As featured in Food Navigator, 29-Feb-2024 by Ryan Daily.
CPG brands increasingly rely on AI technologies to crunch large data stores and find insight to win in the market, but many are falling short of their digital transformation goals due to outdated technology, Capri Brixey, EVP, strategy consulting at Insite AI, told FoodNavigator-USA in a video interview.
Key Insights:
Digital Debt: Outdated systems are stalling progress, requiring significant investments to stay competitive.
Data Overload: Brands struggle with decision-making due to an overwhelming amount of data and market volatility, leading to “paralysis by analysis.”
AI Advantages: AI can streamline administrative tasks, aid decision-making, and improve ROI by optimizing resource allocation.
Strategic Implementation: Brands should adopt AI incrementally, ensuring new systems are adaptable and aligned with current needs.
Starting Small: Begin with small, manageable changes rather than an extensive overhaul to effectively leverage AI capabilities.
For a deeper dive into these insights, watch the full interview with Capri Brixey on FoodNavigator-USA.
How Retailers Can Work With Consumer Brands To Fit Their Private Label Strategies
In this StoreBrands guest post, we explore how retailers can collaborate with consumer brands using predictive analytics to customize assortments, optimize shelf sets, and predict sales trends. Private label sales are growing, and by leveraging AI-driven insights, retailers and consumer brands can work together to build assortments that align with shopper preferences, enhancing the overall shopping experience and boosting category sales.
Decoding Walmart Luminate: Leveraging Predictive Analytics for Success
Anything new takes some getting used to, and a new data engine is no different — especially one as robust as Walmart Luminate.
In March, Walmart will officially transition consumer goods partners into its tiered Walmart Luminate data platform, and supplier partners will need to adjust to this next-generation program. As I wrote earlier, Walmart Luminate is truly game-changing, providing an unprecedented look into the shopping behaviors of more than 140 million households, plus never-before-shared online pickup and delivery data. With Walmart Luminate, brands have access to a level of reporting to grow their businesses in ways they never could before.
Of course, managing and understanding data as complex and revolutionary as what’s inside Walmart Luminate takes time to grasp. AI and predictive analytics can support brands by harmonizing the data and turning findings into actionable outcomes.
Here’s a look at how predictive analytics and our CPG-tailored AI expertise can assist brands using Walmart Luminate.
Supporting brands using Walmart Luminate
Walmart began rolling out Walmart Luminate to brands this fall, providing some CPGs a trial in how they might to use the data, particularly the Charter version of the program (see below summary of the data plans).
Walmart Luminate Charter is the paid tier. It gives brands complete access to shopper behavior data from millions of households, including loyalty data and custom reports on brand-switching behaviors. The full package also debuts behavioral insights on pickup and delivery, and shares robust scoring and recommendations from the Supplier Quality Excellence Program (SQEP). As part of SQEP, all Walmart suppliers are scored based on the condition of packages, pallets and products shipped to their distribution centers, for example. Charter delivers in-depth metrics on how the supplier is performing in those areas. It also has historical data and custom reporting capabilities that’s not available in the basic versions.
Brands that pay for the full service receive an exhaustive and extraordinary set of data that can seem overwhelming at first. For that reason, some CPGs will need assistance sorting it out. Insite AI’s services range from data integration and harmonization all the way to predictive analytics and scenario modeling meant to support each supplier given where you are on your individual AI journey.
Here are four ways predictive analytics and our team of engineers can help simplify the data and get more out of it:
Linking Walmart Luminate to other data-management programs.
Some CPGs may want to integrate Walmart Luminate into other data tools that they’re already comfortable working with, and our engineers can help connect the solutions. For example, a brand may want to continue using Power BI, the data visualization platform from Microsoft, but not know how to integrate Walmart Luminate into the solution. Our team can export the data and insights from Walmart Luminate and link it to a program like Power BI. Brands can get the most out of Walmart Luminate without needing to upend their data analysis habits.
Harmonizing Walmart Luminate insights with additional data sources.
Walmart Luminate provides unprecedented access to customer segmentation data and e-commerce insights, but brands still want to leverage findings from other sources such as third-party panel data, programs like 84.51, macroeconomic data and other external sources. Insite AI’s AI modeling can take the data and harmonize it into one single version of the truth. No matter the source, it’s all big data that feeds into the AI, and our solution harmonizes the information and delivers insights in a format that’s meaningful and easy to understand.
Recommending strategic actions based on the Walmart Luminate insights.
With Large Language Models (LLMS) steeped in retail knowledge, our CPG-focused AI-powered solutions and services can help explain the data. Predictive analytics and AI models can take Walmart Luminate data, create accurate forecasts on a product’s demand and explain why sales may spike or decline in the coming months. Perhaps a rise in gas prices will increase online sales of a product? AI can add a layer of “explainability” to the robust Walmart Luminate data stream. By uncovering the in-depth meaning behind the insights, you can see the trajectory of your business and discover talking points for sales and merchant teams to help drive the business forward.
Enterprise solutions tools that solve business needs within revenue growth management, assortment planning/modular planning, macro/micro space optimization, pricing, pack architecture, forecasting and strategic business planning. By leveraging AI/ML powered solutions, brands can set and visualize multiple goals and obtain proactive and prescriptive recommendations with data explainability. These solutions are embedded within your cloud so your data never leaves the safety of your environment. Let’s say you are wanting to run TPAs (Temporary Price Adjustments) across a few brands or items to see if it will generate a lift in volume and revenue and if that lift will be maintained if you decide to make that your new retail. Our enterprise pricing tool integrates Luminate data alongside other internal and external disparate data sources and utilizes our price elasticities model, using forecasted vs historical data, generating the ideal retail price points to drive the goals of increased volume and revenue.
Walmart Luminate will be a force in 2024 as brands take advantage of the data to learn more about their shoppers and product performance online and in stores. Leveraging AI and predictive analytics, consumer goods companies can harmonize the data and get insights explained.
Whether it’s bringing expertise to sync Walmart Luminate to other internal data solutions or tailoring predictive analytics models to meet backend technical needs, we can craft custom solutions that meet a brand’s specific business goals.
Tailored to Perfection: Creating a Hyper-Local Shopping Experience with AI
Nearly 60% of consumers are more likely to shop at a store that has personalized content.
When done right, retailers can reach higher sales, volume and profit margins, as well as build customer loyalty, by localizing assortments to each store’s shopper preferences and needs. AI-powered insights will help retailers forecast demand around what shoppers are buying by each store beyond just a zip code, and identify which items will perform best, when and at which stores. CPGs that bring these insights demonstrate category thought leadership and will set themselves apart from their competition.
Kristine Joji serves as EVP of Strategy Consulting for Insite AI. She spent 20 years at Walmart where she was recognized as a visionary leader playing a pivotal role in optimizing Walmart’s merchandising strategies.
Four Trends That Will Drive CPGs To Adopt AI in 2024
While food retailers have been actively utilizing AI to forecast shopper behavior and streamline supply chains, the adoption rate among consumer brands lags behind. In this article, we discuss four key trends that will drive CPGs to adopt AI in 2024 and beyond.
Brooke Hodierne serves as EVP of strategy consulting for Insite AI. She previously worked at 7-Eleven as SVP of merchandising for the leading c-store. Before joining 7-Eleven, she held multiple positions at Giant Eagle, notably as VP of own brands.
3 Questions Every CPG Should Ask When Implementing AI
Written by Gopalakrishna Tadiparthi, SVP of machine learning and artificial intelligence
In the rapidly evolving landscape of the CPG industry, AI is poised to revolutionize various aspects of the industry with the multifaceted advances in the technology. In my many years working in the field of AI and ML, it’s been true that CPGs are slow to adopt the technology for their analytics needs. But why is this when predictive analytics can offer so much to their business?
According to a 2023 RIS News survey, only 13% of the consumer goods companies surveyed felt they were adopting AI/ML technology to improve analytics, compared to 23% of retailers. The study specifically added that more than a third of brands using AI have not used the technology at all for pricing, demand planning and key functions like optimizing promotions.
While there may be several reasons why CPGs haven’t fully invested in AI, one plausible reason is they haven’t armed themselves with enough knowledge and education before committing. It’s one thing to understand that AI will undoubtedly help a brand optimize assortments in stores, but it’s another to embrace and trust how AI will work inside a company’s existing infrastructure and processes (own plumbing).
From my view, as the head of AI/ML technology at Insite AI for three years — or lead plumber, if you will — and having spent more than 12 years at dunnhumby working on advanced analytics, I have three questions that all brands should ask an AI partner before working together.
These key questions will help brands better understand AI and implement a solution with more confidence.
Question 1: “Can you build the solution internally?”
At Insite AI, the answer is yes. One myth around AI is that a consumer goods manufacturer always needs to turn its data over to a source outside the company to receive AI-powered insights. They don’t. A dependable AI platform brings the toolkit to a customer’s house and integrates directly into their cloud. There’s no need to take the data out of the brand’s internal systems. Insite AI brings its AI expertise to CPGs and customizes algorithms that fit their architecture.
Many vendors and AI companies require that CPG send the data externally and require the CPG company to work on the standard solution. A major problem here is that there is no guarantee that a brand’s data will play nice inside that AI company’s solution.
Question 2: “Can you harmonize multiple, varying sets of data?”
For Insite AI, that’s affirmative. CPGs have rich data. They possess data from disparate sources and as a result the data. Converting heterogeneous to homogeneous data is a major benefit of AI done right. Predictive analytics and retail-focused large language models (LLMs) can take a wide range of data sources and harmonize the results all in one place to help tell a more succinct and effective story.
“At the heart of data is storytelling, and brands can struggle to find a story to tell if data is lying in piles around the house.”
Gopalakrishna Tadiparthi, SVP of Machine Learning and Artificial Intelligence, Insite AI
An AI platform harmonizes this data all in one location. Just as Insite AI can come to a customer’s house to build a custom product that works, we also clean up the house, too. We bring our toolkit and pick up the scattered data, knowing which sources of information are best to use for specific problems and implement accordingly.
It’s paramount to have one AI platform distill varying data sources such as sales data, macroeconomic insights, third-party panel data analysis, weather, etc., into one location, one single source of truth.
Question 3: “Can your AI platform explain the data?”
Absolutely, with Insite AI, it can. After harmonizing the data, Insite AI’s proprietary AI model can explain the decisions. It’s not enough for any data or insights program to deliver results, the AI needs to explain the “why” behind the numbers. Insite AI prides itself on the models we use for “explainability.”
Traditionally, CPGs use statistical linear regression models. Through these models, they are telling stakeholders a story. But, with disparate data sets, it can be hard to tell a story. Insite AI has developed a three-step process, where we take all the data in, find what’s useful, and then build a machine learning model that makes the data parsimonious, or explainable for the end user.
How can CPGs lead in AI adoption
In conclusion, the pathway for Consumer Packaged Goods (CPGs) to excel in AI adoption lies in the judicious handling of their vast data resources. The integration of AI/ML and advanced predictive analytics transforms this data into enduring strategies for brand growth. Notably, an effective AI model must not only optimize data utilization but also prioritize efficiency and security.
CPGs should embrace change, select AI partners wisely, and collaborate with retailers to unlock shared value. Ultimately, the correct implementation of AI/ML not only enhances sales but propels brands toward the achievement of their annual business objectives. The convergence of strategic foresight and technological prowess positions CPGs to thrive in the evolving landscape of artificial intelligence.
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.
Food Navigator: Insite AI Harmonizes Retail Data Into Actionable Solutions for Brands
Insite AI leverages artificial intelligence and predictive analytics to identify actionable recommendations for brands from data insights, like those provided by Walmart’s recently upgraded Luminate data platform, which tracks the behaviors of its 180 million weekly shoppers and provides suppliers with insights into their business, including customer behavior, product performance and category trends, Kristine Joji, EVP strategy consulting, Insite AI told Food Navigator-USA.
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.
The Art Of Assembling An Industry-Recognized Leadership Team To Best Serve Consumer Brands
At Insite AI, we pride ourselves in creating a company culture that fosters innovation, collaboration, and agility. Leveraging our team’s firsthand expertise in CPG, Retail, and AI, we provide genuinely valuable insights and impactful recommendations to the market.
In this Forbes Technology Council article, Insite AI Founder & CEO, Shaveer Mirpuri, shares guidance for assembling a truly differentiated industry-recognized team. View article.
Former exec and board of 2 early stage VC backed companies (IPO and acquired). Subsequently invested in 15 tech companies in e-commerce, AI, consumer brands, and manufacturing, including 3 JVs with F500 corporates. Prior consultant to Walmart’s ex CEO on AI. American Chamber of Commerce named 2019 top 3 in entrepreneurship.
More than Halloween: Maximizing consumer trends in candy
A recent Advantage Solutions survey of Halloween shopping habits found that more than a third of shoppers reported price as their main purchase driver—the top ranking influence within the survey.
In this article, Brooke Hodierne (former SVP of Merchandising at 7-Eleven), shares recommendations for candy brands can make more informed pricing decisions, maximize their presence in stores, and collaborate with retailers to foster continued growth in the candy category beyond Halloween.
Brooke Hodierne serves as EVP of strategy consulting for Insite AI. She previously worked at 7-Eleven as SVP of merchandising for the leading c-store. Before joining 7-Eleven, she held multiple positions at Giant Eagle, notably as VP of own brands.
Manage Consent
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional
Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
