10x ROI & streamline reset process by 80%

Let's explore how AI is changing category management, generating store-level "snowflake" assortment & space recommendations - in minutes, not months!

HIVERY Curate

The world’s first truly hyper-local category management solution

HIVERY Curate is a category management tool that leverages artificial intelligence and operations research techniques to optimize shelf space and assortments – reducing the time involved in category relays from months to hours. No more dreaded relay process and rapid category scenario planning.

The planograms generated by HIVERY Curate are all space-assortment aware and can incorporate your own unique business rules and constraints.

Augmenting your decisions instantly.

Lead category management transformation

With HIVERY Curate, its simple: Your data + our AI = True store-level assortment & space recommendations. Watch video.

Industry research and insight

Forrester Research: AI In retail needs to start at the backend with smart automation

According to Forrester Research, retailers & CPGs should focus on backend operations and augment decision making with smart analytics to frequently asked questions.

Gartner Research: Emerging trends in retail and how artificial intelligence is accelerating this

Retail CIOs can leverage artificial intelligence to capture deeper insights, anticipate customer needs and proactively deliver across every touchpoint.

Future of Category Management: New capabilities needed to remain indispensable

We often hear that the future of Consumer Packaged Goods and retail sectors begins with Artificial Intelligence (AI), but how exactly can AI shift category management.

Video: Curate helps CPGs category advisors and non-category advisors

At HIVERY, we get a lot of questions about HIVERY Curate and how an AI-driven true store-specific category management solution can transform category and brand professionals' roles at major CPGs and retailers.

Podcast: Shifts in category management and how AI is being used and more

Among other topics, Zach explores the "snowflake" effect of product categories and space; how each store is so different (as a snowflake), yet when it comes to space assortment, the retail industry continues treating each store and their SKU supply the same.

Podcast: Science of retail optimization & methods we use

Mark Lawrenson

Mark Lawrenson has graduated with honours in mathematical applications and was previously a researcher at CSIRO/Data61. In this podcast he explains the science of optimization methods; the science of finding the best alternatives from many alternatives and impact to retail.

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Novel approach to transferable demand

We have been at the leading edge of category management technology for few years now. We help some of the world’s biggest manufacturers predict how shopper demand will transfer when products are added to or deleted from a store’s shelf. The key is deciphering a store’s unique purchase behavior.

Borrowing ideas from deep learning method, 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.

Discussion & demo

Ryan Callery, Sales Director
Linkedin

Across my 14 years in the CPG world, one common theme is a desire to make my partners look good. To make them heroes in their organisations by delivering outstanding results through new thinking and technology. I believe the best path to this goal is paved with data that's not just big, but better. Carefully curated and implemented. And that's what we offer here. I joined HIVERY because we help customers make better use of data to answer complex questions in simple ways. We do this at the most granular level - by store and UPC. HIVERY democratizes the power of predictive & prescriptive AI to put it in the hands of your category management professionals and brand teams; let's schedule time to chat about your initiatives and determine if/how HIVERY can help!

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