whole foods consumer intelligence

Data democratisation simply means making data accessible to all employees in an organisation, and presenting it to them in a way that they can understand and make sense of it. Also known as citizen access, it usually doesn’t require one to have technical knowledge of data handling in order to access such data. In 2020, a study reported that 81% of the business leaders surveyed indicated that data democratisation to be a key initiative. 

Data democratisation comes with a number of benefits; 

  • It makes data accessible across teams, instead of being restricted only to the IT department at organisations 
  • It provides ownership and access to individuals and teams across the organisation and in turn, enable quicker decision making 
  • It proactively helps organisations detect anomalies and work on suitable solutions 
  • It helps teams improve customer experience 

While there are a number of global organisations like Airbnb and Amazon that have democratised their data, in this blog, we’ll specifically look at how the global retailer, Whole Foods, innovated with technology tools, democratized its data across functions, and how it accelerated the retailer’s growth, revenues, customer experience and competitive advantage. 

At the Helm of Data & Technology Innovation 

Whole Foods, the American multinational supermarket chain, has a global presence across 500 locations. For many years, the retailer stood out in the market for its focus on ‘natural’ produce and products with minimal processing. At a very early stage (around 2010), the retailer started leveraging technology to bring down costs and enhance customer experience.  

In 2013, it partnered with Infor to automate its inventory, merchandising and supply chain, and in turn reduce the end cost of organic products offered at its stores. This led to the global retailer reducing costs by as much as USD 300 million by 2017.  

Similarly, in 2014, recognising that many of its customers preferred shopping online for groceries, the company partnered with Instacart to enable customers to shop online and deliver produce to their homes within an hour.  

According to a study by Harvard Business Review, this move could have been one of the first steps retail chains took to fend off the competition from ecommerce players like Amazon, which started eating into the physical stores market. 

True to their predictions, by around 2016, Whole Foods started publicly indicating severe losses, because of competitor stores offering the same experience at a lower price, and ecommerce players eating into their market share. Eventually, a year later, in 2017, Amazon acquired Whole Foods for USD 13.7 billion.  

Today, even though Whole Foods is a subsidiary of Amazon, it is still widely known for its technology innovation. Let’s look at some key tech milestones the company took, and how it worked in their favour. 

The Nielsen Partnership 

In 2016, when competing players started offering natural produce at lower costs, Whole Foods turned to Nielsen to leverage its market, consumer and competitor data intelligence to offer a more enhanced customer experience. Its partnership focussed on achieving three primary goals; 

  • To create a customised natural and organic product hierarchy, to give a detailed view of the retailer’s product categories and the ingredients in them 
  • To use this categorisation to drive better decision-making on what to stock and what to reorder 
  • To proactively predict consumer needs and thus, stay ahead of the curve 

The Dunnhumby Partnership 

In early 2017, Whole Foods partnered with Dunnhunby, a global customer data science company, to optimise product merchandising and categorisation using predictive technology, and to enhance its loyalty program.  

The partnership would help Whole Foods better understand and plan its pricing, promotions and product selections to increase sales and meet customer demands. During the initial stages, Whole Foods CEO said that the company saw a ‘healthy basket over two times what they usually see for non-members’. Moreover, it saw over 50,000 customers signing up for its loyalty program. 

Amazon’s acquisition of Wholefoods 

In 2017, Amazon acquired Whole Foods for USD 13.7 million, in a landmark deal by the tech giant.  

A Merit expert says, “There were many speculations around why Amazon wanted to invest in a retail giant, with arguments ranging from promoting their private labels to growing their online grocery delivery business. While all these are valid reasons, there’s a bigger reason for the acquisition, and that is customer data.” 

Whole Foods has years of consumer data including the preferences of 300 million customers which provides massive insights into FMCG market intelligence. Amazon could very well leverage this data to predict what products to stock in each store, and how to boost sales through cross selling and upselling on its online and in-store platforms. 

Here’s what the deal and data intelligence partnership led to; 

  • Unlike earlier, Whole Foods started using its data to offer promotions and discounts on Amazon Prime membership products 
  • It partnered with Amazon’s online grocery delivery service to deliver perishable goods to customers in short durations 
  • It moved to a centralised procurement model, where regional teams would manage procurement, demand and supply of local products, and national teams would manage the same for produce from national brands 
  • As an extension to the centralised operations, at the store-level it also started stocking perishable produce on a need-to-basis. Meaning, instead of storing them in the stock room, it started estimating demand and stocking produce directly from delivery trucks to stores, as a move to avoid excess perishable inventory 

In addition, there are some significant data democratisation moves the retail giant took as well. It moved from elaborate, complicated excel sheets to using reporting dashboards for its financial and operational data. It powered these insights with Tableau, wherein individual stores got access to store-level performance data, and insights into how each store is performing. These insights helped store managers understand how and why other stores were performing better, when they needed more staff, how many cash registers they needed to keep open during which times, and what products they needed to source and stock at what time. 

Today, 18,000 employees across Whole Foods use dashboards to measure performance and draw proactive insights into what they can do better to enhance customer experience and drive growth.  

It’s safe to say that at every point, the retail giant has chosen to adopt innovative tools to stay one step ahead of competitors, which makes it one of the few retail chains to stick to its original motto of delivering fresh, natural produce, while continuing to evolve with the times and staying relevant. 

Merit’s Expertise in Retail Data and Intelligence  

Our state-of-the-art retail data harvesting engine collects raw data and provides actionable insights  

  • Three to four times faster than standard scrapers  
  • At lower cost  
  • With Increased accuracy (up to 30% compared to standard scrapers)  

Our powerful, new scraper engine can gather massive data sets from multiple sites and geographies in real-time so you can stay informed on customer behaviours and market trends.  

Merit’s eCommerce and retail data engine provides a high degree of confidence in insights generated from analytics – thanks to confidence in the data quality and access to enriched data.  

To know more, visit: https://www.meritdata-tech.com/service/data/retail-data/ 

Related Case Studies

  • 01 /

    A Bespoke Retail Data Solution for Better Insights and Forecasting

    A pioneer in the retail industry with an online solution providing easy access to global retailer data, had the challenge of creating retailer profiles through the data capture of financial and operational location information.

  • 02 /

    AI Driven Fashion Product Image Processing at Scale

    Learn how a global consumer and design trends forecasting authority collects fashion data daily and transforms it to provide meaningful insight into breaking and long-term trends.