New Product Introduction

How to Determine the Best New Product Launch Strategy Based on Target Consumer Profile

BACKGROUND: New Product Introduction

INGREDIENTS: New Product Introduction

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• Consumer Segmentation Schema
• Store List with exact longitude and latitude
• GIS Tools
• Advanced Data Visualization Software to perform the majority of the analysis and share the results
• (Optional) An advanced analytics services company to perform the analysis
   

RECIPE: New Product Introduction

• Create or obtain a consumer profile of the target product
• Determine the trade area for each store
• Determine store consumer composition
• Calculate the store indexes
• Validate the store indexes
• Select the stores and determine strategy

RESULTS: New Product Introduction

The stores identified by this methodology as target stores, usually obtain sales per store of 300% to 400% higher than the store the methodology identifies as having low potential. Despite the fact that the product is being sold in fewer stores, there are far greater per store efficiencies gained in productivity, and inventory turn levels.

DATASETS AND FILES: New Product Introduction

DETAILED STEPS: New Product Introduction

​Segmentation Schema Overview: A consumer segmentation schema will be leveraged for this case study, and many of the other case studies showcased at www.ConsumerGoodsAnalytics.com. A consumer segmentation schema is a system that classifies each U.S. household into one of many consumer segments (usually 40 to 70), based on characteristics like demographics, lifestyle, psychographics, etc. Please visit our page of preferred vendors for our custom recommendation on consumer segmentation schema providers for your needs.

COMMON QUESTIONS: New Product Introduction

1. 1. Step 1 – Create or Obtain a Consumer Profile of the Target Product

      In this case, we used a special software, provided by the segmentation schema provider, to create the profile. Please reach out to your consumer segmentation schema provider to determine the best way to build or purchase a profile for your specific needs. There are thousands of syndicated profiles available - likely even predefined profiles for your products and your competition, or a new profile can be built if needed. A consumer profile states the index of the target product, for each of the consumer segments in the segmentation schema. As part of this step, a chart that represents the index towards the target product of every segment should be created and used to understand the details of the top and bottom segments.

   2. Step 2 – Determine the Trade Area for Each Store

      A trade area is a geographic area within which a store draws most of its customers. If consumer specific information, with latitude and longitude, is available, the trade areas can be defined very precisely. However, in many cases consumer information is not available or retailers do not share it with manufacturers. In that case, another good approach is to calculate the trade area based on urbanicity or population density. 
      For every store, calculate the number of households within a fixed radius (5 miles in this case). We showcase a simple visual explanation of how the methodology works. In real life, a GIS (Geographical information system) is required, since there are multiple stores and trade areas that usually overlap, therefore the calculations are quite complex. 
      After the number of households per store is calculated, a percentile analysis can be developed to cluster all the stores into several groups. Then a specific trade area radius should be assigned to each group of stores, assigning the smaller radius to the stores with more households, and the larger radius to the stores with less households (radius between 3 to 15 miles were used in this example).

   3. Step 3 – Determine the Store Consumer Composition 

      Using the trade area for each store, the number of households for each segment should be calculated. We showcase a simple visual explanation of how the methodology works. In real life, a GIS (Geographical information system) is required, since there are multiple stores and trade areas that usually overlap, therefore the calculations are quite complex.

   4. Step 4 – Calculate the Store Indexes

      A store potential index represents how good of a match a store is with respect to the target product. For example, in a high indexed store, most of the households belong to the consumer segments that are indexed high for the product profile; the opposite for a low indexed store. The calculation of the index is done by using a weighted average for each segment/store combination, as explained in the calculation table. We also recommend calculating an index for every zip code, calculated the same way. This is not used in the analytical results, but is very helpful in visualizing the indexes in a map, and comparing the index of every store with the indexes of the surrounding zip codes.

   5. Step 5 – Validate the Store Indexes

      After performing calculations, we recommend performing some visual validation of the results, to make sure they make sense based on your best knowledge of the business and geographies. Visualizing the indexes of every zip code, for the whole U.S.A., is a good way to validate the results. You can also zoom-in to review regions or cities that you are familiar with, and make sure that it is the best area for Product A, based on high indexes, and is close to what you expect. Also, comparing the index of every store, with the indexes of the nearby zip codes, is an excellent way to validate the results.

   6. Step 6 –Select the Stores and Determine Strategy

      Now that the analytical work is done, and we have all the indexes and results, it is time to determine the strategy. We recommend creating a scatterplot that includes the store index, as well as the households within 5 miles (calculated in Step 2 above). In some cases, the ACV (All Commodity Value) of each store is available, which states the annual dollar sales of each store, for all goods. If the ACV is available, replace the “households within 5 miles” with the ACVs. 
      The best stores to launch Product A, are the ones with a high index and high population (Priority A in this case). We recommend grouping the stores into several groups, using the criteria of index & population in descending order. You can determine the best launch strategy for you based on these store groupings, your business knowledge, and your budget.

      For example:

      Stores Priority A: Launch the product in an end-cap, with many facings and in-store training personnel. 
      Stores Priority B: Launch the product with a large display and several facings.
      Stores Priority C: Launch the product with a small display and few facings.
      Stores priority D: Do not launch the product at all.
       
   • Q: Why is this approach better than launching in all stores - Blanket Approach?
     • A: 
       If the product is launched in all stores, then the product will perform poorly in the stores with low indexes/population. The retailers usually analyze product productivity for the total chain, therefore the product will be less productive, and run the risk of being eliminated from the assortment chain-wide. By launching only in stores that are good matches for this new product (high indexes/population), there are efficiencies gained in productivity, turns and inventory level. There are fewer stores carrying this product, but these stores are very productive and will likely be more stable in the face of retail space shifts.​​
   • Q: What if I don’t have a consumer profile of the target product?
     • A: 
       There are thousands of syndicated profiles available, likely for your products and your competition. If the profile for your specific product is not available, usually the consumer segmentation schema provider can help you build the profile you need. There are several methodologies to build a consumer profile like:
       -Combining attributes and characteristics from other profiles
       -Doing primary research or surveys
       -Using your own consumer database
       -Using data from syndicated panel providers