Accelerate Your Sales, Revenue Growth, and Category Management Initiatives
Groceryshop | September 19-21, 2023 | Mandalay Bay, Las Vegas
Connect with Insite AI at Groceryshop and find out how our revolutionary approach can accelerate your top initiatives. Our team of AI and strategic consulting teams have walked in your shoes, giving them unparalleled insights into your industry-specific hurdles. Our Strategic Advisors are consumer brand and retail veterans from Coca-Cola, PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others.
Let us guide you in tackling your organization’s distinct challenges head-on. Through our collaborative approach, we craft a tailored solution to elevate your product assortment, pricing strategies, trade promotions, and demand forecasting.
Don’t miss this opportunity to expedite your success and lead your organization toward a more efficient and profitable future.
We’re the only partner that lets you dial in multiple scenarios, and confidently predict how they would perform on a forward looking basis against multiple KPIs, with details down to the most granular level, regardless of complexity. Make confident decisions at either the big-picture strategic or tactical level involving commercial aspects such as assortment, pricing, trade, space, and planning. In one click, foresee the results of exactly what will happen in any given scenario. Our unique capabilities take in multiple conditions and assumptions; alternatively, decision makers can rely on us to leverage the technology on their behalf. Act with extreme certainty, speed, save significant time, and ensure your actions will achieve commercial results.
Define your specific objectives, and receive new and creative ways to reach them.
Are you seeking to grow volume? Maximize prices? Grow shelf space? Improve trade effectiveness? Outperform a competitor? Rationalize spend? Our capabilities “goal seek” the exact new strategies or tactical outputs to achieve this, taking into account all of your business dynamics, beliefs, and nuances. Get multiple novel strategies that are truly implementable and actionable. Fuse your vision with our technological levers that incorporate an incredible number of factors. See the forward looking and granular articulation on the recommendation’s performance. This is something any large team of experts aren’t capable of.
Explainable assortment, space, pricing, and trade promotion decisions.
Harmonizing data and searching it for insights is old news, and few companies see value from it. We provide internal and external narratives that are defensible and truly differentiated. In one click, our capabilities explain and decompose the “why” on a forward-looking basis; and the data is presented in a powerful, immediately understandable manner. Incrementality, demand transference, price elasticities, cross elasticities, attributions, shifts, patterns, and factors affecting your existing or recommended actions are clearly articulated.
Former SVP of Merchandising at 7-Eleven, Brooke brings nearly 20 years of grocery and convenience retail experience to Insite AI. She understands what it takes to build valuable partnerships with retailers, and in her role as EVP of Strategy Consulting, she advises consumer brands on ways to elevate strategic business planning, achieve category leadership, and create optimal shopping experiences for their consumers.
Former leader at Coca-Cola, Dr Pepper Snapple, and Delhaize, Capri brings extensive strategic leadership experience from both retail and supplier roles in the consumer goods industry. She was recognized as a Senior-Level Top Woman in Convenience in 2022 and has also received recognition for her leadership in collaborative/joint business planning with top retailers across multiple channels and formats.
Kristine is a highly accomplished retail executive and former VP of Merchandising at Walmart. Kristine led strategic initiatives that resulted in substantial revenue growth for the company across Grocery and prior to that Personal Care. Widely recognized as a visionary leader, she played a pivotal role in optimizing Walmart’s merchandising with large CPGs.
Why Insite AI?
A Consultative Approach
Our team becomes an extension of your team. Our Strategic Advisors are consumer brand and retail veterans from PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others. Our top priority is ensuring you have the guidance and support you need to achieve your goals and maximize the value of your investment.
Everyone else starts from scratch, yet Insite AI has already invested over eight figures of capital and several years into building leading edge technology; creating unmatched advantages for tackling your top initiatives.
We deeply tailor our engagements and fully configure our solutions to meet the unique needs of your brand. Insite AI is a true innovation partner providing CPGs with fully customizable solutions built to solve their unique challenges, enabling them to adapt quickly to changing market conditions and outperform their competition.
Navigating Uncharted Waters: How I’ve Used AI To Help Brands Prepare for Unexpected Events
AI can not only produce plans A, B and C, but also plans X, Y and Z.
Having spent roughly the last seven years in data science at leading global brands, including Bacardi and Anheuser-Busch InBev, I can tell you that brands are too often working in “react mode.” Even at leading brands that already have an army of data scientists and AI-enabled sales and commercial teams, the organization can be challenged by events or market shifts it didn’t see coming.
During my time in the large CPG world, I saw how the brands I worked for — and competitors — reacted to economic shifts, technological changes, unexpected category product trends and more. There’s a lot that can throw a brand off track.
For any global brand looking to scale-up results or that is newer to AI and machine learning, there’s room to improve when it comes to preparing for the unexpected. There are learnings to gather that better manage product assortment, pricing and demand forecasts.
Unexpected Events Impacting Retail
The greatest and most timely example of an unexpected event is the COVID-19 pandemic, which upended all industries, but especially retail. At AB InBev, being a global company, we were able to prepare for shifts in the market and jumped to work with professors at MIT. Understandably, not all brands prioritized using data and technology to help, which has left many of them still learning from what happened.
Natural disasters are also events that can ravage operations regionally and have impacts on a global scale. But there are several other examples that CPGs may not immediately consider such as:
Economic crises. Sudden economic downturns, financial market crashes or currency devaluations that squeeze consumer spending and purchasing power.
Technological disruptions. An emergence of a new disruptive technology — or the obsolescence of an existing one — can throw a wrench into business models. Think of the advent of streaming services impacting traditional media consumption.
Geopolitical events. Unforeseen trade disputes, political instability or international conflicts can greatly disrupt supply chains, sourcing and trade.
Social and cultural shifts. A large cultural movement that causes a shift in consumer values and preferences can impact brands. It can be a sudden reaction to a brand or larger changes like consumer attitudes towards sustainability and ethical sourcing.
Regulatory changes. Unexpected changes in policies or legislation can disrupt business. Product label changes and safety standards, taxes and tariffs can impinge on production costs and market access.
The analysis of these events happening around the world simultaneously can greatly complicate brand strategies. Brands need the right talent in place to understand every shift occurring and how it impacts the total value chain.
Where My AI Efforts Helped Brands
It is my belief that AI’s role isn’t so much to unequivocally predict an event, but the technology can better prepare brands for unexpected scenarios. Here are five ways I’ve used AI to help protect and better manage brands during unexpected events.
1. Enable teams to run endless test-and-learn scenarios (mimicking the many events listed above). AI can not only produce plans A, B and C, but also plans X, Y and Z.
2. Analyze historical data, current market trends and external elements to identify patterns and indicators that may precede such events. This analysis helps alert brands to the potential of unexpected events.
3. Monitor economic indicators, social media sentiment, news reports and even weather patterns, to identify any warning signals. For instance, predictive technology can detect sudden shifts in consumer sentiment or emerging global risks, such as geopolitical tensions or economic instability. By incorporating this information into predictive models, the CPGs were in a better position to anticipate and prepare for unexpected events.
4. Use technology to make snap decisions in real time to chart a new course of action for a brand and make effective moves immediately to limit any damage incurred. For example, if there is a sudden surge in demand for certain products due to panic-buying (like the toilet paper scare of the pandemic) or a shift in consumer needs (like shelf-stable foods during a storm), predictive technology recognizes patterns and quickly predicts behaviors going into and out of the trends.
5. Analyze customer behavior, such as the current downward turn in online grocery shopping or shifts in pricing sensitivity. These real-time insights empower consumer brands to adjust their production, distribution and marketing strategies accordingly.
The power of predictive technology isn’t so much to be a crystal ball, but to aid brands in delivering a collaborative and communicative relationship with retailers during challenging times.
Why Technology Is a Table Stake
The power of predictive technology isn’t so much to be a crystal ball, but to aid brands in delivering a collaborative and communicative relationship with retailers during challenging times.
I saw this firsthand through the brands I worked with, and the technology developed much stronger relationships with retailers. Brand teams can utilize retailer data and run daily reports — especially since constant communication during major events is extra important. These insights can help retailers optimize their inventory management processes and place core, valuable products onto shelves and maintain stockouts.
Data-driven capabilities and AI can be a necessary assistant during unexpected events. The tools can support how crisis teams manage unforeseen events.
The ability to provide data-driven recommendations and support helps build trust inside and outside an organization, and improve operational efficiency. The learnings also guide brands to weather any storm and better expect the unexpected.
To learn more about how Insite AI can help brands mitigate unexpected events, contact us.
Why Are CPGs Still Making Multi-Billion-Dollar Decisions Using Spreadsheets?
“Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt.”
Many CPGs still rely on the trusted yet limited capabilities of spreadsheets as primary tools for assessing and taking action on assortment, trade spending, space and promotions planning. While effective for certain applications, spreadsheets were invented in 1979; Excel was invented in 1985. These are not intuitive or enabled tools that can provide the timely, precise details required to make multibillion dollar decisions. With the retail landscape moving faster than ever, it is time to break free from the constraints of spreadsheets and leverage the transformative power of 21st century technologies. The future belongs to those who embrace innovation and adapt to the evolving industry landscape.
Limitations of Spreadsheets
Relatively easy to learn and use, spreadsheets are a popular option for conducting data analysis among CPGs. They offer a familiar and accessible interface for handling data, performing calculations, and creating visualizations. However, when it comes to fast decision-making in the dynamic world of consumer brands, spreadsheets reveal their limitations. While they can handle considerable amounts of data, they can be slow and unstable, particularly when data is complex. Spreadsheets further struggle to efficiently process and consolidate diverse data sets, leading to manual efforts (heavily reliant on already limited human resources) and potential inconsistencies. Excel can also impede collaboration and sharing at a time when there is more data than ever before to leverage. The bottom line is that spreadsheets are not intuitive and they require human intervention for use and to create value.
Market Volatility
Organizations are constantly trying to evaluate market volatility, competition, emerging markets and channels, and consumer behavioral shifts to assess where to allocate resources. Not having the right products and package sizes in the right place at the right time with the right price results in lost sales opportunities. If performance data shows gaps to targeted objectives, the organization will spend the year working to re-assess remaining planned actions and investments. This makes dependence on historic data troublesome. The gap between the “look back” and the “look forward” is a missed opportunity, especially in light of the market and supply chain volatility of the past three years in the CPG industry in particular.
Modern Approaches
Today, purpose-built CPG-tailored software can ingest billions of data points from disparate sources to assess category maturity, predict future performance and assess the value of investments, allowing brands to appropriately allocate resources. It can also make more precise financial predictions. If resources are not allocated properly, expected results are not achieved. The resulting “gaps” can take a long time to close. Spreadsheets simply indicate what those gaps are; they do not indicate how to solve them. They can only hold data.
CPG-tailored technology uses timely data to project into the future, reducing dependence on historical data alone. Unlike spreadsheets, CPG focused software can “learn” from repetitive patterns and algorithms; it does not simply report data.
CPG-specific software with modeling capabilities uses multiple data sources in real time, incorporating everything from product sales and gas prices to labor department data and demographics. Because their models (accelerated by different prediction, product and pricing engines), are continuously finding data points and learning, they are able to provide forward-looking and prescriptive insights. It can signal package optimizations–e.g. whether there should be more gallon sizes of milk in a particular store versus single-serve cartons. The technology also finds those “needles in the haystack” that can be key differentiators from one store’s assortment to the next. By allowing all data to work together, teams can respond swiftly to market changes and adapt strategies dynamically, providing a competitive edge in a fast-paced industry.
Collaboration & Pinpointed Goals
Moving beyond spreadsheets enables greater collaboration and agility. Cloud-based platforms and data-sharing technologies have begun to facilitate seamless communication across departments, breaking down silos and fostering a collaborative culture. As part of that evolution, good software can facilitate better annual business planning, factoring in supply chain, labor and other costs into input assumption fields. The beauty of this is that it gives visibility to everyone in an organization and makes highly accurate predictions. This elevates target-setting, breaking out targets by function. It measures and compares achievements and lets retailers and suppliers work together to meet goals. Retailers and CPGs can then enable the Joint Business Planning process with these same powerful tools and more collaboratively agree upon a set of metrics and activities that will achieve aligned business objectives that are very specific to categories, investments or activities. Progress against all objectives is part of the modeling, constantly assessing and improving accuracy of predictions, reducing or eliminating the replanning that results from gap closure and volatility.
Lack of Trust & Familiarity with AI
Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt. Despite evidence to support the use of AI, its effective application to broad data sources and existing processes is still nascent in the CPG industry. Just 11% of CPG organizations have adopted ML/AI tools. This stems from various factors, including concerns about the accuracy and reliability of AI algorithms, and a lack of clarity on how to apply the forms and functions of AI models to existing business processes.
There is tremendous efficiency to be gained using technology over spreadsheet, regardless of whether it incorporates a little AI or a lot of AI. Good software does not necessitate adding people (nor replacing people) to make that happen. It’s a small investment compared to what the returns can be when technology is used to augment teams and enable them to act with exponential speed and precision. Any returns can be high with clearly measurable objective-setting and ROI.
Conclusion
The move away from spreadsheets is not just a call for change; it is an opportunity for growth and innovation. By embracing cutting-edge software and analytics, the full potential of data can be unlocked, allowing CPGs to make informed decisions and drive sustainable business growth. The time to act is now, as the CPG landscape continues to evolve rapidly. Those who adapt to change will be the ones to thrive and capitalize on the transformation opportunity.
To learn how you can evolve to be a more agile and AI enabled company, contact Insite AI.
Seeing AI Through a Practical Lens (Featured on C-Store Dive)
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.
Weathering Retail M&A: How CPGs Can Ride the Waves With AI (Featured on CSP Daily)
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.
Download Guide
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.
Download Guide
Predicting Availability at the Shelf: The Power of AI
In this episode, we’re talking about how AI is being used with both CPG companies and retailers to better predict consumer behavior.
Listen to our latest podcast episode on predicting shopper behavior. In this episode, we discuss key strategies for optimizing at-shelf availability, distribution, assortment, and total portfolio optimization.
First appeared here: https://www.foodprocessing.com/podcasts/food-for-thought/buyer-behavior-artificial-intellgience/
Transcript
Erin: Welcome you to the Food For Thought podcast.
Ryan: Thank you very much. I am very pleased to be here.
Erin: Let’s start talking a bit about what Insite AI is all about. What does the company do and who is your end-user?
Ryan: Yeah, thank you very much for the forum. Insite AI, we deliver artificial intelligence solutions focusing on hyper-localization, so solutions that are really tailored or customized to specific problems or platforms that customers are looking for or problems they’re looking to solve. We primarily focus on CPGs. Every now and then, we do connect with retailers, but our primary customer is the CPG, and we do a wide range of things in the artificial intelligence space for them.
Erin: With all of the mountains of data out there, it can definitely seem overwhelming with what to do with it and what direction to take with it. Can you talk about that a little bit more and how InsiteAI incorporates the data that CPG companies are feeding into it?
Ryan: This is actually a question that is one of the first hurdles that we have to overcome when we’re dealing with CPGs. Usually, there is a fairly limited data set. We know that there’s a universe of data out there, but what’s actually usable and complete and clean is a fairly limited data set. So one of the things that we’re finding is that as we start to bring in these mountains of data, one of the first things that we really have to align on is what is clean and complete and what is very accessible. The second part really then factors into the remainder of the mountain, which is all of these really small data points.
A really good example that’s coming out very frequently right now is things like mobility, right? Like external data that can really enrich some of the answers from a predictive system that allows you to kind of drive and do different calculations than you would be able to do if you didn’t have it. But you have to figure out how to incorporate that, how to plug it in where it makes most sense, what type of calculations can leverage it best. And when we’re talking specifically in the food production industry or food services industry, that type of data is hugely important to understand where are people gonna be, what are their patterns, things like that.
With COVID as well we’re seeing a lot of that data that can also be incorporated in understanding, “Will people leave their houses? Will people go to specific restaurants? What type of behaviors will they exhibit?” And some of those COVID data sets will drive, you know, kind of these knock-on forecasts or knock-on patterns that will essentially affect that consumer behavior.
There is a mountain of data out there that the best approach if you’re a CPG or food manufacturer is to analyze each of those data sets against that core base data set that you have. Really start to understand what the value of those components are to your specific business. Artificial intelligence and leveraging data is a little bit like alchemy in the sense that when you’re developing a specific potion, it’s so custom that as you start to try and take that somewhere else, sometimes it just doesn’t work. A data set that was very usable and sensitive to a specific partner is not as usable or sensitive to another partner and essentially it can take you backwards in accuracy. The data really needs to be deciphered and tested. And from that you need to drill down into those couple needles within the haystack of external data within that mountain that can really support the answers in a positive way and support your accuracy increase.
Erin: I like how you mentioned the alchemy aspect. You used a term that I’m not as familiar with that I’d like you to elaborate on. What exactly is clean and complete data?
Ryan: Thank you for giving me the opportunity to elaborate on that because this is something that every single company, partner, student, anyone that’s leveraging any artificial intelligence to drive predictions or understand trends or do analytics is going to come up against. And essentially, the cleanness of data really comes down to the fact of, “Is everything in the right column? Is everything categorized correctly, spelled correctly, do all the characters align the same?” Because what you’re using in artificial intelligence is really being able to crunch a bunch of numbers in a very small amount of timeframe.
You’re crunching a mountain of data, numbers, and attributes and you’re correlating and calculating everything. To get the best answers, everything in there needs to be in an ideal state – as clean as possible. And that way, you don’t get anomalies or mathematic or statistical outliers driven by formulas. Because if you’re getting those, you want them to be relevant and based on the math you’re using. You don’t want them to be irrelevant based on something that’s unclean, that sends accuracy down or confuses the system or the output.
The second piece of that, which is the completeness, is something that we deal with a lot, especially in CPG. Sometimes we get data sets where we might only have a certain number of brands, or we might only have a certain amount of markets, or we might only have data for a specific period. Where you have a projection or a prediction that needs to be executed, but you only have a slice or a sliver without having the complete picture. And in some of these cases, you can kind of work around it.
We’re dealing with a specific set of brands and we know our brand really well. We have all of our data as well as some retailer data and some of the market data, then we can start to put certain pictures together. But, for example, if you’re dealing with very localized, specific, fragmented data such as receipt data, partial loyalty data, or partial sales data, that’s where it becomes a little more tricky because you can start to get these signals that actually send you in the wrong direction.
When we talk about the alchemy piece of it, that’s really one of those things where you need to understand not only the mass behind it and how to actually approach the artificial intelligence. You really then need to understand what your raw materials are to play with so you can produce the best results out of those raw materials versus just applying a methodology in a cookie-cutter fashion.
Erin: I know in doing the research for this particular episode that Insite AI’s tool can help the food processors listening to this episode with behavioral predictions. Walk me through what that’s all about.
Ryan: The principles around artificial intelligence can be achieved through lots of different means. You start to crunch the data, and you’re not just dealing with quantitative data, you’re dealing with qualitative component, but you realize you not only understand kind of what’s going to happen, but you can start to understand the fragments and the segments and the clusters behind what’s driving that future or that prediction.
And I think for us, that’s what really allows us then to start to understand behavior in a very segmented fashion. We are seeing so many microsegments that have popped up. And it’s driven by a combination of different trend timelines specifically driven by the current external environment. We’re seeing timeline-driven components. We’re seeing buying behavior or shopper behavior components as well. You’ve got an ecosystem that, prior to this, was fairly understood. Now what we’re seeing is that ecosystem that we understood is not only changing in these short bursts, but they’re also fragmenting within those changes.
Just to understand that behavioral component of it is really important. That behavioral component of is really telling you which groups of shoppers or consumers are changing, how quickly they’re changing, what groups they’re falling into. And then ultimately, how can you then focus on that level of understanding to then predict their buying pattern? And this, we believe, is going to become hugely more important for really everyone in manufacturing and retail to understand because it means that some of these behaviors are going to be short-term and some of them are gonna be long-term. And you really have an ability to actually influence and drive and set some of those behaviors within these, kind of, short burst trend cycles. And so that’s one of the huge benefits that we’ve seen. And it really is, you know, all about understanding your customer or your consumer at the level in how they’re thinking about the category they’re purchasing in.
Erin: I love what you’re talking about with helping identify and isolate potential short-term or longer-term behavior. If we’ve learned anything in the last year, it’s how disruptions can so severely impact things across the entire supply chain. Can this tool able to help with forecasting when disruptions occur?
For instance, let’s say you are a food manufacturer and you’re looking at your consumer data, you can see that, “We make this particular brand of oat cookie, or we use a certain kind of oat in our product formulation.” Is this the kind of tool that processors can use to say, “We can see people in the Midwest or people in the East are really buying a lot of this particular oat cookie, we need to make sure that we have ordered even more of the oats that we need?” Is that how this tool can help processors?
Ryan: Yes, it’s absolutely one of the ways that kind of leveraging these techniques and these tools you can benefit. First, understanding, “Hey, if I have issues further up the supply chain or sourcing components, I can start to take a look at those and analyze, forecast, and game plan for when I’m going to have that I know.” That leads in to having a trickle-down strategy to say, “Okay, now, I have my kind of core materials and understand what I’m gonna have. Here’s what I can do with those.” Absolutely, we can predict based on those materials, what we know, what we’re going to be able to do with them, and ultimately, what results we’re going to get through the demand and the purchasing.
One of the most interesting things that I’ve seen and been working on over the last couple months is not only just understanding the disruption from the sourcing component or disruption from the global pandemic component where things are happening and we’re trying to figure out, “Okay, what do we have? What can we do?” It’s also from the standpoint of saying, “Hey, you know, if there is a major area of our business that we didn’t see coming, that we didn’t necessarily have that strategic plan for, leveraging the artificial intelligence and the speed and the scenario planning that you can do with tools like this really gives you the ability to essentially disaster control or crisis control your environment.”
If you have a system in the background and you have potential situations where you can’t source materials for certain products, or you can’t now deliver certain products to the store, what this will allow you to do is understand the issue at hand. It also lets you take a look at a larger kind of portfolio level and say, “What other things do I have and how should I approach leveraging those other, products, brands, avenues, and where? Where should I leverage them? If I have an issue where there’s a certain batch of product that I can’t use or I can’t deliver, do I just shift things from other places?” And what’s the impact to that, right? That’s kind of one view or thought process.
There’s also the situation of, “I just can’t get anything. I’ve got a ship that’s stuck in Panama. I’ve got a whole product line that’s going to go down or potentially a whole brand that’s gonna go down for a period of time. What should I put in? What would be the best bet from a retailer perspective? What would be the best bet from a CPG profitability or volume perspective?” Being able to look at that further in real-time and to then say, “Well, is that the strategy I wanna execute? Do I want to push the customers or the consumers one way or another because I now know what the outcome of this crisis is if I just do the normal thing? Maybe I can start to look at multiple different types of outcomes, where I’m doing something non-traditional. I’m pushing them into a different area.”
The last part would be competitive information. You can also gain some competitive intelligence. The timeframe you’d will probably a lot shorter because competitors aren’t going to tell you what’s going on, but at the retail level, you’ll start to understand some of the challenges going on. When you have systems that can move very quickly and analyze in real-time or take in non-traditional data or qualitative data, you can start to then do the same thing with competitive landscape data. This helps you to understand, “Okay, my competitor’s going to have a supply issue. My competitor’s going to have a sourcing issue. How do I capitalize on that? How do I go to a retailer and partner with them or make sure I’m satisfying the consumer with other products that potentially I wouldn’t have had that opportunity if there wasn’t an issue with that competitive landscape?”
There’s so much that can be wrapped into that with what you can do. And it really is all about understanding what’s happening in a moment and being able to not only project, you know, the outcome, but also to strategize along the lines of, “Hey, listen, you know, something’s going wrong. Let’s at least see what the best outcome we have in line with the strategy that we actually want to execute.”
Erin: Are there any trends or behaviors that your teams or clients are picking up on that you can share? I don’t know if that’s competitive information, but is there anything that you guys have noticed that you’re able to share?
Ryan: I think the biggest thing that we’ve seen that’s been happening in the market is the formulation of some of these microsegment behaviors that are actually changing the way that people buy products in stores, but more importantly, really how they buy products in channels. What we are seeing a lot of is really the last year or so has trained the new generation that they don’t need to do things the way that they were doing before. And so some of the anticipated behavior is that things are gonna go back to normal to a certain extent. And in some cases, that is going to be true.
I think one of the biggest challenges and biggest insights that we’ve been able to reveal is that you really have to understand the segment of the shopper in your specific category and how it’s changed over the last 18 months to be able to actually secure that shopper with long-term loyalty. If you think classic brands back in the day, big brands that everybody remembers, where you’re either one thing or another, everyone kind of picks a side and it’s very polarized and it becomes very loyal to products, it’s no longer really that way. The landscape’s much more diverse, and there are a lot more options and a lot more areas where someone can purchase. And so what we are really seeing is that those behaviors are being solidified right now. And essentially, consumers have had the opportunity to test to be part of different behavioral groups, to test different shopping experiences.
This is all opportunities for the retailers and the CPGs to actually secure new customers that they wouldn’t have had access to before. Because of that, now we’re in a situation where the biggest or largest advantage that a CPG can get for itself and a retailer as well is to really deep dive and understand those segments and really understand what’s making those segments tick, how they’re buying, what the makeup is so that they can then formulate those larger segments, push people back into the kind of macro clusters where they were before, where you can manage them more efficiently. Otherwise, these fragments are going to continue and it’s going to become very, very difficult to satisfy large groups of customers because they all are going to buy in these really fragmented and separate ways.
We are seeing a lot of that across multiple categories and partners that we work with. And I think it’s still changing and there will be some settling that happens over the next two years or so. But it is going to look, we believe, fundamentally different. And the current consumer understanding and different roles and constructions that we have previously adhered to we believe are gonna be broken and changed. And whoever figures that out first, you know, and embraces it and strategizes against it is going to see significant wins.
Erin: You have definitely given our listeners a lot to think about and ponder on and so much information about what Insite AI’s tool can help them with. So last question for you is, for our listeners, if anyone wanted to reach out, learn more, maybe hop on board and use this tool as well, how could they do so?
Ryan: Yeah, the best way is you can contact me directly. My email is ryan@insite.ai. I’ll be happy to connect with you, connect you with the right team members. You can also go onto our website and get an understanding of what we do. But what we find is the best way to understand about this is to talk to us. Genuinely bring the challenges that you have. We are going to listen. We are going to break that down, really understand where we can help, really understand where it’s better for you to do things yourself or with other vendors, and approach it in just a really pragmatic way to make sure that you get the best results. So engage with us with your questions, and we’ll genuinely help you try and resolve those challenges, whether it’s something small or something large.
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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.
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