Location Intelligence - A Critical Tool in Retail Performance Management
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Access the full event here: https://event.on24.com/wcc/r/1816077/DFF9ECD078983862B13E8F3DA3C7144A Retailers have more data at their disposal than ever before. Every day new sources and types of data become available. It is critical that retailers are able to consume and analyze this data in order to understand the discrete success factors that drive store success. Location Intelligence combined with leading edge data science is how best in class retailers are finding insights they need to drive growth in today’s Unified Commerce environment. Join Gary Sankary from Esri and Joe Whitley from Environics Analytics to learn how advances in GIS technology and DataScience are enabling retailers to create interactive management decision support tools that that drive top line growth and bottom line performance.
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Location Intelligence - A Critical Tool in Retail Performance Management
- 1. #RSP18 Location Intelligence - A Critical Tool in Retail Performance Management SPONSORED BY:
- 3. May 6-8, 2019 SAVE THE DATE: Convene, New York City www.retailinnovationconference.com
- 4. #RSP18 #RSP18 Prize Pack: Register & Attend to Win Join all our #RSP18 sessions live for the best chance to win • $10 Starbucks gift cards - 1 winner per session • Free passes to #RIC18 - 1 winner per day
- 5. How are we doing? #RSP18 Questions, Tweets, Resources, Survey
- 6. #RSP18 Speakers Gary Sankary Esri Joe Whitley Environics Anlytics Debbie Hauss Retail TouchPoints
- 7. Location Intelligence A Critical Tool for Retail Performance Management Joe Whitley joe.whitley@environicsanalytics.com Gary Sankary gsankary@esri.com
- 9. Data is Everywhere promotions product mix customer records point of sales planograms mobile search social media people footfall coupons trafficbarcode scans online habits adclick
- 10. But what DRIVES your success? Trade Area Development Competition Cannibalization Consumer Behaviors Demographics Spending Movement Store Characteristics Brand Concept Neighborhood Type of Location Transportation Network Operational Data Point of Sale Data Store Team
- 11. Today’s Agenda Discuss some of the new data sources available to retail today Review strategies to analyze and use insights from disparate data Modeling and Site Model Development Location Science and Spatially Enabled Analytics
- 13. Challenges Changing Demographics • Generational Differences in Eating Out • Service Expectations • Pricing Models Competition • Blurring of Dining Segments • New Concepts/Trends • Home Delivered Meal Plans Multiple Channels • Dine-In • Home Delivery • Take Out • Catering
- 14. Challenges Changing Demographics • Generational Differences in Eating Out • Service Expectations • Pricing Models Competition • Blurring of Dining Segments • New Concepts/Trends • Home Delivered Meal Plans Multiple Channels • Dine-In • Home Delivery • Take Out • Catering Strategy • To better understand the metrics, at the local level, that drive success • Engage with customers with more relevant offers and experiences • Grow sales in existing restaurants • Find new market opportunities for continued growth
- 15. Urbanicity Typing Trade Area Analysis Key Metrics Analysis Customer Research Site Model Development Site Model Development Process
- 16. Urbanicity Typing Trade Area Analysis Key Metrics Analysis Customer Research Site Model Development Who is my customer and where do they live and work?
- 17. Urbanicity Typing Trade Area Analysis Key Metrics Analysis Customer Research Site Model Development Who is my customer and where do they live and work? What stores are similar in terms of population and employment density patterns?
- 18. Urbanicity Typing Trade Area Analysis Key Metrics Analysis Customer Research Site Model Development Who is my customer and where do they live and work? What stores are similar in terms of population and employment density patterns? How far are my customers willing to travel?
- 19. Urbanicity Typing Trade Area Analysis Key Metrics Analysis Customer Research Site Model Development Who is my customer and where do they live and work? What stores are similar in terms of population and employment density patterns? How far are my customers willing to travel? What are the key drivers of store performance?
- 20. Urbanicity Typing Trade Area Analysis Key Metrics Analysis Customer Research Site Model Development Who is my customer and where do they live and work? What stores are similar in terms of population and employment density patterns? How far are my customers willing to travel? What are the key drivers of store performance? What is the sales potential for future store locations?
- 21. Types of Site Models
- 22. Actual Store Sales Model Estimated Sales Stores AnnualSales Limitations in available data Inaccurate data or data that has not been properly validated Statistical Overfitting
- 23. Trade Area Challenges Defining accurate trade areas Different methodologies feed different model examples
- 27. Mobile Data • Visualize where people move based on cell phone usage • Enable retailers to see where customers live, here they go during the day, where they work and where they play • Review market penetration and movement in and around shopping centers, competitor locations, attractions and events. 27 ©2018EnvironicsAnalytics
- 28. ©2018EnvironicsAnalytics How do customer trade areas change throughout the day? All Trips Early morning Midday
- 29. Spatial Interaction Models 29 What they are How they work Interactions and what if scenarios Takes into account multiple dimensions that affect your business Used as a predictor of performance and a decision support tool
- 30. Spatial Interaction Models (SIMS) Potential Attracti veness DistanceAffinity Attractivenes s
- 31. Total Dollars in a specific geography for a product or service Spatial Interaction Models (SIMS) Potential Attracti veness DistanceAffinity Attractivenes s
- 32. Attractiveness of a specific location for a branch or a store Total Dollars in a specific geography for a product or service Spatial Interaction Models (SIMS) Potential Attracti veness DistanceAffinity Attractivenes s
- 33. Attractiveness of a specific location for a branch or a store Distance the customer must travel to a location based on where they live and/or work Total Dollars in a specific geography for a product or service Spatial Interaction Models (SIMS) Potential Attracti veness DistanceAffinity Attractivenes s
- 34. Attractiveness of a specific location for a branch or a store Distance the customer must travel to a location based on where they live and/or work Consumer preferences for a brand Total Dollars in a specific geography for a product or service Spatial Interaction Models (SIMS) Potential Attracti veness DistanceAffinity Attractivenes s
- 35. Site Detail Report Site Information Model Results Site ID 1 Address 123 Main Street Sales Forecast On Premise $1,200,000 City Anywhere Take-Out $350,000 State OH Delivery $125,000 ZIP Code 43065 Catering $125,000 Urbanicity Urban Fringe Total Sales $1,800,000 Model Data Total Demand Actual On Premise $7,500,000 Market Potential Take-Out $850,000 Delivery $550,000 Catering $650,000 Competition #Primary 3 #Secondary 3 Competitor Impacts Sister Stores 0 Site Characteristics Store Size 3,500 Parking (1=Poor, 2=Average, 3=Good) 2 Attractiveness Accessibility (1=Poor, 2=Average, 3=Good) 1 In Shopping Center (1=Yes, 1=No) 1 Total GLA (Trade Area) 550,000 Operations Manager Rating (1= Poor, 2=Average, 3=Good) 2 Affinity Consumer Experience Index(100=Average) 125 Core Customer Profile Index Score 250 MarketSpecificSiteSpecific
- 37. Trade Area Market Potential Site Detail Report Site Information Model Results Site ID 1 Address 123 Main Street Sales Forecast On Premise $1,200,000 City Anywhere Take-Out $350,000 State OH Delivery $125,000 ZIP Code 43065 Catering $125,000 Urbanicity Urban Fringe Total Sales $1,800,000 Model Data Total Demand Actual On Premise $7,500,000 Market Potential Take-Out $850,000 Delivery $550,000 Catering $650,000 Competition #Primary 3 #Secondary 3 Competitor Impacts Sister Stores 0 Site Characteristics Store Size 3,500 Parking (1=Poor, 2=Average, 3=Good) 2 Attractiveness Accessibility (1=Poor, 2=Average, 3=Good) 1 In Shopping Center (1=Yes, 1=No) 1 Total GLA (Trade Area) 550,000 Operations Manager Rating (1= Poor, 2=Average, 3=Good) 2 Affinity Consumer Experience Index(100=Average) 125 Core Customer Profile Index Score 250
- 38. Trade Area Market Potential Site Detail Report Site Information Model Results Site ID 1 Address 123 Main Street Sales Forecast On Premise $1,200,000 City Anywhere Take-Out $350,000 State OH Delivery $125,000 ZIP Code 43065 Catering $125,000 Urbanicity Urban Fringe Total Sales $1,800,000 Model Data Total Demand Actual On Premise $7,500,000 Market Potential Take-Out $850,000 Delivery $550,000 Catering $650,000 Competition #Primary 3 #Secondary 3 Competitor Impacts Sister Stores 0 Site Characteristics Store Size 3,500 Parking (1=Poor, 2=Average, 3=Good) 2 Attractiveness Accessibility (1=Poor, 2=Average, 3=Good) 1 In Shopping Center (1=Yes, 1=No) 1 Total GLA (Trade Area) 550,000 Operations Manager Rating (1= Poor, 2=Average, 3=Good) 2 Affinity Consumer Experience Index(100=Average) 125 Core Customer Profile Index Score 250
- 39. Trade Area Market Potential Site Detail Report Site Information Model Results Site ID 1 Address 123 Main Street Sales Forecast On Premise $1,200,000 City Anywhere Take-Out $350,000 State OH Delivery $125,000 ZIP Code 43065 Catering $125,000 Urbanicity Urban Fringe Total Sales $1,800,000 Model Data Total Demand Actual On Premise $7,500,000 Market Potential Take-Out $850,000 Delivery $550,000 Catering $650,000 Competition #Primary 3 #Secondary 3 Competitor Impacts Sister Stores 0 Site Characteristics Store Size 3,500 Parking (1=Poor, 2=Average, 3=Good) 2 Attractiveness Accessibility (1=Poor, 2=Average, 3=Good) 1 In Shopping Center (1=Yes, 1=No) 1 Total GLA (Trade Area) 550,000 Operations Manager Rating (1= Poor, 2=Average, 3=Good) 2 Affinity Consumer Experience Index(100=Average) 125 Core Customer Profile Index Score 250
- 40. To Recap There is an abundance of available data in the marketplace What is of critical importance to key decision makers today is site location, traffic generation and assortment planning The art and science of site potential model development requires an ability to take a multidimensional look at site and marketplace drivers and measure the effects on estimating site performance Site potential modeling is a scalable solution ranging from basic descriptive models to more advanced and complex spatial interaction models
- 41. Location Intelligence A Critical Tool for Retail Performance Management Joe Whitley joe.whitley@environicsanalytics.com Gary Sankary gsankary@esri.com
- 42. #RSP18 Q&A / Speakers Gary Sankary Esri Joe Whitley Environics Anlytics Debbie Hauss Retail TouchPoints
- 43. #RSP18 Register for more sessions now thru September 17th! Join Our Next Session: The Payoff Of Post Purchase Engagement: Benchmarks & Best Practices For Driving Repeat Purchases webinars.retailtouchpoints.com/retail-strategy-and-planning-series/2018/ September 18th 2:00 PM Eastern
Notas del editor
- Social media, mobile devices and the cloud are creating Digital Swarms. Heat maps of location data and the interaction of people and customers on top of the real world. All your assets – whether they be fixed or mobile – have a location, and your database, customer activity, social feeds, foot fall and traffic patterns, Everything from coupons and promotions to associate and customer interaction, your stock and how and where your customer search is streaming in to you mobile devices and big data sources, a digital swarm that can be connected and analyzed because just about all of it is associated with a location. In other words, location data is everywhere, all the time.
- Social media, mobile devices and the cloud are creating Digital Swarms. Heat maps of location data and the interaction of people and customers on top of the real world. All your assets – whether they be fixed or mobile – have a location, and your database, customer activity, social feeds, foot fall and traffic patterns, Everything from coupons and promotions to associate and customer interaction, your stock and how and where your customer search is streaming in to you mobile devices and big data sources, a digital swarm that can be connected and analyzed because just about all of it is associated with a location. In other words, location data is everywhere, all the time.
- For example, let's take a look at the casual dining restaurant segment that I believe we can all relate to. Several years ago, casual dining was simple a brick and mortar location that relied heavily on local residential and workplace draw. However, the marketplace has changed dramatically offering many new challenges.
- We are experiencing a radical shift in consumer purchase behaviors and expectations that is mainly driven by a changing demographic, menu preferences, and pricing. In response to a changing consumer, restaurant segments are blurring, where the full-service, fast casual, QSR segments continue to chip away at share through changes to their marketing plans, changes to their menu mix, price, and service. Operators are also responding consumer preferences by providing alternative channels such as home delivery, take-out, and catering. This translates to a need for operators to better understand the drivers of their success, both Internal and external, and respond to those drivers in order to stay competitive in the marketplace.
- We are experiencing a radical shift in consumer purchase behaviors and expectations that is mainly driven by a changing demographic, menu preferences, and pricing. In response to a changing consumer, restaurant segments are blurring, where the full-service, fast casual, QSR segments continue to chip away at share through changes to their marketing plans, changes to their menu mix, price, and service. Operators are also responding consumer preferences by providing alternative channels such as home delivery, take-out, and catering. This translates to a need for operators to better understand the drivers of their success, both Internal and external, and respond to those drivers in order to stay competitive in the marketplace.
- Site model development is both an art and a science and is it involves a careful blending of statistics, measurement and logic to develop an actionable and reliable tool that can be applied to a prospective location to estimate potential. Therefore, it is important to take into account critical information that impacts performance. This includes customer insights, urbanicity, trade area extent, as well is statistically defined variables on demographics, activity, competition and site and situational characteristics. Collectively these variables help explain variations in historical sales as inputs into the model. We normally describe each of these components shown as building blocks since the learnings and insights gained from each proceeding step feeds into the final development of a site model.
- Since the key objective of a site potential model is to predict consumer behavior, the first step of the process begins with developing a firm understanding of the customer which today cannot be overemphasized. Given the complexity of the marketplace with a changing demographic, multiple channel options and a more rigorous competitive landscape, advanced analytics combined with demographics, segmentation, and available custom data sources on usage behavior for brick and mortar and other channels, cotenency preferences, etc., each play a vital role for the development of a site potential model.
- Urbanicity plays an important role in terms of how far consumers are willing to travel to a store location based on where they work and live, the competitive and demographic landscape of a store trade area and the effects on channel usage behavior and preferences. With that in mind it is important to ensure there is consistency in the store sample by classifying stores based on similar urbanicity classifications that we call urbanicity typing. We have defined 15 homogenous types that are used to classify every BG in terms of employment and population density that can be applied to existing and prospective locations. Typically, depending on sample size, and for reasons mentioned, each urbanicity group would normally require a separate model
- Measuring the trade area extent in terms of how far customers are willing to travel to a site has increased in complexity over the years. This is because the trade area is more greatly influenced by where people shop, work and live relative to a site. Trade areas are also influenced by urbanicity, competitive alternatives and local activity. We will be talking more on this topic which includes how advances in technology and data are used to define a representative trade area for existing and prospective sites.
- Key Metrics is a process of using available data sources, the components of the preceding analysis and advanced analytics to identify key variables and their interactions that explain store sales across the sample that is used for model development. For this analysis it is important to take a multidimensional view of those factors that can influence store sales including demographics, demand, competition, activity, and consumer behavior as inputs into the analysis. It is also very important to not only consider the trade area extent but also the effects of distance on those variables selected in the key metrics analysis.
- Site model development is the culmination of insights learned from each of the preceding steps where the site model weights each of the variables identified in key metrics based on their relative significance. The final model, if properly validated, then serves as a useful tool for site and market planning.
- There is a tendency in the marketplace to refer to the term "site model" as a predictor of sales which is simply not the case. Instead site models should be referred to as a decision support tool that fits the specific business needs of the client which calls for a scalable approach. For example, site screening models, analog models and site scoring models are very good tools that require less data and analytics but serve as an important input for identifying locations based on certain minimum thresholds. These are descriptive models that are not associated with a predictive outcome such as sales or profits. As we move up the food chain we now apply more advanced analytics and additional data sources since the models are now required to predict an outcome for a prospective location.
- With any model there are challenges and it is extremely important to ensure that the proper procedures are established for data collection, best business practices used to build the model, and proper techniques for model validation to ensure the model works properly in the market place. Our many years of experience in building models have identified several reasons why models do not work as expected for site and market planning. One of the contributors in statistical overfitting the model, or adding additional variables to the model until all residuals are explained. Although these models look good on paper, they are only met with disappointment when used in the marketplace. With a growing trend of using artificial intelligence and machine learning techniques in the market place, these "instant models" do have a tendency to overfit and are met with disappointment in actual application. Other reasons for a model not meeting desired expectations has to do with limitations in available data on the customer, sales, site and situational characteristics including externally available data sources. Since the model only sees the variables that are actually used, proper discretion must be used when setting proper expectations on the intended use and application of the model. In some instances, for example, we have ruled against developing a predictive sales model because of limited information and suggested the development of a descriptive model with a favorable outcome. Also, with any model that is developed there should be guidelines established to ensure the models are properly validated.
- One way insights and analytics can be used to support management decision is through the development of Spatial Interaction models that appeal to companies that are seeking to utilize advanced analytics and technology for their decision making. Although SIMs are not for everyone, I would like to share with you what they are, and how they work in the marketplace.
- As the name implies, SPATIAL INTERACTION MODELS combine data and advanced analytics to predict sales for a retail, bank or restaurant location. The modeling process is comprised of the interaction of four key measurable elements. This is one of the more complexed models that can be built and they appeal to the decision maker because they are predictive, interactive, and offer the ability to utilize these models for scenario building. At the risk of oversimplifying the process, there are 4 key elements for the development of a spatial interaction model. The first element is total dollars available to spend for each component area of geography. However, not everyone has the same preference for a retail or restaurant location. Attractiveness refers to a combination of measures which make a location desirable to the consumer. Includes size of the unit, management, signage, visibility, parking, etc. Distance measures the likelihood consumers are willing to travel from home, work, or shop to a given location and can be modeled in many different ways. Another element is Affinity which measures a consumers preference for retail or restaurant location over other options that are available. The customer analysis that was described earlier when we described the modeling process is an important component when measuring affinity. These four measurable elements become the foundation for the development and application of a spatial interaction model which leads us to the next slide where we illustrate the relationship between these elements as an input for estimating sales for a prospective location.
- The first element is total dollars available to spend for each component area of geography which can come from customer transaction data or syndicated research sources on consumer spend for products and services. . However, not everyone has the same preference for a retail or restaurant location.
- Attractiveness is the combination of quantifiable factors that make a location desirable to the consumer. This includes size of unit, merchandise offering, signage, visibility, accessibility, etc.
- Distance measures the likelihood consumers are willing to travel from home, work, or shop to a given location and can be modeled in many different ways.
- Another element used is a measure of affinity which measures customer preference for a retail or restaurant location over other options that are available to the consumer. The need for a detailed and comprehensive customer analysis that we described earlier is an important component when we measure affinity. Collectively, These four measurable elements become the foundation for the development and application of a spatial interaction model which leads us to the next slide where we illustrate the relationship between these elements as an input for estimating sales for a prospective site.
- This is an illustration of the relationship between the four elements described including the modeled variables and how they would appear on a site detail report one of the tools we would use to support a business decision. Here you see a thmatic map with estimated demand for casual dinng, Included is the prospective location, competition and major shopping centers. We also show thematically the demand for casual dining and full service restaurant patronage. The adjoining site detail report shows the variables that contribute to the sales estimate for this location based in their respective weights. Also note the urbancity type assignment and the sales by channel which can be accomplished through the development of additional models.
- Breaking it down further, we show where the demand is located relative to the site and the relative distance from the demand to the location. The model measures the amount if demand that can be effectively drawn to the prosepctive location.
- This is where the effects of competition based on type of competitor and their location realtive to the site and demand plays an important role in the final sales estimate.
- The attractiveness of a prospective site relative to other options is also taken into account in measuring how much demand can be expected to be drawn to a site.
- An finally, affinity which relates back to our knowledge of the customer including their propensity to frequent this location relative to other options. It must be noted that the science behind the development of a spatial interaction modes rests with our ability to measure these impacts and contribution to total sales interactively. One of the main advantages of this approach is that the decision maker can now utilize what if scenarios when evaluating a site placement decision. Size of the unit, measuring the impacts of a new store or competitor, changing the size of a store, etc., all fall within the reach of the development and application of this tool to support the decision making process.