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Location Intelligence The Power Of Where
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Use Location Intelligence In Your Category Management Process

Use Location Intelligence In Your Category Management Process

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  • Simply stated, Location Intelligence is the representation of business data on a map.
  • You might be thinking, “That’s great, but what is the relevance to the CPG industry how can I leverage Location Intelligence in consumer products marketing. ?”
  • Location Intelligence can give your company better analytic insight. Understanding your category becomes easier. The gaps and trends become obvious. You can identify threats and opportunities. With AWhere Location Intelligence software, you cam associate multiple layers of data to visualize the relationships. You can harness the power of index analytics to see the texture in the data. You can perform cluster stores by metric, index, profile, or geography and really understand your accounts at a store level. You can develop a route penetration strategy – are your drivers selling every high opportunity store? See which stores are in high potential zip codes and see their volume index. Know where to take action.
  • Location Intelligence can give your company better analytic insight. Understanding your category becomes easier. The gaps and trends become obvious. You can identify threats and opportunities. With AWhere Location Intelligence software, you cam associate multiple layers of data to visualize the relationships. You can harness the power of index analytics to see the texture in the data. You can perform cluster stores by metric, index, profile, or geography and really understand your accounts at a store level. You can develop a route penetration strategy – are your drivers selling every high opportunity store? See which stores are in high potential zip codes and see their volume index. Know where to take action.
  • Location Intelligence can give your company better analytic insight. Understanding your category becomes easier. The gaps and trends become obvious. You can identify threats and opportunities. With AWhere Location Intelligence software, you cam associate multiple layers of data to visualize the relationships. You can harness the power of index analytics to see the texture in the data. You can perform cluster stores by metric, index, profile, or geography and really understand your accounts at a store level. You can develop a route penetration strategy – are your drivers selling every high opportunity store? See which stores are in high potential zip codes and see their volume index. Know where to take action.
  • Location Intelligence can give your company better analytic insight. Understanding your category becomes easier. The gaps and trends become obvious. You can identify threats and opportunities. With AWhere Location Intelligence software, you cam associate multiple layers of data to visualize the relationships. You can harness the power of index analytics to see the texture in the data. You can perform cluster stores by metric, index, profile, or geography and really understand your accounts at a store level. You can develop a route penetration strategy – are your drivers selling every high opportunity store? See which stores are in high potential zip codes and see their volume index. Know where to take action.
  • Location Intelligence can give your company better analytic insight. Understanding your category becomes easier. The gaps and trends become obvious. You can identify threats and opportunities. With AWhere Location Intelligence software, you cam associate multiple layers of data to visualize the relationships. You can harness the power of index analytics to see the texture in the data. You can perform cluster stores by metric, index, profile, or geography and really understand your accounts at a store level. You can develop a route penetration strategy – are your drivers selling every high opportunity store? See which stores are in high potential zip codes and see their volume index. Know where to take action.
  • Now, let’s talk about the Power of Where. What are the specific capabilities of Location Intelligence? Location Intelligence software, such as the Awhere tool, uses available information to develop a geo-coded data base of demographic statistics. Location intelligence allows users to see all the data points mapped to a particular address, like a store location, trading area, or region. Then this tool presents the information graphically, using pie charts, bubbles, bar charts, etc., all displayed on a map. By using map representations, all types of category management analysis can be performed. You can review distribution, out of stocks, sales by region, point of sale merchandising, clusters of stores that meet certain criterial, all at the click of a button, and voila! The data is graphically displayed.
  • Today, spreadsheets drive category management. The human eye can see visual patterns 65,000 times faster on a picture, such as a map, than in tabular form, such as a spreadsheet. When you look at a spreadsheet with 1000 store locations with specific data, such as out of stocks, point of sale merchandising, merchandising rack locations, or even sales growth versus prior year, it’s difficult to assimilate all the information your have available.
  • You can take in large amounts of information visually. In this case, you can visualize sales across 3400 stores. It’s easy to see that sales are strong in the southeast and along the east coast for this product. When you see the same data on a map, relationships become apparent.
  • You can hone in on opportunities by spotting store condition trends, knowing your distribution voids, seeing the presence of merchandising racks and POP, seeing volume trends compared to merchandising efforts, asking “Is my merchandising working? Are my demos in the right stores? Is my delivery frequency right for my stores? How many stores carry all 3 sku’s?”
  • On this map, these are store locations have been ranked as “high performing” (indexing 100+ to the national average sales-shown in green), “average performing” (indexing at 100 to national average-shown in yellow), and “under performing” (indexing below 100 to national average or below-shown in red). You can see clearly that a group of high performaning stores are concentrated in the area just south of Chicago.
  • Location Intelligence tells you where so you can see why it’s happening. Distribution? Demographics? Weather? Competition? A multi-layered mapping tool lets you link the map layers to produce consumer segmentations that would be difficult to see in an Excel spreadsheet. We saw that the high performing stores were concentrated just south of Chicago. Now, we can drill down and visually understand the demographics of these stores. We can see that relative to Chicago, these stores have a higher number of African Americans living in the area. This gives us insight into our consumer, that we may not have understood from looking at a spreadsheet or without performing consumer research. Demographic comparisons can be visually made and insight into the product’s consumer is gained. This is the power of where!
  • With a Location Intelligence tool, you can analyze specific retailers, such as Wal-Mart, to understand their strengths and weaknesses. You can see their business visually, across the nation in one glance. From this map, you can easily determine that Wal-Mart is strong in the eastern half of the United States.
  • You can perform analysis at a glance and instantly understand a retailer market position. Here we can see with a click of a button, that Wal-Mart has 3417 stores in the USA, each selling and average of $3625 per period of your product, with a total annual retail sales dollars of $12.4 million dollars in the selected period. Let’s say that you know that your product consumers tend to be Hispanic. Let’s add some store trading area demographic data.
  • With Location Intelligence, you can select stores that meet a certain criterion. Let’s say you know your product indexes higher to Hispanics. Let’s figure out some opportunities. Here we can see (in the click of a button), that 607 Wal-Marts (18% of total stores) index strongly above national average for Hispanic shoppers. These are the stores you should be focusing our promotional efforts on. You can use indexing to compare relative strength of markets, in this case, we are looking for strong Hispanic markets, shown here in green bubbles. It’s no surprise that many of the southern states, including Florida, Texas, New Mexico, and California skew Hispanic. What might be a hidden opportunity is the concentration of Hispanics in northern Georgia, North Carolina, and Nebraska. Overall, these concentrated Hispanic stores account of 18% of all Wal-Mart sales, but 23% of your product sales.
  • With Location Intelligence you can cluster stores by metric. Clusters of stores are developed to apply a common assortment, merchandising, and/or coverage strategy. Stores are clustered based on demographics, sales rates, store characteristic (square footage, ACV, stores with merchandiser racks), or some combination of metrics. Here our mission is to cluster stores to isolate stores with a distribution void of a particular top selling sku. Now we can take action.
  • Using Location Intelligence
  • Let’s look at another chain, Publix, headquartered in Lakeland, Florida. It has a concentration of stores in it’s home state. You know your product indexes higher to senior citizens. The green bubbles represent the store with an index of 150 or above to the national average of that age group. You can see that of the selected area, only 25% of there locations, but they represent 46% of the total sales. Publix should develop programs that target and reward seniors. Seniors are their bread and butter.
  • You can use Location Intelligence to create a proximity query and know the impact of competition on your business. With the widespread expansion of Wal-Marts across the US and internationally, many retailers are asking the question of “what’s the impact on my business?” By creating a proximity query, you can understand the impact of local competition. Here the question is posed, what is the impact of a competitor in the area. Here, the question is asked by Publix about Wal-Mart. You can see that when a Publix has a Wal-Mart located within one mile of the store, sales are down 43 points for this product.
  • With Location Intelligence you can cluster stores by metric. Clusters of stores are developed to apply a common assortment, merchandising, and/or coverage strategy. Stores are clustered based on demographics, sales rates, store characteristic (square footage, ACV, stores with merchandiser racks), or some combination of metrics. Here our mission is to cluster stores to isolate stores with a distribution void of a particular top selling sku. Now we can take action.
  • With Location Intelligence you can cluster stores by metric. Clusters of stores are developed to apply a common assortment, merchandising, and/or coverage strategy. Stores are clustered based on demographics, sales rates, store characteristic (square footage, ACV, stores with merchandiser racks), or some combination of metrics. Here our mission is to cluster stores to isolate stores with a distribution void of a particular top selling sku. Now we can take action.
  • Here we are looking at Category Development Indeses by area of the country in tabular format.
  • Using Location Intelligence, we can look at the same data and it comes to life. Clearly the category is very strong in the northeast and weaker in the south. We’d make our first investments in distribution where the category is stronger.
  • You can visually compare your brand sales to the category development. Are you strong where you should be strong, or did you just spot an opportunity?
  • Add business metrics and now you really have Location Intelligence.
  • A lot of useful information is collected by our government. The Census Bureau collects demographic information, such as ethnicity/race, age, income, education, and household characteristics like the number of children under 18 years, etc. Another source of valuable information is the Mux Credit Bureau which collects income information. The National Weather Service collects all sorts of weather data, and in fact were the original developers of the geo-coding system.
  • There is a lot of information you can collect and transform into Location Intelligence in your own company. It can all be mapped to store locations. You can gather POS Sales, merchandizing efforts, displays, POS materials, spoils and shrinkage, product distribution, new products cut in, out of stocks, category segment sales, stores size, store conditions, sales call activity, rack placement by store, and your consumer addresses or zip codes gained through loyalty programs.
  • Ok, I think I have demonstrated that Location Intelligence is a powerful tool. But, you might still be wondering, how exactly does it work.
  • The first step is to get geo-codes for your store locations. This means you must correlate store locations with lattitude and longetude coordinates. You will need complete store addresses including zip codes.
  • Input your store location data into the GPS Vsualizer’s Easy Batch Geocoder to convert multiple addresses to GPS coordinates.
  • Insert address list and start geo-coding.
  • You need to identify a common field; store number, TDLinx number, internal number, etc.
  • All types of demographic data is indexed to the national average for analysis.
  • The FIPS code is the common field that relates the block number to all the other variables.
  • Once your store locations are geo-coded, you need to develop customized store trading areas. A store trading layer, is linked to a block group layer containing demographics, to develop store trading area with demographic profiles. A store trading area is developed for each of the stores in the mapping tool (let‘s assume a 2 mile radius around the store address). Then the underlying layer, containing consumer demographics was connected with the trading area layer to produce a demographic profile for each store. The process of assigning store demographic profiles can be quickly and easily accomplished for thousands of stores; thus allowing a user to identify and target stores that meet a certain demographic profile. For example, a user might want to identify all stores that have a high index of families with two or more children under the age of 18. Stores matching that profile will be highlighted on the map and easily recognized, identified, and targeted for promotional execution.
  • Using Location Intelligence, you can not only customize your store’s trading area, but also you can assign multiple trading areas for each store based on consumer shopping missions. For example the trading area (distance a consumer will drive) for a soda is relatively small, let’s say 2 miles. However, for a larger ticket item like a lawn mower, a consumer might drive further, seeking out the best deal, let’s say 10 miles.

Transcript

  • 1. Location Intelligence
    Introduction
    1
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 2. Rick PensaCEO: Insight, Information, & Consulting Services, Inc.
    38 years in the CPG industry
    Sales
    Sales Management
    Trade Promotion Management
    Category Management
    Consumer Segmentation
    & Targeting
    Location Intelligence
    Data visualization
    2
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 3. Objectives
    Define Location Intelligence
    Demonstrate how Location Intelligence can bring added value to the Category Management process
    Questions & answers
    3
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 4. Have you ever looked up a Starbucks on Google Maps?
    4
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 5. Ever listed driving directionsto get there?
    You’ve been using Location Intelligence!
    2009 Copyright: Insight Information & Consulting Services, Inc.
    5
  • 6. Location Intelligence
    Location Intelligence is the representation of business data on a map.
    6
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 7. Consumer Products Leaders Find Gold In Their Demand Data:Next Gen BI Uncovers It
    “They are leveraging new insights gleaned from next-generation business intelligence (BI) tools to devise strategies to meet today’s pressing needs while also laying the groundwork to respond to tomorrow’s challenges.”
    Source: Consumer Goods Technology
    2009 Copyright: Insight Information & Consulting Services, Inc.
    7
  • 8. Poll:
    Have you ever used a mapping tool?
    2009 Copyright: Insight Information & Consulting Services, Inc.
    8
  • 9. Location Intelligence
    CPG Industry Applications
    9
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 10. Why is Location Intelligence Important to CPG?
    Stores performance…
    • Have you ever struggled to understand why seemingly equal stores perform differently?
    10
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 11. Why is Location Intelligence Important to CPG?
    Store cluster management…
    • Is your retail customer asking you how to cluster stores in a more effective way?
    11
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 12. Why is Location Intelligence Important to CPG?
    Consumer profiles…
    • You know your category segments appeal to different consumers. Do you know which stores?
    12
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 13. Why is Location Intelligence Important to CPG?
    Seeing your store level data…
    • When you see your chain’s store data on a spread sheet do you come away scratching your head?
    13
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 14. Why is Location Intelligence Important to CPG?
    Store causal information…
    • Do you know which stores should get key merchandising racks, demos and coverage?
    14
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 15. Poll:
    Are you concerned that you might be missing key insights in your store level data?
    2009 Copyright: Insight Information & Consulting Services, Inc.
    15
  • 16. Location Intelligence
    The Power of Where
    16
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 17. The Power of Where
    Today, spread sheets drive category management.
    17
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 18. The Power of Where
    Imagine visualizing sales across 3,000 stores.
    18
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 19. The Power of Where
    How many stores carry all 3 sku’s?
    19
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 20. The Power of Where
    The human eye processes visual data
    65,000 times faster than in tabular form.
    20
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 21. The Power of Where
    Caucasian
    Location Intelligence software correlates
    store sales by location with associated
    geo-point demographic information.
    21
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 22. The Power of Where
    Wal-Mart Store Locations
    Location Intelligence gives
    the ability to look at major retailers.
    22
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 23. The Power of Where
    Instantly understand retail’s store metrics.
    23
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 24. The Power of Where
    Drill down to specifics, looking for opportunities.
    24
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 25. The Power of WhereSales & Facings By Size Segment
    Segment Facings
    Segment Sales
    Are facings correctly assigned?
    25
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 26. Poll:
    Do you use store clustering analysis to identify opportunities?
    2009 Copyright: Insight Information & Consulting Services, Inc.
    26
  • 27. The Power of Where
    Cluster Analysis
    Cluster analysis is a grouping of stores by a common trait.
    In this example, stores are clustered by distribution voids.
    27
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 28. The Power of Where
    “Grab” an area and hone in.
    28
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 29. The Power of Where
    Drill down to specifics, looking for opportunities.
    29
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 30. The Power of Where
    Create a Proximity Query: know the impact of competition.
    30
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 31. The Power of Where
    Seasonal Demand Forecasting
    Warm (90-100) in Aug 2009
    Hot (100+) in Aug 2009
    Compare historical sales data against historical causal data.
    In this case, we look at weather to perform demand forecast planning with store-level precision.
    31
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 32. The Power of Where
    Causal Demand Forecasting
    Correlate Incidents Of Influenza Doctor Visits
    32
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 33. The Power of Where
    CDI by Area-Tabular Format
    33
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 34. The Power of Where
    Visual CDI by Area
    34
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 35. The Power of Where
    CDI
    Very High Index
    CDI vs. Brand Sales By Area
    BDI n
    BDI x
    Very Low Index
    35
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 36. Location Intelligence
    Data Sources
    36
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 37. Business drivers
    • Census or weather data
    Region boundaries
    • States, counties, zips
    • 38. Sales territories
    Country boundaries
    • Stand ard “shape” files
    • 39. Built-in or Internet
    Road maps
    • e.g. MS Virtual Earth
    Data Sources
    Map Multiple Layers from Multiple Sources
    Business metrics
    • Spreadsheets or DB
    37
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 40. Data Sources
    Broker Data:
    Sales calls, POS  Shipments, Voids, OOS, Merchandising Compliance,
    Demos & Racks
    POS Data:
    Wal-Mart RTL,
    Publix, Kroger
    More…
    Syndicated Data:
    Nielsen, IRI, SPINS, Panel
    Government & Credit Industry Data:
    Corporate
    Data:
    US Census (proj 2009)
    Mux Credit Bureau
    National Weather Service
    Factory Shipments
    38
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 41. Data Sources
    What can you map to a store location?
    39
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 42. Location Intelligence
    How It Works
    40
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 43. How It Works
    • Prepare store point data
    • 44. Organize retailer store lists with addresses (include zip codes)
    • 45. TDLinx
    • 46. Lead generation sites
    • 47. Account store lists
    • 48. Geo-Code store points
    41
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 49. How It Works:
    Geo-Codes
    Geo-Codes: Map coordinates in latitude and longitude
    • Free Source of Geo-Codes:
    www. batchgeocode. com
    • Paid Source of Geo-Codes:
    www. geocode. com
    42
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 50. How It Works:
    Geo Codes: www. batchgeocode. com
    43
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 51. How It Works:
    Geo-Codes: www. batchgeocode. com
    44
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 52. How It Works
    Identify a common field, like store number,
    to link address and geo-code.
    45
    Longitude
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 53. How It Works:
    How does demographic information get assigned to a postal code and address?
    Federal Information Processing Standards (FIPS) codes are issued by the National Institute of Standards and Technology (NIST) that identify each block area.
    For Example :
    XX XXX XXXXX
    13 113 13113
    STATE COUNTY BLOCK GROUP
    NC MACON TALLEY MILL CREEK
    46
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 54. How It Works:
    Raw Demographic Counts
    Block#/(%) Total 13113 U.S.
    13113 = Caucasian – 3,000/ (46%) (74%)
    13113 = Afr. American – 1,500/ (23%) (12%)
    13113 = Hispanic – 2,000/ (31%) (10%)
    13113 = Total Pop – 6,500/ (100%)
    Index Values:
    Caucisian 62 (46/74)
    African American 191 (23/12)
    Hispanic 310 (31/10)
    47
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 55. How It Works
    48
    FIPS Code Is The Common Field
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 56. How It WorksProximity Based Store Trade Areas
    Store Trading Areas
    49
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 57. How It Works
    Customized Store Trading Areas
    50
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 58. How It WorksACV Based Store Trading Areas
    Block group dollar purchases
    Store All Commodity Volume (ACV)
    Find block groups around each store that account for 80% of store ACV
    Collapse block groups into store point
    Store demographics built from selected block groups
    51
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 59. Location Intelligence
    See Your Data At The Speed Of Sight
    Capitalize onThe Power of Where in the Category Management Process
    Store Performance
    Store Cluster Management
    Consumer Segmentation
    Store Level Data
    Gain valuable insights that give you the edge
    52
  • 60. 53
    Questions
    2009 Copyright: Insight Information & Consulting Services, Inc.
  • 61. See Your Data At The Speed Of Sight!
    Thank You…
    For more information on Location Intelligence for Category Management please contact Rick Pensa.
    As a thank you for your participation, Insight, Information & Consulting Services, Inc. is offering a complimentary 1 hour consultation on Location Intelligence.
    Rick Pensa 770-425-4243 bpensa@insightinformation.net
    2009 Copyright: Insight Information & Consulting Services, Inc.
    54