Retail

5 Reasons why Syndicated Data Struggle to Deliver Granular Insights

By Lea El Hage • December 05, 2017

The retail landscape is undergoing a dramatic transformation, signaling the start of the analytics arms race. The conventional weapons of the past (i.e., syndicated data, targeted loyalty mailings, etc.) are simply not going to be as effective in this new battle against Amazon and new, non-conventional brick-and-mortar formats.  For convenience, grocery, and discount, fresh food offerings and in-store experiences are becoming a core differentiator as the center store moves online.

A primary problem is that most grocery, drug, mass, and other retailers and their suppliers rely on slow, aggregated legacy syndicated data. Syndicated data has five major problems that limit growth and the ability to deliver contextually relevant, localized shopping experiences:

  1. 1. Lack of granularity limits measurement & localization:  Because of their lack of granularity, legacy syndicated insights have become insufficient to activate growth solutions with localized and/or personalized strategies. Brands cannot measure displays, media, and other activities if they are limited to weekly sales and volume figures at the lowest level that is delivered to them (8-30 days post purchase). Full store basket-level data, enabled by SwiftIQ, a modern retail analytics software, allows for complete store coverage and basket-level analysis like cross purchase correlations, basket size, seasonality, and dayparts to know when to run and optimize promotional offers.
  1. 2. Sample data is less accurate and actionable: Syndicated data is relevant for forecasting national market shares. It is highly inaccurate and in other contexts and should not be used for tactical execution.
  2. 3. Unequipped for Foodservice: The most profitable and important area of emphasis is food service and the grocery perimeter. Syndicated data is built off UPC data whereas foodservice data is largely PLU-based and unavailable from syndicated providers. SwiftIQ, which captures every UPC and PLU, brings this insight to light.
  3. 4. Closed systems: Syndicated data is built on closed systems, which limit the retailer and vendor collaboration, speed, and agility:  A lack of powerful APIs limits the ability to embed granular retail data into machine learning forecast and other third-party systems.
  4. 5. Speed to insights: SwiftIQ can process basket-level data on-demand can also handle real time data answers whereas syndicated data is limited to measuring the previous 15 days’ transactions. Notably processing basket-level data for a grocer with large baskets takes 8-15x the computer power vs. even high volume, low basket size retailers like convenience stores and quick service restaurants.

To compete in this new world, it’s more important than ever that retailers adopt a data-first, analytics-centric approach across the board, from supply chain, to store, to online. Until retailers offer a seamless, convenient, and compelling shopping experiences, they will be behind.

SwiftIQ brings this new foundation to help retailers, brands and suppliers transform their retail execution through a software that powers decision automation, retail localization and vendor collaboration to create efficiencies and compelling customer experiences in-store and online. Its state-of-the-art retail analytics platform, the Retail Execution Cloud and its artificial intelligence applications, empower retailers to maximize the value of their data and drive profitability by processing hundreds of billions of records of granular insights from transaction, customer, and shipment data.

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About SwiftIQ 

SwiftIQ provides retailers, brands, and distributors with a leading, on-demand insights and decisioning platform from the most highly granular, real-time store, customer, and supply chain data to create compelling shopping experiences and maximize profitability. SwiftIQ uses high scale on-demand processing and artificial intelligence to convert billions of records of shipment, customer, and transaction data into prescriptive and predictive analytics to optimize store-level execution, such as category management initiatives, promotions, customer experiences, merchandising actions and pricing strategies. SwiftIQ analyzes over $100 billion of transaction-log level data serving convenience, grocery, drug and other retailer formats. Named a 2017 CIO Top 25 Artificial Intelligence Solution Provider, 2016 Vendor of the Year by RaceTrac and 2016 Gartner Cool Vendor for CPGs, SwiftIQ has also been recognized by McKinsey & Company, Forrester, Forbes, NACS and Progressive Grocer for its achievements.

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