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.

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

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.

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?

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.

Funding Incremental Growth Through Effective Trade Promotion Optimization

How does product demand with price change? And, how does your pricing affect sales volume and margins? Keep these factors in mind to harness the power of pricing elasticity and make the best decisions.

Avoid Costly Pricing Mistakes by Getting to Grips with Price Elasticity

PRICE ELASTICITY PRIORITIES

Put simply, price elasticity measures how demand for products changes with price – how shopper behavior changes in relation to price. For every CPG, a key theme is how pricing affects sales volume and margin? If your product has an elasticity of -2.00, it means that a 1% price increase will mean a 2% fall in volume. Knowing your elasticities will ensure you can plan price changes carefully and model the optimum mix of volume and margin. It also ensures you can collaborate successfully with retailers to get the most out of trade promotions.

According to Nielsen, price elasticity normally varies between 0 and -3.5 in CPG products. But, as we know, price elasticity varies between categories, between brands, and even between individual SKUs in a range. How can CPG companies get the right combination of factors to avoid costly mistakes and find the price sweet spot?

TO HARNESS THE POWER OF PRICING ELASTICITY AND MAKE BETTER DECISIONS, THESE FACTORS ARE CRITICAL:

  1. Ensure you understand price elasticity in a granular way, right down to individual store levels

    Historically, some CPGs set prices nationally without taking into account local price sensitivities for various regions. In the same way as it’s now best practice to optimise assortment at the store level, the same applies to price elasticity, which can vary greatly by geography and individual retailer. A 2016 study by McKinsey found that companies using store-level data outperformed those using aggregated or national data by 2.2 times. Whilst strategies may start off at the national level, giving your account and marketing teams localized data will enable them to strengthen retailer relationships and adjust the marketing levers to maximise local and regional success. It’s also critical to factor in the price elasticity of shopper segments at different retailers and avoid assumptions. Shoppers at upscale or premium grocers may be just as price sensitive as those at value-based discounters.

  2. Understand price elasticity at the product and brand level

    Consumers can demonstrate high levels of brand loyalty, but that doesn’t mean they will universally accept price increases across the range, as sensitivities can occur even down to different pack sizes and formats. If you do need to raise prices, find the items that have the lowest level of elasticity – here you can more safely raise the price without eroding volume. Before changing prices across a whole brand, model the effects on each SKU individually to predict outcomes. That way, you are taking into account the nuances of the various categories in which these products sit and make smarter adjustments by looking at the entire picture.

  3. Ensure you take into account cross elasticity and price thresholds for both your own products and those of competitors

    It can be easy to fall into the trap of focusing on the price of individual items instead of looking at a range or category holistically. Do you understand how the brands inside your portfolio compete with each other in relation to price and do you understand the pricing dynamics within each range? Price gaps to your competitors should be considered in detail – especially when the brands are highly substitutable. For example, raising the price of your mid-range pet food could take it so close to the price of a competit

So how do you optimise pricing at scale across the enterprise?

Getting to grips with price elasticity and cross-price elasticity has been a recurring challenge for even the biggest CPGs – this is because it’s challenging to accurately model volume and margin at scale, across retailers and geographies, right down to individual factors. Platforms like Insite AI sit inside your private cloud, running millions of what-if scenarios in real time so you can fully model and accurately forecast the impacts of the most granular of pricing decisions. Your CPGthen has the internal capability to maximise brand growth and harness the full potential of each channel, whether retail, discount, online, or wholesale.

How Nestle-Purina and Boston Beer are Navigating the AI Journey

Presented in conjunction with the Promotion Optimization Institute.

Artificial Intelligence (AI) is the hot new thing for planning, Category Management and Revenue Growth Management.

Wherever you are a top CPG or an emerging brand, getting game-changing results takes vision, planning and the right partner.

In this panel-style session you’ll hear from two segment leaders, Nestle-Purina and Boston Beer along with AI innovator Insite AI.

They’ll talk about their visons for transforming planning and how they are navigating the AI journey. Q&A included.

Watch The Webinar

Fill out the form below to request access to the webinar

"*" indicates required fields

This field is for validation purposes and should be left unchanged.

Contact Us

contact@insite.ai

12600 Hill Country Boulevard
Bee Cave, TX 78738
USA

Is It Possible for CPGs and Retailers to Both See Profit Boosts?

Both CPGs and retailers are under immense pressures for different reasons, operating with different objectives. See how a well-built, joint business plan can deliver incremental profit pool growth for both sides.

It is if they work together.

Relations between CPGs and their retail partners have fallen to their lowest level in 5 years, according to a 2021 report by management consultants Bain & Company. According to the firm, the main cause has been a short-sighted approach on both sides that has favored short-term sales targets. CPGs and retailers alike face their share of challenges: CPGs are under immense cost pressure, especially with rising raw materials and logistics costs. Meanwhile, retailers have had to defend their market share against new entrants, such as deep discounters. In addition, the trend toward smaller store formats and the rapid growth of online commerce has further upset the balance. However, according to Bain, a well-devised, joint business plan can deliver more than 10% of incremental profit pool growth for both the brand and retailer in a single year providing they work together intelligently.

The report, which focused on CPGs and retailers in Europe, concluded that only one in four joint annual plans actually manages to create value for both parties, and that instead of building incremental value, 59% of trade promotions merely subsidize baseline sales. They suggest that the traditional, strategic moves from CPGs like launching a new, above-the-line television campaign or new brand extension, are running out of steam and losing effectiveness.

The growing friction between CPGs and retailers is real. In 2016, the UK’s largest grocery retailer, Tesco, delisted nearly all of Unilever’s products after the manufacturer asked for a 10% price increase across its iconic brand portfolio. The CPG’s price increase was blamed on the pound’s fall against the euro and the US dollar. In 2016, Unilever had a 32% share of all the UK’s ice cream and frozen dessert sales, 21% share of all table sauces, and 19% share of butter and margarine sales according to Euromonitor. The disagreement was rapidly resolved, but this example shows what can happen when negotiations fail

The only sustainable solution is to collaborate closely and grow profits together.

According to Bain & Company’s research that was published in 2021, the best CPGs are able to deliver a 10% increase in joint profit pool growth in a single year by doing 5 things differently:

  1. The best CPGs understand both their own and their retailers’ strategies
    Getting deep under the skin of the retailer’s P&L enables the CPG to appreciate their business strategy, their most important KPIs, and ultimately how value is delivered to the end consumer. By listening to the other side’s needs and being able to articulate how a CPG initiative will build value is much more likely to get retailers onside.
  2. Deliver up to 20% more incremental sales and profit for both parties through an assortment optimization program
    Legacy approaches to store assortment and clustering can lead to high levels of item cannibalization. Limited shelf space often means an ongoing battle between CPG and retailers on which SKUs to stock, how much space to allocate in which stores, and whether a product should be delisted.CPGs had previously been held back without the means to optimize assortments at the lowest, most granular level. However, a cloud-based platform can create optimized assortment decisions at scale, right down to individual store level. This gives clear direction on product mix, space elasticity, and pricing, and a truly optimized assortment has the potential to significantly drive volume and profit for both parties. The latest platforms are able to run millions of what-if scenarios in a matter of seconds, giving real-time insight that allows a proactive assortment approach. The results ensure customers get the value they need, whilst the CPG and the retailer can make the most out of every bit of shelf space.
  3. Take a medium-term view to deliver a business plan
    Both CPGs and retailers have monthly, quarterly, and annual revenue and profit targets to hit, as well as shareholders and stakeholders to satisfy. Delivering a successful joint business plan is not a quick fix, and therefore it will take bold leaders who are prepared to invest at least a couple years in order to deliver a properly-executed joint profit plan. As a CPG, you should have a 3-year plan for each of your most important retailers with an imperative of not being distracted by only focusing on monthly sales targets.
  4. Build trust and transparency
    Strong relationships are built between people. Beyond the written business plans and agreements, invest in building strong relationships with all the key people within your retail partners. Build these not only with c-suite executives or VPs, but with the people who will actually deliver the day-to-day strategy. This includes category managers, buyers, and shopper marketing directors. Build a real understanding of the KPIs they track. What does success look like to them? How are they being appraised? Then, arm yourself with data that gives them the confidence to buy into your CPG’s initiatives, whether it’s store assortment, a new range, or a price change. If you’ve got granular data to prove something, then you’re halfway there.
  5. Break down silos within your CPG
    Most CPGs have teams dedicated to their most important retailers to bring people together from commercial, category, sales, marketing, manufacturing, and logistics.Whilst people from these disciplines do work together on projects, it is incredibly challenging to avoid a siloed mentality. There are many widely-cited solutions to the siloing issue, and hundreds of management books have been written about it. However, taking a pragmatic approach to ensure teams are collectively responsible for delivering the retailer’s P&L with common goals and objectives is the key. Ensure the collective responsibility is mandated from the highest level of leadership within your CPG so it has a real sense of purpose and gravitas.

3 Ways CPGs Use AI to Increase ROI

Every CPG strives to maximize its return on the money and effort it places into investments. The question is:

Which parts of these investments will drive the biggest and most certain returns?

Optimized category management, assortment planning, and revenue growth management are the key factors to success, profits, and market share.

Don’t take chances on your success – using artificial intelligence to make decisions ensures optimal outcomes. Here’s how CPGs are using it:

1. Category Management

Spend precious technology and data dollars on the product most likely to help you succeed. This means using a solution that continually monitors and uses data to create all outcome scenarios possible.

These scenarios use past, present, and future-possible data to show you and your retail partners which products should be on shelves and at what price.

Key benefits of AI-driven forecasting in category and assortment:

  • Selling the right quantity of product that accounts for upcoming sales, trends, advantage over competitors, and promotions
  • Better optimization of assortment ratios
  • Improved understanding of demand drivers and customer behavior, right down to the SKU level
  • Decreased lost sales / missed sales opportunities
  • Better alignment between products and locations where demand exists
  • Improved GMROI

An AI-based forecasting system, is a focused on the CPG major upgrade to existing systems where brands attempt to manually cluster and define data and patterns. Unsurprisingly, results are often disappointing and lead to lost sales and market share.

Store-level forecasting is a difficult endeavour with traditional systems, especially when CPGs are dealing with millions of possible product, price, and store combinations.

By alleviating the need for so much manual intervention and by accounting for so much information at any given time, artificial intelligence-based forecasting can deliver far more accurate decisions.

2. Revenue Growth Management

Advanced forecasting combines with historic sales with seasonality, trends, product lifecycle effects, and statistically-tested assumptions to improve accuracy. Increased accuracy is the single biggest driver of direct savings, revenue, and total return on
investment. Improvements to revenue growth management will be realised in the following ways:

  • Customized decisions for granular levels such as individual store, SKU, particular time, price elasticity, and demand changes
  • Making optimal pricing decisions to make you more money and take market share away from your competitors
  • Opportunity for improved use of strategic discounting

3. Operational Efficiency

In the face of increasing competition, CPGs are all looking to ensure their products remain a focus over their competitors, and that their retail accounts keep them top of mind.

More accurate simulations for decisions can help CPGs optimize category, assortment, trade fund, and pricing/promotion. This is while ensuring they’re able to offer retailer partners the right discounts and provide exact pricing recommendations for optimal outcomes on either side. It’s important that assortment is accurate at individual store levels, using both micro and macro views for trends, seasons, demand, and changes to ensure customer demands are being served at a granular level. Product imbalances often occur right after seasonal peaks. One of the biggest reasons CPGs fall into this trap is because they’re often forced to act retroactively with top-up orders, transfers, or pricing changes. Providers such as Insite AI provide highly accurate, data-driven scenario decisions at store levels ahead of time to keep ahead of competitors.

Contact Us to found out how you can increase ROI with the power of AI.