How have the best assortment planning practices evolved? What can category analysts and shopper insight professionals learn to grow their professional skills in the new year? And how is AI shaping the future of category management? Read on to discover how CatMan 3.0 will change every retailer’s approach to assortment planning on the Category Management Association (CMA). Then, learn how AI-driven assortment simulation ushers category management into the future. Information in this piece is adapted from a podcast interview with Steven Soderlund, first featured on the Retail Maverick podcast. Soderlund is a CMA board adviser and analytical consultant for major retailers.  Category Management 3.0: How Category Management Has Evolved Category management was first articulated as a practice around the 1980s. The original eight-step process outlined retailers’ and marketers’ supply and purchasing strategies for the next decade. Then in 2016, customer insight professionals standardized some much-needed updates. CatMan 2.0 incorporated new data sources, advances in analytics capabilities, increased retailer diversification, and greater shopper empowerment. Yet, for all the innovations, 2.0 still largely focused on brick-and-mortar retailers. And, while 2.0 involved greater joint business planning among retailers and manufacturers, 2.0 did not deal with new fulfilment methods with the necessary complexity. Enter: CatMan 3.0.  The latest formal category management process keeps much of what worked. Managers are still developing consumer insights and capturing information about shoppers. They’re still executing and iterating plans.  What’s changed? CatMan 3.0 incorporates full integration of the omnichannel mentality into the assortment planning process. It’s all about how to manage that change. Omnichannel Retailing and CatMan 3.0 Omnichannel retailing harmonizes brick-and-mortar store and eCommerce services. It integrates marketing channels. This, in turn, alters how analysts should conceive of shelf space in the store. In the past few years, online sales have grown to 15-20% from 5%, seemingly “overnight.”  In stores, it’s increasingly critical to dedicate shelf space to fulfil online orders. Same-day shipping and curbside pickup orders make an increasing bulk of sales. New technology even lets customers peruse local stores' products from home. You don’t want bares shelves after items are picked up. Other technologies like Shopify enable more physical stores to go online in a manner of minutes.  CatMan 3.0 Impact on Category Analysts CatMan 3.0 offers analysts a more robust list of metrics to track customer behavior. When following best practices, category managers ought to use the new, diverse metrics when they measure category performance. The 3.0 edition also offers clearer, more detailed, more defined categories. This makes it easier to cultivate more nuanced decision sets.  Your customer decision tree will vary among similar categories: the customer won’t behave identically in the candy aisle in a store as they do browsing for chocolate online. Granular categorization lets you map out customer behavior more readily.  Categorize by Buyer Behavior: Brick-and-Mortar vs. Online 3.0 lets you increasingly define categories by consumer behavior and experience. Previously, you may only have used broad strokes to categorize products by behavior. For example, you might sort impulse purchases from routine/needs-based purchases. Now, insight professionals can build categories around shopper patterns. A routine purchase, like laundry detergent, is better understood as customer experience in some contexts.  Is the customer buying detergent every eight weeks out of habit? Or are they moved to try experiential products with new scents?   In some cases, speed is not as paramount online, as online shopping is already convenient. Customers may be more willing to experiment or purchase something they saw mentioned recently on Facebook. In contrast, displays in a brick-and-mortar store act as line-of-sight “billboards.” When you understand a customer’s patterns, you know which category displays more often alter behavior.  Takeaways for Category Managers and Shopper Insight Professionals New categorization is possible because there’s more data available than ever. But how can you make data measurable and actionable? The growth of omnichannel (i.e. eCommerce and direct-to-consumer) is an opportunity for all categories to grow. Retailers can meet shopper needs in new ways. But, more complexities in the behaviors of consumers complicate things. For category management, it’s more critical than ever to measure shopper behavior. Then, it’s vital to apply measurements effectively.  Fortunately, there are new tools to do just that. AI-driven solutions make CatMan 3.0 work.  With AI machine learning, category management will evolve past the spreadsheet. Category analysts and managers will get in front of real people again. CatMan 3.0 empowers managers to do the fun stuff—like creative storytelling—while advanced AI solutions do the rest of the work.   Pandemic Upends Customer Purchasing Habits, Prediction Models  Why are AI solutions so critical? Post-pandemic consumers are used to pandemic innovations. So many retailers rose to the challenge of the pandemic. Services like curbside pickup and same-day delivery are here to stay.  Now, a customer who's about to make dinner might choose a recipe on Pinterest. But, they realize they're missing an ingredient. That's not a problem—they can order that ingredient and it'll be at their door in an hour using services like Instacart.  Some customers may never return to brick-and-mortar stores. Others may split their time more evenly between online and physical shopping.  SKU Innovation, Big Data Change Demand Forecasting Tech innovations have changed the game for category managers. CatMan 3.0 advises retailers on the best way to take these into account. When you build your approach for 2022, keep in mind: SKU innovation Multi-channel innovations Retail store options Changing product preferences Social media influences Growing datasets A growing list of retail and merchandising rules and constraints These innovations factor into the customer behavior models of the future. For example, SKU rationalization empowers retailers to uncover brand growth potential. As demand transfers, SKU rationalization creates space to swap-out underperforming products and reduce out-of-stocks.  With AI, you can model different swap-out or churn rates and see the impact on revenue.  Below is a real AI assortment simulation prepared for a client presentation. They wanted to know the break-even point of their assortment change (ie adding new items vs deleting old poor performing SKUs) at stores. Below you see it would require this client to change at least 13% of assortments to get to the break-even point. This was all powered by using HIVERY Curate and created in a matter of minutes, not days or months.   Further in the Curate solution, if they wanted, the Category analysts and shopper insight professionals can view are the actual assortments that are being deleted and new ones added.  They can also view by individual stores. Today any Category analysts and shopper insight professionals would need to upskill to manage this level of increased information complexity. With solutions like Curate that are powered by artificial intelligence, such analysis can be done in a few clicks. This means you can conduct any “what if” scenario planning at the store level and up prior to implementing any major resets.  Artificial intelligence can do this category-by-category, retailer-by-retailer and manage all associated merchandising rules and assortment constraints with category goals. The aim is to improve product placement efficiency on the shelf. The future is artificial intelligence (AI) and it's available today.    What’s the Best New Approach to Assortment Planning? As retail changes, the available data broadens and merchandising rules/constraints will be complicated. The big data companies, Numerator, Nielsen and IRI, generate a lot of useful syndicated data. They also offer panel data, which can inform your consumer behavior maps.   Meanwhile, eCommerce data also grows. Clickstream and other solutions enable broad data collection in the eCommerce space. You can't afford to ignore it. But, you must avoid drowning in the data. The required legwork has only increased. You still need to discern the appropriate regional segmentation of assortments per retailer—but now you have more information to sort through. What's the best way to pull the signal from the noise? The answer is AI-driven solutions. With AI-driven solutions, you can do the exercise that matters in minutes. Build a story with actionable insights.  How do AI and ML Solutions Make a Difference? Ai-driven solutions get you to the actionable result faster. You can create more accurate predictions now than you ever could before. A solution like HIVERY Curate turns big data into a sales forecast. But, it doesn't stop there. The AI program then takes granular data at the assortment level and cultivates an actionable plan that has been already qualified. Meaning you know the likelihood of your plan if executed in retail space. Curate has simulated the assortment plan's financial impact and included your category goals and those retail and merchandising rules and constraints.  How Machine Learning Works to Make Assortment Predictions HIVERY Curate optimizes assortment and space using big data. Typically that's retail data and POS data from all stores and all items.  HIVERY Curate incorporates omnichannel data into its recommendations. This includes all sales velocities as well as the category goal and merchandising rules and assortment constraints. Once the AI looks at the data, it makes predictions: which products will sell in which environments? How quickly? Thus, with Curate's plan, you can create the most appropriate amount of space for each category.   Test Hypotheses With Machine Learning As we know machine learning is just a sub-area of AI. It's where algorithms learn unique patterns in data beyond human abilities.  This means you can test category management strategies hypothetically not possible before For instance, you could go into HIVERY Curate and ask “if I take out these 5 items, what will happen to this category?” or “What new items should I be adding in? What is the optimal assortment for these selected stores? What is the impact on sales of reducing distribution for a supplier? What happens to volume and sales if we deleted 18 & 20 pack bottles and cans? Where will the demand transfer to?  HIVERY Curate will run simulations and generate forecasts given your merchandising rules and assortment constraints or any other parameters that you like it to include. HIVERY Curate's forecasts aren't guesswork. It is trained. The simplicity is that it does not need information like demographic or seasonality.  The demographic and seasonality traits of each store are already “embedded” in the POS data (ie point of sale or point of purchase).  Further, AI solutions like HIVERY Curate are trained to understand important economic principles like product cannibalisation, elastic of demand for each SKU and demand transference. So in a few clicks of the mouse, it delivers actionable recommendations with associated planograms should you want to execute that category reset. With machine learning, you have a quick and easy way to test any assortment hypothesis. A predictive model no longer requires retail disruption, you just need to test the hypothesis and see the impact before. How AI-Driven Solutions Apply Economic Concepts As mentioned, AI-Driven solutions apply known economic concepts before they generate an actionable plan. For instance, HIVERY Curate specifically understands concepts like: Assortment elasticity Cannibalization Incrementality Demant transferrence Consider item transferability vs. cannibalization. A marketing team may be too attached to brands to predict accurately. The team may be overly-optimistic about a new scented deodorant product within their brand.  Will the new product expand the brand's consumer base? Or will it simply cannibalize the brand's existing deodorant products? An AI-driven solution takes a logical, mathematical approach to predict incrementality. It learns how economic rules have been applied before, then applies the most similar scenarios to the proposal. Thus, the team gains a more accurate sense of whether the new product will upend the market or cannibalize sales.  Can Machine Learning Predict New Product Development Before Data Exists? Yes, machine learning solutions can predict how customers will react to new products. A machine can also continually update its model based on new data. Category managers can use HIVERY Curate to predict a new product's impact. To do this, a manager assigns a new product a "sister item." Curate then discerns relationships.  HIVERY Curate examines the sister product's relationships to other products in different categories and in different types of stores. Then, the AI incorporates transferability. This empowers the AI to note the difference in relationships and contexts between the new item and the sister item.  This, plus input POS data, enables Curate to generate an accurate model of customer response to a product that's still in development.  Alternatively, you might want to leverage market research data points like new product penetration rates from say a Nielsen BASES report.  You can add percentage data and see the impact all at the store level. AI-Driven Data Analysis, Human Creativity: CatMan 3.0 Assortment planning demands our due diligence. The future of category management requires us to use giant sets of data, those merchandising rules and assortment constraints. That makes tools like HIVERY Curate critical. HIVERY Curate frees us from number-crunching tasks. With the data managed by artificial intelligence, we can do what humans do best: create stories. Free the creativity of category managers everywhere. Explore Catman 3.0. Request a demo of HIVERY Curate today - the world’s first true store level strategy simulation & optimization solution. Get a PDF version of this post

Cantaloupe Inc a digital payments and software services company that provides end-to-end technology solutions for the unattended retail market, today announced its partnership with HIVERY, a data-science company that specializes in AI technology to streamline category management for retailers in the consumer packaged goods (CPG) industry. Enhance™, a user-friendly AI and ML technology-based solution will be available to Cantaloupe Seed customers through its integration with the Seed™ platform, specifically Seed Pro™ and Seed Office™, making it an even more intelligent tool for customers.... learn more

In this podcast, we will cover: • How category management (Cat Man) practice has evolved from Cat Man 2.0 to now Cat Man 3.0, and what it means for category analysts, category management and shopper insight professionals; • Impact of the growing channels such as e-commerce and omnichannel on decisions category managers have to make around assortment, and how customer decision tree (CDT)varies between categories, retailers and channels; • Why in the pre-pandemic world the shoppers were relatively predictable but in the post-pandemic world it has changed and now requires a new approach; • Today's predictive retail analytics are not working, what solution can retailers and CPG brands rely on and use to make effective assortment and category decisions? • How do artificial intelligence and machine learning solutions like HIVERY Curate make a difference to assortment planning and category decisions? • Should retailers and CPG brands be concerned about the accuracy of AI-driven assortment recommendations? • How does applied artificial intelligence and machine learning actually make assortment predictions when it comes to such things as SKU innovation or new product development when data does not exist? • Can artificial intelligence and machine learning understand economic concepts like assortment elasticity, cannibalization, incrementality and demand transference?

As you might have guessed we are so passionate about data and believe it has its own ideas that it is in fact our registered trademark tagline Data Has a Better Idea™. At HIVERY it has actually been our philosophy from the day we started. You can learn more about this in the section: So, why HIVERY? We are just different. In this blog, we will discuss how machine learning specifically learns and uses data to discover these new ideas. Why is “data” in Data has a better idea™ in important? Machine learning can detect epilepsy in children, with a 73% success rate. This test method wasn't applicable to children's developing brains until now. Machine learning, a sub-area of Artificial intelligence (AI) is actually the Computer Science discipline that looks at algorithms that “experience” data to find new ideas. More specifically it's the practice of machine learning algorithms that finds patterns and connections that human intelligence can't. In fact, data is so important now that it is the competitive differentiator for successful companies.  You might have heard expressions like data is the new oil or data is the new currency or data is the new gold!  Put in another way, some reports suggest that over 90% of data in the world was generated over the last few years, yes 90%!  Further, according to Forbes report, 2.5 quintillion bytes of data is being created each day.  It's no wonder that data is an important asset and an important part of decision making. It's also no wonder why if there is a new and better way to analyse this data using computers has generated a lot of interest.   Machine learning algorithms are good at finding patterns and connections that we can't. They are essentially good at communicating “data’s ideas” to use.  Machine learning algorithms are modern age “interpreters”.  They provide a new competitive advantage. Industries; from airlines, mining, finance, insurance to health and retail are able to discover data indeed has a better idea.  Keep reading to learn about how data has a better idea for business intelligence and decision making. Why Data Has a Better Idea™? As mentioned above machine learning (ML) is a method of analyzing big data using carefully designed models or algorithms. This is what a data scientist key job is. Designing and testing algorithms that can look at data patterns without needing much human interaction and that can learn over time -  get better. Cheaper, more powerful computational processing and affordable data storage make this possible. A wide variety of industries now make use of this type of AI, from healthcare to retail, to transportation. Business intelligence (BI) helps vendors identify important data points and patterns. It helps reduce costs, increase efficiency, and identify opportunities for a business. This is due to the fact that datasets have become too large and complex traditional data analysis and modelling approaches are often used by Business intelligence (BI) teams have become ineffective.  ML algorithms help you understand customers on a deeper level, and they learn associations more quickly and thoroughly than traditional BI approaches. You could analyze a smaller dataset with an Excel spreadsheet. However, to understand larger patterns, you must use ML to process big datasets, find patterns and create sophisticated demand forecasts.  In retailing, for example, any retailer or category management professional knows when assortment is done right, they enjoy more sales, higher gross margins, leaner operations, and most importantly, more shopper loyalty  In reality, this is hard than it sounds.  Trying to get assortment planning right means you are constantly monitoring, strategizing and managing SKU performance.  ML can help with SKU rationalization and SKU optimization allows businesses to only keep the products that generate ideal revenue. An ML can show which specific stores an SKU introduction will perform better, what SKUs should be removed and what will be the impact to revenue and volume which taking into account demand transference and cannibalization, all in a few minutes, not months.  Data Has A Better Idea™ With Machine Learning  There are many ML methods to design the right algorithm model. Each has its own advantages and disadvantages.  Let's review, at a high level, four popular ones. Supervised Learning Supervised learning trains an algorithm using labelled examples. It already knows the desired output. The model modifies itself by comparing its outputs to the correct answers. You should use this where historical data consistently predicts future events. Supervised learning is especially useful for binary classification, multi-class classification, and regression modelling. Unsupervised Learning Unsupervised learning works against data that has no historical labels. There is no predetermined right answer. The system must find a pattern and figure out what is happening on its own. This works well to find groups of customers with similar attributes. Simply put, it looks for any meaningful connection in the dataset. It's useful for clustering, anomaly detection, association mining, and dimensionality reduction. Semi-Supervised Learning Semi-supervised learning combines both labelled and unlabeled data for training. The labelled data points the algorithm in the right direction. Semi-supervised learning is more affordable than fully supervised learning. This method is best for machine translation, fraud detection, and labelling data. Reinforcement Learning Reinforcement learning discovers through trial and error which actions yield the greatest rewards. Its goal is to learn the best policy within a set of defined rules. The algorithm gets positive or negative cues as it tries out various ways of completing a task. Reinforcement learning is often used for robotics, video gameplay, navigation, and assortment planning. Machine learning can be leveraged by retailers and consumer package goods when conducting assortment optimization. It finds the sweet spot between operational capability and maximum profit. Machine Learning for Retailers Machine learning is a great way to optimize your operating strategy. Data has a better idea™ for your business! Find out what it can do for your business by contacting us for an AI-powered solution.

Sydney, NSW, Australia — December 7, 2021 — Business NSW honors HIVERY with the Excellence in Export State Award. Recently, the Australian organization Business NSW honored HIVERY with the Excellence in Export award. This award highlights HIVERY's commitment to exporting sophisticated AI-driven assortment simulation solutions to some of North America's biggest retailers and Consumer Packaged Good (CPG) brands. During their recent awards ceremony, Business NSW gave HIVERY the Excellence in Export title. Business NSW's virtual award ceremony honored the top businesses in New South Wales, naming HIVERY amongst thousands of state entries. This announcement came shortly after Forbes Asia included HIVERY in their 100 to Watch list. In April 2021, HIVERY reported that its clients' total sales growth is projected to reach $1 billion. That includes an annual 10% sales growth and reducing labor resource hours by as much as 80%, boosting profits and freeing up resources. HIVERY's investors include industry giants, like Blackbird Ventures and The Coca-Cola Company, who saw the company's incredible growth and potential. In a statement, HIVERY said, "We would like to thank our talented people, our customers, our partners including Microsoft and Databricks, and our eminent investors--Blackbird Ventures, One Ventures, AS1 Partners, The Coca-Cola Company and Australia's national science agency, CSIRO -- who all believe in our vision that Data has a better idea™" HIVERY's products use data, analytics and AI technology to streamline category management for retailers. In particular, HIVERY Curate offers assortment strategy simulations that allow retailers to maximize profits and perfect their strategies. Other products include HIVERY Enhance, which focuses exclusively on vending machines, and HIVERY Promote, which creates trade promotional calendars. HIVERY offers solutions for vending, food service, customer insights, sales management and revenue growth, covering the full spectrum of business needs. These services contributed to HIVERY's rapid growth in Australia, Asia, the United States and Europe. Launched in 2015, HIVERY started as an AI-driven solution for vending machines. As the company and interest grew, HIVERY expanded its reach to assortment and category management for large retailers and CPG manufacturers. HIVERY has offices in Australia, Japan and the United States, enabling the company to reach a global client network. About HIVERY. Data has a better idea™ At HIVERY we believe that 'data has a better idea'. Pioneering hyper-local retailing, HIVERY offers retail AI-driven strategy assortment simulation & optimization solutions to a growing number of CPG companies and retailers across the globe. For more information, press only:    Address: HIVERY, Level 2,15, Foster St, Surry Hills, NSW 2010, Australia, Email ID: franki@hivery.com, Contact number: +61 409 213 683 Download the PDF version

Sydney, NSW, Australia — December 2nd, 2021 — Meeting growth and expansion demands, HIVERY continues to move forward, attracting key hires at the industry's forefront. Humble beginnings in 2015 launched HIVERY to the cutting-edge global force that it is today. From Australian roots, it branched to North America, Asia, and Europe. To keep up with its unprecedented success, HIVERY needs top talent to match its state-of-the-art development. Over the years, HIVERY has added raw talent to its force of AI innovators. Now, two more seasoned professionals have joined the team. To meet the increase in customer demand, representatives from industry leaders want to be a part of HIVERY's revolution. HIVERY is pleased to announce the hiring of Matt Schilb, VP of Product and Dirk Herdes, VP of Retail,  to help drive the next generation of assortment retail analytics.  Matt Schilb Schilb comes with an impressive record of success. Previously employed by Walmart, RSi, and most recently as Vice President of Product & Technology at NielsenIQ, he's aware of the need for data in decision-making, and its benefits for both companies and consumers. "HIVERY’s motto of Data has a better idea™ is something that really resonates with me," Schilb said. "People are flooded with information, and the ability to make effective decisions has become super difficult. We have decision fatigue, which in retail can lead to subpar assortment outcomes or a bad shopper experience. The ability to simulate different assortment strategies using AI and then quantify those scenarios in minutes is where HIVERY excels." Committed to ever-evolving AI technology, Schilb is familiar with its vast scope of results. His proven leadership and passion for design-driven innovation are the perfect forward-thinking fit for HIVERY to meet client needs while producing high-quality output. Dirk Herdes Herdes joins HIVERY following a successful 15 year run at The Nielsen Company where he rose to  Senior Vice President of Retail. An expert in leveraging data and analytics, he sees the massive opportunity and need for merchandising solutions to adapt to both current and future demands of the industry. "Retailers are inundated with data, never-ending merchandising rules, and SKU proliferation," Herdes noted. "Combined with increased supply chain complications and rising consumer demands, this is making optimal assortment planning and execution very challenging. The current process is labour-intensive and too slow to respond to evolving customer needs. HIVERY was created to change this, providing a simple interface but powerful AI model that can simulate scenarios at store level to scale, quantify the impact of those decisions, and then automate planogram generation at the most granular level. This is where the industry is heading and I want to be part of the transformation." Herdes has a passion for helping companies leverage their data and technology in new ways.  Finding the right AI-powered solutions, that enable users to easily interact with their data and make decisions that help customers fuel their growth - which has been HIVERY's belief from the start.  HIVERY is Different Schilb and Herdes recognize that HIVERY takes a unique approach to data, and this method of using artificial intelligence in assortment planning is the way of the future. Wanting to be a part of this growth mindset, they're perfect for the revolutionary development that HIVERY continues to deliver. With HIVERY Curate, for instance, users, with a few clicks, can make rapid assortment scenario simulations to determine the best possible decision to make around SKU rationalization, SKU introduction and space simultaneously. Simulations take into account any category goal, merchandising rules entered by the user with the impact of cannibalization and other key metrics considered to deliver optimal assortment planograms from store-level and up. About HIVERY. Data has a better idea™ At HIVERY we believe that 'data has a better idea'. Pioneering hyper-local retailing, HIVERY offers retail AI-driven strategy assortment simulation & optimization solutions to a growing number of CPG companies and retailers across the globe. For more information, press only:    Address: HIVERY, Level 2,15, Foster St, Surry Hills, NSW 2010, Australia, Email ID: franki@hivery.com, Contact number: +61 409 213 683 Download PDF version

Sydney, NSW, Australia — November 17, 2021 — HIVERY is delighted to announce that Red Bull's John Showalter will be joining them to deliver AI assortment simulation to more CPG and Retailers. HIVERY is proud to announce that John Showalter (Red Bull) is joining the company with the aim of bringing AI assortment simulation to more retailers and CPG. "Red Bull Core Values are all about having an entrepreneurial mindset, looking for better, smarter ways of doing things and differentiating ourselves” said Showalter.  “In the last five years at Red Bull, I started asking the question of myself and of the team, where does data science meet category?  What new and emerging technology was coming into the marketplace where we could continue down the path of making us differentiated -  HIVERY did just that." "Over the past thirty years, we have progressed a lot as an industry, but I don't think we've encountered anything revolutionary, until now. I feel that AI augmentation of human decision-making around assortment strategy and planning is going to have a major impact on the industry moving forward." said Showalter, VP of Client Services. John is a seasoned professional with more than thirty years of experience in the category management and insight niche. In the past, he has held senior roles at Bayer, Kellogg’s, the Coca-Cola company and Red Bull. He is committed to enhancing capacity and capability in category insights, space planning, shopper research and insights, and training & capabilities.  John is always looking for ways to embrace new technology and do things "smarter". When HIVERY partnered with John, they recognised there was synchronicity in their approach to technology within the sector. John appreciated the potential of the HIVERY technology, specifically what HIVERY Curate has to offer, prompting him to move to the company. HIVERY employed John to help support and drive the company's continued customer growth.  HIVERY's customers have increased by over 900%  (from five to fifty-four)  over the past few years. With John's assistance, HIVERY is on track for a Series B target in mid-2022. Their aim is to reach the target by demonstrating continued strong growth to hit about $10 million ARR. In his capacity as a CMA-certified professional strategic advisor, John has worked on the Omnichannel Project and CMA's Evolution of Category Management.  HIVERY provides AI-powered solutions for retailers and CPGs. HIVERY aims to assist its clients by providing options for category management, vending machine technology, assortment optimization, promotional calendars and planograms. HIVERY Curate is one of HIVERY's most advanced AI solutions. It provides store-level assortment simulation and optimization, enabling retailers to create rapid, straightforward options for complex situations. It is prescriptive and conversational in nature, requiring little domain expertise to use, augmenting users' ability by navigating the assortment optimization cycles rapidly. This is significantly faster than traditional methods. About HIVERY. Data has a better idea™ At HIVERY we believe that 'data has a better idea'. Pioneering hyper-local retailing, HIVERY offers retail AI-driven strategy assortment simulation & optimization solutions to a growing number of CPG companies and retailers across the globe. For more information, press only:    Address: HIVERY, Level 2,15, Foster St, Surry Hills, NSW 2010, Australia, Email ID: franki@hivery.com, Contact number: +61 409 213 683 Download PDF version

In this webinar hosted by Category Management Association; IDC’s Jon Duke, Vice President of Research and Retail Insights, and HIVERY's John Showalter, VP of Client Services, discuss the coming merchandising challenges for retailers and CPGs as well as the next generation of software solutions that help solve them. As investment in AI in retail accelerates, and its adoption increases, we are rapidly moving from the future-focused, more-visionary-than-practical approach into a world where next-gen tools deliver practical value. International Data Corporation (IDC) is the premier global provider of market intelligence and advisory services. IDC examines consumer markets by devices, applications, and services for clients. Jon Duke, IDC's Vice President of Research and Retail Insights Jon leads the Intelligent Product Merchandising and Marketing Strategies practice and is responsible for delivering IDC Retail Insights’ authoritative perspective on how retailers can leverage technology solutions to achieve key operational, tactical, and strategic objectives across merchandising and marketing functions. Jon provides global, fact-based, retailer-driven research and analysis that IT buyers and merchant users can use to gain an advantage. International Data Corporation (IDC) is the premier global provider of market intelligence and advisory services. IDC examines consumer markets by devices, applications, and services for clients.  John Showalter,  HIVERY’s VP of Client Services John is a seasoned category management and shopper insights professional with 30+ years of experience. John has held senior roles at Kellogg, Bayer, The Coca-Cola Company and Red Bull driving business outcomes and capabilities in Category Insights, Shopper Research and Insights, Space Planning as well as Training and Capabilities. He is a CMA Certified Professional Strategic Advisor and has worked on CMA's Evolution of Category Management in an Omnichannel World project. Get the IDC research report when available? Sign up, and we'll send you the latest IDC research report on this topic once available in a few weeks.