MapInfo Predictive Analytics Group

Transcription

1 Welcome to the Science of Site Selection Online Seminar What s New at MapInfo: New Online Seminar: Predictive Analytics for Commercial Real Estate Developers, Leasing Agents, and Brokers Go to: for 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 Go to: for more information or to enroll 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 Extensive field knowledge results in ground truth analysis 1

2 MapInfo Predictive Analytics Offerings tailored to all firm sizes, budgets, and needs Expectation More Focused Solutions Specific Answers Smart Site Solutions AnySite (site selection, segmentation) Custom Modeling Solutions AnySite Gravity Model TargetPro (advanced segmentation) Standard & Customized Outsourced Research Value and Predictive Power Location Intelligence Component for Business Intelligence Software Directional AnySite Online and SOAP web services Data MapInfo Offerings 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 2

3 MapInfo Offerings Smart Site Solutions (S 3 ) 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 3

4 MapInfo Offerings Modeling Solutions (continued) 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 Steps in Developing a Comprehensive Location Research Program 4

5 Location Research Steps 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) 5

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7 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 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 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 7

8 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.) Location Research Steps 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 8

9 New Opportunities 9

10 Location matters Location helps model $ flows realistically 242 z 2285 z $1,128,279 $44,548 $751, z Mississippi River 1738 z $0 10

11 Use Drive Distance/Time to collect realistic market information Customer Penetration High High Med-High Med-High Med Med Med-Low Med-Low Low Low Store Store Location Location Customer Penetration for a Retail Outlet in Hamilton, Ont Store Maturity Analysis 11

12 Store Maturity Analysis 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. Monthly Sales Trend 12

13 Maturity Curve - Sample Gradual Awareness Growth Store sales are mature in their 37 th month of operations. Monthly Sales Trend Honeymoon Effect 13

14 Customer and Competitive Profiling Primary Factors Impacting Store and Sales Performance Demographic/Customer Profile Competition Site and Location Characteristics Operations/Merchandising Marketing/Brand Awareness/Regionality 14

15 Customer Profile Analysis 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. 15

16 Targeting Customer Segments PSYTE Strip Center Profile Top PSYTE Advantage Clusters 16

17 Top PSYTE Advantage Clusters Core Customers Create Trade Areas 17

18 Trade Areas Based on Customer Data, Not Assumed Radius Definitions 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 Analyze Sales Distribution Patterns 18

19 Determine Trade Area Overlap Between Stores $70 Customer Performance Per Capita Sales by Distance $60 Sales per Capita $50 $40 $30 $20 $10 $ Driving Distance (miles) 19

20 Normal Curve Demographic Example Normal Curves - Small Market Percent Households with Income $50,000+ $500 $450 $400 $350 HH Base INC > 50% INC < 15% Sales per Household $300 $250 $200 $150 $100 $50 $ Drive Time (minutes) Competitor Analysis 20

21 Competitive Profile Analysis 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. Normal Curve Competition Example Competitors within

22 Competitive Synergy Competitive Thresholds Sales Performance Retail Synergy/Competitive Intensity Under certain conditions, the presence of competition can provide a synergistic lift in store sales performance. Site Profiling 22

23 Site Profile Analysis 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 23

24 Smart Site Solutions Market Examples Create Market Demand Surface 24

25 Establish Site Search Parameters Site Search Parameters Sales Potential: GT $8.5 million Cannabalization: LT 10% Seed Points: Shopping centers Buffer Around Existing Stores 25

26 Identify Target Locations Green Field Market Analysis Site Search Parameters Sales Potential: GT $8.5 million Store Spacing 10 miles apart Seed Points: Shopping centers 26

27 Target Sites Identified Market Optimization and Site Model Example 27

28 Create Market Demand Surface Establish Site Search Parameters 28

29 Run Site Evaluator Model Site Forecast and Reports 29

30 Summary 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. Paul Thompson Questions? [email protected] 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: Be sure to check out our new Predictive Analytics for Commercial Real Estate Developers, Leasing Agents, and Brokers online seminar. Go to: 30