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.

Digital debt is costing us innovation. According to a recent Microsoft study, 64% of people struggle with finding time and energy to get their work done, and those workers are 3.5x more likely to say they struggle with innovation. 

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.

CPGs & Joint Business Planning: A Retailer’s POV

A former executive at 7-Eleven and Giant Eagle, Brooke Hodierne, EVP – Strategy Consulting, discusses where CPGs can evolve joint business planning and take more control

Joint business planning (JBP) is mission critical for retailers and their consumer goods partners. It’s a months-long process that runs from the starting line, through various checkpoints and past the checkered flag. JBP is when retailers address goals, category strategies and marketing initiatives, and CPGs bring insights, innovation and investment in the pursuit of growth.

After going through various stages of the process to see where the parties’ strategies align, they then settle on product assortment, pricing, promotions, shelf space, marketing and e-commerce decisions. The process is deliberate, but generally powered by old data and slide presentations. It needs a boost.

In my view as a former retailer, CPGs can light that fire and revamp JBP through new data and near real-time data and insights. CPGs can leverage more accurate and intelligent predictive analytics to chart a better course at the beginning of JBP, maintain their efforts throughout the year, collaboratively work to “gap close” and, frankly, drive more of the conversation. 

This is the type of intelligence that will keep CPGs at the top of a retailer’s list. 

Where CPGs Can Level Up During JBP

No matter the technology or industry advancements, a part of JBP will always be like playing three-dimensional chess. Both retailers and CPGs hold back just enough information for competitive reasons while being as transparent as necessary to drive win-win and mutual benefit.

It’s understandably complicated, but within that chess match, there are ways CPGs can help improve the process overall. Here are tips to gain a better standing in JBP: 

Be Insight Rich

You’ve heard the saying, “data rich but insight poor,” and this can pertain to many CPGs. The companies might be swimming in data but often they either don’t have access to it, can’t digest and harmonize it, or can’t synthesize it quickly enough to make it actionable. This often occurs during JBP and it can be obvious to a retailer when a CPG purchases data for the sake of saying yes but doesn’t shape it to a specific retailer’s customers or goals.

Honor the Deadline

A retailer’s internal planning deadlines need to be taken seriously. For years, retailers granted extensions to certain brands while negotiations continued, but in a world where teams are under-resourced or in the middle of reorganization, CPGs going into a JBP thinking there will be an exception will miss the boat. If a deadline isn’t met, the decision will be made for you. The retailer, with or without you, will make a final decision on the brand plan, space, pricing and promotional strategy. It can even come down to a retailer not including a brand’s new item introduction. Instead, they’ll choose the competitor’s new item because they followed the process.

Fair Share Isn’t Always Fair

Every category is different — especially when it relates to  allotted shelf space in stores. For CPGs entering a JBP with a retailer, they need to know their place in the category and be prepared to not always earn their “fair share” of space. From the retailer’s view, there always will need to be extra space reserved to make room for private brand introductions and innovations that excite a category from smaller, emergent, challenger brands. A CPG can’t merely expect to receive their fair share, so plan ahead and prioritize the brands and products that will deliver the most growth and differentiation.

Keep Stakeholders in the Know

Perhaps the single biggest issue to disrupt a JBP is when Sales teams don’t bring key decision makers along for the journey. In large, matrixed organizations, it’s especially important to expand discussions early and often with members of finance, revenue growth management and marketing.

CPGs that can improve processes around these tips can come to a JBP with better expectations and an understanding of a retailer’s priorities and constraints. But there are also ways CPGs can win over a JBP meeting.  

Where CPGs Can Shine During JBP

Lest we forget, retailers also have a lot of room to improve in how they handle JBP meetings. But, with technology like AI, CPGs are in a grand position to rewrite the game. They can change the tone of meetings with precise, accurate, forward-looking data. They can earn more control over how their products are received by bringing rich insights that help grow an overall category. Here’s where CPGs can win in JBP:

Bring Clear-Eyed Data

Retailers look to CPGs for data and insights. CPG organizations can leverage AI to run “what if” scenarios in real time that foster forward-thinking, collaborative conversations with retail buyers. The data accounts for all the ways retailers can play on their chessboard, which helps them develop rich category plans with more clarity. Using technology to detail the why, and share the explainability factor goes a long way with a buyer, and helps them also explain their decisions to their leadership teams.

Invest Toward Category Growth

Rich data that can present a predictive view of the entire category — not just how your brands sit within it — ultimately will win over a retailer. Data that highlights an investment in overall category growth and that arms a CPG to be the smartest person in the room when it comes to their product and the total category can be a massive game changer.

Forget the Rear View

For years, JBP relied on CPGs coming to the table with insights based on historical data. Brands looked backward, referencing what happened a year ago to predict what will happen in the year ahead. It’s simply not accurate. Do you behave the exact same you did last year? I know I don’t so why would we believe a customer would? There is so much change in shopper behavior and macroeconomic trends that it can’t be relied upon. The windshield is bigger than the rear view for a reason. Let’s all start looking down the road. 

AI Can Power Your JBP

Just as AI and machine learning can revolutionize how CPGs perform annual business planning, CPGs can leverage the technology to vastly improve how they meet with retailers and master JBP. 

To learn more about how AI can create smarter scenarios for an in-depth view of business planning, click here.

How AI Will Revolutionize Annual Business Planning

Annual business planning is one of those constants, like taxes and change, that nearly every organization can count on each year. It is enormously important to consumer goods organizations, and is a complex and ongoing process throughout a fiscal year where brands continuously shift priorities and strategies to meet performance gaps and adjust to fluctuating business conditions.

And this is all still largely done on spreadsheets.

Planning tool evolution (or lack thereof) aside, CPG organizations typically inform their annual planning decisions with historical sales trends and year-over-year performance data to paint a predictive view of how the year ahead might play out.

It is a strategy built on looking backward to go forward. This model has been reliable; learning from history has always been a competency, rather than a liability, and the consumer goods industry has typically been one of stability and predictability. However, history also tells us what worked before is not always going to be what works going forward (just ask Blockbuster Video).

CPGs (as most of us do) often miss black swan events, those rare sea changes in the market, because they are repeating what was done before. In our current environment of ever-advancing artificial intelligence and machine learning capabilities, we can now more accurately look ahead, better preparing brands for what may seem unpredictable. Further, the benefit of AI is continuous learning and an ongoing, realistic view of the direction in which a brand’s portfolio is heading, providing predictive outcomes against which to work and to plan.

The application of AI to annual business planning is a tipping point in organizations’ operations, resourcing, and capabilities. With smarter, evolved predictive market analytics, CPGs can lead the market in making the annual business planning process more manageable, and more importantly, more accurate.

It All Begins With Reliable and Relevant Data

The last few years may have produced some of the most historically unreliable data on consumer behavior. The COVID-19 pandemic, inflation and record-high costs resulted in brands facing highly unpredictable situations. Across the board, supply, labor, health, and macroeconomic trends created one hurdle after another for the production and delivery of goods of any kind.

When it comes to annual business planning, brands working backward to look forward aren’t fully armed to make the best decisions about what part of history will repeat itself. AI-powered predictive analytics integrate multiple sources of data, stabilizing volatility and creating a continuous learning model, enabling it to constantly import new data, test, learn and readjust to only deliver the most relevant information.

Produce Actual Insights on Category Futures

AI capabilities, when applied to annual planning, shift mindsets on portfolio investments. With predictive analytics at its heart, the future performance of categories and product classes/packs informs the most appropriate growth targets and levels of investment, optimizing profitability and effort. Imagine the efficiencies that could be attained through knowing, before hindsight is available, which categories are shifting in maturity? The cycle of growth and decline in any category (and the creation of new categories), based on consumer behavior and sentiment, is the moving target within which brands bet on growth investments and performance, all of which begins with the annual business planning process.

• Emerging / Growth categories. These categories are where new entrants, or even evolving established products, begin defining new niches within an existing category. At one time, ‘energy’ was not a category, but is now one of the largest categories in any cold vault, with most trend data pointing to continued growth ahead. Winning in newly defined space is both potentially a higher risk and a bigger reward. This is a category that will see many new competitors enter the category, but there is a big growth potential, and AI can help brands identify where to invest and take advantage of the white space in the market.
  
  
• Mature categories. These more developed categories face limited incremental space availability and more competition within existing space. But small amounts of growth in these categories can be worth more dollars in totality, since household penetration is likely higher in a mature category. Here, AI can enable brands to appropriately optimize strategic goals and investments to maximize potential.
  
  
• Declining categories. In these categories, space is often shifted to emerging categories as a result of sustained declines overall. Which is not to say that a category will eventually be eliminated, but sized appropriately, it could eventually evolve into a growth category with new entrants and evolution of offerings. AI can help brands optimize portfolios, but the technology can also help identify how to disrupt a declining category to bring back growth trends.

Shifting from Setting Targets to Closing Gaps

Annual business planning is just getting started once the targets are set. This continuous cycle on which nearly all business routines are anchored is one of measuring progress and performance against targets and plans, closing gaps, adjusting strategies and solving challenges that arise. AI can quickly help teams optimize strategies to focus on the best opportunities to shift resources and priorities to achieve plan goals. Further, if teams are using AI continuously in this process throughout the year and make it an ongoing part of reporting and performance measurement, trends could be better predictive and prescriptive analytics can used to take the most efficient and effective action possible.

AI Is Annual Business Planning

It’s important to note that AI doesn’t remove the human in the middle of the data. AI helps find the most impactful needles in the haystack for teams to consider and around which to develop strategies.

AI/ML never stops learning, so organizations and teams can be prepared for fluctuations and changes in near real-time, removing inefficiency in guesswork, creating options for action, and ultimately, enabling plan achievement. At its core, AI technology is annual business planning. Customized solutions are designed to look at where a brand / organization is sitting relative to the category and market, identify where the consumer / trends will go, harmonize data streams to inform financial deliverables, and then manage to and against those targets in aggregate through continuous learning.

Put the Spreadsheets Away

Establish leadership in the industry by shifting the paradigm on annual business planning. Free up resources currently mired in planning and re-planning to get back to the business of thought leadership. Take advantage of what innovative technologies offer and evolve dynamically beyond the complexity of a static spreadsheet. Enabling the future means finding better ways to work smarter: the thoughtful application of AI in your data environment is the best way to do that now.

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

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.

Getting to Grips with Price Elasticity. Contact us today.

Demand Transference: Making Data-Driven Assortment Choices and Tradeoffs

Using AI to Address Challenges Around Demand Transference and Omni-Channel Modeling

In this informative webinar CPG and Artificial Intelligence Experts from Insite AI will discuss how to incorporate AI into your CatMan and RGM processes. We will discuss using AI-driven Demand Transference and Omni-Channel Modeling to drive improved decision-making to improve your competitiveness and strengthen your channel relationships.

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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:

1. Decide on the perfect prices for maximum sales, revenue, or profit generation
2. Create optimized pricing at the most local, granular level
3. Evaluate and select the best promotions and scenarios for optimized volume
4. Use competitive cross-price elasticity to game plan against your competitive set

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:

1. 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.

2. 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.

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?

Unlock Agility by Making Sense of Demand Transference

Consumer preference and behavior changes lead CPGs to renovate and rationalize their portfolios while satisfying retailer needs. But being mindful of how demand transfer affects both sides allows for optimal assortment mixes and accurate transferable demand calculations.

Making Sense of Transfer Demand to Unlock CPG Agility

Consumer needs are changing fast and a new innovation is equally as likely to come from a well-funded technology startup as it is another CPG. Consumers are hungry for newness and have growing appetites for plant- or animal-free products, purpose-led brands, and sustainability. These changes have led CPGs to renovate and rationalize their portfolios, while retailers are seeing a growing number of SKUs from which to choose and are increasingly shifting to smaller store formats, meaning even less room on shelves. How do you rationalize ranges at scale without losing sales to competitors? One of the critical factors to follow closely is transferable demand.

Put simply, transferable demand is about understanding the effects of listing or delisting products. It considers how demand moves between products, brands, and even categories.

How can your CPG create optimal assortment mixes and calculate transferable demand accurately?

And how can you do it across millions of different brand and SKU combinations? If you’re able to get it right, you’ll work with the confidence to make optimal range and pricing choices in a matter of hours or days instead of weeks and months. Major assortment, pricing, and range delisting decisions are going to be far reaching across the enterprise, and their monetary effects could go one of two ways. Historically, the performance of a go-to-market strategy that lists a new or removes an existing SKU or product won’t be known until weeks or even months ahead. But, the right intelligence on transfer demand can ensure ranges are evolved safely, without losing category turnover or share.

Previously, optimizing an assortment and predicting transferable demand was a computational impossibility. Picture this scenario: you need to choose 300 products out of a possible set of 500. There are more ways to choose 300 products out of 500 than there are atoms in the entire universe.

So what do you do and how do you predict transferable demand?

An AI solution that can sit within your organization’s private cloud, looking at data from multiple sources, is the start. including sales and category data down to the most granular EPOS information. Inputs can also include everything from sales and category data, EPOS metrics, 3rd-party data, shopper behavioral insights, and 3D spatial information and planograms.

Across tens of thousands or millions of households, the optimal solution looks at the level of item loyalty within a particular category; for example, what percentage of shoppers are buying the same product every week. It identifies products that shoppers are willing tosubstitute and where shoppers switch brands regularly or buy from an extended repertoire.

It also looks at a product’s importance to customers in terms of total value, far beyond simple metrics such as rate of sale per store. For example, a niche, slow-selling SKU could be so important to some shoppers that its delisting could lead them to move their entire spend to other brands or even retailers. But how can you predict these results ahead of decisions being implemented?

Using an output that makes decisions at scale with accurate forecasts on the share of sales that would be reallocated to other brands in the brand portfolio and the share at risk of being lost, either to other brands or from the category entirely, is the holy grail of the CPG’s toolset.

The importance of consumer decision trees (CDTs)

Consumer decision trees are a visual representation of how consumers choose products according to different need states. Products are grouped into clusters that share similar product attributes, and it’s within these clusters that product switching is most likely to take place. The different product attributes are shown in a hierarchy: by brand, price point, taste, texture, ingredient lists, pack sizes, and more. For example, in the growing dairy-free milks category, there are variables such as soy, almond, oat, chilled, ambient, sweetened, non-sweetened, organic variants, barista variants, and flavors.

Using consumer decision trees, an AI solution can help CPGs identify demand transfer between different clusters of consumer need states, identifying how the introduction of a new product or range will interact with existing products. Opportunity gaps within a category can be quickly identified and the transfer demand from other products accurately predicted. For example, if you’re a CPG in the highly-competitive laundry category and you launch a sustainable, earth-friendly brand, a good AI tool will be able to predict the level of possible brand cannibalization, as well as the opportunity size in gaining share from competitors.

Unlike many off-the-shelf AI platforms, Insite AI is highly adaptable to meet a CPG’s specific business challenges. We understand that your enterprise needs to be agile in meeting consumer needs and reacting to a volatile economic climate while maintaining growth and dealing with the complexity and limits of the supply chain. With advanced AI analytics, you will have the organizational capability and intelligence needed to move faster and smarter.

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.

Better predict availability at the shelf through AI. Contact Insite AI.