Operating in an intensely competitive market, consumer packaged goods (CPG) companies are faced with unique and complex data and analytics challenges, especially amidst all of the changes in consumer behavior in recent years.
Using tools like traditional business intelligence (BI) and spreadsheets, CPG organizations can conduct high-level reporting. But when it comes to answering ad hoc questions or determining exactly what’s driving changes—across millions upon millions of data points—manually slicing and dicing data is certainly a less-than-ideal process.
There’s a growing need within the CPG industry for companies to pull together all relevant information for easier and faster decision-making. Here’s how AI-powered analytics is helping address this challenge—and provide the answers to critical business questions—across five common use cases.
In This Post
Category management
Question: What was the daily market share for Snack Brand X vs. Snack Brand Y during the last week of February?
Category management teams can struggle to keep up with the pace of change in the industry when they don’t have timely—and, thus, accurate—data to support answering questions like these. From assortment ranking to optimizing shelf space to personalizing customer interactions, they need to be able to quickly derive data-driven insights in parallel with changing consumer behavior.
Using traditional analytics tools and waiting a week to have a report turned around won’t cut it. After all, speed to insights often equates with speed to shelf.
Instead, using AI-powered analytics, category managers can leverage up-to-date data to quickly answer critical category questions. Here’s how:
- Using an AI-analytics platform with visual data prep tools, they can combine data across customers, internal sources, and syndicated data providers like IRI and NielsenIQ. No data gets left behind.
- Automated customer segmentation and insights enable them to understand key drivers around successful (or unsuccessful) promotions, track anomalies, and segment customers to better understand behaviors and preferences.
- Leveraging machine learning, they can easily understand critical seasonal patterns through trend insight capabilities.
Answer: Market share for Snack Brand X plummeted by 1.75% on Feb. 27 but went back up to normal patterns on Feb. 28, while market share for Snack Brand Y did the complete opposite. Now we can investigate why this may have happened for each snack.
Shopper insights
Question: What are some underlying factors driving purchasing behavior for Toothpaste Brand X across the Midwest?
To answer questions like these in order to improve shopper experiences, shopper insights teams must have the right types of data to understand shoppers’ attitudes, behaviors, and preferences, ultimately leading to better targeting and more revenue.
However, integrating sales data with data from syndicated market research providers and consumer surveys is a technically complex and often time-consuming task. Also faced with bottlenecks from overburdened data teams, shopper insights teams are unable to receive dashboards and reports in a timely manner.
Instead, here’s how they can leverage AI-powered analytics to help improve decision-making and strategic planning at their organization:
- AutoML (rather than manual reporting) saves teams substantial time on carrying out forecasting, regression, cluster analysis.
- Deep automated insight generation helps them better understand trends, customer segmentation, and market basket analysis.
- An AI analytics platform with natural language search enables self-serve, ad hoc data exploration and analysis, enabling shopper insights teams to simply ask questions of their data on their own.
Answer: We can see that for Toothpaste Brand X in the Midwest, purchasing is at its highest when the marketing source comes from an email opened between noon and 2 p.m. on a mobile device. Now we have granular enough data on which to base our next marketing campaign for this product in that region.
Omnichannel analysis
Question: How well is each channel (online, offline, e-commerce, and brick-and-mortar) contributing to overall sales and productivity?
Implementing and maintaining effective omnichannel analytics requires not only significant resources, but also ongoing support. Integrating and then analyzing data from multiple sources—e.g., online and offline sales, social media, mobile apps, and in-store interaction—is a highly complex undertaking.
AI-powered analytics helps CPG organizations better understand how channels interact and influence each other along the customer journey:
- Gaining a holistic view into all available data across channels, teams have live connections with all data sources, enabling them to stay updated on critical shifts in the market in real time so they can create responsive marketing campaigns.
- Predictive analytics powered by AI enables demand forecasting, giving teams the ability to analyze historical sales data, seasonality, and other factors to optimize inventory levels.
- With real-time market basket analysis driven by AI, teams can more easily identify cross-sell and upsell opportunities, as well as identify the best times to engage with customers.
Answer: Even in May 2023, sales still hadn’t returned to December 2019 peak levels for brick-and-mortar. We can confirm we need to make a big change in our targeting for this channel.
Self-service analytics
Question: How can I figure out—on my own during a meeting when this comes up—how profits were affected by discounts last month?
To answer these types of questions, many CPG organizations using legacy BI tools need dashboards to be pre-built by central IT or the data engineering team. But by the time they receive the dashboard, the data is often outdated, leaving them with more questions than answers. What’s more, these tools can only go so far to answer complex questions with predefined logic built into dashboards.
Today especially—when market changes are affecting the business outlook on a daily basis—category management and shopper insights teams are unable to move at the speed required to keep pace with competitors.
AI-powered self-service analytics eliminates these organizational bottlenecks by putting data-driven decision-making into the hands of more users:
- Domain experts can ask their own questions in natural language: i.e., a Google-like search requiring no coding know-how.
- They then receive charts and data visualizations based on their question, with automated insights highlighting the right areas to explore.
- By putting answers directly into the hands of the domain experts (vs. the data team), they’re equipped to make data-driven decisions on their own, without waiting for the latest dashboard.
Answer: Based on the bar chart showing total revenue from the discounted products by category for last month, I can show the team right away that Product Y took a major hit.
Supply chain optimization
Question: How can we mitigate potential risks and disruptions that could impact our supply chain?
Teams need to make supply chain decisions as quickly as changes occur (and before disruptions happen), but legacy analytics providers can’t provide a quick enough turnaround of data analysis.
CPG organizations already face a wide range of issues getting value out of supply chain analytics. Seasonality, promotions, and changing consumer preferences are constantly causing varying demand patterns, but when you add in market volatility in recent years from the pandemic and other world events, supply chain complexity is only increasing.
Here’s how AI-powered analytics can help CPG brands overcome common challenges associated with supply chain management:
- Teams can dive into analysis in one place, unifying data across all channels (e.g., ERP systems, TMS, POS, supplier, demand, production).
- With AI-based anomaly detection and alerting, supply chain analysts have real-time visibility into inventory levels, production progress, and order fulfillment.
- Using automated root cause analysis, supply chain analysts can identify causes of supply chain disruptions or quality problems, as well as use ad hoc analysis to quickly identify potential issues and plan efforts (e.g., for quick answers during supplier meetings) to reduce overstocking and stockouts.
Answer: Based on the day-to-day trends we’ve been tracking throughout the year, we’re confident about adjusting our inventory for the demand fluctuation predicted next week.
Want to learn more about AI-powered analytics?
Major CPG firms like Haleon are using Tellius’ AI analytics platform to get answers more quickly, find deep insights more easily, and make informed decisions backed by data.
To learn more about informing every business decision from all your data, check out our newly released eBook, The Tellius Guide to AI Analytics for CPG Companies.
