Solution Provider Spotlight: HIVERY

July 10, 2023
by Heather Martin, VP of Marketing & Sales at HIVERY

Tenure With Current Company: 3
Years of Industry Experience: 15 years

Give us a brief history of your company, when and how did you get started and what is your high-level mission statement?

HIVERY emerged from a unique challenge in the Coca-Cola Founders Program: How to improve vending machine sales while reducing restocking? The problem was evident in hospital vending machines that were uniformly stocked, despite consumption in the OR waiting room and the OR staff lounge. This led to frequent out-of-stocks and increased costs. Traditional retail research methods and datasets were limited. However, HIVERY had access to the sales data. In collaboration with CSIRO’s Data61, Australia’s national science agency proprietary machine learning and applied mathematics algorithms were developed, which could predict sales for each item in every machine and recommend the ideal space allocation. This enabled optimized assortment, tailored restocking strategies, and revenue optimization. The success of this approach led to the development of HIVERY Curate, which leverages machine learning models with store-level data, like Netflix’s recommendation system. Today, HIVERY operates across over 100 retailer-category combinations in all classes of trade. Our advanced retail analytics enhance decision-making and provide an accelerated path to execute those insights at the shelf. At HIVERY, we are revolutionizing how CPG professionals collaborate across their organization and with their retail partners, validating and partnering on the portfolio, category assortment, and space decisions. Our mission, therefore, is simple: To assist retailers and consumer package goods (CPG) brands in adopting a data-driven, dynamic category and assortment planning approach. By doing so, we enable them to respond to shopper trends and insights more quickly while also reducing food waste.

What would you like CMA and SIMA members to know about your brand/company?

We firmly believe that Data Has A Better Idea™, and therefore we leverage retail-specific store-level data to uncover assortment insights. Shoppers vote with their wallets on the shelf at each specific store; however, for years, we have been making decisions within our businesses without considering the shelf. We want to make the shelf central to all product, portfolio, category, and assortment decisions by leveraging retailer-specific data and bottom-up analysis. This enables more accurate decision-making and delivering locally relevant, effectively merchandised, and operationally efficient plans with retailer-supplier transparency and collaboration at scale.

What is the most common question you hear from current and potential customers, and how do you answer it?

We are stuck in a doom loop of having too much data and too little action. What do you do at HIVERY that is different?

Shoppers vote with their wallets in stores, and we make this visible to retailers and CPG manufacturers to enable them to make decisions and deliver locally relevant, effectively merchandised, and operationally efficient plans at scale. With technology like AI as your co-pilot, you can enhance your decision-making by qualifying any strategy to iterate faster and act, knowing your strategy will be executable in store. Recent customer quote: “This looks amazing. I mean upwards to double-digit growth on everything like that. Stunning”– Category Leadership at an American food manufacturing & processing conglomerate.

Any white space in the industry or areas you are looking to expand into?

To look at future white space for the industry, we must look at the past. Let’s start with the term “category management”. It was coined by Brian F. Harris, a former professor at the University of Southern California and the founder of The Partnering Group (TPG). Harris first introduced the concept of category management in the late 1980s, and it quickly became a popular approach to managing product categories in the retail industry. The goal of category management is to optimize the sales and profitability of each product category by taking a holistic view of the category and working with suppliers to develop strategies that will benefit both the retailer and the supplier. Source: https://en.wikipedia.org/wiki/Brian_F._Harris

For decades, technologies like Apollo Space Management, developed by Harris, revolutionized how we work. These technologies gave us incredible “superpowers,” transforming our abilities.

The transformative impact of generative AI and natural language models inherently differs from older technologies. These new technologies and methods can significantly impact our collaboration and application of expertise – tasks we previously believed had a lower potential for automation are, according to a recent McKinsey report, now ripe for disruption: Applied expertise, decision-making abilities, and collaboration with a system and humans. Applying a combination of technology with expertise and stakeholders could enable up to an 87.5% increase in productivity gain to our industry, or close to 2X.

At HIVERY, this is where we see “white space” potential. We see the revolution that will allow consumer package product (CPG) manufacturers and retailers to validate decisions faster, collaborate internally faster, collaborate externally faster (CPG/retailer), and make better assortment and space decisions. In short, generate a faster time to value. You can read more about this in this blog post.

Overall Technical Automation Potential of Generative AI graphic
Source: McKinsey & Company, The Economic Potential of Generative AI: The Next Productivity Frontier – June 14, 2023

What is the essential thing that needs to be addressed in the category management and shopper insights disciplines going forward?

Understanding consumer behavior in the retail industry goes far beyond traditional market research and shared panel data. It involves delving deep into the dichotomy between what consumers say and what they do. By leveraging store-level data and advanced AI models, we can inform portfolio and price pack decisions with the only insights that matter – what will happen at the shelf and how do I execute it?

How are you thinking about the next 3-5 years in retail?

There is a doom loop in CPG and retail: there is no shortage in the amount of data available, but gathering that data, analyzing it at a granular enough level to be actionable – but not too granular to be impossible to understand, and leveraging it enable decisions and actions that impact the shopper at each store is challenging. As such, we take an average-of-averages approach, which means leaving revenue and shopper satisfaction on the table.

In the next 3-5 years, we intend to help bridge the gap between stated preferences and actual shopper behavior. This has enormous application across annual operations planning (marketing, sales, innovation), Joint Business Planning (JBP), category planning, line reviews, assortment finalization, and planogram generation. Leveraging data at the glandular level, decoded by machine learning algorithms, and interacting with it by natural language models allows for insights into the nuance of shopper behavior by store. By analyzing every single transaction across a retailer, we can capture shoppers’ unfiltered, real-world decisions. As an industry, this allows us to drive the most appropriate overall strategy, creating the ultimate win-win for the brand, category, retailer, and, most importantly, the shopper.