Big Data

FMCG Retailers' Analytics Improve with Supplier's 1st Party Data

By Stacy Klimkowski • December 10, 2015

Fast moving consumer goods retailers and their suppliers typically follow category management practices to make decisions on what items to carry in their stores. According to the Category Management Association, the formal definition of Category Management is “a retailer-supplier process of managing categories as strategic business units, producing enhanced business results focused on delivering consumer value.” While large merchants like Wal-Mart, Kroger, Meijer, and Target have been on the leading edge of supplier data collaboration, many retailers manage their business using pure intuition, basic intelligence solutions or inefficient, legacy syndicated data platforms.

Syndicated data is traditionally one way, with the supplier buying the retailer’s daily transaction data from a 3rd party vendor.  On the other hand, collaboration portals can be more granular (basket-level data), near real-time (next day), and allow both companies to combine their data to unlock deeper insights to solve many merchandising, marketing and supply chain problems. Although retailers own transaction data, suppliers have a wealth of information on product attributes, location demographics and cross retail format trends that can enhance the retailer’s data.



Advanced analytics from combined data enables mutual benefits that lead to optimized promotions, strategic partnerships, operational efficiencies to reduce costs, and higher ROIs.  Retailers that do not share data will find certain competitive disadvantages while suppliers that recognize the value of their own data sharing practices can help enrich retailers’ data to reinvent category management and retail execution. According to the GMA online retailer-direct data report, there are over 30 benefits from 1st party retailer data sharing.  Some of the most valuable include:

  • Minimizing inventory and out of stocks while enhancing shelf availability
  • Strengthened sales forecast accuracy
  • Advanced price and promotion optimizations
  • More accurate item/SKU rationalization
  • Lessened inefficiencies and cost of retail execution
  • More personalized and localized customer experiences


Undoubtedly, the proliferation of data processing frameworks that can analyze billions of records in seconds and the diminishing costs of cloud storage have facilitated data sharing.  However, building a vendor portal is timely and costly. SwiftIQ has broken down many  of the major barriers (data blending, lag time to market, reporting that allows for actionable insights) to empower anyone with on-demand mobile access to be a retail data scientist.


Overview of SwiftIQ:

SwiftIQ uses high scale data processing and machine learning to deliver contextually relevant insights and digital experiences for retailers and brands. Its platform unifies and analyzes data primarily from in-store transactions as well as online behavior and third party sources to predict and inform category captains, shopper marketing, assortment, supply chain and content delivery decisions. SwiftIQ’s unique ability to process billions of basket-level transaction records in near real-time and convert that into on-demand mobile visualizations, dayparts, affinities and attribution fosters a new level of retailer/supplier collaboration and innovation.


Since launching its category captain and consumer behavioral analytics platforms in late 2014, SwiftIQ now analyzes over $60 billion in offline, receipt-level point of sale data. The company serves 5 of the global leading category suppliers and several billion-dollar retailers. SwiftIQ, named a Top Innovator twice by DataWeek, has also been recognized by Forrester, Forbes, NACS, ProgressiveGrocer and ComputerWorld for its achievements. 


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