIM systems models▲ Network Optimization Models

- Simple location-allocation models- Location-allocation with uncalibrated SIM sub-models- Location-allocation with calibrated SIM sub-models

6

The Focus of Attention

What is being estimated ……▲ Business volumes at a store/branch▲ Business volumes in the network▲ Cannibalization from other facilities in

the network▲ Impacts of competitors▲ Impacts on competitors

7

The Dependent Variable▲ A - Business volumes

- Customers- Transactions- Sales- New customers or sales (banking)

▲ B - Market shares; market share increases- A challenge to measure

▲ C – Profits- Uncommon

8

Scenarios In Site Models

▲ Add one; add several▲ Drop one; drop several▲ Moves of facilities▲ Completely new network/system▲ Change store/branch attribute(s)▲ Change competitor attribute(s)▲ Change demand base▲ Change transportation infrastructure

9

Outputs From Site ModelsReports/tables

▲ Show projected sales at one location or many

▲ Show sales at all network locations

▲ Show incremental impacts at one store

▲ Show incremental impacts at each store (in market) in network

▲ Show sales estimates or impacts on all sites including competitors

Maps▲ Can play key role in

facilitating site model input

▲ Small role in output typically

▲ Show the locations of adds, drops, changes

▲ Can show the existing flows or changes in flows or assignments

10

Families of Independent Variables Used in Site Evaluation Models

1. Demand side variables2. Facility3. Site 4. Situation5. Competition6. Other attractors and detractors7. Management quality8. Other idiosycratic variables

11

Independent Variables Used in Site Evaluation Models

1. Demand side variables

All these relate to small demand areas - like BGs CTS – or to “trade areas”

• Actual demand estimates (clout type estimates)

• Census type variables• Urbanity and settlement

context variables• Car ownership data• Other imputed behavioral

data• Other imputed attitudinal

data

2. Facility• Sq. feet of selling space• Number of POS positions • Length of time store at this

location• Age of facility• Time since last renovation• Number of floors• Number of sales staff • Front window display space • Layout efficiency indices• Other variables - depending

on context

12


4. Situation variables• Vehicular traffic flows

on relevant streets• Pedestrian traffic flows• General vehicular

access (e.g. dist. to nearest highway ramp)

• What kind of shopping environment is this?

• Reputation of the area• Nearness to shopping

centre(s)

3. Site Variables• Size of site• On site parking places

(non shared)• Visibility of

store/branch• Signage quality of

store branch• Number of curb cuts• Busy-ness• Grade/flatness

13


6. Other attractors and detractors

(These modify demand or attractiveness)

• Universities, high schools

• Sports stadia• Arts or theatre districts• Tourist hangouts• Sleezy bars• Dangerous places

5. Competition Variables

• Count of number of competitors by nearness

• Geographic potentials re competitors

• Indices of “attractiveness” and “power” of near competitors

14


7. Management Quality Variables

• Typically not available or poorly measured

• Often need a special survey or work session

• Put in models not because one will leverage this effect in implementation but rather to ensure model is correctly calibrated

8. Other idiosyncratic variables

• Distance to nearest theatre

• Distances to liquor stores

• Sunny side of the street

• Right next to X

15

The Measurement of Explanatory Variables

▲ Most variables are measured at interval/ratio scale: average household income, years old, parking places, competitor potentials, etc.

▲ But some can’t be: signage, visibility, available parking, “power” of competitor banners, pedestrian flow volumes, and many others

▲ We believe that it is fine to use subjectively scored or ranked variables in these cases (as if they were interval-ratio level)- For example, have bank regional managers evaluate

appearance, signage, etc. using 5 point scales- BUT critical to use “score recipe book” with example cases and

real world “pictures” to be consistent (… story) - Certainly better to use approximative variables than to just

leave them out because we could not achieve a high level of measurement status

16

The Competition Variable Definition Problem

Most common definitions are:1. how far is the nearest competitor from this

site?2. how many competitors are with N miles of

this site?▲ Both are very crude and insensitive

measures, … think about it▲ 2 sites: one with one competitor at 1.99

miles away will have same value as another site with competitor at .2 miles and 5 more competitors 2.01 to 2.2 miles away

17

The Competition Variable Definition Problem

▲ One very good solution is to define a geographic potential that measures aggregate access to a set of weighted points (here competitors)

▲ These are classical “geographic potentials” (in the gravity model family) in which we add up attractiveness scores of competitors discounted by “distance” from each reference facility

▲ These improve the performance of regression type models a lot

18

Measuring Distances Customers to Facilities

Trade-off between rigor and simplicity; inexpensive and quick versus expensive and time consuming

▲ Straight line Euclidian distances (simplest and most used)- Only lat/long coordinates needed- Good if street/road network is pervasive

▲ Manhattan or rectangular distances - Only coordinates needed, plus grid orientation (don’t just

use N-S, E-W)- Good if pervasive rectangular street network

▲ Either of above with barriers- Barriers defined as vectors of impassible areas- Interstates, railway lines, rivers, ravines, etc.- Fugitive barrier pieces; need to create own

19


▲ Shortest path street distances- Need good up-to-date street/road/highway

files and these are expensive- For large problems computation time is an

issue▲ Shortest path travel time distances

- Same as above when you believe that the times you have for network segments/links are accurate

- Used in our industry as if they were all good

20


▲ If the project is broad scale and exploratory you can work with Euclidian or rectangular without losing conclusion validity- Even more true when road network is dense

▲ When the project is very detail-oriented and it matters exactly what part of the block the site being tested is on and/or the store type is local or convenience - with very small trade areas - then you really should use at least barrier distances or best shortest path distances or travel times

21

The Trade Area Problem▲ Most types of site models have the site as the

unit of statistical observation- Exception is SIM types of models

▲ So they require a prior definition of store/branch trade area in order to tie demand to stores/branches

▲ GIS and simplistic retail paradigm has trade areas as (1) entities and (2) polygons - when they are neither of these things

▲ These deficiencies profoundly affect quality of models … and quality of sales estimates …and the “what-if” power of resulting models

▲ But not necessary to live with this…

22

The Trade Area Problem▲ If we create simple TAs - so they can be easily

defined and used in a site model - they will almost surely be inferior packages of demographics ...so the model suffers (heavily)

▲ If we create sophisticated concept of trade areas then likely can not do a good job of defining these for new store locations for use in the site evaluation model - esp. if they use customers data in defining the TA

▲ In general analysts do not want to build (real) predictive models of trade areas to use these to define demographics to put these into other predictive site evaluation models

23

Customer Origin Studies▲ How far do my

customers come to store?

▲ Where do they come from?

▲ What impact does distance have on dollars derived form demand area (i.e. my sales)?

▲ How it is used:- Comparing stores- Targeting

promotions- Simple trade area

definition0

50

100

150

200

250

300

350

400

450

500

0-1 k

m1-

2 km

2-3 k

m3-

4 km

4-5 k

m5-

6 km

6-7 k

m7-

8 km

8-9 k

m9-

10 km

10-1

1 km

11-1

2 km

12-1

3 km

13-1

4 km

14-1

5 km

15-1

6 km

16-1

7 km

17-1

8 km

18-1

9 km

19-2

0 km

20+ km

Distance from Location

Num

ber o

f Tar

get C

usto

mer

s

24

A (Real) Model of Trade Areas▲ Define 75% customer trade areas (polygons God –

forbid) for existing mature stores▲ Dependent variable: the distance to the 75% mark in

one of 8 quadrants about each store (8 variables for each store)

▲ Independent variables:- Demographics in quadrant between here and the store- Average drive time to here to the store - Competitors between here and store- Traffic flows, etc.

▲ Can then predict for a new store how far out is the trade area that we have defined for existing stores

▲ Run this model first for any new stores; define TA polygon; get demographics and use these in main site evaluation model

25

Closest Facility Patronage

010203040506070

% closestfacility

% secondclosestfacility

% other

Closest (etc) Facility By Product

Perc

ent

CheckingSavingsGICsInvestmentsLoansMortgages

26

Types of Retail Trade Areas▲ Circular or ring; trimmed or clipped circles▲ User defined or hand drawn▲ Closest customers (or percentage of

customers)▲ Thiessen (Voronoi) polygons▲ Drive distance or drive time ▲ Applebaum (trade-off) and variants▲ Breakpoint (Reilly) areas▲ Huff-type probabilistic trade area

27

Deterministic Versus Realistic Trade Areas

28

Typical Huff Type Trade Areas

29

Courtesy of CSCA at Ryerson University, Toronto

Probabilistic (Huff Style) with 3Probabilistic (Huff Style) with 3--D VisualizationD Visualization

30

Weighted Demographics for Trade Areas

▲ A cheap surrogate for Huff type trade area demographics to use in regression and similar models

▲ Now very common to “add up” or average variables like average household income or % families with children for a trade area (“trade area demographics”) for all BGs in the polygon “trade area”

▲ With weighted demographics one computes a weighted average for each variable of interest where weights are inverse functions of distances to the store/branch- Have the weights go to zero at the edge of the polygon

“trade area”▲ Almost guaranteed to increase site evaluation model

power - by better measuring the demand side surrogates for potential demand

31

Regression Site Model

Statistical observations are all stores ‘and their trade areas’

Yi = a + b1X1i + b2X2i + b3X3i + b4X4i + …. + Ei

Yi is dependent variableXi are the independent variables

b are the regression coefficientsa is the regression constantE is the error term

▲ Typical to have 7-11 X’s with some representatives from each family: demand, facility, site, situation, competition, etc.

▲ Should have at least 25 stores for statistical rigour -so typically can not be done for “a market”

32

Database Analogue ModelObservations are all stores ‘and their trade areas’▲ Create a database of all existing stores▲ Put in attributes - all the independent variables listed above

(demand in TA, facility, site, situation, competition etc): standardize all variables

▲ Create weights for the key drivers (based on correlation or regression for example)

▲ Define closeness (in terms of “matching”) in multivariate space based on sums of squared deviations between “new store” and each other store pairwise

▲ When user keys in “a new store” and defines all relevant attributes the algorithm searches the database and finds “close matches”, and puts them in a table on screen - along with their annual sales figures

▲ Possibly also computes the average sales of all N closest matching analogues

▲ User can use the outcomes in many ways because no single number is produced

33

Key Site Modeling Options: Pros and ConsDatabase Analog Models▲ Simple to build and use▲ Can perform very well in practice (but sometime we are not

really sure why) ▲ Tends to rely on simple trade areas too much▲ Not great where there are few good analogs (e.g. new

markets) ▲ Management tends to love them

Regression Models▲ Simple to build, program and understand▲ Tends to under-estimate great performers and over-

estimate poor performers▲ Weak in handling trade area demographics▲ Weak in handling competition ▲ Weak in estimating cannibalization

▲ can yield good projections in standard settings/contexts

34

Key Site Modeling Options: Pros and ConsSimple “Gravity Models”▲ Generally positioned as “uncalibrated”▲ Typically “black box” too▲ Simple to construct, program and understand▲ Requires a reasonable amount of data

▲ handles competition much better than above, but not rigorously because of lack of contextual calibration

▲ Can handle trade area demographics much better than above – because trade area concept not used

▲ Deals theoretically with cannibalization (but not rigorously)

▲ Tends not to give projections that are ‘way out’▲ May not work well in some markets if defined for

average market

35

Key Site Modeling Options: Pros and Cons

Elaborate Spatial Interaction Models▲ Complex/esoteric to construct▲ Easy to understand basic parts▲ Theoretically rigorous and satisfactory ▲ Requires lots of good data to make it excel

including good demand data, ‘from work ‘demands, street data for shortest path distances

▲ Requires special programs and skills to calibrate models

▲ Handles demand side surrogates variables (demographics etc.) and competition very rigorously

▲ Generally fits the best (in those situations in which we can do these models)

36

Key Site Modeling Options: Pros and ConsSimple Network Optimization Models

▲ Should not be one-at-a-time optimization (yet common) and if so these are still optimization models of systems of facilities

▲ In these models there are typically simple (crude) assumptions made about allocation of demand over the facilities (e.g. closest facility assignments)

▲ These operation research models tend not to deal well with actual site evaluation model process

▲ They tend to permit one to find the best K new facilities, or the best K to drop etc (i.e. not just de novo scenarios)

▲ These do allow crude assessments of the optimal number of facilities per market

▲ Run quickly (typically heuristic algorithms)

37

Key Site Modeling Options: Pros and ConsSophisticated Network Optimization Models▲ These high end models are our specialty – especially for

banks ▲ Theoretically very rigorous/robust ▲ Requires a lot of good data (like SIM)▲ We embed the most powerful SIM models to evaluate

each site within the operations research L-A algorithm process

▲ We have designed a proprietary optimization algorithm that takes the embedded SIM model into account as the system is optimized

▲ The roll-out process is necessarily more complex and takes longer

▲ The software design requires special software and skills to make it fast (use matrix oriented language )

▲ Great flexibility in final product that effectively answers any question within network optimization process

38

Site Screening Models▲ A “quick-and-dirty” approach to identifying areas where

high business volume new facilities can be located▲ Typically based on regression (or analog) model -

without lots of local knowledge built in▲ Add a sophisticated tapered demographics-based

demand side component▲ Create and input a measure for “competitive potential”▲ Inject a site at every block group (BG) centroid and/or

every highway intersection and/or every arterial street intersection and run model automatically for a “standardized facility” to produce business volume estimates for all these points

▲ Rapidly evaluate 1000’s of sites/locations▲ Map results using colored dots of a surface

39

Site Screening - Conceptual Framework

These models make use of simple site evaluation models – like regression- with typically just demographics and competitor predictor variables

40

Site Screening With Model Results

41

Site Screening Making Action Clear

42

Different Models for Different Situations for The Same Client

▲ For all non-SIM models you do not have to restrict plan to have “one site model”

▲ If enough statistical observations then modelers should try different models for different subsets of observations:- Different regions- Big versus medium versus small markets- Downtown versus mid town versus suburbs versus exurbs- Street sites, small shopping centers, larger malls

▲ Can have different models even if have the same sets of explanatory variables for all markets

▲ But if different sets of explanatory variables … then a very good a priori reason to have multiple models

43

Shopping/Banking from Work▲ One of the largest deficiencies of most site models is their

failure to deal with non-residential based patronage behavior▲ The demand demographics used in almost all are based on

the census – i.e. night time demographics▲ There are estimates for BGs and CTs of day time

demographics - pop and hhd counts but not great estimates of relative demographics like family size, income, education mother tongue; very difficult to do

▲ Nevertheless these variables can be used in modeling▲ Unfortunately it is tricky to impose a priori weights on these

versus night time ones - even when one knows that 40% of the patronage is based on trips from work

▲ In SIM models must have whole model obey demand accounting constraints; if all demand is conceptualized as coming from residential areas then one needs additional conditions to take work place demand into account so that it all adds up

▲ There are some good proprietary approaches to this

44

Cannibalization▲ Most retailers who can afford site models have big

networks and would like to estimate cannibalization ▲ Cannibalization becomes more important when

network become full and mature because very hard to find low cannibalization sites to add

▲ Very difficult to estimate cannibalization effects in a standard regression and analogue site modeling setting (poor theory at the base of problem)

▲ Need to have a variable or variables that captures nearness to close own-banner sites in the model (and different vars than nearness to competitor sites)- Sometimes these variables do not want to come

into regression models esp. because of multicolinearity

- Sometimes they do not accurately capture the cannibalization effects because the model has structural flaws

▲ So really need a SIM model to deal well with the important cannibalization effects

45

Spatial Interaction Models - Concept▲ SIMs do not go after direct estimation of store sales

or branch business volumes▲ Rather estimate flows from demand areas to facilities▲ Households (and areas) patronize multiple facilities ▲ “Areas” patronize multiple facilities even if persons

patronize single facilities but a fortiori because persons do

▲ So “areas” patronize multiple facilities not just one (despite GIS) and are modeled as such

▲ Probabilistic assignments of household demands via areas are very realistic not just a fetish of advanced modelers

▲ So what should be the role of retail “trade areas” per se in good site evaluation models? Minimal!

46

Spatial Interaction Models - Concept

▲ Spatial Interaction Model allocates (potential) dollars from each demand area or neighborhood (BG) to each branch based on:

▲ Probability of patronage- Store/branch attractiveness for this facility

(multidimensional) - Distance between the neighborhood and the facility- Location, number & power of competitors- (perhaps also ‘intervening opportunity’)- Number of local detractors & attractors near facility- Other idiosyncratic socio-economic behaviors

▲ Actual dollars assigned affected by the amount of demand for product(s) in the demand area or neighborhood ($ per household * households)

47

SIM Assignments

Each demand area can interact with each supply point - so it a natural matrix type model

Areas have choices

i

j = 1

j = 2

j = 3

48

SIM Model What-If Type Questions“What-if” questions to ask (one change focus):▲ What will be the bus volumes there if we open a new branch at

X? (bus volumes can be in terms of 6 products - for example)▲ How much bus volume will we lose of we close a branch at Y?▲ What happens if my competitor opens a new super store across

the street from our store 218?▲ How much will a proposed new store Z cannibalize our other

near-by existing sales, by store?▲ For the new branches we are looking at, what is the best branch

format to use in each case?▲ Project business volumes for the new store out 5 years.

(maturation process)▲ Of the 5 new sites we are considering now find the one that

maximizes expected “incremental sales” 5 years out?▲ If we close branch 563 how much of its business will be retailed

and at which of our existing branches; (also which competitors gain most)?

▲ Of the 3 existing sites which we are proposing to close which results in the largest total retention?

49

SIM Model What-If Type Questions“What-if” questions to ask (multiple change focus):▲ Many multiple changes are not recommended unless the models fit very

well and are very sensitive▲ Also ever try to answer a what if question when the issues involved are

not all built into the model▲ What will be the bus volumes increases by branch if we open the 3 new

branches simultaneously How much bus volume will we lose of we close a branch at Y?

▲ How much will these 3 proposed new branches cannibalize our other near-by existing sales, by branch?

▲ What happens to our store sales in the first 3 years if my competitor opens up 2 new stores in the north end of the market at X and Y as planned?

▲ Of the 5 new sites we are considering now find the 2 (or 3) sites that that maximize expected “incremental sales” 5 years out?- Best done as network optimization question

▲ If we close branches 419, 563,and 622 how much of their business will be retained and at which of our existing branches; (also which competitors gain most)?

▲ Of the 6 existing sites which we are proposing to close which 2(or 3) result in the largest total loss … largest total retention?

50

SPATIAL INTERACTION MODELSDOLLAR FLOWS FROM DEMAND AREAS (ROWS) TO FACILITIES (COLUMNS)

DEMAND OWN FACILITIES COMPETITOR'S FACILITIESfor demand areas

AREAS W1 W2 W3 W4 W5 W6 W7 . C1 C2 C3 C4 C5 . ROW SUM1 280 12 132 54 170 0 0 . 0 210 34 18 0 . 2562 45 0 3 0 2 15 0 . ? ? ? ? ? . 1873 0 34 0 167 9 0 0 . ? ? ? ? ? . 3514 0 23 0 14 0 119 51 . ? ? ? ? ? . 1365 23 0 17 8 0 54 59 . ? ? ? ? ? . 2366 87 0 19 0 12 0 9 . ? ? ? ? ? . 1187 0 0 187 0 52 0 0 . ? ? ? ? ? . 4568 19 159 0 34 0 0 0 . ? unknown flows to competitors . 2209 0 26 0 71 0 0 194 . ? ? ? ? ? . 23510 0 0 0 9 31 61 21 . ? ? ? ? ? . 38411 0 0 37 0 18 0 42 . ? ? ? ? ? . 42912 0 56 34 0 3 152 0 . ? ? ? ? ? . 19813 6 221 0 0 0 0 0 . ? ? ? ? ? . 9714 28 2 82 19 0 21 36 . ? ? ? ? ? . 28515 0 0 145 52 8 8 0 . ? ? ? ? ? . 269. . . . . . . . . . . . . . . .. additional demand areas . . . . . . . . . . .. . . . . . . . . . . . . . . .

1198 0 0 47 234 0 28 0 . ? ? ? ? ? . 1871199 25 0 0 12 39 0 0 . ? ? ? ? ? . 2291200 2 290 0 5 121 12 21 . ? ? ? ? ? . 331

for stores COLUMN 9987 19845 12309 8320 9952 23295 7829 . XC1 XC2 XC3 XC4 XC5

SUM (store sales) . or ??? or ??? or ??? or ??? or ??? or ??? 271256sometimes these are known or we can come close

51

Calibrating SIM Models▲ All SIM models have “parameters” and all need to be

calibrated▲ Popular in GIS arena to say “we have a gravity model

for that is “ready to go” and “does not need your data”

▲ Many assumptions must be made that are never set out (especially by model vendors)

▲ Can not calibrate a SIM model by just fitting the store sales or the ‘end state flows’ ; poorly understood;- There are an infinite number of parameter combos that will

fit the sales perfectly – and some of the have a positive distance decay effects

▲ Must fit the “cells” of the matrix (not column sums) to benefit from the true power of the SIM

52

Calibrating SIM Models▲ Best methods depends on how complex the model is▲ Use Nakanishi-Cooper linearization and calibrate

using least squares▲ Recast as a multinomial logit model and calibrate

using standard non-linear optimization algorithms ▲ Deal directly with complex non-linear models using

special non-linear search algorithms to find parameters

▲ We prefer the latter as it is very powerful and flexible▲ But need experienced math or OR geeks and some

non trivial computing times even on fast boxes▲ We have calibrated models with up to 17 nonlinear

parameters (in addition to demand estimation) for several thousand origins by up the 1000 facilities

53

An Example Banner Facility Network - in One Market

54

Example Banner Facilities + Competitors

55

Example Banner + Competitor Facilities + Demand Polygons

56

Testing The Addition of a New Branch

57

Testing The Addition of a New Branch

Branch Name

Current Status in Scenario

Actual Volumes

($MM)Total Projected Volumes ($MM)

Absolute Difference

($MM)

Percent Change

(%)Myers Park 145.90 140.55 -$5.35 -3.67%Providence 32.82 31.12 -$1.70 -5.18%Kenilworth 66.93 65.09 -$1.84 -2.75%Dilworth 34.64 34.44 -$0.20 -0.58%Plaza 86.95 86.76 -$0.19 -0.22%One Wachovia Center 156.91 155.9 -$1.01 -0.64%Three Wachovia Center 231.46 231.02 -$0.44 -0.19%South Boulevard 45.88 45.23 -$0.65 -1.42%Park Road 44.53 43.88 -$0.65 -1.46%Cotswold 84.56 74.87 -$9.69 -11.46%Tyvola 123.32 122.54 -$0.78 -0.63%Tyvola Road II 112.84 111.23 -$1.61 -1.43%New Branch Opened 0.00 45.87 $45.87 NA

Market Totals 1,166.74 1,188.50 $21.76 1.87%

Market Impact Report

Facility Type: Free StandingSize: Mega# FTE's: 12# ATM's: 4Parking: yesVisibility: Excellent

Attributes

Business volume projections

Cannibalization impacts

58

Test New Branch Zoom-In

Branch Name

Current Status in Scenario

Actual Volumes

($MM)Total Projected Volumes ($MM)

Absolute Difference

($MM)

Percent Change

(%)Myers Park 145.90 140.55 -$5.35 -3.67%Providence 32.82 31.12 -$1.70 -5.18%Kenilworth 66.93 65.09 -$1.84 -2.75%Dilworth 34.64 34.44 -$0.20 -0.58%Plaza 86.95 86.76 -$0.19 -0.22%One Wachovia Center 156.91 155.9 -$1.01 -0.64%Three Wachovia Center 231.46 231.02 -$0.44 -0.19%South Boulevard 45.88 45.23 -$0.65 -1.42%Park Road 44.53 43.88 -$0.65 -1.46%Cotswold 84.56 74.87 -$9.69 -11.46%Tyvola 123.32 122.54 -$0.78 -0.63%Tyvola Road II 112.84 111.23 -$1.61 -1.43%New Branch Opened 0.00 45.87 $45.87 NA

Market Totals 1,166.74 1,188.50 $21.76 1.87%


59

5 Year Projections by Product▲ Select the best number

and location of branches from the network optimization model to run what-if scenarios

▲ The selected branch network scenario is used to calculate 5 year projections by product mix

Branch # Benchmark: Current ScenarioBranch Name:

Product DescriptionYear 1 Year 2 Year 3 Year 4 Year 5

Retail Demand Deposits 20.67 22.22 22.89 23.61 23.75Business Demand Deposits 18.35 19.54 19.89 19.99 20.21Retail Time Deposits 5.67 5.98 6.56 6.71 6.82Retail Lending 14.44 15.67 15.89 15.97 16.14Retail Investment 4.12 4.18 4.21 4.35 4.52Retail Insurance 3.28 3.43 3.49 3.57 3.61Total Balance 66.53 71.02 72.93 74.2 75.05


Projections ($MM)

W5455Downtown

Current ScenarioCurrent Scenario

Projections can be:- Market share- New product

sales- Sales- Business

volumes by product

60

SIMs are Market Specific▲ It is very wise to assume that each major market

will require calibration of its own SIM ▲ Regression models are such that rarely enough store

observations in a market (say >25) to do a different model for each market

▲ Sometimes you can get away with one SIM for several markets of the same type (e.g. big southern cities, medium sized mid-western cities, big city suburbs, etc.)- Easily tested by checking how well one market’s

parameters work in another market- But you still have all the work to set up the files for each

city to do the tests▲ Because hard to estimate the number of models

needed it is also hard to price out projects in proposals

61

Network OptimizationFor chain stores, branch banking

62

What is Network Optimization?▲ The focus is on finding the optimal locations of sets of multiple

facilities in a system (… a network) where the success of one location is influenced by the location of other locations

▲ The problem paradigm deals with locations of facilities and the assignment of demand (or whatever) to those facilities- both simultaneously being “optimized”

▲ The problem is an OR problem: - the well-celebrated location-allocation problem

▲ There are about 1400 papers since 1960 in the literatures of OR, Management Science, Industrial Engineering, Transportation Science, Geography, Civil Engineering, Applied Mathematics, Marketing, Applied Economics and Ag Economics

▲ Much of the literature focuses on algorithms … from heuristics to mixed integer programming algorithms

▲ Geographers had been involved mainly in simple location-allocation problem types like the celebrated p-median problem

▲ Sophisticated models deal with special circumstances like fixed costs, multilevel hierarchical systems, probabilistic demands etc

63

What is Network Optimization?▲ Think of it as thousands of “what-if” scenarios done

within a site evaluation modeling framework - one after the other or concurrently asking questions that use the words “best” or “optimal”

▲ The “what-ifs” are done systematically as part of the solution procedure

▲ The question above was a network optimization question- Of the 5 new sites we are considering now ... find

the 2 (or 3) sites that that maximize expected “incremental sales” 5 years out?

▲ Standard site evaluation questions are: what are the sales at this location or that location (or even two locations) and/or what are the impacts or selecting this location on sales at other locations… but whenever the words “best” or “optimal” are used it implies many “what-if type site evaluation runs” (onerous) and often not really possible

64

Network Optimization What-If Questions▲ (de novo -What is the best network of 16 facility sites in

the market - to optimize total sales or market shareGiven many sites identified for possible new stores/branches

in the market ….▲ Which are the best 10 new locations for max of Z▲ If we must close say 5 facilities in the market which 5 are

the best to close for long term revenue? And ..what do we lose?

▲ How does the answer above change if we must ensure retention of the VP’s favorite 7 branches (i.e. hands-off a specific 7)

▲ If we close these 5 locations and we are to add 3 - which are the best 3 to add (and what are sales at each and for system)

▲ Which 15 branches of the 21 in this market are the best to retain … with minimal loss of market share by year 5?

▲ What is the optimal number of branches in this market for profit surrogate: ratio of market share to gross annual operating costs?

65

Network Optimization Model Structure

▲ Optimal location-allocation in an interdependency environment

▲ Location-allocation (L-A) model traditionally have demand assigned to closest (lowest cost of travel) facility in system

▲ Many algorithms depend on this for their efficiency and even their possibility

▲ In retail, closest assignments are very unrealistic (unless delivery system)

▲ Best to use Huff type models for retail patronage within L-A model …. but many L-A algorithms don’t work (well) any more with these additional non-linearities

66

Network Optimization Model Structure▲ How many facilities is best? – additional optimization

question .. beyond where and what assignments▲ Fundamental “trade off”

- the more facilities the better off are customers (re travel) - the fewer facilities the lower are capital and operations

costs; also bolstered by economies of scale ▲ This can not be “solved for” unless we incorporate

the construction and operational (capital) costs –and this is uncommon in retail site modeling contexts

▲ So typically someone in management/finance says “add 4 or 5 facilities” and this becomes the “budget constraint” in the optimization process

▲ When asked to add 8 new facilities an a market modelers tend to try 6, 7, 8, 9, 10 to be rigorous

67

The Network View and Strategy

Market Share as a Function of Number of Optimal Locations

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Number of Optimal Locations

Mar

ket S

hare

(%)

Diminishing Marginal Returns

68

Network OptimizationOptimal 15 Additional Branches

69

Network OptimizationOptimal 25 Additional Branches

70

Optimal 5 “Drops” From Network

71

Calgary: 11 Existing Facilities

72

Calgary: 7 Clean Slate Optimal Networks

73

Calgary New Optimal Network Modified by Fixed Cost Reality

Not cost effective to close a facility and open one really near-by

74

Also Do Sensitivity AnalysisAfter Network Optimization

▲ Use the Spatial Interaction Model to perform what-if scenarios from a newly derived set of optimal sites:- Opening- Closing- Relocating- Consolidating- Change of format

▲ Size/layout▲ Change of staff specialists▲ Change in hours of service

- Competitive changes- Transport surface changes

75

When to Do Optimization▲ Optimization is sophisticated and expensive▲ When there are large numbers of what if

questions to be posedEspecially when: - the network is large - there are significant additions, deletions,

consolidations and/or changes- when you already have a good site model and

this is the next logical step - when you have not got the time/resources to

assign persons to running what if scenarios all day

- when you have one or two master geeks to do runs and manage the process

76

Selling Models▲ Don’t really “sell” them; best if sales persons have limited roll unless

they have very good domain knowledge▲ Prospective clients have to need help and “know models help”▲ Make sure you have your (good) name in the ring when folks go

looking for site modellng help or a supplier▲ Get a good name by doing great work for a fair price … then word of

mouth marketing does it from there ▲ Talk to clients about their problems at length; spend time in the

organization (free), listening to pain statements and data issues; few consultants still do this

▲ Clients do need some good (and bad) news stories to become really motivated

▲ Don’t over promise, be on-time, provide a full report with all model details, so they do not feel ‘stuck’

▲ Data quality is key issue: use best data available, be frank when it comes to the quality issues in client data – get it right – don’t compromise

▲ Models can be run for retailers by consultant with report results being delivered via web the next day – not everyone needs software on their desk

▲ Environics delivers solid model roll-out software that works and updates of data and software at fair prices

77

Conclusions▲ Projecting sales and market share is difficult; it needs careful

thought and rigorous methodology▲ There are many ways of doing it badly▲ Invariably simple unsophisticated approaches tend to yield poor

results▲ Many of the problems stem from lack of knowledge and

simplistic GIS approaches to measuring demographic-based demand and competition and the lack of software fordoing it right

▲ Business analyst is making progress on some of these fronts ▲ There are many good ideas for doing better job and I have

noted just some of them in this paper ▲ Most of the ideas start with Huff type spatial interaction models

which are theoretically rigorous and not as data hungry as some critics have claimed

▲ There are many examples of situations in which these approaches have worked superbly - including embedding these in network optimization models

78

POSTSCRIPT Academic Paper on Trade Areas

A great new 60 page paper has just been published on retail trade areas entitled “What’s In A Trade Area?” - by Tony Hernandez, Tony Lea, and Philip Bermingham of The Centre For The Study of Commercial Activity at Ryerson University in Toronto. The paper reviews all major trade area concepts, methods and issues and is the most complete and up-to-date paper yet on this topic. A must read for retail and GIS analysts.

You will find more information on the paper here:http://www.csca.ryerson.ca/publications/2004-03.html

It also explains how to order the publication The price in Canadian funds is $107.00 per copy or $88.00 US.

http://www.csca.ryerson.ca/publications/2004-03.html

Documents

Site Evaluation and Sales Estimation Modelling For