Accelerate Your Sales, Revenue Growth, and Category Management Initiatives
Groceryshop | September 19-21, 2023 | Mandalay Bay, Las Vegas
Connect with Insite AI at Groceryshop and find out how our revolutionary approach can accelerate your top initiatives. Our team of AI and strategic consulting teams have walked in your shoes, giving them unparalleled insights into your industry-specific hurdles. Our Strategic Advisors are consumer brand and retail veterans from Coca-Cola, PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others.
Let us guide you in tackling your organization’s distinct challenges head-on. Through our collaborative approach, we craft a tailored solution to elevate your product assortment, pricing strategies, trade promotions, and demand forecasting.
Don’t miss this opportunity to expedite your success and lead your organization toward a more efficient and profitable future.
Former SVP of Merchandising at 7-Eleven, Brooke brings nearly 20 years of grocery and convenience retail experience to Insite AI. She understands what it takes to build valuable partnerships with retailers, and in her role as EVP of Strategy Consulting, she advises consumer brands on ways to elevate strategic business planning, achieve category leadership, and create optimal shopping experiences for their consumers.
EVP, Strategy Consulting
Former leader at Coca-Cola, Dr Pepper Snapple, and Delhaize, Capri brings extensive strategic leadership experience from both retail and supplier roles in the consumer goods industry. She was recognized as a Senior-Level Top Woman in Convenience in 2022 and has also received recognition for her leadership in collaborative/joint business planning with top retailers across multiple channels and formats.
EVP, Strategy Consulting
Kristine is a highly accomplished retail executive and former VP of Merchandising at Walmart. Kristine led strategic initiatives that resulted in substantial revenue growth for the company across Grocery and prior to that Personal Care. Widely recognized as a visionary leader, she played a pivotal role in optimizing Walmart’s merchandising with large CPGs.
Why Insite AI?
A Consultative Approach
Our team becomes an extension of your team. Our Strategic Advisors are consumer brand and retail veterans from PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others. Our top priority is ensuring you have the guidance and support you need to achieve your goals and maximize the value of your investment.
Everyone else starts from scratch, yet Insite AI has already invested over eight figures of capital and several years into building leading edge technology; creating unmatched advantages for tackling your top initiatives.
We deeply tailor our engagements and fully configure our solutions to meet the unique needs of your brand. Insite AI is a true innovation partner providing CPGs with fully customizable solutions built to solve their unique challenges, enabling them to adapt quickly to changing market conditions and outperform their competition.
Fueling Growth: AI Unleashed in the Energy Drink Industry
The energy market has evolved considerably over the last 30 years. While many suppliers have disappeared, the number of new entrants continues to rise. With increased competition and what seems like constant innovation, consumer preferences and shopping patterns continue to evolve with the category. Without infinite space, retailers are under pressure to design the optimal assortment with the best combination of SKUs, flavors, brands and package sizes to maximize selling space and win the most consumer dollars.
In six months, the landscape for this rapidly evolving segment may look dramatically different. Whether its volume, shifting brand loyalty or cross category migration, retailers need to know what will happen next—not yesterday–to maximize profits in valuable selling space.
A Profitable Segment
Constant category changes can make it challenging to stock the right SKUs. In addition to the continual bombardment of new products and suppliers, Covid-era shopping patterns have shifted back to normal. With only a handful of brands having long-term staying power, retailers continually ask themselves, “How do I balance my assortment of core brands/flavors with new ones?” and “How many cooler doors do I utilize for energy drinks versus other beverages?”
Choosing the right size cans and multi-packs is another dilemma. During Covid-19, purchasing of multi-packs and larger pack sizes increased as more consumers drank beverages at home. Today, retailers must meet the needs of changing consumer mobility patterns, with increased shifting purchasing behaviors between immediate consumption to at home consumption. While in-line square footage is a bit flexible, cooler space is fixed.
Demanding More Space
The growing energy drink industry continues demanding more square footage. But brands must “prove” their entitlement. Traditionally, suppliers used historic data to make predictions. But, more so than most other CPG categories, the energy drink market is in a constant state of flux, making this data seriously unreliable. AI tools use real time data, allowing vendors to more accurately predict how many items will sell in what space and which will perform best moving forward versus looking backwards.
AI can provide a granular level of clarity around purchase decisions and consumer preferences. As the energy segment evolves with added need states, functionalities, and more, understanding the ‘why behind the buy’ becomes crucial for achieving long-term category success
Consumers’ preference for new brands and formulations versus legacy brands varies by store, channel and demographics. AI can indicate which shoppers stick with tried-and-true labels and which ones are more adventurous. Using data points to form useful insights, AI can help indicate what flavors, package sizes and formulations will resonate with certain groups.
AI’s ability to track shifting purchasing patterns yields data that can dramatically impact suppliers’ go-to-market strategies. When brand is not the primary motivator, it can help retailers avoid duplicating similar flavors, package sizes and formulations. Consequently, space becomes more profitable.
The Right Price
Over the past 18 months, cost increases have impacted almost every consumer category. While products must be profitable, brands must clearly understand the impact raising prices can have on shopper demand. AI can help. If the everyday retail price increases from $2.49 to $2.69, for example, AI can clearly project new sales volume. AI can also compare pricing to that of competitors. This helps retailers assess consumers’ sensitivity to particular prices, including associated risks. This is a win for both supplier and retailer.
Technology and modeling strategies can cross categories. The increased consumer need-state for Energy has caused a surge of other cross- category entrants in the added caffeine space. Where typically, brands focused on hydration benefits, we see more brands entering the space. But some newer items contain high caffeine levels, blurring the lines between categories and threatening energy drinks’ market share if energy drink prices go too high.
AI can predict which shoppers will remain loyal to energy drinks and which will not. It can also determine what this means to the vendor’s base business and how the vendor can protect and defend its energy space.
Immediate Consumption vs. Planned Purchasing
As consumer household penetration for Energy drinks continues to grow, developing broad brush pricing strategies for all channels from a historical POV will limit opportunities. Some retailers are losing traffic. Higher prices are prompting many consumers to plan purchasing online or in discount, club and grocery channels. These purchases frequently involve more economical multi-packs. Data and predictive modeling can track cross-channel migration and associated purchasing behavior. And it can do so down to the individual flavor, ingredient profile, brand and package type level.
In the dynamic world of energy drinks, AI holds the key to success. With the market ever-evolving and competition intensifying, the optimal assortment is crucial to attract customers and maximize profits. AI offers real-time data to predict consumer behavior, helping brands to make informed decisions on SKUs, flavors, and pricing. By discerning brand preferences, tracking shifting patterns, and unlocking cross-category insights, AI elevates strategic planning to new heights.
Embrace the AI advantage to secure lasting success in this dynamic landscape. Contact Insite AI.
Refine Wine: Navigating Regional Preferences With Precision
Unlike the beer category, where summer sales can make or break a brand, wine experiences less seasonal volatility. Instead, brands are tasked with answering cyclical purchasing trends among different varietals year-round. Further, consumers have very distinct preferences across geographic regions that brands need to keep in mind.
Throw in heavy state-by-state regulations, pricing compliance, and managing product availability through distributors, and wine is one full-bodied, complex category.
Even though there are a lot of variables to consider for wine brands, the ultimate mission is the same: optimize product, placement, and pricing. AI and machine learning can help brands cut through the complexities and get the right bottles (or boxes, cans and any other emerging packaging style) onto the right shelves at the right time.
Here’s how AI-enabled technology can refine the wine category and accelerate the most critical opportunity metrics.
AI harmonizes massive amounts of data
One thing wine does have in common with beer is there are a lot of brands competing in the space, and it can be tough for consumers to decide what to buy. For brands, it can be equally confusing to strategically assess what products should go where — and which ones can’t go where based on distributor input and state-by-state regulatory compliance.
The level of data that brands are working with is a highly complex stew, overwhelming in its diversity and complexity, that AI can harmonize and distill to deliver simplified, optimized, and precise product recommendations at a granular, even store level. Each state and distributor network has a matrix of stores and retailers they serve, sliced by state line, retailer requirements, and wineries and distilleries.
Just navigating excellence in execution within this complex web is difficult. Brands are often short on resources and capacity thresholds to properly elevate strategic assortment optimization based on consumer behavior and preferences.
With AI-enablement, that overwhelming digital debt can be managed, breaking the silos between data and functions, regulatory compliance, and complex distributor networks and availability of inventory. Using billions of data points, AI/ML delivers pinpoint forecasting and recommendations on where products should go.
For example, one retailer may have several distributors delivering wine to one store. It presents a tremendous amount of data overlays to manage. Brands can help by harmonizing the data they have from their distribution partners and more to create models for that buyer, factoring in sales and distinct distributor data to deliver highly intelligent strategic assortment plans that the industry has not seen before.
Product and Placement: Identify preferences across regions
As mentioned above, where wine gets tricky is getting products out to the right regions. For example, there arenearly 7,500 wineries in the U.S., up nearly 4% from 2022. Each winery sells better in different parts of the country.
Wine is very regional by nature, so it’s not surprising that wine drinkers in Michigan may prefer wines produced nearby vs. in northern California, where consumers may prefer other wine attributes.
But AI allows for more granularity than that. AI can recommend products to place by store clusters or at individual stores, knowing what customers like and what distributors are able to deliver to precisely place the best options available.
Insite AI worked with a national beer brand to optimize the craft beer assortment inside two leading grocery chains to leverage the peak summer season. The result was a 3% lift in sales for the overall category in those retailers, growing annual revenue by $20 million.
While the wine category can have additional variables in play, it doesn’t mean brands and retailers can’t see similar optimization and granular-insight-driven success.
Pricing and Promotion: Optimize premium to value brands
Another issue in wine is the wide-ranging price points in the category. AI helps brands understand elasticities of price across a diverse category. Again, what price points work best across varietals and regions can be very different.
The economies of scale are not a benefit or a factor in wine; to drive incremental and sustainable growth, brands need to take a localized approach. It can be very time-consuming using legacy models to devise pricing and promotion strategies. AI/ML can recommend precise moves in seconds.
There’s the saying that wine gets better with age. That may be true in a home cellar, but retailers need to move product, know trends and what sells better during certain seasons. Distributors play a key role here in revenue growth management by deploying similarly effective AI/ML strategies to predict what will generate the greatest returns.
Uncork efficiencies with AI
The intricacies of the wine category make it one of the most complex to manage. Regulations by state, in addition to working through multiple distributors to get as much product coverage as possible is a challenge.
There’s an increasing benefit for larger wineries to partner with specialized distributors that have a wider network across states, too. Recently, several wineries have formalized more exclusive distributor partnerships to gain simpler, and broader coverage.
While the complexity can be high, the benefits of new technology in managing it makes the possibilities for incremental growth in addition to significant advancement in efficiencies across multiple business functions considerable.
Wine brands can leverage AI to forecast demand and predict strategies that better meet the needs of the value chain. Contact Insite AI to learn how AI-powered solutions can get more granular than ever imagined in a category where granularity is a must.
Why Are CPGs Still Making Multi-Billion-Dollar Decisions Using Spreadsheets?
Many CPGs still rely on the trusted yet limited capabilities of spreadsheets as primary tools for assessing and taking action on assortment, trade spending, space and promotions planning. While effective for certain applications, spreadsheets were invented in 1979; Excel was invented in 1985. These are not intuitive or enabled tools that can provide the timely, precise details required to make multibillion dollar decisions. With the retail landscape moving faster than ever, it is time to break free from the constraints of spreadsheets and leverage the transformative power of 21st century technologies. The future belongs to those who embrace innovation and adapt to the evolving industry landscape.
Limitations of Spreadsheets
Relatively easy to learn and use, spreadsheets are a popular option for conducting data analysis among CPGs. They offer a familiar and accessible interface for handling data, performing calculations, and creating visualizations. However, when it comes to fast decision-making in the dynamic world of consumer brands, spreadsheets reveal their limitations. While they can handle considerable amounts of data, they can be slow and unstable, particularly when data is complex. Spreadsheets further struggle to efficiently process and consolidate diverse data sets, leading to manual efforts (heavily reliant on already limited human resources) and potential inconsistencies. Excel can also impede collaboration and sharing at a time when there is more data than ever before to leverage. The bottom line is that spreadsheets are not intuitive and they require human intervention for use and to create value.
Organizations are constantly trying to evaluate market volatility, competition, emerging markets and channels, and consumer behavioral shifts to assess where to allocate resources. Not having the right products and package sizes in the right place at the right time with the right price results in lost sales opportunities. If performance data shows gaps to targeted objectives, the organization will spend the year working to re-assess remaining planned actions and investments. This makes dependence on historic data troublesome. The gap between the “look back” and the “look forward” is a missed opportunity, especially in light of the market and supply chain volatility of the past three years in the CPG industry in particular.
Today, purpose-built CPG-tailored software can ingest billions of data points from disparate sources to assess category maturity, predict future performance and assess the value of investments, allowing brands to appropriately allocate resources. It can also make more precise financial predictions. If resources are not allocated properly, expected results are not achieved. The resulting “gaps” can take a long time to close. Spreadsheets simply indicate what those gaps are; they do not indicate how to solve them. They can only hold data.
CPG-tailored technology uses timely data to project into the future, reducing dependence on historical data alone. Unlike spreadsheets, CPG focused software can “learn” from repetitive patterns and algorithms; it does not simply report data.
CPG-specific software with modeling capabilities uses multiple data sources in real time, incorporating everything from product sales and gas prices to labor department data and demographics. Because their models (accelerated by different prediction, product and pricing engines), are continuously finding data points and learning, they are able to provide forward-looking and prescriptive insights. It can signal package optimizations–e.g. whether there should be more gallon sizes of milk in a particular store versus single-serve cartons. The technology also finds those “needles in the haystack” that can be key differentiators from one store’s assortment to the next. By allowing all data to work together, teams can respond swiftly to market changes and adapt strategies dynamically, providing a competitive edge in a fast-paced industry.
Collaboration & Pinpointed Goals
Moving beyond spreadsheets enables greater collaboration and agility. Cloud-based platforms and data-sharing technologies have begun to facilitate seamless communication across departments, breaking down silos and fostering a collaborative culture. As part of that evolution, good software can facilitate better annual business planning, factoring in supply chain, labor and other costs into input assumption fields. The beauty of this is that it gives visibility to everyone in an organization and makes highly accurate predictions. This elevates target-setting, breaking out targets by function. It measures and compares achievements and lets retailers and suppliers work together to meet goals. Retailers and CPGs can then enable the Joint Business Planning process with these same powerful tools and more collaboratively agree upon a set of metrics and activities that will achieve aligned business objectives that are very specific to categories, investments or activities. Progress against all objectives is part of the modeling, constantly assessing and improving accuracy of predictions, reducing or eliminating the replanning that results from gap closure and volatility.
Lack of Trust & Familiarity with AI
Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt. Despite evidence to support the use of AI, its effective application to broad data sources and existing processes is still nascent in the CPG industry. Just 11% of CPG organizations have adopted ML/AI tools. This stems from various factors, including concerns about the accuracy and reliability of AI algorithms, and a lack of clarity on how to apply the forms and functions of AI models to existing business processes.
There is tremendous efficiency to be gained using technology over spreadsheet, regardless of whether it incorporates a little AI or a lot of AI. Good software does not necessitate adding people (nor replacing people) to make that happen. It’s a small investment compared to what the returns can be when technology is used to augment teams and enable them to act with exponential speed and precision. Any returns can be high with clearly measurable objective-setting and ROI.
The move away from spreadsheets is not just a call for change; it is an opportunity for growth and innovation. By embracing cutting-edge software and analytics, the full potential of data can be unlocked, allowing CPGs to make informed decisions and drive sustainable business growth. The time to act is now, as the CPG landscape continues to evolve rapidly. Those who adapt to change will be the ones to thrive and capitalize on the transformation opportunity.
To learn how you can evolve to be a more agile and AI enabled company, contact Insite AI.
Beers & Sunshine: How Brewers Can Lead the Season Through Assortment and Space Elasticity
How a national brewer optimized and cultivated a successful assortment in the craft beer category, increasing revenue and market share
The most important months for alcohol sales are underway. From Memorial Day to Labor Day, retailers see the largest percentage of sales in beer and alcohol. To capitalize on that demand, however, retailers rely on their beer partners to deliver the most profitable assortment available.
The craft beer sector can be one of the most complex categories, requiring a retailer to choose among hundreds of unique breweries local to their stores; then there are thousands of regional breweries and national craft companies. Next, how many IPAs should be carried? What about sours, stouts, pilsners, maybe a gose with hibiscus or a near-beer pale ale? How many four-packs, singles or cases? Cans or bottles? The options are seemingly endless.
Factoring in the importance of the summer — the National Beer Wholesalers Association ranks the top three beer holidays as Independence Day, Memorial Day and Labor Day — retailers need powerful insights that deliver reliable visibility into seasonal trends like summer.
Beer brands need to help retailers measure the price elasticity of craft beer in the summer and perfect assortments to take advantage of summer habits, trends and taste profiles. Insite AI and precise predictive modeling can set brands up to lead the way.
In this blog, we explore further through a national brewery client that optimized an assortment for two national specialty retailers, resulting in an increase of sales of nearly 3% for the craft beer category, growing annual revenue by $20 million.
Understanding the Complexity of Craft Beer
Year after year, the total number of craft breweries entering the U.S. market continues to climb. But is there enough space for them? Or, better yet, how can we truly expect a retailer to know what to put on the shelf or in the cold vault?
In the problem of too much beer, we worked with a national brewer to optimize assortments at two national specialty retailers, one with 300 stores and another with 500. As a result, it was the first time the national brewer had been awarded a category captaincy to help rein in and optimize assortments.
For both chains, the brewer helped make the most out of the retailers’ craft beer shelf space, something incredibly important for the big four months. The National Beer Wholesalers Association has reported summer beer sales represent anywhere from 20-40% of a company’s sales.
Driving Revenue and Market Share
Working with the national brewer, we leveraged AI-powered predictive analytics at a store-by-store level, as opposed to store clusters.
Every store came with unique space constraints and localized options to consider. With Insite AI, the brewer was able to take multiple sources of unstructured data and deliver granular insights to help the brand forecast performance over a two-year period. Insite AI deployed targeted assortment capabilities inside the brand’s cloud environment to analyze key data points across multiple retailer accounts. Insite AI then applied sophisticated models that delivered visibility into areas of growth and decline, and predicted innovation trends.
They quickly delivered critical insights on demand transference. The platform highlighted the incrementality associated with new products to add to an assortment, suggested what to remove, and looked at other market factors.
For the national brewer, the modeling led to huge success for the two retailers, generating:
A 3% lift in sales above “business as usual.”
$20 million in annual revenue increases for the retailers.
Significant market share gain.
These numbers are significant for a category where retailers are looking to pull back on inventory and players in the space. It is more important than ever for brands to become trusted and credible thought partners to their retailers with business planning and decision-making.
With Insite AI, the brewer can now create multiple assortments within seconds and recommend the best one for its retailer partners. Alongside the consulting engagement, they provide an AI model that is continuously learning so brands can deliver updates to optimally run the category and maintain a captainship.
Building Better Assortments
Brands across the CPG space can refine and optimize assortments, space planning and trade promotions through AI modeling. Machine learning distills the months of manual work required to understand movements and trends within a category to minutes. AI serves as an accelerant to internal teams and ways of working.
There’s a lot of noise around AI and what it can or cannot do. In this article, Brooke Hodierne, former SVP of Merchandising at 7-Eleven explores the practical applications and challenges of implementing AI in the convenience store industry. She discusses the potential benefits of AI technology and emphasizes the importance of aligning AI initiatives with actual business needs and objectives rather than pursuing AI for its own sake. She also addresses the obstacles and skepticism faced by businesses, highlighting the need for realistic expectations and understanding AI’s limitations.
About the Author: Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services. Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.
The CPG’s Guide to AI
Empowering Consumer Brands with Clear and Actionable AI Insights
Research confirms leading consumer brands who harness the value of consumer insights and artificial intelligence (AI) better predict the needs of their customers, improve category performance, accelerate growth, and outpace the competition.
But how can you get started? With data overload, an abundance of options and unclear direction, many companies opt to do nothing. This is no longer an option. You will be left behind. Armed with the right data, AI-driven CPG brands are working hand in hand with their retail partners to better meet consumer demand. By turning mounds of overwhelming data into actionable intelligence, these CPGs are scoring big with retailers and end consumers alike.
In this guide:
How consumer brands can leverage AI today.
Top 5 AI/ML Use Cases in CPG
Going beyond Power BI and advanced analytics
Making the case for AI in your organization
Top questions to ask for a fruitful AI journey
Harness the power of AI to ensure you have the right products on the right shelves at the right time. Download this guide to begin your AI journey toward becoming an AI-driven, category-leading consumer brand.
How Brokers Can Lead the Way in AI Adoption
Positioned between brands and retailers, brokers can leverage AI and precise data to find a common truth — and pave the way for CPGs to adopt AI
Brokers play a crucial role in the CPG and retailer community. They possess extensive knowledge of the market and categories across every store, including consumer preferences, trends, and pricing dynamics. Brokers effectively represent brands and lead as strategic partners in navigating the retail industry. Leveraging their expertise, brokers help retailers streamline their supply chains, expand their product offerings, and ultimately enhance customer satisfaction. Their ability to effectively bridge the gap between suppliers and retailers as a total solution makes brokers essential in optimizing retail operations and driving business growth.
By sitting between the CPG and the retailer, brokers hold a unique position, with an opportunity, or even a responsibility, to become leaders in how CPGs of all sizes adopt AI. The technology is currently in its infancy for effective adoption, with limited clarity on exactly how CPGs will allow AI to change ways of working. However, brokers can shape the ways this technology creates efficiencies, reduces the digital overload, and pioneers its broad application to the industry overall. In doing so, they differentiate themselves and fulfill their promises to their CPG partners in helping them gain a competitive edge in this dynamic retail landscape.
Through business intelligence and predictive analytics, brokers can ascend to new heights among CPG partners. They can also strengthen their standing among retailer partners. Moreover, brokers can be a bridge between both, using high-powered AI to uncover common data truths and drive growth across the store.
Here are top ways brokers can lead the way in AI:
1. Present Accurate Demand Planning and Predictive Market Analytics
In 2023, retail sales are expected to grow more than 4%, generating nearly $5.23 trillion, according to the National Retail Federation. NRF also said more than 70% of those sales will be inside physical stores.
How close to reality will that forecast of 4% growth turn out to be? Brokers can provide a precise view of what’s happening in the market and what is likely to happen through AI-powered demand planning and market-level trend forecasts. These data and insights help inform forecasting from the highest level. Brokers can help predict future buying behavior across channels and subcategories. They can inform retailers of trends and shifts in the marketplace, and they can provide the most granular store-level view into inventory and click-and-collect service. All of these efforts, powered by AI, continuously learn, adapt, and create an enterprise environment enabling strategic decision-making, rather than an increased digital workload. Brokers can become a single source of truth in developing a precise view of enterprise market and demand planning.
2. Assist With Store Execution and Assortment
At a store-by-store level, across retail channels, brokers can leverage AI to customize insights for CPGs in any category. AI can be custom-tailored to each of the brands with which brokers work, to build the most impactful product mix and decision-enabled portfolio.
Further, they have the unique perspective of working with brands at all points in their journey of scaling and growth. For larger brands, some brokers have a responsibility to effectively build a mature portfolio with multiple opportunities in the retail environment. In that role, they fill gaps where large CPGs lack visibility and provide solutions where larger CPGs cannot internally manage the need for additional capabilities. For emerging, growth, and niche brands, brokers have a different, more targeted set of responsibilities to deliver that those brands might not be able to generate themselves.
All brands are looking to achieve category thought leadership and mutual growth with retailers they serve. AI application to assortment optimization, demand transference, and predictive analytics can help them achieve a greater share of the category and effective increases in visual inventory. Smaller brands aiming to get a stronger foothold in a category can tap into brokers and their ability to lead with AI-driven insights to bring retailers data-informed strategies on how they’ll grow a category overall.
3. Optimize Promotions and Trade
Even without a robust services suite, as sales partners to CPG brands, brokers, enabled by AI, can boost acumen in understanding elasticities of price, space, and market. AI modeling shows how the interconnected dynamics in availability, leakage, allocated category space, pricing and promotions impact sales and profitability.
Brokers that embrace this technology will lead by using learning models to predict the most effective promotional outcomes, optimized for their partners’ established goals and the current macroeconomic environment.
The technology allows for actionable insights on how to execute the best overall plan, and the best use of promotions, in the most impactful locations, and in the most deserving regions. The technology backs brokers with the unique and differentiating capability to plan efficiently as partners and lead with the optimization of portfolios, brands, and categories, in ways CPGs are currently not leveraging themselves. Brokers can align a pricing strategy that maximizes sales, revenue, and profits for their partners.
4. Become a Bridge to a Common Truth
Possibly the greatest strength a broker can leverage through AI is an ability to lead the data capabilities that solve problems and enable more efficiencies for CPG clients, in addition to relieving their own ‘digital debt’ that continues to grow for the industry overall.
Common truth, or insights driven by the integration of multiple sources of data, narrow the focus to that with the greatest impact on the outcome. And those that excel at — or adopt these integrated models to find the common truth — will be the bridge-builders and the leaders in the industry. This becomes a powerful position for brokers, solidifying them as intelligence-driven category advisors.
Brokers have a tremendous opportunity to enhance their offerings to CPGs through the adoption of AI. AI and machine learning solutions can enable brokers to analyze vast amounts of data, including market trends, consumer behavior, and competitor insights down to the store level. By harnessing these insights, brokers can further establish themselves as thought leaders and strategic advisors, providing CPGs with valuable market intelligence, helping them to be more agile, make more data-driven decisions, and outpace the competition.
For more on how Insite AI can help brokers become innovation leaders in the industry, contact us here.
Funding Incremental Growth Through Effective Trade Promotion Optimization
How does product demand with price change? And, how does your pricing affect sales volume and margins? Keep these factors in mind to harness the power of pricing elasticity and make the best decisions.
Avoid Costly Pricing Mistakes by Getting to Grips with Price Elasticity
PRICE ELASTICITY PRIORITIES
Put simply, price elasticity measures how demand for products changes with price – how shopper behavior changes in relation to price. For every CPG, a key theme is how pricing affects sales volume and margin? If your product has an elasticity of -2.00, it means that a 1% price increase will mean a 2% fall in volume. Knowing your elasticities will ensure you can plan price changes carefully and model the optimum mix of volume and margin. It also ensures you can collaborate successfully with retailers to get the most out of trade promotions.
According to Nielsen, price elasticity normally varies between 0 and -3.5 in CPG products. But, as we know, price elasticity varies between categories, between brands, and even between individual SKUs in a range. How can CPG companies get the right combination of factors to avoid costly mistakes and find the price sweet spot?
TO HARNESS THE POWER OF PRICING ELASTICITY AND MAKE BETTER DECISIONS, THESE FACTORS ARE CRITICAL:
Ensure you understand price elasticity in a granular way, right down to individual store levels
Historically, some CPGs set prices nationally without taking into account local price sensitivities for various regions. In the same way as it’s now best practice to optimise assortment at the store level, the same applies to price elasticity, which can vary greatly by geography and individual retailer. A 2016 study by McKinsey found that companies using store-level data outperformed those using aggregated or national data by 2.2 times. Whilst strategies may start off at the national level, giving your account and marketing teams localized data will enable them to strengthen retailer relationships and adjust the marketing levers to maximise local and regional success. It’s also critical to factor in the price elasticity of shopper segments at different retailers and avoid assumptions. Shoppers at upscale or premium grocers may be just as price sensitive as those at value-based discounters.
Understand price elasticity at the product and brand level
Consumers can demonstrate high levels of brand loyalty, but that doesn’t mean they will universally accept price increases across the range, as sensitivities can occur even down to different pack sizes and formats. If you do need to raise prices, find the items that have the lowest level of elasticity – here you can more safely raise the price without eroding volume. Before changing prices across a whole brand, model the effects on each SKU individually to predict outcomes. That way, you are taking into account the nuances of the various categories in which these products sit and make smarter adjustments by looking at the entire picture.
Ensure you take into account cross elasticity and price thresholds for both your own products and those of competitors
It can be easy to fall into the trap of focusing on the price of individual items instead of looking at a range or category holistically. Do you understand how the brands inside your portfolio compete with each other in relation to price and do you understand the pricing dynamics within each range? Price gaps to your competitors should be considered in detail – especially when the brands are highly substitutable. For example, raising the price of your mid-range pet food could take it so close to the price of a competit
So how do you optimise pricing at scale across the enterprise?
Getting to grips with price elasticity and cross-price elasticity has been a recurring challenge for even the biggest CPGs – this is because it’s challenging to accurately model volume and margin at scale, across retailers and geographies, right down to individual factors. Platforms like Insite AI sit inside your private cloud, running millions of what-if scenarios in real time so you can fully model and accurately forecast the impacts of the most granular of pricing decisions. Your CPGthen has the internal capability to maximise brand growth and harness the full potential of each channel, whether retail, discount, online, or wholesale.