Why AI Projects Fail 

Written by Gopalakrishna Tadiparthi, SVP of machine learning and artificial intelligence

Amid all the excitement and buzz circling around AI, it might be a bit sobering to learn that most AI projects fall short of their goals.

Gartner originally laid claim that 85% of AI projects fail, largely due to erroneous data. Another more recent Gartner study predicts that half of AI deployments in the finance sector will be delayed or shut down by 2024.

In retail, the success rate of AI projects can be similarly soft and there are a few reasons why — notably data and a lack of education around how to use AI. Surely, erroneous or unclean data can hinder projects, but also brand teams that aren’t empowered to learn and grow with the technology can stall a project’s success. Some companies come to the technology with a “set it and forget it” attitude, not embracing the pivotal partnership humans play in making AI-powered insights and predictive analytics thrive.

Truthfully, there is no single defining reason why AI projects fail, but as consumer goods companies implement AI, there are some common missteps and reasons. Here are three to watch:

1.  Limited or under-used data

Companies receive and purchase data from many vendors and retailers. The power of AI and ML is in the fuel (i.e., the quality of data along with the quantity of data). Since the data is from disparate sources, they need to be harmonized. The additional efforts in harmonization and the unknown value of the new data are preventing companies from using the data.

In machine learning and AI, there’s never enough data. A primary reason AI projects fail is when companies don’t feed the machines enough quality sources of information to make accurate decisions and recommendations. Companies need to keep shoveling the furnace coal and fueling AI algorithms so that they continue to learn and adjust in real time.

Keeping the data clean is also important. Many AI programs struggle when an AI solution provider removes a client’s data from its internal IT infrastructure. Insite AI works directly within a client’s cloud.

Machine learning platforms need to fit within a brand’s architecture, using their internal data sources inside their framework. Keeping the technology in-house ensures clean and effective results.

2.  Failure to customize AI models

Many companies focus on obtaining the latest and greatest technology. Technology FOMO is a real thing. However, teams must focus on the business problem they are solving for — then pick the appropriate model to solve the problem. Often, if a business issue is unique to a brand, the company will need a customized AI model. And, to get the most out of a machine learning tool, CPG teams throughout the organization need to learn the customized model inside and out to address their specific business needs.

For example, marketing teams can identify brand-switching behaviors that inform campaigns to reduce leakage. At the same time, finance teams can forecast demand around products to influence budgets. There are many examples, but the key is for each user to identify the variables that will help grow their forecasting needs.

For instance, if a CPG is standing on the principle of always delivering competitive pricing. Teams need to learn how to use the models and focus on the variables to help forecast a competitive pricing strategy. Then, CPGs need to think about how to derive insights from the models to get the most out of the learnings.

3.  Lack of continued learning and resources

Brands cannot be complacent because they implemented an AI system. They have to scale the process and embrace the data-driven decision-making culture. Just as there’s never enough data for an AI program, brands can’t let up on feeding the machines insights. Of course, this requires a steady diet of internal resources and investment.

Brands need to put a plan in place that manages how teams use AI long term. Companies that go heavy early with the modeling can weaken results later if they’re not continuing to feed the AI on an ongoing basis. Brands need to understand and plan the time and costs associated with a more efficient and effective use of AI.

As the AI algorithms continue to learn and deliver insightful, accurate predictive analytics, CPGs will grow their brands and develop stronger categories overall. Brands that plan accordingly can use AI proactively, getting out in front of large events that can alter a category, rather than using the tools in react mode. 

The success of an AI program inside a brand’s business requires data analysts and category teams to be empowered to learn the models and leverage the insights. C-level executives should build a culture around AI that ensures the technology keeps learning and so do the brand teams working directly with it.

Learn more with Insite AI

Insite AI helps tackle common pitfalls that companies face when working with AI models. With a focus on knowing consumer brands at their core, Insite AI harmonizes key data and uses it to enrich the training process of the AI models being used. Insite AI partners with brands to create custom solutions that solve their unique business problems and is transparent in how it communicates the model training process and explanation of the AI/ML methodology. 

Contact us to see how AI can directly impact key teams within your organization.

Connect at Groceryshop

Connect at Groceryshop

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.

The Leading Partner for Large Consumer Brands

Know the precise impact of your decisions.

We’re the only partner that lets you dial in multiple scenarios, and confidently predict how they would perform on a forward looking basis against multiple KPIs, with details down to the most granular level, regardless of complexity. Make confident decisions at either the big-picture strategic or tactical level involving commercial aspects such as assortment, pricing, trade, space, and planning. In one click, foresee the results of exactly what will happen in any given scenario. Our unique capabilities take in multiple conditions and assumptions; alternatively, decision makers can rely on us to leverage the technology on their behalf. Act with extreme certainty, speed, save significant time, and ensure your actions will achieve commercial results.

Define your specific objectives, and receive new and creative ways to reach them.

Are you seeking to grow volume? Maximize prices? Grow shelf space? Improve trade effectiveness? Outperform a competitor? Rationalize spend? Our capabilities “goal seek” the exact new strategies or tactical outputs to achieve this, taking into account all of your business dynamics, beliefs, and nuances. Get multiple novel strategies that are truly implementable and actionable. Fuse your vision with our technological levers that incorporate an incredible number of factors. See the forward looking and granular articulation on the recommendation’s performance. This is something any large team of experts aren’t capable of.

Explainable assortment, space, pricing, and trade promotion decisions.

Harmonizing data and searching it for insights is old news, and few companies see value from it. We provide internal and external narratives that are defensible and truly differentiated. In one click, our capabilities explain and decompose the “why” on a forward-looking basis; and the data is presented in a powerful, immediately understandable manner. Incrementality, demand transference, price elasticities, cross elasticities, attributions, shifts, patterns, and factors affecting your existing or recommended actions are clearly articulated.


Connect at Groceryshop

Name(Required)

Meet our Team:

View on LinkedIn

Brooke Hodierne

EVP, Strategy Consulting

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.

View on LinkedIn

Capri Brixey

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.

View on LinkedIn

Kristine Joji

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.

Most Mature, CPG-Proven Capabilities

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.

Deeply Tailored to Meet Your Goals

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.

Why Are CPGs Still Making Multi-Billion-Dollar Decisions Using Spreadsheets?

“Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt.”

Many CPGs still rely on the trusted yet limited capabilities of spreadsheets as primary tools for assessing and taking action on assortment, trade spending, space and promotions planning. While effective for certain applications, spreadsheets were invented in 1979; Excel was invented in 1985. These are not intuitive or enabled tools that can provide the timely, precise details required to make multibillion dollar decisions. With the retail landscape moving faster than ever, it is time to break free from the constraints of spreadsheets and leverage the transformative power of 21st century technologies. The future belongs to those who embrace innovation and adapt to the evolving industry landscape.

Limitations of Spreadsheets

Relatively easy to learn and use, spreadsheets are a popular option for conducting data analysis among CPGs. They offer a familiar and accessible interface for handling data, performing calculations, and creating visualizations. However, when it comes to fast decision-making in the dynamic world of consumer brands, spreadsheets reveal their limitations. While they can handle considerable amounts of data, they can be slow and unstable, particularly when data is complex. Spreadsheets further struggle to efficiently process and consolidate diverse data sets, leading to manual efforts (heavily reliant on already limited human resources) and potential inconsistencies. Excel can also impede collaboration and sharing at a time when there is more data than ever before to leverage. The bottom line is that spreadsheets are not intuitive and they require human intervention for use and to create value.

Market Volatility

Organizations are constantly trying to evaluate market volatility, competition, emerging markets and channels, and consumer behavioral shifts to assess where to allocate resources. Not having the right products and package sizes in the right place at the right time with the right price results in lost sales opportunities. If performance data shows gaps to targeted objectives, the organization will spend the year working to re-assess remaining planned actions and investments. This makes dependence on historic data troublesome. The gap between the “look back” and the “look forward” is a missed opportunity, especially in light of the market and supply chain volatility of the past three years in the CPG industry in particular.

Modern Approaches          

Today, purpose-built CPG-tailored software can ingest billions of data points from disparate sources to assess category maturity, predict future performance and assess the value of investments, allowing brands to appropriately allocate resources. It can also make more precise financial predictions. If resources are not allocated properly, expected results are not achieved. The resulting “gaps” can take a long time to close. Spreadsheets simply indicate what those gaps are; they do not indicate how to solve them. They can only hold data.

CPG-tailored technology uses timely data to project into the future, reducing dependence on historical data alone. Unlike spreadsheets, CPG focused software can “learn” from repetitive patterns and algorithms; it does not simply report data.

CPG-specific software with modeling capabilities uses multiple data sources in real time, incorporating everything from product sales and gas prices to labor department data and demographics. Because their models (accelerated by different prediction, product and pricing engines), are continuously finding data points and learning, they are able to provide forward-looking and prescriptive insights. It can signal package optimizations–e.g. whether there should be more gallon sizes of milk in a particular store versus single-serve cartons. The technology also finds those “needles in the haystack” that can be key differentiators from one store’s assortment to the next. By allowing all data to work together, teams can respond swiftly to market changes and adapt strategies dynamically, providing a competitive edge in a fast-paced industry.

Collaboration & Pinpointed Goals

Moving beyond spreadsheets enables greater collaboration and agility. Cloud-based platforms and data-sharing technologies have begun to facilitate seamless communication across departments, breaking down silos and fostering a collaborative culture. As part of that evolution, good software can facilitate better annual business planning, factoring in supply chain, labor and other costs into input assumption fields. The beauty of this is that it gives visibility to everyone in an organization and makes highly accurate predictions. This elevates target-setting, breaking out targets by function. It measures and compares achievements and lets retailers and suppliers work together to meet goals. Retailers and CPGs can then enable the Joint Business Planning process with these same powerful tools and more collaboratively agree upon a set of metrics and activities that will achieve aligned business objectives that are very specific to categories, investments or activities. Progress against all objectives is part of the modeling, constantly assessing and improving accuracy of predictions, reducing or eliminating the replanning that results from gap closure and volatility.  

Lack of Trust & Familiarity with AI

Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt. Despite evidence to support the use of AI, its effective application to broad data sources and existing processes is still nascent in the CPG industry. Just 11% of CPG organizations have adopted ML/AI tools. This stems from various factors, including concerns about the accuracy and reliability of AI algorithms, and a lack of clarity on how to apply the forms and functions of AI models to existing business processes.

There is tremendous efficiency to be gained using technology over spreadsheet, regardless of whether it incorporates a little AI or a lot of AI. Good software does not necessitate adding people (nor replacing people) to make that happen. It’s a small investment compared to what the returns can be when technology is used to augment teams and enable them to act with exponential speed and precision. Any returns can be high with clearly measurable objective-setting and ROI.     

Conclusion

The move away from spreadsheets is not just a call for change; it is an opportunity for growth and innovation. By embracing cutting-edge software and analytics, the full potential of data can be unlocked, allowing CPGs to make informed decisions and drive sustainable business growth. The time to act is now, as the CPG landscape continues to evolve rapidly. Those who adapt to change will be the ones to thrive and capitalize on the transformation opportunity.

To learn how you can evolve to be a more agile and AI enabled company, contact Insite AI.

Seeing AI Through a Practical Lens (Featured on C-Store Dive)

Guest article featured on C-Store Dive. See full article.

There’s a lot of noise around AI and what it can or cannot do. In this article, Brooke Hodierne, former SVP of Merchandising at 7-Eleven explores the practical applications and challenges of implementing AI in the convenience store industry. She discusses the potential benefits of AI technology and emphasizes the importance of aligning AI initiatives with actual business needs and objectives rather than pursuing AI for its own sake. She also addresses the obstacles and skepticism faced by businesses, highlighting the need for realistic expectations and understanding AI’s limitations.

About the Author: 
Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.

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

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

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

About the Author: 
Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.

The CPG’s Guide to AI

Empowering Consumer Brands with Clear and Actionable AI Insights

Research confirms leading consumer brands who harness the value of consumer insights and artificial intelligence (AI) better predict the needs of their customers, improve category performance, accelerate growth, and outpace the competition.

72% of executives consider AI as a business advantage

But how can you get started? With data overload, an abundance of options and unclear direction, many companies opt to do nothing. This is no longer an option. You will be left behind. Armed with the right data, AI-driven CPG brands are working hand in hand with their retail partners to better meet consumer demand. By turning mounds of overwhelming data into actionable intelligence, these CPGs are scoring big with retailers and end consumers alike.

In this guide:

  • Demystifying AI
  • How consumer brands can leverage AI today.
  • Top 5 AI/ML Use Cases in CPG
  • Going beyond Power BI and advanced analytics
  • Making the case for AI in your organization
  • Top questions to ask for a fruitful AI journey

Harness the power of AI to ensure you have the right products on the right shelves at the right time. Download this guide to begin your AI journey toward becoming an AI-driven, category-leading consumer brand.

Download Guide

Name(Required)

Mastering Joint Business Planning: An Insider’s Guide

When it comes to joint business planning (JBP), Category Advisors bring a source of truth to the table, fostering credibility and trust in the JBP, yet oftentimes they take a backseat in the planning process. Join us for a panel discussion with guests and former sales and merchandising leaders from Walmart and Coca-Cola, as they share their perspectives on how Category Advisory can lead JBP to improve outcomes. Gain a deeper understanding of retailers’ data challenges and find out how you can become their go-to partner by bringing the right data and insights to the table.

Key topics addressed in the webinar:

  • Understand the pivotal role of category advisory in Joint Business Planning.
  • Discover how to establish credibility and trust as an objective third-party advisor.
  • Empower sales growth through actionable and clear insights.
  • Employ emerging technologies to stay ahead of the competition.

Don’t miss this opportunity to elevate your advisory skills and drive mutual success.

Panelists:

  • Kristine Joji, Executive Vice President, Strategy Consulting, Insite AI
  • Capri Brixey, Executive Vice President, Strategy Consulting, Insite AI.

View replay

Name(Required)

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.

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