Borrowing ideas from deep learning methods, HIVERY is able to generate data from random noise (i.e. random numbers) to infer incrementality and demand transferability. It's the world’s first AI driven cannibalization model, and it drives HIVERY's assortment recommendations.

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AI Models Transferable Demand With Unparalleled Accuracy 

If you get the fundamentals right, everything else often falls into place. And in retail analytics, there are a number of fundamentals to grasp.

Check out our latest blogs on AI, retail, big data, shopper insights, category management, trade promotion optimization and more

Relax and enjoy our diverse range of podcast on our Retail Maverick channel

Other resources you might find interesting...

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Transferable Demand White Paper Download Request

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.

 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:

 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.

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!

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!

Want this guide email to you for later reading? No problem, request it here

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

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.

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

HIVERY Curate Now Available on Microsoft AppSource - Media Release.pdf

HIVERY today announced the availability of HIVERY Curate on Microsoft AppSource, an online cloud marketplace providing tailored line-of-business solutions.

HIVERY Curate Now Available on Microsoft AppSource

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

FINALIST EVENT - 2021 Premier's NSW Export Awards

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

Driven to make a greater impact in the retail industry and beyond, HIVERY has developed a new category space in retail analytics involving a powerful assortment strategy simulation and optimization solution.

HIVERY announced as a finalist for the 2021 Premier’s NSW Export Awards

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. 

HIVERY makes finalist for AmCham’s prestigious 2021 Alliance Award in Artificial Intelligence

HIVERY announced as a finalist for AmCham’s Alliance Award in Artificial Intelligence

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

Thanking the Export Finance Australia for supporting HIVERY's continued US expansion

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.

Brand is the Founder/Principal at Elverston Consulting. He is a senior strategic advisor providing upper leadership consultative services in retail technologies. Brand specialises in disruptive, non-traditional technology integration, large scale development and implementation and technology identification. Brand is one-of-a-kind leader in the retail industry. And if you like this podcast, you must get Brand's new book: Proclivity: It’s more intuitive than you think, resist making it hard.

Discover new emerging retail risks and why even the biggest retailers continue to face inventory management challenges

Discover new emerging retail risks and why even the biggest retailers continue to face inventory management challenges.

Meet Brand Elverston:
Brand is the Founder/Principal at Elverston Consulting. He is a senior strategic advisor providing upper leadership consultative services in retail technologies. Brand specialises in disruptive, non-traditional technology integration, large scale development and implementation and technology identification. Brand is one-of-a-kind leader in the retail industry.  And if you like this podcast, you must get Brand's new book: Proclivity: It’s more intuitive than you think, resist making it hard. 
https://www.amazon.com/dp/B096Z5TWTT/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1623774186&sr=8-1

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.

Brand Elverston on new emerging retail risks and inventory management challenges by Retail Mavericks

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:

1. Is used to augment and qualify human decisions
2. Can find the right balance of risk and reward for different retail growth strategies; all at store-level
3. Can rapidly and iteratively simulate different strategies to achieve your growth plans

Presented by
Heather Martin, Vice President of Sales at Hivery
Jackie Wightman, Sales Director at Hivery

CMA + HIVERY on Using AI to simulate the impact of human judgement

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.  

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

Watch On Demand Webinar Now: Using AI to simulate the impact of human judgement

Category Management Association (CMA) hosted a HIVERY webinar where we shared 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 
•	Enable pinpointed advertising in markets to amplify the success of existing brands and new items

Designed for front line users in sales, marketing and category management, learn how ML and OR is being used to augment traditional tasks from category strategy to planogram creation - all in minutes not months. 

Presented by 
Scott Anderson, Chief Revenue Officer at Hivery
Zach Simpson, Director, Customer Success at Hivery

CMA + HIVERY On Simulating New Item Success - From Innovation To Execution At The Retail Shelf

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 

Watch On Demand Webinar: Simulating New Item Success - From Innovation To Execution At The Retail Shelf

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.

HIVERY makes Forbes Asia 100 to Watch list

This article showcases our top picks for the best Sydney based Machine Learning companies. These startups and companies are taking a variety of approaches to innovating the Machine Learning industry, but are all exceptional companies well worth a follow.... read more.

HIVERY 1 of 54 Most Innovative Sydney Based Machine Learning Companies

54 Most Innovative Sydney Based Machine Learning Companies

Coresight Research Retail-Tech Landscape comprises selected startups globally that can help retailers improve their digital offerings and the overall customer experience.  The landscape report spotlights innovators that are disrupting the retail industry in various categories.

Retail-Tech-Landscape-Innovator-Holiday-2021-Shopping-List—Technology-Essentials-Jul-29-2021.pdf

Coresight's Retail-Tech Landscapes Report (no registration required)

If you liked this report, you may also like:

Coresight Research full report: The Age of Precision Category Management: Hyper-Localized Assortment Optimization Using Advanced Technologies

Coresight Research X HIVERY webinar: Deborah Weinswig, CEO & Founder of Coresight Research, and HIVERY’s VP, Client Services, NA, Steve Weir, and Director of Customer Success, Zach Simpson explore how retailers & CPG companies are using advanced technologies in machine learning and data science to simulate merchandising strategies and find incremental value. 

Summary-Coresight-Research-x-HIVERY-The-Age-of-Precision-Category-Management—Technology-Driven-Assortment-Optimization-Jun-17-2021.pdf

Coresight Research X HIVERY webinar's key summary takeout (no registration required)

Retail-Tech Landscape: Retailers’ Technology Shopping List for Holiday 2021 contents:

Five Key Trends in Retail Technology for Holiday 2021

Retail-Tech Landscape: Retailers’ Technology Shopping List for Holiday 2021—Infographic

Retail-Tech Landscape: Retailers’ Technology Shopping List for Holiday 2021, by Category

Download Coresight Research Retail-Tech Landscape comprises selected startups globally that can help retailers improve their digital offerings and the overall customer experience.

Coresight Research Retail-Tech Landscape Report

On May 12, 2021, Deborah Weinswig, CEO and Founder of Coresight Research, hosted an online event in conjunction with HIVERY, provider of an artificial intelligence (AI) and data science (DS)-powered solution that harnesses store-level data for category management optimization, enabling precision in category management and localized assortment.

Learn how HIVERY is pioneering hyper-local retailing, and offers simulation & optimization solutions to a growing number of retail and CPG clients in North America. 

In this free report, Coresight presents three key insights from the event, which explored the age of precision category management.  

The full report is available for free and can be accessed by registering details here.

Click here to watch the full on-demand webinar or download the summary.

Download Our Free Webinar Summary No registeration Required. Coresight Research x HIVERY: The Age of Precision Category Management and The Power of Retail Simulation & Optimization

Summary: Coresight Research x HIVERY: The Age of Precision Category Management and The Power of Retail Simulation & Optimization

There’s an old adage: You get what you get, and you don’t get upset. Clearly, that doesn’t apply to retail. For some time now, the axiom has been customers want what they want, when they want it. And COVID-19 didn’t seem to change that. Apparel shoppers want choices, and they want the stores and clothes to reflect their local tastes and demands.

Coresight Research says the pandemic “accelerated consumer disinterest in homogeneous physical retail experiences.” Instead, shoppers are seeking experiences that are local, distinctive and diverse. The firm advises retailers consider localizing in-store experiences and product offerings. Coresight adds that mall owners can support its tenants with localized center experiences. They good at adding datasets but lack the ability to model based on goals and constraints - this is HUGHE difference....Read More

Meeting Shopper Demand with Localized Assortment Optimization  

In this podcast you will learn:
• What Operations Research and Optimisation is about and how most of the models used today assume one thing: consumers are rational!

• How can statistics explain consumer choice? Dr Berbeglia is working on new models to help predict what consumers will choose given certain events or triggers. This research has applications for policy marketers, category management and shopper insights professionals.

• Does social influence impact consumer choice? Dr Berbeglia illustrates, for example, how popularity of media can influence consumer rationality and hence choice.

• What new consumer rationality experiments are being performed by Dr Berbeglia in his labs. One interest is "the compromise effect". The effort that dictates that a consumer will choose a middle option over a given set of choice alternatives as it's easier to justify that choice, it reduces cognitive dissonance and loss aversion.

• Another phenomenon Dr Berbeglia is exploring is the "the decoy effect", the effort going into consumer choice when the third option is presented against existing two options.

• Dr Berbeglia talks about applications and effects of these models from choice of COVID19 vaccine to organ donation to retail and assortment, category and merchandising planning.

• Hear what Dr Berbeglia thinks about how these consumer choice models will be used in machine learning and AI models in the future.

Interesting further reading and watching:

• Compromise effect and consideration set size in consumer decision-making, Jaewon Yoo, Hyunsik Park & Wonjoon Kim 

• The decoy effect: how you are influenced to choose without really knowing it, Gary Mortimer, Associate Professor in Marketing and Consumer Behaviour, Queensland University of Technology

• Dr Dan Ariely classic experiment Pricing the Economist 

Dr Berbeglia specialises in Operations Research, Game Theory, Transport Optimisation, Revenue Management, Pricing Optimisation, Impact of Social Influence In Markets. Gerardo’s research has been published in leading journals and conference proceedings, including Transportation Science, INFORMS Journal on Computing, European Journal of Operational Research, Operations Research Retters, ACM conference on Economics and Computation (EC), Conference on Web and Internet Economics (WINE) and the International Joint Conference on Artificial Intelligence. His recent research focuses on revenue management and quantitative models that account for social influence in online markets (such as iTunes or Google Play). Gerardo teaches Operations, Optimisation and Decision Making, and Supply Chain Analytics on the MBA programs and Master of Business Analytics program.

Are consumers rational really? How does this impact shopper insights, category management, marketing and future AI models? Dr Gerardo Berbeglia, Associate Professor of Operations, shares his views and predictions

Are consumers rational really? How does this impact shopper insights, category management, marketing and future AI models? In this podcast, Dr Gerardo Berbeglia, Associate Professor of Operations, shares his views and predictions.

Meet Dr Gerardo Berbeglia:

Dr Berbeglia specialises in Operations Research, Game Theory, Transport Optimisation, Revenue Management, Pricing Optimisation, Impact of Social Influence In Markets. Gerardo’s research has been published in leading journals and conference proceedings, including Transportation Science, INFORMS Journal on Computing, European Journal of Operational Research, Operations Research Retters, ACM conference on Economics and Computation (EC), Conference on Web and Internet Economics (WINE) and the International Joint Conference on Artificial Intelligence. His recent research focuses on revenue management and quantitative models that account for social influence in online markets (such as iTunes or Google Play). Gerardo teaches Operations, Optimisation and Decision Making, and Supply Chain Analytics on the MBA programs and Master of Business Analytics program.

In this podcast you will learn:
• What Operations Research and Optimisation is about and how most of the models used today assume one thing: consumers are rational!

• How can statistics explain consumer choice? Dr Berbeglia is working on new models to help predict what consumers will choose given certain events or triggers. This research has applications for policy marketers, category management and shopper insights professionals.

• Does social influence impact consumer choice? Dr Berbeglia illustrates, for example, how popularity of media can influence consumer rationality and hence choice.

• What new consumer rationality experiments are being performed by Dr Berbeglia in his labs. One interest is "the compromise effect". The effort that  dictates that a consumer will choose a middle option over a given set of choice alternatives as it's easier to justify that choice, it reduces cognitive dissonance and loss aversion.

• Another phenomenon Dr Berbeglia is exploring is the "the decoy effect", the effort going into consumer choice when the third option is presented against existing two options.

• Dr Berbeglia talks about applications and effects of these models from choice of COVID19 vaccine to organ donation to retail and assortment, category and merchandising planning.

• Hear what Dr Berbeglia thinks about how these consumer choice models will be used in machine learning and AI models in the future.

Interesting further reading and watching:

• Compromise effect and consideration set size in consumer decision-making, Jaewon Yoo, Hyunsik Park & Wonjoon Kim https://www.tandfonline.com/doi/abs/10.1080/13504851.2017.1340567

• The decoy effect: how you are influenced to choose without really knowing it, Gary Mortimer, Associate Professor in Marketing and Consumer Behaviour, Queensland University of Technology
https://theconversation.com/the-decoy-effect-how-you-are-influenced-to-choose-without-really-knowing-it-111259

• Dr Dan Ariely classic experiment Pricing the Economist https://www.youtube.com/watch?v=xOhb4LwAaJk

Dr Gerardo Berbeglia on consumer irrationality impact on category management and future AI models by Retail Mavericks

Not one, but 10: that’s the number of venture debt deals now completed by Dr Michelle Deaker’s OneVentures first debt fund, which has now put half of its $80 million in capital to work...Read more.

HIVERY scores debt from OneVentures in 10th credit deal

• 2:00 - 7:02: What current challenges are faced by the category management discipline. Key insight is how time consuming and extremely labour intensive the process is as well as variability from team to team and time to time.

• Why the industry has limited to clusters planograms generation and how this generates an average of averages approach.

https://soundcloud.com/retail-mavericks/jeff-ireland#t=2:00

• 7:03 - 10:15: How artificial intelligence/machine learning techniques have been solving for these category management challenges: speed in delivery, speed in category strategy simulation, running unlimited tests and what-if scenarios, and optimising for them.

https://soundcloud.com/retail-mavericks/jeff-ireland#t=7:03

• 10:16 - 14:54: The shifts in category management: from clusters based on averages to clusters based on store-level analysis and insights.

• What "space-assortment aware" means and how it is applied at HIVERY.

https://soundcloud.com/retail-mavericks/jeff-ireland#t=10:15

• 16:12 - 22:25: What the most common constraints or business rules are used by retailers or CPGs before building planograms. For instance, shopper safety and global deletes or additions, forcing in new products or minimising assortment churn.

https://soundcloud.com/retail-mavericks/jeff-ireland#t=16:11

• 22:25 - 24:35: How a machine learning model is used to challenge these constraints or business rules by running different and rapid scenarios. For example, if you removed a particular SKU or brand, it would present the outcome of those scenarios.

https://soundcloud.com/retail-mavericks/jeff-ireland#t=22:25

• 24:36 - 29:57: What the benefits of store-level analysis are for any cluster dimension or count you use.

With 18 years in category management, Jeff Ireland is an Analytic Consultant at HIVERY. Before HIVERY Jeff was the Senior Manager of the Category Strategy and Analytics Team at The Coca-Cola Company focusing on the creation of revolutionary AI products deployed at Walmart. This was HIVERY Curate. So, Jeff decided to leave Coke to join HIVERY to continue to bring this revolutionary solution to retailers and CPGs.

During Coke, Jeff was the Category Advisor to Walmart in the sports drinks category where he focused on helping provide effective store level space recommendations for Walmart. Jeff also worked at Retail Solutions Inc. as a Senior Product Manager and Senior Analytical Consultant as well as at Newell Brands as a Category Advisor.

With 18 years in category management, Jeff Ireland is an Analytic Consultant at HIVERY. Before HIVERY Jeff was the Senior Manager of the Category Strategy and Analytics Team at The Coca-Cola Company focusing on the creation of revolutionary AI products deployed at Walmart. This was HIVERY Curate. So, Jeff decided to leave Coke to join HIVERY to continue to bring this revolutionary solution to retailers and CPGs.

During Coke, Jeff was the Category Advisor to Walmart in the sports drinks category where he focused on helping provide effective store level space recommendations for Walmart. Jeff also worked at Retail Solutions Inc. as a Senior Product Manager and Senior Analytical Consultant as well as at Newell Brands as a Category Advisor.

Based in Bentonville, Arkansas, Jeff Ireland ex Coke, Newell Brands and IRI talks about the challenges and future of category management analytics

Based in Bentonville, Arkansas, Jeff Ireland ex Coke, Newell Brands and IRI talks about the challenges and future of category management analytics.

With 18 years in category management, Jeff Ireland is an Analytic Consultant at HIVERY. Before HIVERY Jeff was the Senior Manager of the Category Strategy and Analytics Team at The Coca-Cola Company focusing on the creation of revolutionary AI products deployed at Walmart. This was HIVERY Curate. So, Jeff decided to leave Coke to join HIVERY to continue to bring this revolutionary solution to retailers and CPGs. 

During Coke, Jeff was the Category Advisor to Walmart in the sports drinks category where he focused on helping provide effective store level space recommendations for Walmart. Jeff also worked at Retail Solutions Inc. as a Senior Product Manager and Senior Analytical Consultant as well as at Newell Brands as a Category Advisor.

In this podcast you will learn:
• 2:00 - 7:02: What current challenges are faced by the category management discipline. Key insight is how time consuming and extremely labour intensive the process is as well as variability from team to team and time to time.

• Why the industry has limited to clusters planograms generation and how this generates an average of averages approach.
https://soundcloud.com/retail-mavericks/jeff-ireland#t=2:00

• 7:03 - 10:15: How artificial intelligence/machine learning techniques have been solving for these category management challenges: speed in delivery, speed in category strategy simulation, running unlimited tests and what-if scenarios, and optimising for them.
https://soundcloud.com/retail-mavericks/jeff-ireland#t=7:03

• 10:16 - 14:54: The shifts in category management: from clusters based on averages to clusters based on store-level analysis and insights.

• What "space-assortment aware" means and how it is applied at HIVERY.
https://soundcloud.com/retail-mavericks/jeff-ireland#t=10:15

• 16:12 - 22:25: What the most common constraints or business rules are used by retailers or CPGs before building planograms. For instance, shopper safety and global deletes or additions, forcing in new products or minimising assortment churn.
https://soundcloud.com/retail-mavericks/jeff-ireland#t=16:11

• 22:25 - 24:35: How a machine learning model is used to challenge these constraints or business rules by running different and rapid scenarios. For example, if you removed a particular SKU or brand, it would present the outcome of those scenarios.
https://soundcloud.com/retail-mavericks/jeff-ireland#t=22:25

• 24:36 - 29:57: What the benefits of store-level analysis are for any cluster dimension or count you use.
https://soundcloud.com/retail-mavericks/jeff-ireland#t=22:25

Jeff Ireland on the challenges and future of category management analytics by Retail Mavericks

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HIVERY is mentioned in Valuer.ai's article The 50 Best Startup Cities in 2021!

The age of precision category management is upon us. Effective assortment and space localization can be a very challenging task for CPG companies and retailers, but if done right with the right methods, the incremental value generated is significant. Whether they are using a cluster approach or not, the ability for retailers and CPG companies to leverage store-level data in new ways opens up new opportunities to have the right product assortment to meet shopper demand across any store.

You can watch the full recorded session with Deborah Weinswig, CEO & Founder of Coresight Research, with HIVERY’s VP, Client Services, NA, Steve Weir, and Director of Customer Success, Zach Simpson, in which they discussed the signiﬁcant incremental value to retailers and CPG companies in optimizing assortment at the store level using advanced technologies in machine learning (ML) and data science (DS). 

In this on-demand mini-webinar, we cut to the question: 

Why do many retailers choose to take the top-down "cluster" approach vs store-specific?

The age of precision category management is upon us. Effective assortment and space localization can be a very challenging task for CPG companies and retailers, but if done right with the right methods, the incremental value generated is significant. Whether they are using a cluster approach or not, the ability for retailers and CPG companies to leverage store-level data in new ways opens up new opportunities to have the right product assortment to meet shopper demand across any store.

Recorded on Wednesday, May 12, Deborah Weinswig, CEO & Founder of Coresight Research, was joined by HIVERY’s VP, Client Services, NA, Steve Weir, and Director of Customer Success, Zach Simpson, to discuss the signiﬁcant incremental value to retailers and CPG companies in optimizing assortment at the store level using advanced technologies in machine learning (ML) and data science (DS).

They will explore the following:

1. Key industry trends that are impacting CPG and retailers with a focus on effective product localization, in-store execution and harnessing the power of data to meet shoppers’ expectations

2. How both retailers and their suppliers can achieve the goal of shopper-centricity through the hyper-localization of assortment and space

3. The power of ML and DS models for driving effective localization and space planning. A key to driving category share and incremental revenue lift

Watch full version here for free here 

https://www.hivery.com/case-studies/coresight-webinar-age-of-precision-category-management

Why do many retailers choose to take the top-down approach vs clustering?

Recorded on Wednesday, May 12, Deborah Weinswig, CEO & Founder of Coresight Research, was joined by HIVERY’s VP, Client Services, NA, Steve Weir, and Director of Customer Success, Zach Simpson, to discuss the signiﬁcant incremental value to retailers and CPG companies in optimizing assortment at the store level using advanced technologies in machine learning (ML) and data science (DS). 

1. Key industry trends that are impacting CPG and retailers with a focus on effective product localization, in-store execution and harnessing the power of data to meet shoppers’ expectations

2. How both retailers and their suppliers can achieve the goal of shopper-centricity through the hyper-localization of assortment and space

3. The power of ML and DS models for driving effective localization and space planning. A key to driving category share and incremental revenue lift

Meet Shopper Demand

The age of precision category management is upon us. Effective assortment and space localization can be a very challenging task for CPG companies and retailers, but if done right with the right methods, the incremental value generated is significant. Whether they are using a cluster approach or not, the ability for retailers and CPG companies to leverage store-level data in new ways opens up new opportunities to have the right product assortment to meet shopper demand across any store.

On Wednesday, May 12, Deborah Weinswig, CEO & Founder of Coresight Research, joined by HIVERY’s VP, Client Services, NA, Steve Weir, and Director of Customer Success, Zach Simpson, to discuss the signiﬁcant incremental value to retailers and CPG companies in optimizing assortment at the store level using advanced technologies in machine learning (ML) and data science (DS). They will explore the following:

1. Key industry trends that are impacting CPG and retailers with a focus on effective product localization, in-store execution and harnessing the power of data to meet shoppers’ expectations

2. How both retailers and their suppliers can achieve the goal of shopper-centricity through the hyper-localization of assortment and space

3. The power of ML and DS models for driving effective localization and space planning. A key to driving category share and incremental revenue lift

Webinar (On Demand) Coresight & HIVERY on Age of Precision Category Management

Watch on-demand webinar: Age of Precision Category Management: Using Hyper-Localized Assortment Optimization to Meet Shopper Demand

What are some of the key barriers faced when switching to store-specific analysis?

What are some of the key barriers faced when switching to store-specific analysis.mp4

How do analytical thinking and technology/data literacy complement each other when working with large data sets to draw shopper or category insights? 

How do AI recommendations factor in online (i.e. e-commerce) vs offline (in-store) purchases

On-demand mini-webinar: How do analytical thinking and technology/data literacy complement each other when working with large data sets to draw shopper or category insights?

How do analytical thinking and technology/data literacy complement each other when working with large data sets to draw shopper or category insights?

Why is there lower than expected usage of advanced analytical methods by the retailer/CPG industry?

Why is there lower than expected usage of advanced analytical methods by retailer/CPG industry

What is the financial benefit to retailers of such store-level analysis compared to clustering?

What is the financial benefit to retailers of such store-level analysis compared to clustering.mp4

How do AI recommendations factor in online (i.e. eCommerce) vs offline (in-store) purchases?

On-demand mini-webinar : How do AI recommendations factor in online (i.e. eCommerce) vs offline (in-store) purchases

How do AI recommendations factor in online vs offline purchases?

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