Category Archives: Location Intelligence

Location Intelligence – The Power Of Where

I have been talking about the power of seeing data on a mapping tool, and this month I would like to highlight a powerful data visualization tool which can turn your thousand line spreadsheets into powerful data visualization tools.

The aWhere CPG tool has been developed with the Consumer Goods Company in mind, as the next level of reporting for Category Management projects. The Category Management capabilities built into the aWhere CPG mapping tool include the following:

  • See store level sales information such as high performing or low performing stores instantly with multiple colors and store icons.
  • See store data such as out-of-stocks (OOS), segment or SKU sales depicted with map graphs which are a pie chart/bubble chart combination and/or bar charts.
  • Build custom consumer profiles from retailer store level POS data and the US census demographic data included in aWhere CPG.
  • Cluster stores on the basis of demographics, indexes, sales thresholds or any combination of these to identify stores that are over or under performing their sales opportunity.
  • Evaluate store coverage or in-store conditions reporting impact on POS sales by seeing where demo activity, store coverage, plan-o-gram type or in-store merchandising is driving sales (or not).
  • Map together syndicated data such as IRI or Nielsen with retailer store level POS data to see a true picture of product sales and shares by market.

Seeing data from a 1,000 line spreadsheet mapped suddenly allows even the least analytical user to quickly see trends and gaps within the data. We called it making decisions at the speed of sight!

Recently, I was asked to work on a hardware project where I developed store level consumer profiles in order to build demographically based merchandise assortments. My client was having problems with out of stocks. It was clear that brass hardware sold better in certain markets/stores and brushed nickel sold better in other markets/stores. The trick was to predict where the best assortment of price tier, finish and styles would sell by store. This would be especially important for new store openings so that the new store had the best mix of price tier, finishes and styles in the highly stylistic bath hardware category.

Here is how we did it:

  1. We knew the store level POS sales for each SKU stocked, so we segmented the SKU's by price tier, finish, style and other characteristics.
  2. Each of these product characteristics were built into a matrix across all stores, and the sales for each characteristic were indexed to the chain average sales. The result was some stores indexed very high for each characteristic grouping and some stores indexed low.
  3. Each store was scored with a demographic profile using the aWhere CPG multi-layer mapping and layers intersect capabilities. Some stores were blue collar/low style stores and some stores scored as high income/high style stores and other stores scored in between across 4 key demographic profile consumer segments.
  4. The demographic profiles for each characteristic grouping were weighted for the high sales index stores and a profile for each characteristic was built.
  5. We then went to the map and clustered all stores that matched the profile for each product characteristic, and evaluated their sales index for the product characteristics.
  6. The final step was to build store cluster assortment recommendations for each of the store cluster groups.

The results were amazing and they highlighted the consumer driven nature of the bath hardware category. Our project resulted in a 20% reduction of out of stocks and 10% increase in sales by simply having the correct assortment on the shelf.

To learn more about CPG applications of Location Intelligence, please register for our free webinar that will show you how to leverage the power of mapping in your Category Management analyses. If you are in a data poor category, you need to see this.

Monday, November 16th 11am EST

https://www.gotowebinar.com/register/196219058

Wednesday, November 18th 11am EST

https://www.gotowebinar.com/register/569854898

For more information please contact Rick Pensa at bpensa@insightinformation.net or call him at 770-425-4243.

The Power of Where – Location Intelligence

Have you ever run a Google map from your location to the nearest Starbucks, and listed the driving directions? Well you just ran some location intelligence. Location Intelligence is the visualization of relationships between facts and data points about a specific location or set of locations in order to identify opportunities take action and measure the results of those actions. When you see 1,000 store locations and the specific data such as Out of Stocks (OOS), existence of a merchandising rack, or Point of Sale (POS) sales data in a spreadsheet it is difficult for the normal person to perceive gaps, regional trends, and opportunities/threats. That is where Location Intelligence comes in; when you can see those same 1,000 store locations on a map, the regional trends, relationships between OOS and POS sales, and opportunities/threats become obvious.

The power of location intelligence has come to category management by leveraging location information your company is already collecting to learn the why behind the what. When you can see threats and opportunities before your competitor sees them, you can act before they react. You can quickly see which stores are underperforming or over performing and see the underlying influences in a manner that would be nearly impossible in a spreadsheet. 

Figure #1 Sales Data in Spreadsheets

AWhere data in spreadsheets

Figure #2 Performance by Store Location

AWhere Performance by Store Location  

The human eye can see visual patterns 65,000 times faster on a picture, like a map, than in a tabular form such as a spreadsheet. The data in Figure #1 is difficult to interpret. The map displayed in Figure #2, visually represents the data on the spread sheets, so that the relationships become apparent. Here, the red stores are underperformers, the yellow stores are average performers, and the green stores are high performers. The high performing stores can be easily and quickly viewed in relationship to their location and the associated demographics. While location intelligence provides excellent data visualization capabilities; there is so much more that can be applied to category management.

Location intelligence allows users to see all the data points mapped to a particular address, like a store location, trading area, or region, in graphic representations such as pie charts, bubble charts, and bar charts; all displayed on a map. By using mapping representations, the user can perform all types of category management analyses, such as distribution analysis, out of stock analysis, regional differences in POS sales, and cluster analyses of stores that meet certain criteria within the data. A multi-layer mapping tool, allows the user to link together map layers to produce a consumer segmentation analysis that would be difficult in an Excel spreadsheet.

An example would be a store trading area layer linked to a block group layer containing demographics to develop store trading area demographic profiles. In Figure #3, a store trading area was developed for each of the stores in the mapping tool (i.e. a 2 mile radius around the 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. In this example, we will look at the racial profiles in each trading area.

Figure #3 Demographics by Store Location

In Figure #2, we saw that the stores in south side of Chicago were high performing stores for our product. In Figure #3, we are looking at demographic profiles for each store. Compared to other parts of Chicago, these stores have a higher incidence of African American consumers in their trading area. This gives insight into who's buying our product without extensive consumer research. This information is not easily visualized from a spreadsheet. This is the power of WHERE!

Location intelligence is not a difficult discipline to bring into your organization, but certain elements need to be in place to be successful in you implementation. A successful location intelligence implementation will require:

  • Tools – Select the right tool(s) to facilitate the power of location intelligence within your organization.
  • Data – Identify, organize, source, and structure geographic data sets within your organization.
  • Data Visualization – Craft the best mapping tools and analytics to reveal the most about your data.
  • Human Resources – Identify and train the right people with analytic skills to advance and evangelize the organization for location intelligence.

Insight, Information & Consulting Services, Inc. can help your organization craft a location intelligence strategy, identify data sources, organize your data, and develop hard-hitting, information packed data visualization mapping tools. There are many GIS, location intelligence tools on the market, and they run the gamut from basic to extremely complex. A basic tool such as Map Point by Microsoft, is a good starter tool for visualizing location intelligence data, but it lacks multilayer mapping and important location intelligence analytics tools found in the AWhere CPG location intelligence software. There are many professional GIS tools that require individuals with highly skilled GIS training to operate. The GIS tool I am using for examples in this article, come from AWhere CPG, from the AWhere Company, which delivers multilayer mapping capabilities, sophisticated geo-demographic analytics tools, and it has been designed and optimized to facilitate category management within the CPG industry. For more information on Location Intelligence or to sign up for our webinar on Location Intelligence, coming up on October 1st, 2009, please email us at www.insightinformation.net.

Data Visualization – Location Intelligence

This is a new web log to address the exciting new concept of visualizing business intelligence data in easy to use tools even on a map.  Nearly 80% of business data is location driven, and it only makes sense to see data on the familiar metaphor of a map.  The focus of this "blog" is to deliver insight into tools that deliver information from your data.  We are pioneering the use of mapping tools to view information in the Category Management process in the Consumer Package Goods (CPG) industry.

We will publish tips and analytic techniques used in Excel, Access and using spatial analysis tools to expand Category Management data visualization with the use of maps.  The tool I use is the AWhere SIS spatial analysis suite of tools to produce output such as:

  1. Shopping mission driven trade areas
  2. Loyalty card shopper analysis
  3. Map causal impact on POS sales
  4. Develop consumer profiles using POS data

The following map shows how store coverage data collectedon handhelds can be mapped to show impact on POS sales at the store level:

Mapjpeg_better

AWhere For Retail Link Data

Ever wish you could see Wal-Mart Retail Link store level data on a map?  The AWhere folks have developed a "Lite" version of AWhere SIS that only links to spreadsheet data.  We are in the process of developing Excel templates that are populated by Retail Link output, cleaned up with a macro and mapped in the AWhere SIS "Lite" tool.  The whole process is a clean end to end turnkey output of Retail Link data for your stores mapping Out Of Stocks, distribution, POS Sales, Mark Ups and basket information at the store level and aggregated by Wal-Mart DC.

Does anyone have any ideas or wishlist output that I can build into the templates?

Location Intelligence

This is a new web log to address the exciting new concept of visualizing business intelligence data on a map.  Nearly 80% of business data is location driven, and it only makes sense to see data on the familiar metphor of a map.  The focus of this "blog" is the use of mapping tools in the Category Management process in the Consumer Package Goods (CPG) industry.

We will publish tips and analytic techniques using spatial analysis tools to expand Category Management data visualization with the use of maps.  The tool I use is the AWhere SIS spatial analysis suite of tools to produce output such as:

  1. Shopping mission driven trade areas
  2. Loyalty card shopper analysis
  3. Map causal impact on POS sales
  4. Develop consumer profiles using POS data

The following map shows how store coverage data collect on handhelds can be mapped to show impact on POS sales at the store level:

Mapjpeg_better