As first published in a Wall Street Journal article, Kroger recently made news in the alcohol category for appointing a private distributing company (Southern Wine & Spirits) to lead the process of recommending assortment strategies instead of traditionally using supplier as a category captain. In a Fortune.com interview, Kroger’s spokesperson Keith Dailey stated that all [alcohol] producers will benefit from “unbiased plans that are customer-and data-driven.”
The shift away from a supplier category captain reflects the sentiment of retailers and smaller suppliers that category captains favor their own products for more optimal shelf placement. This begs the question; can category captains provide unprejudiced plans? The answer is YES, however, it’s a collaborative effort between the retailer and supplier.
As a former consumer product category manager for over 6 years, and “captain” for a leading grocery retailer, I can offer up my advice to retailers and suppliers on how to foster and create accurate category plans. Whether you are a retailer or a supplier, read on to find out how!
1. Use unified data sources to enhance analytics and avoid confusion:
Providing an impartial review starts with working with the retailer’s 1st party data to ensure that there is single ownership of the data. Both retailers and suppliers must appreciate how each categorize data; Suppliers can add their vast knowledge of their products and respective categories to this data. There are also numerous other benefits to mutual data sharing which you can read more about in the previous blog article FCMG Retailers' Analytics Improve with Suppliers 1st Party Data.
2. Factor in cross-category basket-level data to understand the bigger picture:
Often times, category mangers look at data in silos. For instance, a category captain might make recommendations on space allotment for candy at the front end checkout lane, however, will base this solely off of data from the candy segment. Important categories like snacks, beverages, and general merchandise are disregarded in their recommendation. This fragmented way of looking at the data isn’t necessarily the manager’s fault; The high cost of syndicated data systems, and their prior lack of adequate on-demand cross-category insights can be a limiting factor to even to the largest category players.
3. Leverage predictive analytics to eliminate “guess work”:
Syndicated data sources are useful for understanding high level market trends, but generally do not provide store-level insights. Fair share indices, calculated off of market trends, are used by category managers to determine distribution opportunities for products and/or brands. As a result, plans can fail to account for regional and store-level performance variances. Access to store-level and cross-category data can allow category managers to better understand store-level performance and provide localized recommendations to generate higher dollar opportunities. Additionally, when data is used with predictive analytics, it can be used to forecast sales, segment store clusters, and can take the guess and grunt work out of the opportunity review.
In order to make this all possible, retailers also have a role in facilitating balanced category captaincies.
1. Provide store-level category data to discover bigger opportunities:
It is imperative to provide suppliers full access to basket-level POS data in order for them to produce plans that will generate the highest dollar return. Aggregated data constrains the ability to discover hidden relationships between products and/or categories. Providing full-store category data as a unified data source can eliminate the shortcomings of supplier owned classifications and data manipulations. New technologies can identify product level relationships with basket analyses across brands, categories, and manufacturers.
2. Make the same data available to all of your suppliers for a level playing field:
The high cost of syndicated data sources can burden smaller suppliers. Making retail data available to not only category captains, but all other vendors can create a system of checks and balances and gives everyone a fair shot at maximizing sales. According to the Retailer/Supplier Shared Data Study 62% of retailers and suppliers that collaborate with data realized greater ROI’s as they were able to improve upon out-of-stock issues and better manage inventory levels. Setting up a data sharing portal for suppliers may be easier and more cost effective than you might think; New business intelligence ecosystems makes data sharing faster, more secure, and more profitable than ever before.
3. Give store managers a voice:
Allow store managers access to data and have a say in what products should go on shelf. Many retailers are already practicing this, however, store managers are making such decisions without any access to store data. Data visualizations and business intelligence platforms can empower most non-technical user to be a data scientist and make validated store decisions.
In sum, creating and facilitating an environment for balanced category captaincies is a two-way street; Retailers must provide full store POS data visibility to all stakeholders to allow for optimized plans. Category leaders should rely on 1st party data as well as contribute their data to accurately discover and understand each retailer’s unique needs. Suppliers that create localized store specific decisions, rather than making high level decisions based on regional averages, will be positioned to win for the long term.
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.