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:
- 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.
- 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.
- 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.
- The demographic profiles for each characteristic grouping were weighted for the high sales index stores and a profile for each characteristic was built.
- 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.
- 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
Wednesday, November 18th 11am EST
For more information please contact Rick Pensa at firstname.lastname@example.org or call him at 770-425-4243.