MapInfo Predictive Analytics Group

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• New Online Seminar:

Predictive Analytics for Commercial Real Estate Developers, Leasing Agents, and Brokers

Go to: http://mapinfoevents.webex.comfor more information or to enroll

• MapWorld 2006

If you are in retail, restaurant, or commercial real estate don’t miss this opportunity to attend the strategic site selection seminars. Mike LaFerle, VP of Real Estate for Home Depot, is the keynote speaker for MapWorld 2006.

Go to: www.mapinfo.com/mapworld2006for more information or to enroll

Welcome to the Science of Site Selection Online Seminar

What’s New at MapInfo:

MapInfo Predictive Analytics Group

• The largest full-service research organization devoted exclusively to serving the restaurant, retail, and real estate industry

• Staff of approximately 120+ consulting professionals - part of 700+ MapInfo, Corporation

• Offices in Ann Arbor, Michigan; Dublin and Newport Beach, California; Toronto, Canada

• Acquired Compusearch (opened in 1973), Dec 2000 • Thompson Associates (opened in 1959), Jan 2003

• Staff skill set includes extensive domain expertise in each of the restaurant, retail, real estate, and financial services verticals

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Offerings tailored to all firm sizes, budgets, and needs

MapInfo Predictive Analytics

Custom Modeling Solutions Smart Site Solutions AnySite Gravity Model AnySite (site selection, segmentation) TargetPro (advanced segmentation)

Standard & Customized Outsourced Research

AnySite Online and SOAP web services

Data Software – Directional Specific Answers More Focused Solutions Value and Predictive Power Expectation

Location Intelligence Component for Business Intelligence

Standard and Customized Research

MapInfo provides an array of outsourced Consulting Services to meet its client’s needs:

• Custom Mapping

• Sales Transfer Studies (cannibalization) • Sales Forecasting System Application • Field Research

• Market Research – • Focus Groups

• Customer Surveys and Interviewing • Expert Testimony

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Smart Site Solutions (S

3

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Predictive Analytics solution that systematically determines the number of supportable stores, placement, and priority of proposed brand locations

• Assess those factors that determine demand for a given brand

• Final solution produces the number, placement, and priority of potential locations that meet client volume and cannibalization parameters

• Can be run in existing markets (for in-fill opportunities) or in new markets for initial optimal development solution

• Can de developed as single engagement deployed within AnySite Online or as a turn-key application within AnySite

MapInfo Offerings

Modeling Solutions

Predictive Analytics solutions that provide site specific sales potential estimates to better ascertain successful deployment

• Developed by marrying customer data (via POS, survey, or customer list) with demographic,

segmentation, business, competitive, and other data to develop a Customer and Competitive Profile to explain sales performance

• Takes into consideration density class of locations, regionality, and multiple layers of demand for a given brand

• Can be developed to incorporate a Site Profile to explain and account for those site based factors that effect sales potential

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Modeling Solutions

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Predictive Analytics solutions that provide site specific sales potential estimates to better ascertain successful deployment

• Includes Outlier Analysis to understand underperforming assets

• Allows user flexibility to refine trade area extents based on local knowledge

• Includes customized reports comparing proposed site with market and peer group existing units for validation

• Typically delivered as a turn-key solution within AnySite software. Can also be applied by MapInfo on client’s behalf

• Options include: Site Analog Matcher, Site Evaluator, Market Optimizer

MapInfo Offerings

Steps in Developing a Comprehensive Location Research Program

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1. Assemble Data Inputs

a. Demographic/Psychographic Segmentation data (PSYTE) and/or Hhold data

b. Brand locations c. Unit Sales History d. Competition

2. Complete/Process Customer Surveys a. POS or Customer Source Survey (CSS)

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Customer Source Survey (CSS) Analysis

• Customer frequency (per month) – Once – 41% – Twice – 20% – Three – 11% – Four – 9% – Five – 4% – Six – 3% – Seven – 1% – Eight – 2% – Nine – 1% – Ten – 8% – Average visits/month – 3.0 • Average Expenditures:

– Bar $12.23 ($7.51 min/$19.75 max) – Dining $21.87 ($16.40 min/$31.76 max) – Merchandise $17.55 ($7.15 min/$79.66 max)* – Expenditure Party $26.97 ($17.53 min/$36.30 max) – Expenditure Person $13.32 ($10.41 min/$17.68 max) * large variation

• Customers went to: – Home – 36% – Work – 25% – Other – 11% – Recreational Activities – 9% – Shopping – 6% – Hotel/Motel – 5%

• Customers who made round trips – Work – 22%

– Home – 12%

– Other combinations – 55%* – *not round trips

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Location Research Steps

3. Develop Customer & Competitive Profiles a. Develop by each activity generator b. Develop across Density classes c. Understand differences by region d. Determine competitive brand strength e. Develop Modeling “engine”

4. Complete “Outlier Analysis”

Compare existing unit performance to modeled • Identify units that are underperforming 5. Complete Smart Site Solutions Analysis (S3)

a. Utilize modeling engine to evaluate a host of “seed” locations

b. Set parameters of Volume & Impact (e.g., only show opportunities >$700k and <10% impact) c. Identify Number, Target and Priority of

development by geography (DMA, county, etc.)

6. Develop Site Profile

a. Collect Site Surveys on a host of existing brand locations

b. Perform statistical analysis to determine those site factors that explain variances in unit performance

7. Develop Site Evaluator Modeling Application a. Program modeling engine and site profile

results into AnySite Software

b. Custom design reports and mapping output for brand

c. Develop add-on functionality 1. Analog Matcher

2. Scenario Manager 3. Market Optimizer

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Location matters

z

M is_sis sip_{pi R} iver $1,128,279 242 1184 1738 2285 $0 $751,543 $44,548

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Store Store Store Store StoreStoreStoreStore Store Location Location LocationLocationLocationLocationLocationLocationLocation

Customer Penetration High Med-High Med Med-Low Low Store Store StoreStoreStoreStoreStoreStoreStore Location Location Location Location LocationLocationLocationLocation

Location Customer Penetration High Med-High Med Med-Low Low

Use Drive Distance/Time to collect realistic market information

Customer Penetration for a Retail Outlet in Hamilton, Ont

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• Stores do not open 100% mature.

• Stores need to be open for a period of time to generate market awareness and to shift existing consumer shopping behaviors.

• Strip and Mall units typically differ in their maturity curves. • Stores mature based on a maturity curve.

• The Real Estate group needs to know the maturity rate and the Marketing group needs to work to accelerate each store towards reaching maturity.

• Reaching mature sales sooner equals dollars in your pocket and a more solid lock on market share within that territory.

Store Maturity Analysis

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• Store sales are mature in their 37th_{month of operations.}

Gradual Awareness Growth

Maturity Curve - Sample

Honeymoon Effect

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Customer and Competitive Profiling

Primary Factors Impacting Store and Sales Performance

• Demographic/Customer Profile • Competition

• Site and Location Characteristics • Operations/Merchandising

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Everything starts with understanding the customer!

• Basis of most location modeling and market research. • Defined via Customer Surveys/POS/Client data. • Statistically determine those demographic and

lifestyle factors important in strong sales performance.

• Identify the characteristics of the most productive customers and where the greatest geographic concentrations of those customers are located. • Highlight the significance of distance to sales

penetration.

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PSYTE Strip Center Profile

Targeting Customer Segments

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Core Customers

Top PSYTE Advantage Clusters

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Trade Area Extension The contiguous geographic area from which a store’s greatest concentration of sales originate.

•Urban Trade Areas

•Suburban Trade Areas

•Exurban Trade Areas

Trade Areas Based on Customer Data, Not Assumed Radius Definitions

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Determine Trade Area Overlap Between Stores

Customer Performance Per Capita Sales by Distance

$0 $10 $20 $30 $40 $50 $60 $70 0 5 10 15 20

Driving Distance (miles)

S a le s p e r C a p it a

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Normal Curve – Demographic Example

Normal Curves - Small Market Percent Households with Income $50,000+

$0 $50 $100 $150 $200 $250 $300 $350 $400 $450 $500 0 5 10 15 20 25 30

Drive Time (minutes)

S a le s p e r H o u s e h o ld HH Base INC > 50% INC < 15% Competitor Analysis

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• Determine which competitors impact performance – category vs. chain

• Evaluate which kinds of competition are strongest: – Sister, Direct, Indirect

• Assess positioning within the trade area, not just presence in the trade area (not only how much but also where):

– Intercepting – Adjacent

• Identify when conditions are over/under saturated with competition.

• Determine when the presence of competition actually improves store performance.

Competitive Profile Analysis

Competitors within 360

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Competitive Thresholds

Retail Synergy/Competitive Intensity

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Under certain conditions, the presence of competition can provide a synergistic lift in store sales performance.

Competitive Synergy

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• Critical evaluation of location and site characteristics. • Key factors include:

– Visibility

– Parking and site layout – Co-tenants

– Size of center, type, “the drawing power” – Retail activity and image – established vs. new – Ingress/egress and accessibility

– Traffic count/Drive by influence

• Determine which site and location factors are associated with better performing stores

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Smart Site Solutions – Market Examples

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Sales Potential: GT $8.5 million

Cannabalization: LT 10% Seed Points: Shopping centers

Site Search Parameters

Establish Site Search Parameters

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Identify Target Locations

Site Search Parameters

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Target Sites Identified

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Create Market Demand Surface

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Run Site Evaluator Model

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Site Selection is a scientific process and follows the scientific method: – Data is gathered. The more accurate and granular the data the more

accurate the model and its predictions.

– Statistical methods are used to search for relationships within the data.

• Competition

• Customer profiles – Specific PSYTE clusters • Cannibalization – Sales Transfer

• Site Specific Attributes

• Trade area rules – this step should not be trivialized as it is the foundation of YOUR business

– Combine model predictions with YOUR experience to create very reliable market expansion strategies.

– Monitor the model and alter as market conditions change.

Summary

Questions?

Paul Thompson

[email protected] 416.594.5290

For copies of presentation slides, or sales information: [email protected]

Interested in learning more about the use of Predictive Analytics and modeling in site selection and sales forecasting?

Be sure to register for MapWorld 2006! Go to: www.mapinfo.com/mapworld2006 Be sure to check out our new “Predictive Analytics for Commercial Real Estate Developers, Leasing Agents, and Brokers” online seminar.