Working with two leading multi-billion dollar global brands, Coca-Cola and Anheuser-Busch has validated the cutting edge work that SwiftIQ is doing around retail data analytics. So what is it like working with these CPG giants? The 2015 and 2016 Consumer Goods Technology (CGT) Visionary list named two of our champion partners at Anheuser-Busch, and Coca-Cola as forward thinking visionaries, and gave insights into what is on the top of their minds when it comes to data analytics. The following are the top three takeaways from these leaders, but also coincide with what we at SwiftIQ are seeing across vendors in this space:
1. Enhanced data Granularity is being used to make Context Specific Store-Level Decisions
CJ Watson, formerly the Vice President of small format sales at Anheuser-Busch and now an executive at Wal-Mart successfully implemented near real-time POS analytics that delivered more effective category leadership initiatives to localize merchandising, minimized out of stocks, and allowed non-technical sales and category analysts to access and analyze full store transaction data. His vision for proactively using basket-level data for dayparts, item correlations and to improve on-shelf availability, earned him one of eleven spots on the 2015 Consumer Goods Technology Visionary list. With granular data access, his team was also able to determine unique store-level performance factors and used that information to appropriately customize 2,000 planograms. Also making the list in 2016, is Coca-Cola’s Group Director of Category Strategic Advisory, Clint McKinney, who also recognized that streamlining data, and shortening the time from analytics to insights would make his team more efficient. Using near real-time data allows for increased speed to insights, and in turn, more actionable results.
2. Tailor insights to the needs of your customer:
One of the key principles of category management and shopper marketing is to know what your customers want before they do. Many eCommerce businesses have been able to achieve this with armies of software engineers that can manipulate data and provide advanced analytics, however, many retailers lack significant technical resources and therefore, have not implemented this practice. As a forward thinking leader, CJ realized the need for advanced analytics and worked on building daypart solutions for the convenience store channel. His team partnered with SwiftIQ in 2014 and worked to identify key dayparts that retailers can merchandise around to drive traffic and profitability. Having the appropriate merchandise at the right store locations, available at the right time of day, targets the shopper during their path to purchase and ensures the right products are on shelf, thus increasing conversion and sales. Clint and Coca-Cola are doing an excellent job building profitable, data-driven bundling programs around a retailer’s key foodservice businesses. Not only are they able to match highly correlated products and categories, but they are able to quantify the return on investment, not only for sales, but for incremental revenue driven by other non Coca-Cola items during a promotion.
3. Leverage new technology to help achieve goals:
According to a June 2016 McKinsey report, Playing catch-up: How to partner with the retailer of the future, almost all retailers can now gather and mine valuable data which consequently diminishes the significance of data that CPG manufacturers provide. Leveraging new technology that can generate insights that aren’t simply descriptive or explanatory, but predictive and prescriptive-offering fact-based answers to the questions, “what will happen?” and “what should we do to get the most benefit out of what will happen,” will drive collaboration and help meet the needs of shoppers. New data processing frameworks now have the capabilities to process large datasets and offer more prescriptive analytics. Both CJ and Clint have had success implementing new technology retail analytics platforms allowing them to mine billions of records, in near-real time, for enhanced insights and increased collaboration with their retail partners. In addition, they both realize that AB and Coca-Cola have valuable context around their products and the product’s attributes (size, segment, consumption behavior, etc.) that enrich a retailer’s transaction data to help identify non obvious patterns in which they can activate against.
With all three of these learnings, the first step in the process is to secure a robust data set that allows for advanced analytics and insights. There are many more benefits to having access to such data which you can read about here: FCMG Retailer’s Analytics Improve with Suppliers 1st Party Data. Additionally, you can find examples of how customers use SwiftIQ’s platform to achieve success at retail in the ebook here: Driving Value with Retail POS Data.