Using Location Data

Review a sample use case of how a big box supplier leveraged Narrative to buy location & purchase history data to determine competitor & loyal shoppers

Problem

BigBoxSupplier is opening new stores on the West Coast. In preparation, they want to begin marketing efforts in the new regions and have turned to Narrative to help them better understand who their potential new customers are. 

Through Narrative, they are looking to accomplish the following.

 

Data Needs

  • Identify shoppers at their 3 main competitor stores in California, Nevada, and Oregon in the past 90 days
  • Build loyal (4+) versus non-loyal (1-2) competitor shopper lists based on visit frequency
  • Understand household cleaner, soda/beverage, and home improvement purchase history against all loyal shoppers

 

Narrative Solution

    • Location Data -- Using competitor store datasets (lat/longs) with a refined polygon, BigBoxSupplier can collect MAIDs seen in-store at their competitor’s West Coast locations
    • Frequency -- To understand shopper loyalty, Narrative allows buyers to choose the frequency (once per, uncapped, etc.) of when to buy a data point. If left uncapped, BBS will buy all instances of a MAID seen at a store location, allowing them to understand which MAIDs visited just once, twice or 4+ times in their recency window
    • Purchase History -- BBS buys specific item category purchase history data to enhance their list of loyal shoppers 

     

    Summary

    Using Narrative’s Acquire Platform, BigBoxSupplier is able to - without any data of their own - purchase MAID lists of competitor shoppers and determine loyalty to specific stores based on visit frequency. To gather more information on shoppers, purchase history data can be layered on top of device lists. This can be in the form of specific items (disinfectant wipes), item category (household cleaners), brand, and even vendor in some instances, depending on the data available. 

    BigBoxSupplier can now execute marketing efforts to different pools of users - loyal v non-loyal, purchase history categories - to more effectively reach the right customers with the right messages.