Weathering Retail M&A: How CPGs Can Ride the Waves With AI (Featured on CSP Daily)

Guest commentary featured on CSP Daily News. See full article.

With AI, CPGs can weather the storm and gain some control during the stressful M&A process. CPGs can use AI and bring thoughtful insights to the table that ease any tension in the process and give them more control at the same time. CPGs can look to AI to support difficult conversations and arm the newly formed retailer with accurate predictions around store space, total units, unique demand, loyalty and more.

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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Be the Smartest CPG in the Room During Joint Business Planning (Featured on Consumer Goods Technology)

Guest article originally featured on Consumer Goods Technology. See full article.

Joint business planning is the lifeblood of a brand’s success at a retailer.  During these meetings, retailers are looking to CPGs to bring them deep insights and category stories.

Brands should come to retailers with truly powerful insights that more accurately predict how categories will perform in the future, assist retailer partners to make intelligent decisions and advance the outcomes of joint business planning meetings. Machine learning, AI and predictive analytics can help CPGs ultimately create advantage for themselves and the retailer.

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.

Tapping Real-Time Market Data and Performance Forecasts to Pick the Next Winners

Navigating Economic Uncertainty: How Shifting Data is Revolutionizing the CPG Relationship

Data is Shifting the CPG-Retailer Relationship (featured on MarketScale podcast)

CPGs and retailers have always had strong ties. With consumer behavior shifts, more competition, and new technology tools, that relationship is evolving. In the center of the CPG-retailer relationship are AI and machine learning.

In this podcast, we explore the ways in which shifting economic trends are transforming the customer relationship between CPG companies and consumers. Join our host and expert guests as they share insights on how to leverage data to gain a competitive edge, foster innovation, and drive growth in uncertain times.

Through real-world stories and practical advice, you’ll discover new ways to think about data and learn how to use it to create meaningful connections with your customers. Whether you’re a seasoned industry veteran or just starting out, Navigating Economic Uncertainty offers valuable insights and actionable strategies to help you succeed in today’s challenging market.

First appeared here: https://marketscale.com/industries/retail/data-is-shifting-the-cpg-retailer-relationship/

To learn more about how AI can create efficiencies in resources and accuracy in both macro and micro-trend planning, click here.

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:

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 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.

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Avoiding Disastrous Category Management & Assortment Decisions

There have never been more choices available for consumers, which leads to massive challenges for category and assortment managers. Which means being able to predict what happens when product change decisions are made is critical.

How to Avoid Disastrous Category Management and Assortment Decisions

Category management as a discipline started to become popular in the 1980s. Fast forward to 2021, and there’s never been more choice for consumers, creating even more of a category challenge for both CPGs brands and retailers.

The number of SKUs in a given category has exploded, particularly over the last 10 years. According to Nielsen, there are 58% more baby food SKUs, with up to 300 for the largest assortments. Similarly, there are +81% coffee SKUS and +42% in healthcare.

There are new products coming into the market all the time, and making the wrong SKU rationalization decisions can be disastrous for a CPG. For the retailers carrying your products, it’s a complex situation. They want to integrate new products into their assortment, sometimes whilst maintaining the same or shrinking available shelf space. Removing a product can have unintended consequences and can lead previously-loyal shoppers of that product to ditch your entire brand for your competitor, or even leave the retailer entirely.

A retail category manager might choose to remove an obscure, low-margin, and slow-selling product variety – logic often dictates that’s the right thing to do. But that item could be the reason that some of the store’s most profitable customers visit in the first place.

One of the dangers of traditional category management for both brand and retailer is that decisions are made in a simplistic manner by looking at metrics like sell through and margin, but without looking at the assortment from a shopper’s perspective. Then there’s the highly complex task of looking at the store holistically and getting to a granular understanding of how a single change in one category can affect the performance of multiple others. Simultaneously, shopper segmentation and profiling is becoming more complex, as are their tastes, preferences, and behaviors.

It’s becoming clearer all the time that, for many established CPGs and retailers, decades-old approaches are still being used to make critical assortment decisions. Often these decisions use primarily historical data, which in today’s faster-and-faster-moving environment, is like looking in a rearview mirror.

Instead of looking back, the top CPGs use all available data, analyze it in real time, and then make well-founded decisions on which moves to make by being able to accurately predict the effects of change on sales, margins, and revenue. Understanding how categories work together and how changes to them impact consumer behavior and satisfaction are the keys to category and assortment success. Not only will your CPG come out ahead, but accurate forecasts that present the most compelling business case to your retailers and channel partners will build and strengthen hard-won, long-term relationships. Simultaneously, you’ll be able to hone in on optimal plans forpricing and promotions and know which marketing levers to pull in order to grow your market share in the respective category.

Talk to the category management experts at Insite AI to learn how our solution will give you the edge you need.

How AI Helps CPG Leaders Optimize Shelf Space

The stakes for shelf space are high, and the competition for it is fierce. Optimizing space is complex and nuanced, but using the right tools to analyze space elasticity helps CPGs win big.

Your brand of tomato ketchup – does it have a high sales velocity because of its huge on-shelf presence, or would it achieve the same sales with half the space? Does it need two facings or three? These questions have perplexed retail and brand leaders for decades. When a CPG has hundreds or even thousands of products in a single retail store, spacial decisions are crucial to maximizing sales and building brands. Position on shelf can be the difference between winning and losing, and CPGs often pay significant fees to retailers to secure prime shelf positions.

Optimizing space is complex and nuanced: too many facings could be a waste of space, whilst too few could mean risk of out-of-stocks and lost sales. There are many variables to consider when it comes to space elasticity and demand, and the top considerations are both quality and quantity of space. Space quality can include factors such as in-store location, shelf height, and which other products are in proximity. According to Nielsen, there were 20,000 new product launches in the US between 2008-2013, but 85% failed and stole spaces. In 2017, out-of-stocks led to $54 billion of missed opportunity.

So what is space elasticity exactly? Put simply, it’s the relationship between rate of sale and space allocation, but it varies across different products and categories. Elastic products show a substantial increase in sales when more space is given to them. As you allocate more space to elastic products, a point of diminishing returns is reached, where the sales increase rate drops dramatically. In contrast, inelastic products show little or no increase in sales when more space is given to them. To maximize your returns, you want to hit a sweet spot, and that can be challenging.

Optimization of this across all your SKUs can represent millions of dollars in additional revenue. With inelastic SKUs, you have the opportunity to maintain sales even after reducing footprint in store. With elastic SKUs, you want to increase their space in store to the sweet-spot point.

AI and data science are helping CPG players make these decisions fast and at scale. The right platforms incorporate spacial awareness, using computer-generated, 3D representations of stores to optimize space allocation and shelf-placement decisions. Using advanced algorithms, they crunch through millions of data points and what-if scenarios in a matter of hours, using a combination of historical sales data, EPOS data, 3rd-party data, and consumer insight data. This is done together with information about product margins and the costs of various in-store locations.

The output is tangible, optimized, go-to-market recommendations. Instead of seeing countless and meaningless possibilities, you need to get to the best decisions that will build your relationship and business case with the retailer. Further, you need to be able to understand space elasticity down to the most granular level: by SKU, by store format, andby retailer. The prize is the ability to drive millions of incremental value rapidly and at scale, driving your growth in the categories that matter.