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A five (5) min guide that will walk you through what you need to know about assortment, simulation, optimization & AI

Oct 11, 2021 | By

HIVERY

Retail is a tough game, and optimizing your choices can be incredibly difficult. With the ever-growing number of SKUs to choose from, limited shelf space, and supply chain complexity, retailers and Consumer Packaged (CPG) companies are increasingly under pressure to get assortment right. Enter Artificial Intelligence (AI).   You can develop assortment strategies or review how your stores are doing, and hope for the best, but that may not give you the best results.  Question: Have you considered using computers to simulate and help you optimize your category assortments? Analytical assortment optimization powered by machine learning can greatly improve both decision and execution in rapid time. Here are a few key facts about this field and how you can use it in your category assortment planning.   What Is Machine Learning?  This is a segment of the field known as Artificial Intelligence (AI) and is designed to mimic the ways humans can learn things by importing data. While AI is a favorite topic in science fiction, the goal is to let computers improve their results through data and algorithms. Machine learning (ML) is a subset of AI that uses special algorithms or models to conduct data analysis, finding patterns in data and relationships within the dataset, in order to make predictions. Predictions get better over time as more data is analyzed. Trial and error plays a big part in ML. As the model finds errors or discrepancies between its prediction and the actual dataset, it tries to rectify its thinking so its predictions are close to what is actual. This process is often called "training the model". Actual datasets are split into training sets and validation sets, generally a 90/10 split with 90% for training and 10%  for validation of its prediction or error rate. This is where data scientists might change the features of what the model should be learning from such as price, product, location and/or the model's parameters; which are the properties of the training datasets that are learned during training. Oftentimes, parameters are something that a model learns on its own and adjusts automatically as it tries to reduce its prediction error rate. It is this ML trial and error that is important because it sees the world from a machine construct, not a human construct. This difference allows retailers and CPGs to tap into a new way of looking at, for example, assortment optimization. Below is an illustration of this process of learning:   How Is It Used in Retail?  ML can help retailers optimize their supply, product, and sales. Retailers and CPG brands generate tons of data, and ML can look through this data to find new actionable insights for businesses to achieve their goals. Many retailers use data analysis, and you've probably seen examples work in real-time when you're shopping or visiting a store. If you buy something online and get a "customers who liked this also like..." message, or if you see recommended items that match what you're shopping for, that's an example of a type of ML model. However, there's also a lot of analysis and modelling that goes on behind the scenes. The location of stock on the shelves, and even what stock is in the store, can be improved with machine learning. Retailers and Consumer Packaged (CPG) companies can use ML models to find out where best to put items, how to shelve and advertise, and what things to restock.  ML algorithms can effectively improve profit margin by ensuring a better product mix and uncover previously neglected customer needs.  This means superior SKU rationalization and new product introduction as well as optimal space allocation and localization for those assortments. Basically, the ML algorithms can be made to look at the data and help retailer/CPG assortment teams find out what moves stuff off the shelves. Do people buy a particular item more when it's on an endcap and is it worthwhile to have that item there instead of something else? ML models that are used for assortment optimization can perform core SKU analysis, letting you figure out your optimal SKU introduction for each store.  Some retailers also use ML to help them with loss prevention. If you've ever tried to buy something locked away in a special part of the store, you've seen this idea in action.  ML algorithms can be trained to pick items that are likely to be stolen. The stores then lock them up, cutting their losses on those stock items. While it can be frustrating, it serves as an important tool for keeping costs low.    What Can Analytical Assortment Optimization Do?  Analytical assortment optimization is a specific way to use machine learning to help you make decisions for your business. It can perform "retail simulation," giving you a virtual idea of the impact of your category or merchandising assortment decisions and recommendations on how to improve them.  Historical data is a key aspect of this kind of demand or assortment forecasting. ML algorithms will look at the sales and volume numbers from your entire universe of stores, trying to find patterns in the data. Did something sell really well in one store that can be sold in another? Which stores will this new SKU introduction do better in? The decision can be simulated rapidly and the impact quantified to inform decision making. Again, more data is always better. However, the reality is there are also merchandising rules or constraints like POD (Point of Distribution) thresholds and ensuring certain SKUs get at least, say, 1.5 cases on the shelves. As you begin using analytical assortment optimization to track your sales and store's performance, you'll begin changing these constraints and be able to see the true impact of your decisions. As this happens, the algorithm will be able to better analyze what sort of changes are beneficial at store-level or national-wide.   How Can You Implement It? There are a few ways to go about using data analysis and ML to improve your assortment decisions. If you feel confident about your statistics and programming skills, you can do the analysis on your own and try to improve your store. But if you're like most people and barely know anything about statistics or programming, or simply don’t have time, you may have to seek outside help. Whether you want some planogram analysis, SKU rationalization, or retail optimization, it might be a good idea to pay someone else to help take care of things. Tech companies offer data analysis on all types of business data. From your products to your advertising choices, the analysis can improve your profit margins and get your products moving. Everything from small things all the way up to the big picture with your macrospace allocation and localization can be covered. It doesn't matter if it's big or small; data analysis can help you save money and improve your business!   Learn More If you'd like to learn more about how analytical assortment optimization can help your business, we'd love to help!  You can learn more about our solution, HIVERY Curate which enables you to run SKU rationalization, SKU introduction and optimization at store-level and up. HIVERY Curate can incorporate any merchandising constraints, allowing you to simulate strategic scenarios with accuracy and reliability, guaranteeing executable store-specific recommendations for every cluster dimension or count; eliminating the averages of averages approach. Feel free to contact us with any questions or concerns about machine learning, AI, or data analysis you may have or just see a live demo. Seeing is believing! Get this guide emailed? Want this guide email to you for later reading? No problem, request it here
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news
HIVERY Curate Now Available on Microsoft AppSource
Oct 05, 2021 | By

HIVERY

Sydney, NSW, Australia — October 5, 2021 — HIVERY today announced the availability of HIVERY Curate on Microsoft AppSource, an online cloud marketplace providing tailored line-of-business solutions. HIVERY was established in 2015 with the motto “Data Has A Better Idea”. It is the company's expertise in data science and heritage from the renowned CSIRO's Data61 that makes HIVERY special. CSIRO is Australia's national science agency who also invented Wi-Fi technologies and helped NASA reach the Moon, sending TV images to 600 million people around the world. The company has had remarkable success in providing powerful retail assortment strategy simulation and optimizations to complex assortment challenges to global CPG brands and retailers like The Coca-Cola Company and Walmart. "The market reaction to HIVERY Curate is extremely exciting, so we are expanding our operations aggressively in North America with the help of our investors and business partners like Microsoft,” said Jason Hosking, CEO, HIVERY Toby Bowers, General Manager, Business Applications Group, Microsoft Corp., said, “We’re happy to welcome HIVERY Curate to Microsoft AppSource, which gives our customers access to the best solutions available from our extensive partner ecosystem. Microsoft AppSource offers partner solutions such as HIVERY Curate from HIVERY to help customers meet their needs faster.” Learn more about HIVERY Curate at its page on AppSource. About HIVERY HIVERY is a pioneer of hyper-local retailing and offers retail AI-driven strategy assortment simulation & optimization solutions to a growing number of CPG companies and retailers in North America. 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
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news
HIVERY announced as a finalist for the 2021 Premier’s NSW Export Awards
Oct 04, 2021 | By

HIVERY

The Premier’s NSW Export Awards is an annual program that acknowledges the achievements, hard work and commitment of Australian companies engaged in international business. This award recognizes the important contribution exporters make to Australia’s economy by strengthening the workforce and boosting prosperity for both the community and the state. HIVERY finds itself amongst other prestigious and hard-working organizations in the Advanced Technology category, all having not only survived but thrived and innovated despite the challenges of the pandemic. We are proud to stand beside them as we advance to the finals together. Being one of the finalists for the Premier’s NSW Export Awards means that, today, HIVERY has reached another significant milestone. This recognizes our passion for and dedication to revolutionizing merchandising management and retail analytics through cutting-edge technology. Our team wishes to express our gratitude to the panel of judges - Export Council of Australia - and our fellow finalists.  See video and list here It is a huge privilege for HIVERY to be part of such a prestigious award and to also be distinguished as one of the top businesses for exports in the country. We are looking forward to hearing the outcome of this event which will be held in late October. This year has brought HIVERY into the limelight with The Financial Times Asia-Pacific naming us as one of the high growth top 500 companies with a rank of 77, Forbes Asia putting us amongst the 100 To Watch List and being announced as a finalist for AmCham’s Alliance Award in Artificial Intelligence. Driven to make a greater impact in the retail industry and beyond, HIVERY has developed a new category space in retail analytics. Using the world’s most advanced machine learning and operations research techniques, our goal is to consistently empower global FMCGs, CPGs and retailers to make more informed decisions that will enhance customers’ shopping experiences, optimize shelf space utilization and maximize profits. If you would like to know more about this solution and our other products, contact us
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HIVERY announced as a finalist for AmCham’s Alliance Award in Artificial Intelligence
Sep 16, 2021 | By

HIVERY

Our team is honored to announce that the American Chamber of Commerce has listed HIVERY as a finalist for the Alliance Awards 2021 in the Artificial Intelligence category. AmCham’s inaugural Alliance Awards Gala will recognize pathfinders in six key industries that are actively strengthening the links between Australia and the United States, and these awards are designed to shine a light on their achievements, focus attention on future investment, and inspire the next generation of Australian and American innovators. We thank the panel of judges, the AmCham Alliance Association, Verizon and our fellow finalists. It’s an honor to be recognized for such a prestigious accolade in this category as it signifies our alignment with both Australian and American industries. Our team looks forward to hearing the outcome on December 8th in Sydney. “We are grateful to be a finalist, amongst a cohort of so many other amazing companies,” said Jason Hosking, CEO of HIVERY. “We are proud to be pushing market boundaries by truly transforming retail analytics and merchandising management with practical applications of AI. We are achieving extraordinary results and recently surpassed US$1B in incremental customer sales generated through our HIVERY Curate soluition. It comes down to our ability to help our customers to leverage AI by generating rapid retail strategy simulation and optimization strategies. It is hyper-local retailing at its best.” Using the world’s most advanced machine learning and operations research techniques, HIVERY will consistently empower global FMCGs, CPGs and retailers to make more informed decisions that will enhance customers’ shopping experiences, optimize shelf space utilization and maximize profits.  If you would like to know more about this AI solution and our other products.
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Thanking the Export Finance Australia for supporting HIVERY's continued US expansion
Sep 10, 2021 | By

Export Finance Australia

Founded in early 2015, Red Analytics (Trading as HIVERY) offers retail strategy simulation and optimisation to Retail and Consumer Packaged Goods (CPG) customers around the globe. Headquartered in Surry Hills, NSW, HIVERY has sales offices in Japan and the US, and around 90% of its revenue is generated internationally. HIVERY started as a $US50K experiment after a data hackathon event that proved AI can augment human thinking. This led to the creation of a mathematical model that optimises the products in fleets of vending machines.... Read More
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podcasts
Discover new emerging retail risks and why even the biggest retailers continue to face inventory management challenges
Sep 06, 2021
In this podcast you will learn: • Why "new retail disruptors" are going to be those that can give intelligence around inventory. This is a competitive imperative, it's not an advantage. • What emerging risks that are invisible at the moment retailers are facing as the new normal takes its effect. For example, with decline in human interaction and traffic in physical stores due to Buy online, pick up in store (BOPIS) and mobile checkout, discovery opportunities and cross sell opportunities are reduced. • What new technologies retailers should be investing in and integrating in order to meet new shopping habits of the "frictionless" shoppers. Conventional legacy technologies in store today will not do. • Why retailers are relying on technology suppliers to bring new innovations? Why some retailers who traditionally have not built their own physical technology are starting to use artificial intelligence (AI) algorithms? • What new, smarter technologies in retail are being developed to help with risk mitigation. • Why AI/machine learning models really "force multipliers", accelerating everything inside the retail box and more.
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Watch On Demand Webinar Now: Using AI to simulate the impact of human judgement
Sep 03, 2021 | By

HIVERY

Can human foresight be achieved? To fully leverage the value contained in data, CPG companies and retailers need to bring Artificial Intelligence (AI), specifically Machine Learning (ML), into their retail planning and execution processes. We need to transition from a data-driven world to an AI-driven world. A world that can help us augment human judgement as simply as Google Maps does today. With the proliferation of AI/ML capabilities in our business and lives, we are now able to get closer to understanding the impact of every decision before making that decision. In this fascinating webinar, HIVERY will explore how AI/ML is being used to simulate the impact of human judgement both in our industry and beyond. You will learn how AI/ML: Is used to augment and qualify human decisions Can find the right balance of risk and reward for different retail growth strategies; all at store-level Can rapidly and iteratively simulate different strategies to achieve your growth plans Presented by Heather Martin, Vice President of Sales at Hivery and Jackie Wightman, Sales Director at Hivery Questions on how to leverage AI in your retail strategy design and find optimal store-level assortment and planograms? Send us an email at hello@hivery.com
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news
Watch On Demand Webinar: Simulating New Item Success - From Innovation To Execution At The Retail Shelf
Aug 31, 2021 | By

HIVERY

In this webinar, we shared some of the best practices on how CPG manufacturers are leveraging next-generation Machine Learning (ML) and Operations Research (OR) models to: Simulate where new items will earn distribution in retailers down to the store level To maximize revenue by optimizing your brand portfolio at the store level to create space for new innovation and high performing items Quantify the success of existing brands and new items Designed for front line users in sales, marketing and category management, in this webinar, you will learn how ML and OR are being used to augment traditional tasks from category strategy to planogram creation - all in minutes, not months. Presented by Scott Anderson, Chief Revenue Officer and Zach Simpson, Director, Customer Success
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news
HIVERY makes Forbes Asia 100 to Watch list
Aug 13, 2021 | By

HIVERY

Our team is honored to announce that Forbes has included HIVERY on their 100 to Watch list. This inaugural list places a spotlight on remarkable companies and start-ups on the rise across the Asia-Pacific region. Companies featured on the list have been selected from a pool of over 900 submissions and highlighted for making extraordinary progress and impact despite the challenging climate brought on by the Covid-19 pandemic. “We are honored to be part of this list, amongst so many amazing companies,” said Jason Hosking, CEO of HIVERY. “To me, this is really about recognizing and celebrating our hard work as a collective team, our investors who continue to believe in our vision, our business partners who continue to support us and of course, our customers who are seeing the unfair advantage in transforming their merchandising and category management operations with our products.”    This news comes just as HIVERY has been acknowledged as a leader in providing category management simulations and optimizations by Coresight Research and recognized in the Market Guide for Retail Assortment Optimization Applications in Merchandising by globally recognized independent IT research and advisory company, Gartner. HIVERY is transforming category and merchandising management processes amongst the world's biggest brands in retail and CPG, and has recently secured additional funds from new investors OneVentures (read more here) and existing ones: AS1 Growth Partners and Blackbirds ventures (read more here).  "The Retail and CPG industry has relied on traditional techniques and tools developed back in the 1980s, combined with human ‘gut feel and experience’ to manage merchandise in stores. We are changing this and how category management is done, advancing our industry into the Fourth Industrial Revolution," said Scott Anderson, Chief Revenue Officer at HIVERY Using the world’s most advanced machine learning and operations research techniques, HIVERY will consistently empower global CPGs and retailers to make more informed decisions that will enhance customers’ shopping experiences, optimize shelf space utilization and maximize profits.  If you would like to know more about this solution and our other products, contact us here. We’d love to hear from you.  We wish to express our gratitude to everyone who has helped us get this far.  Here's to a better future we will keep on building.
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