Retail

Basket-Level Insights is the New Black

By Lea El Hage • February 02, 2017

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We constantly read, hear, and talk about the need of implementing omni-channel strategies to create a bridge between the physical and online store, that understanding consumer behavior is key, that brick-and-mortars are now more than ever, the most important channel to focus on, and so on.

Brands and retail companies claim to have found the remedy to answer these challenges by the advent of a game changer, Big Data. This infamous term is everywhere, used by everyone, all the time for numerous hypothetical use cases. Companies have already stepped into war to claim the throne of King of data – with a strategy to possess robust, infinite, and audacious data sets, the ultimate weapon to win the war. But on its own, the term Big Data means nothing and if companies cannot explain the data or insight and resort to “big” it should be a concern.

But what if the reality is other than that. What if, as Harvard Business Review contributor M. Wessel stated in his post You don’t need  Big Data – You Need the Right Data, “the reality is that their relentless focus on the importance of big data is often misleading […] and that the size of data is not the most critical factor, but having the right data is”?

 

So, what is the right data for retail success?

Because of their lack of granularity and accuracy, legacy syndicated market share insights are poor and obsolete to activate growth solutions with localized and/or personalized strategies. Basket-level transaction data is complete and undeniably, the ultimate source of shopper and location data to improve store-level sales, inventory, and category management for the following reasons:

  • Deeper data granularity: Syndicated data platforms typically use modeled/theoretical data, collect basic sales information only from items that have UPCs, are aggregated to a week ending number and are typically based on a limited sample of stores. Hence, they are not reliable for more than high level directional insights, but nothing tactical. In contrast, full store basket-level transaction data allows for complete store coverage and basket-level analyses like cross purchase correlations, basket size, seasonality, and dayparts to know when to run or target offers and they analyze non UPC items like foodservice which are critical for retailer profitability
  • Speed to insights: Systems that 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-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.
  • Type of insights: The key to growth lies in the ability to generate insights that are not simply descriptive or explanatory, which is what syndicated data provides, but predictive and prescriptive. Basket-level data does not only identify the problem but enables recommendation and action plans to overcome it and moreover, anticipate it. With basket-level insights, retailers and brands can build profitable bundles and measure the ROI on promotions, displays and media based on full-store impact, not just sales of the item that was promoted
  • Profitability: Basket-level data enables profitable bundle and promotion analyses versus what is possible with just sales data as opposed to syndicated data that measure estimated ROI

For e-commerce retailer, basket-data is also critical. Walmart acquired Jet which built some impressive basket-level offers in real-time as customers placed data in their cart lowering the customer’s cost if they bought more due to shipping economies.

Another significant source to harness the right data is from loyalty cards. The outcome of shifting from a product-centric mindset to customer-centricity is putting the customer at the heart of retail occupations and strategies, which is why understanding her purchase behavior, personal preferences, needs, and expectations by demographic type have become inevitable. Loyalty data is a subset of basket-data and can help identify what the most valuable households purchase, their behavior over time, their loyalty to a brand and how they switch for price changes or life events like having a baby.  Combining loyalty to basket-level data is the pinnacle of establishing an optimal and efficient relationship with the customer, and therefore, growth.

Considering the pace of the change in the industry, the need for basket-level data is now more than ever real and present. The overall effect of this is the changes occurring in the retailer-manufacturer dynamics, leading to a more challenging relationship: they must overinvest in power partnerships with each other to leverage advanced analytics and turn them into the Right Data.

 

About SwiftIQ

SwiftIQ is an advanced insights and activation platform that helps large retailers and brands generate growth and improve vendor collaboration using 100% basket-level transaction and loyalty data. SwiftIQ uses high scale data processing and artificial intelligence to convert billions of records of POS data into predictive store-level demand forecasts, media measurement and personalization activations. SwiftIQ analyzes over $85 billion POS data serving many billion dollar retailers and 5 of the global leading category suppliers. SwiftIQ, named a 2016 Vendor of the Year by RaceTrac and 2016 Gartner Cool Vendor for CPGs has also been recognized by Forrester, Forbes, NACS, Progressive Grocer and ComputerWorld for its achievements.

 

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