There’s a lot of noise around AI and what it can or cannot do. In this article, Brooke Hodierne, former SVP of Merchandising at 7-Eleven explores the practical applications and challenges of implementing AI in the convenience store industry. She discusses the potential benefits of AI technology and emphasizes the importance of aligning AI initiatives with actual business needs and objectives rather than pursuing AI for its own sake. She also addresses the obstacles and skepticism faced by businesses, highlighting the need for realistic expectations and understanding AI’s limitations.
About the Author: Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services. Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.
CPG’s Guide to Walmart Luminate: Enhancing Results Through AI.
Are you getting the most out of your Walmart Luminate data? The platform offers a goldmine of shopper insights, but making the data actionable can be a challenge. That’s why we’ve created the CPG’s Guide to Walmart Luminate: Enhancing Results Through AI.
This comprehensive guide provides a deep dive into Walmart Luminate, exploring its unique benefits and how to apply predictive analytics to unlock its full potential.
In this guide:
The key differences between the Basic and Charter versions of Luminate.
How AI-powered solutions can harmonize Luminate data with other sources.
Real-world examples of how brands are using shopper insights to optimize strategies.
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The CPG’s Guide to AI
Empowering Consumer Brands with Clear and Actionable AI Insights
Research confirms leading consumer brands who harness the value of consumer insights and artificial intelligence (AI) better predict the needs of their customers, improve category performance, accelerate growth, and outpace the competition.
72% of executives consider AI as a business advantage
But how can you get started? With data overload, an abundance of options and unclear direction, many companies opt to do nothing. This is no longer an option. You will be left behind. Armed with the right data, AI-driven CPG brands are working hand in hand with their retail partners to better meet consumer demand. By turning mounds of overwhelming data into actionable intelligence, these CPGs are scoring big with retailers and end consumers alike.
In this guide:
Demystifying AI
How consumer brands can leverage AI today.
Top 5 AI/ML Use Cases in CPG
Going beyond Power BI and advanced analytics
Making the case for AI in your organization
Top questions to ask for a fruitful AI journey
Harness the power of AI to ensure you have the right products on the right shelves at the right time. Download this guide to begin your AI journey toward becoming an AI-driven, category-leading consumer brand.
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How Brokers Can Lead the Way in AI Adoption
Positioned between brands and retailers, brokers can leverage AI and precise data to find a common truth — and pave the way for CPGs to adopt AI
Brokers play a crucial role in the CPG and retailer community. They possess extensive knowledge of the market and categories across every store, including consumer preferences, trends, and pricing dynamics. Brokers effectively represent brands and lead as strategic partners in navigating the retail industry. Leveraging their expertise, brokers help retailers streamline their supply chains, expand their product offerings, and ultimately enhance customer satisfaction. Their ability to effectively bridge the gap between suppliers and retailers as a total solution makes brokers essential in optimizing retail operations and driving business growth.
By sitting between the CPG and the retailer, brokers hold a unique position, with an opportunity, or even a responsibility, to become leaders in how CPGs of all sizes adopt AI. The technology is currently in its infancy for effective adoption, with limited clarity on exactly how CPGs will allow AI to change ways of working. However, brokers can shape the ways this technology creates efficiencies, reduces the digital overload, and pioneers its broad application to the industry overall. In doing so, they differentiate themselves and fulfill their promises to their CPG partners in helping them gain a competitive edge in this dynamic retail landscape.
Through business intelligence and predictive analytics, brokers can ascend to new heights among CPG partners. They can also strengthen their standing among retailer partners. Moreover, brokers can be a bridge between both, using high-powered AI to uncover common data truths and drive growth across the store.
Here are top ways brokers can lead the way in AI:
1. Present Accurate Demand Planning and Predictive Market Analytics
In 2023, retail sales are expected to grow more than 4%, generating nearly $5.23 trillion, according to the National Retail Federation. NRF also said more than 70% of those sales will be inside physical stores.
How close to reality will that forecast of 4% growth turn out to be? Brokers can provide a precise view of what’s happening in the market and what is likely to happen through AI-powered demand planning and market-level trend forecasts. These data and insights help inform forecasting from the highest level. Brokers can help predict future buying behavior across channels and subcategories. They can inform retailers of trends and shifts in the marketplace, and they can provide the most granular store-level view into inventory and click-and-collect service. All of these efforts, powered by AI, continuously learn, adapt, and create an enterprise environment enabling strategic decision-making, rather than an increased digital workload. Brokers can become a single source of truth in developing a precise view of enterprise market and demand planning.
2. Assist With Store Execution and Assortment
At a store-by-store level, across retail channels, brokers can leverage AI to customize insights for CPGs in any category. AI can be custom-tailored to each of the brands with which brokers work, to build the most impactful product mix and decision-enabled portfolio.
Further, they have the unique perspective of working with brands at all points in their journey of scaling and growth. For larger brands, some brokers have a responsibility to effectively build a mature portfolio with multiple opportunities in the retail environment. In that role, they fill gaps where large CPGs lack visibility and provide solutions where larger CPGs cannot internally manage the need for additional capabilities. For emerging, growth, and niche brands, brokers have a different, more targeted set of responsibilities to deliver that those brands might not be able to generate themselves.
All brands are looking to achieve category thought leadership and mutual growth with retailers they serve. AI application to assortment optimization, demand transference, and predictive analytics can help them achieve a greater share of the category and effective increases in visual inventory. Smaller brands aiming to get a stronger foothold in a category can tap into brokers and their ability to lead with AI-driven insights to bring retailers data-informed strategies on how they’ll grow a category overall.
3. Optimize Promotions and Trade
Even without a robust services suite, as sales partners to CPG brands, brokers, enabled by AI, can boost acumen in understanding elasticities of price, space, and market. AI modeling shows how the interconnected dynamics in availability, leakage, allocated category space, pricing and promotions impact sales and profitability.
Brokers that embrace this technology will lead by using learning models to predict the most effective promotional outcomes, optimized for their partners’ established goals and the current macroeconomic environment.
The technology allows for actionable insights on how to execute the best overall plan, and the best use of promotions, in the most impactful locations, and in the most deserving regions. The technology backs brokers with the unique and differentiating capability to plan efficiently as partners and lead with the optimization of portfolios, brands, and categories, in ways CPGs are currently not leveraging themselves. Brokers can align a pricing strategy that maximizes sales, revenue, and profits for their partners.
4. Become a Bridge to a Common Truth
Possibly the greatest strength a broker can leverage through AI is an ability to lead the data capabilities that solve problems and enable more efficiencies for CPG clients, in addition to relieving their own ‘digital debt’ that continues to grow for the industry overall.
Common truth, or insights driven by the integration of multiple sources of data, narrow the focus to that with the greatest impact on the outcome. And those that excel at — or adopt these integrated models to find the common truth — will be the bridge-builders and the leaders in the industry. This becomes a powerful position for brokers, solidifying them as intelligence-driven category advisors.
Brokers have a tremendous opportunity to enhance their offerings to CPGs through the adoption of AI. AI and machine learning solutions can enable brokers to analyze vast amounts of data, including market trends, consumer behavior, and competitor insights down to the store level. By harnessing these insights, brokers can further establish themselves as thought leaders and strategic advisors, providing CPGs with valuable market intelligence, helping them to be more agile, make more data-driven decisions, and outpace the competition.
For more on how Insite AI can help brokers become innovation leaders in the industry, contact us here.
Passing Costs Onto Consumers: What is the Breaking Point?
Price Increases Are Inevitable in this Inflationary Environment. Avoid These Costly Pricing Mistakes.
Setting prices is complex, and making pricing adjustments is even more difficult. Too often, CPGs aren’t able to predict critical effects of pricing until it’s too late, and those effects can be costly. How can you thoughtfully pass cost increases onto consumers while delivering the
First: The basics of price elasticity
Put simply, price elasticity measures how demand for products changes with price – how shopper behavior changes in relation to price. For every CPG, a key theme is how pricing affects sales volume and margin? If your product has an elasticity of -2.00, it means that a 1% price increase will mean a 2% fall in volume. Knowing your elasticities will ensure you can plan price changes carefully and model the optimum mix of volume and margin. It also ensures you can collaborate successfully with retailers to get the most out of trade promotions.
According to Nielsen, price elasticity normally varies between 0 and -3.5 in CPG products. Price elasticity varies between categories, between brands, and even between individual SKUs in a range.
To harness the power of pricing elasticity to make better decisions, you need to think carefully about the following:
1. Getting your pricing right in the first place
Of course it’s fundamental to price your products properly – this will anchor your products with consumers. But pricing is neither simple nor easy. If the price is too low, then promotions will severely erode margin. If the price is too high, then volumes won’t meet expectations, even when the product is on promotion. The shopper landscape is always in flux and CPGs face cost pressure when it comes to cost of goods, logistics, and marketing costs. An essential thing to understand is how your products will perform at their base price and at promotional prices, while also taking into account whether a product is designed to have a consistent EDLP (everyday low price). Neither consumers nor retailers like price increases. However, armed with the right analytics, you can model price, volume, and profit to prove to retailers that they are not going to lose category value.
2. Ensure you understand price elasticity in a granular way, right down to individual store levels
Historically, some CPGs set prices nationally without taking into account local price sensitivities for various regions. In the same way as it’s now best practice to optimise assortment at the store level, the same applies to price elasticity, which can vary greatly by geography and individual retailer. A 2016 study by McKinsey found that companies using store-level data outperformed those using aggregated or national data by 2.2 times. Whilst strategies may start off at the national level, giving your account and marketing teams localized data will enable them to strengthen retailer relationships and adjust the marketing levers to maximise local and regional success. It’s also critical to factor in the price elasticity of shopper segments at different retailers and avoid assumptions. Shoppers at upscale or premium grocers may be just as price sensitive as those at value-based discounters.
3. Understand price elasticity at the product and brand level
Consumers can demonstrate high levels of brand loyalty, but that doesn’t mean they will universally accept price increases across the range, as sensitivities can occur even down to different pack sizes and formats. If you do need to raise prices, find the items that have the lowest level of elasticity – here you can more safely raise the price without eroding volume. Before changing prices across a whole brand, model the effects on each SKU individually to predict outcomes. That way, you are taking into account the nuances of the various categories in which these products sit and make smarter adjustments by looking at the entire picture.
4. Ensure you take into account cross elasticity and price thresholds for both your own products and those of competitors
It can be easy to fall into the trap of focusing on the price of individual items instead of looking at a range or category holistically. Do you understand how the brands inside your portfolio compete with each other in relation to price and do you understand the pricing dynamics within each range? Price gaps to your competitors should be considered in detail – especially when the brands are highly substitutable. For example, raising the price of your mid-range pet food could take it so close to the price of a competitor’s premium offering that shoppers move to the competitive brand.
So how do you optimise pricing at scale across the enterprise?
Getting to grips with price elasticity and cross-price elasticity has been a recurring challenge for even the biggest CPGs – this is because it’s challenging to accurately model volume and margin at scale, across retailers and geographies, right down to individual factors. Platforms like Insite AI sit inside your private cloud, running millions of what-if scenarios in real time so you can fully model and accurately forecast the impacts of the most granular of pricing decisions. Your CPG then has the internal capability to maximise brand growth and harness the full potential of each channel, whether retail, discount, online, or wholesale. In real-life CPG deployments, Insite AI’s price elasticity predictions are 30% more accurate than tier 1 consultancy models.
With Insite AI, you can:
Decide on the perfect prices for maximum sales, revenue, or profit generation
Create optimized pricing at the most local, granular level
Evaluate and select the best promotions and scenarios for optimized volume
Use competitive cross-price elasticity to game plan against your competitive set
Will Your Latest Price Increase Pass the Test?
Avoid Costly Pricing Mistakes by Getting to Grips with Price Elasticity
Price Elasticity Priorities
Put simply, price elasticity measures how demand for products changes with price – how shopper behavior changes in relation to price. For every CPG, a key theme is how pricing
affects sales volume and margin? If your product has an elasticity of -2.00, it means that a 1% price increase will mean a 2% fall in volume.
Knowing your elasticities will ensure you can plan price changes carefully and model the optimum mix of volume and margin. It also ensures you can collaborate successfully with retailers to get the most out of trade promotions
According to Nielsen, price elasticity normally varies between 0 and -3.5 in CPG products. But, as we know, price elasticity varies between categories, between brands, and even between individual SKUs in a range.
How can CPG companies get the right combination of factors to avoid costly mistakes and find the price sweet spot?
To Harness the Power of Pricing Elasticity And Make Better Decisions, These Factors Are Critical:
1. Ensure you understand price elasticity in a granular way, right down to individual store levels
Historically, some CPGs set prices nationally without taking into account local price sensitivities for various regions. In the same way as it’s now best practice to optimise assortment at the store level, the same applies to price elasticity, which can vary greatly by geography and individual retailer.
A 2016 study by McKinsey found that companies using store-level data outperformed those using aggregated or national data by 2.2 times
Whilst strategies may start off at the national level, giving your account and marketing teams localized data will enable them to strengthen retailer relationships and adjust the marketing levers to maximise local and regional success. It’s also critical to factor in the price elasticity of shopper segments at different retailers and avoid assumptions. Shoppers at upscale or premium grocers may be just as price sensitive as those at value-based discounters.
2. Understand price elasticity at the product and brand level
Consumers can demonstrate high levels of brand loyalty, but that doesn’t mean they will universally accept price increases across the range, as sensitivities can occur even down to different pack sizes and formats. If you do need to raise prices, find the items that have the lowest level of elasticity – here you can more safely raise the price without eroding volume.
Before changing prices across a whole brand, model the effects on each SKU individually to predict outcomes.
That Way, You Are Taking into Account the Nuances of the Various Categories in Which These Products Sit and Make Smarter Adjustments by Looking at the Entire Picture.
3. Ensure you take into account cross elasticity and price thresholds for both your own products and those of competitors
It can be easy to fall into the trap of focusing on the price of individual items instead of looking at a range or category holistically.
Do you understand how the brands inside your portfolio compete with each other in relation to price and do you understand the pricing dynamics within each range?
Price gaps to your competitors should be considered in detail – especially when the brands are highly substitutable. For example, raising the price of your mid-range pet food could take it so close to the price of a competitor’s premium offering that shoppers move to the competitive brand.
So how do you optimise pricing at scale across the enterprise?
Getting to grips with price elasticity and cross-price elasticity has been a recurring challenge for even the biggest CPGs – this is because it’s challenging to accurately model volume and margin at scale, across retailers and geographies, right down to individual factors.
Platforms like Insite AI sit inside your private cloud, running millions of what-if scenarios in real time so you can fully model and accurately forecast the impacts of the most granular of pricing decisions. Your CPG then has the internal capability to maximise brand growth and harness the full potential of each channel, whether retail, discount, online, or wholesale.
Tough Questions to Answer If You Want Category Captaincy
Retailers expect more than ever from their CPG partners as they face growing shopping challenges, keeping up with consumer demand, and adjusting to ever-evolving trends. If you want the ever-valuable category captaincy, be prepared to answer these questions.
If You Want Category Captaincy, Prepare to Answer These Tough Questions from Retailers
As retailers face growing challenges, keeping up with demand, and adjusting with ever-evolving trends, they’re starting to question the role of their category captains. These people or teams have traditionally assisted retail buying departments, acting as unbiased analysts who worked to deliver the retailer’s goal for the category. Mike Gervasio, President of Category Leadership at PepsiCo and Chairman of the Category Management Association, was quoted in Retail Wire as saying, “It took the pandemic to really shake the behavior of the CPG industry; there’s entirely new problems to be solved.” He said that the industry has been accelerated by 5 years in just a matter of months and that companies have to acquire new sets of data and tools in order to deal with new challenges.
Against the shifting backdrop of consumer behavior, retailers have a real need for a different kind of category captaincy from their CPGs in order to keep them onside. CPG leaders need to prepare for these tough questions from retailers.
Is Your CPG Prepared for These Retailer Questions?
How can we grow the joint profit pool?
Relations between CPGs and their retail partners have fallen to their lowest levels in 5 years according to Bain & Company (2021). However, a well devised joint business plan can deliver more than 10% of incremental profit pool growth for both parties in a single year, helping to strengthen relations.
How can CPGs help us to glean better insights from our mass of data?
Whilst retailers sit on huge amounts of EPOS and loyalty card data, they are not as advanced when it comes to AI, data, and analytics. In return for closer collaboration, there is an opportunity for CPGs to use AI platforms to add value for both parties at a much more granular level to grow the joint profit pool.
How do we get to grips with category management for e-commerce channels?
Retailers are having a tough time adapting to e-commerce, curbside delivery, and marketplaces where category management becomes even more complex. In theory, online sales could give consumers access to endless long-tail choices, but at the same time this creates a logistical nightmare. Retailers need support on how to optimize choices when it comes to assortment and pricing for the online world whilst meeting customer needs.
How can you help us to make assortment adjustments faster and in a more agile way?
Retailers are working to adapt their offers and store formats quickly as the trend toward smaller store formats and neighborhood markets means difficult choices need to be made. How much space should be allocated per category? How can we get the most out of everyinch of that space? Retailers and CPGs need the ability to make assortment decisions in real time. Waiting for annual or bi-annual reviews means potential revenue is leaking away.
How do we ensure the category is managed properly?
When one CPG is the captain of a category in a key retail account, there is always the question of how impartial their recommendations are. AI and machine learning can support CPGs with business case modelling so category decisions are transparent and scientific.
How do we deal with such high levels of product innovation?
Record numbers of product innovations are launched every week. New categories, brand extensions, and even new flavor or fragrance variants mean there’s no shortage of variety. Meanwhile, shelf space is shrinking. Retailers need assistance to model every change made on the shelf to make sure they can maximize revenue.
We how can we localize our assortments at scale?
Over the years, retailers have become better with store clustering and assortment localization. But many want to take their assortments to the next level, delivering even more value to shoppers based on local needs, preferences, cultural differences, and even price elasticity. This is an area where technology can help.
You want to raise your prices, but can you help us understand price elasticity?
One of the biggest tensions between retailers and CPGs is price increases. As a CPG, you face pressures on cost of goods, logistics, and marketing. Meanwhile, retailers want to protect their value proposition and price perception. You can help retailers understand price elasticity down to individual store level using a platform like Insite AI.
Can you help us to develop new store formats and optimize the space?
Spacial optimization is key, especially with increasing smaller store formats where every inch needs to count. Add value for the retailer by helping them understand the spacial elasticity of your products. Prove to them the profit opportunity of allocating additional facings to your SKUs.
Shoppers only buy your products when they are on promotion – Help!
Getting pricing right from the start is crucial. Some products are priced too high, so they tend to only generate volume when on promotion. Likewise, a low everyday price that’s too low means that promotions erode margins even further. Use technology to optimize pricing and understand the demand transfer that happens when prices go up and down. Create appropriate promotional strategies accordingly for the optimal revenue outcome.
Funding Incremental Growth With AI
Selecting the right AI solutions to for your category, pricing, and assortment decisions can reduce inventory costs, improve forecast accuracy, and enhance the customer experience. By leveraging these solutions, CPGs can fund incremental growth and achieve their business objectives.
How to Choose an AI Solution for Pricing and Assortment Optimization
Choosing the right solution for critical assortment and pricing decisions means a CPG must practice a lot of due diligence to find the right fit. Having worked across CPGs and teams at all levels, from the c-suite to VPs and category managers, here are some of the questions you should be asking as you evaluate a data science, analytics, or AI-based solution:
Protect the IP that is the heart of your CPG’s strategic competitive advantage
There is nothing more precious than the capabilities, knowledge, and unique intellectual property that’s been crafted over decades (or even hundreds of years in some cases). You want to be sure that any analytics, data-science, or AI-powered solution is going to protect the integrity of these hard-won trade secrets. Therefore, choose a solution that sits within your own private cloud, where no data, learnings, or proprietary information is ever going to leave the organization.
Choose a solution built for CPGs and Consumer Brands
AI is often misunderstood. Some of the world’s largest software companies have promised it as being the remedy to an endless number of business challenges. While some of that might be true, they haven’t always done a good job of explaining what AI or data science actually means for specific business functions in a tangible, applied way. Some promote an AI or data science platform that ends up being completely generic – these solutions are often sold as solving any type of business problem. But, we’re often shown that being all things to everyone makes a master of none. Software and technology without consulting is just a tool, and that tool often ends up being challenging to use and without the promised results. Rather, it’s important to look for a purpose-built solution that’s able to be customized for each unique business case, that serves specific needs, and is able to be intentionally constructed to solve CPG-focused assortment, pricing, demand, and category management challenges.
Think about the importance of solving specific problems instead buying a piece of technology
Start with your specific business problems and translate those into a custom solution that works for stakeholders across your enterprise. This allows for a bespoke solution to be built for individual needs, rather than trying to solve individual problems with a broad solution. CPGs may operate in the same industry, but their IP, modes of operation, and go-to-market strategies vary wildly. Solving big, unique problems is never going to be about buying a piece of technology or a solution that works right out of the box. Instead, look for an AI company whose domain experts will get to know your CPG’s business, its unique challenges, and how best to solve them. Then, get those experts to build a custom solution to do the job by producing desired outcomes.
Funding Incremental Growth Through Effective Trade Promotion Optimization
How does product demand with price change? And, how does your pricing affect sales volume and margins? Keep these factors in mind to harness the power of pricing elasticity and make the best decisions.
Avoid Costly Pricing Mistakes by Getting to Grips with Price Elasticity
PRICE ELASTICITY PRIORITIES
Put simply, price elasticity measures how demand for products changes with price – how shopper behavior changes in relation to price. For every CPG, a key theme is how pricing affects sales volume and margin? If your product has an elasticity of -2.00, it means that a 1% price increase will mean a 2% fall in volume. Knowing your elasticities will ensure you can plan price changes carefully and model the optimum mix of volume and margin. It also ensures you can collaborate successfully with retailers to get the most out of trade promotions.
According to Nielsen, price elasticity normally varies between 0 and -3.5 in CPG products. But, as we know, price elasticity varies between categories, between brands, and even between individual SKUs in a range. How can CPG companies get the right combination of factors to avoid costly mistakes and find the price sweet spot?
TO HARNESS THE POWER OF PRICING ELASTICITY AND MAKE BETTER DECISIONS, THESE FACTORS ARE CRITICAL:
Ensure you understand price elasticity in a granular way, right down to individual store levels
Historically, some CPGs set prices nationally without taking into account local price sensitivities for various regions. In the same way as it’s now best practice to optimise assortment at the store level, the same applies to price elasticity, which can vary greatly by geography and individual retailer. A 2016 study by McKinsey found that companies using store-level data outperformed those using aggregated or national data by 2.2 times. Whilst strategies may start off at the national level, giving your account and marketing teams localized data will enable them to strengthen retailer relationships and adjust the marketing levers to maximise local and regional success. It’s also critical to factor in the price elasticity of shopper segments at different retailers and avoid assumptions. Shoppers at upscale or premium grocers may be just as price sensitive as those at value-based discounters.
Understand price elasticity at the product and brand level
Consumers can demonstrate high levels of brand loyalty, but that doesn’t mean they will universally accept price increases across the range, as sensitivities can occur even down to different pack sizes and formats. If you do need to raise prices, find the items that have the lowest level of elasticity – here you can more safely raise the price without eroding volume. Before changing prices across a whole brand, model the effects on each SKU individually to predict outcomes. That way, you are taking into account the nuances of the various categories in which these products sit and make smarter adjustments by looking at the entire picture.
Ensure you take into account cross elasticity and price thresholds for both your own products and those of competitors
It can be easy to fall into the trap of focusing on the price of individual items instead of looking at a range or category holistically. Do you understand how the brands inside your portfolio compete with each other in relation to price and do you understand the pricing dynamics within each range? Price gaps to your competitors should be considered in detail – especially when the brands are highly substitutable. For example, raising the price of your mid-range pet food could take it so close to the price of a competit
So how do you optimise pricing at scale across the enterprise?
Getting to grips with price elasticity and cross-price elasticity has been a recurring challenge for even the biggest CPGs – this is because it’s challenging to accurately model volume and margin at scale, across retailers and geographies, right down to individual factors. Platforms like Insite AI sit inside your private cloud, running millions of what-if scenarios in real time so you can fully model and accurately forecast the impacts of the most granular of pricing decisions. Your CPGthen has the internal capability to maximise brand growth and harness the full potential of each channel, whether retail, discount, online, or wholesale.
How Nestle-Purina and Boston Beer are Navigating the AI Journey
Presented in conjunction with the Promotion Optimization Institute.
Artificial Intelligence (AI) is the hot new thing for planning, Category Management and Revenue Growth Management.
Wherever you are a top CPG or an emerging brand, getting game-changing results takes vision, planning and the right partner.
In this panel-style session you’ll hear from two segment leaders, Nestle-Purina and Boston Beer along with AI innovator Insite AI.
They’ll talk about their visons for transforming planning and how they are navigating the AI journey. Q&A included.
Watch The Webinar
Fill out the form below to request access to the webinar
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