How CPG Category Management Teams Get Faster Value from Syndicated Data with Self-Service Analytics

category management

With labor shortages, inflation issues, and the recent specter of a recession on the horizon, understanding consumer trends has only risen in importance for category managers and shopper insights teams within the consumer packaged goods (CPG) industry. 

In fact, according to McKinsey, the CPG companies with the “most mature” digital and analytics programs outperform their laggard peers in compound annual growth rate (CAGR) by 60%, highlighting the importance of getting the most out of their data. The industry leaders in CPG are combining granular, geography- and store-based, retailer-specific transactional data from IRI, GfK, or other market research with internal data to unlock new analytical value for their insights and category teams. 

Agile, data-driven decision-making is especially important to CPG companies as consumer preferences and buying patterns are changing faster than ever. Over the last 12 months, food prices have risen by 10.4%, which is the fastest price increase since the early 1970s, according to the Bureau of Labor Statistics (BLS). In light of this rapidly evolving situation, there’s a growing need within the CPG industry for companies to enable their category management and shopper insights teams to pull together all relevant information for better decision-making. This includes invoices, vendor rebates, cost of goods, plan-o-grams, and merchandising information, combined with syndicated data from market research companies. Fast price rises require CPG companies to quickly and efficiently understand sales volume and profitability within brands and categories to improve the overall effectiveness of the retail space allotted in each store location.

Leveraging syndicated data to improve insight generation

Market research companies like Nielsen and IRI collect retailer measurement and consumer insights data for CPG companies to better understand price and consumer trends. Retail measurement data includes information on product movement, market share, distribution, price, and other market-sensitive information that’s crucial for category managers, while consumer insights data can include things like location- and retailer-based transaction, loyalty card, and segmentation data. Overall, syndicated data provides a wealth of information for CPG companies to optimize pricing and assortments by region, chain, and market type, helping them weather the current inflation storm while proactively preparing for the risk of recession.

With this flood of syndicated data and data from buyers coming in, companies are hiring teams of data scientists and data analysts to make sense of it all.

However, across the U.S. hiring landscape, hiring managers are facing major challenges attracting talented data engineers, analysts, and scientists. In fact, hiring managers say the hardest roles to fill are in the data and analytics space, according to a recent Upwork survey of over 1,000 U.S. hiring managers.

Many companies are worried the talent gap in the data and analytics space will be a barrier to technology adoption and lead to worse outcomes for businesses that are not effectively using all the data available to them. The traditional data and analytics space is dominated with difficult-to-learn tools and complex data pipelines. Finding a simple solution to upskill your business users and analysts while providing them with an easy means to discover valuable data across sources and extract insights from the data continues to be a giant hurdle for many CPG organizations.

Finally, the pressure to decrease costs in the face of rising recession risk is driving organizations to more efficiently leverage their existing workforce. Recently, layoffs have hit a number of large CPG companies. These layoffs only exacerbate existing problems at CPG companies trying to become more data-driven. Many data and analytics teams are already overworked, and with more limited resources, the bottleneck to leveraging data to drive better decision-making for category managers and shopper insights teams will only grow.

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Challenges with self-service analytics and modern business intelligence

The concept behind self-service analytics is quite simple: You’re effectively empowering your business users and data analysts to be more self-sufficient with their queries and analysis. Many business intelligence companies in the past decade have promised tools to make data analysis easier. In fact, many analytics companies have recently acquired companies with the intent to improve their core offerings with various self-service features. However, these companies have come up short with bolted-on additions that only pay lip service to the core issues.

Self-service analytics starts with access. Legacy business intelligence software provides access to predefined datamarts. Many CPG companies relying on legacy tools require dashboards to be prebuilt by central IT or the data engineering organization. You may also be playing a game of telephone with your data team as you explore syndicated data provided by your vendor, your retail buyer is inundating you with new POS data, and your requests change on a day-to-day basis. This central IT-driven analytics paradigm does not allow your category management and shopper insights teams to move at the speed required to keep pace with competitors and continue innovating. This is especially true in today’s market environment where global conflicts, inflation, and recession risk are changing the business outlook on a daily basis.

The next step toward a truly self-service analytics organization is discovery. Ad hoc analysis in the past was driven by leveraging SQL to query the data in a very structured manner. Data analysts would have to know how the data was structured in order to get any value. Meanwhile, category managers and insights teams have new business questions and are receiving new data from IRI on a weekly basis. Many of these teams do not have the time or ability to do ad hoc analysis on the data using SQL. The inflexibility to address new business questions and quickly integrate new data led to the rise of search-based business intelligence tools. This new search capability only put a new coat of paint on an old problem as these first-generation search tools were based on keywords—hence you still needed to understand the structure of the data to get answers.

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Finally, we get to the fun part of self-service: analysis. Understanding what factors influence or motivate purchase decisions is at the heart of any category or shopper insights program. Promotion, assortment, shopper, market, and competitor analysis can be time-consuming and resource-intensive. Legacy business intelligence tools can only go so far to answer these questions with pre-defined logic built into dashboards. And, again, any deviation from the standard business logic leads back to new requests of your central IT or data teams.

Simplifying self-service analytics with Tellius

Tellius is the first true self-service analytics platform providing tools for data preparation, discovery, and analysis. The combination of these tools allows category managers and shopper insights teams to more effectively leverage both internal and syndicated data while providing the best data for improved decision-making. It has truly been built from the ground up for self-service analytics.

Tellius provides a simple way to create a unified view of your entire data ecosystem with the ability to connect to your data warehouse, data lake, business applications and syndicated data. The platform offers the ability to both query in-memory to boost performance or query live by pushing down the query to the data source. This provides organizations with the flexibility to manage for performance, cost, or simplicity in their data architecture.

Data preparation on the platform is simplified with a point-and-click interface to visually blend, transform, and clean data. Tellius comes out of the box with dozens of pre-built, no-code data preparation capabilities, which can help automate many of the previously manual processes associated with combining syndicated data with internal data to create new business views. Automating these once manual processes provides category managers and shopper insights teams with easier access to much-needed data for critical business decisions. This allows these teams to bring in new syndicated data to create new business views at the speed required to continue innovating new product categories.

Tellius solves search-related problems with true natural language search capabilities. Simply ask a business question like “How are profits affected by discounting?” or “How does the introduction of a new product influence the sales of a similar product,” and Tellius automatically provides you with recommended visualizations while Guided Insights help to address these questions. Natural language search allows your more business-focused users to more productively leverage data to understand critical product and shopper insights.

To take it a step further, Tellius’ Guided Insights uncover root causes, key drivers, and underlying trends in the data with a single click. Tellius leverages AI-driven analyses to help uncork the bottleneck of delivering insights to category and shopper insights teams. After you’ve run your search and visualized the data, simply click on the area of a chart where you’d like to uncover root causes or find key insights. Tellius generates answers by selecting the right algorithms and ranking the resulting insights. Root cause analysis on the platform allows you to instantly diagnose problems. Guided Insights reduce the time necessary to analyze problems by automating the process for you.

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The core capabilities of Tellius, purpose-built to unite users (business users and analysts) and analytic approaches in a seamless analytics experience so anyone can accelerate answering what, why, and how across all their data

In conclusion

Tellius helps consumer packaged goods companies bring together data to gain a deeper understanding of how to effectively leverage retail space, optimize pricing, and deliver more targeted promotions to new or existing customers. The combination of augmented data preparation, natural language search, and Guided Insights unlocks more value for CPG companies and provides truly augmented analytics. With rising inflation driving difficult pricing questions and the risk of recession making consumer packaged goods companies focus on efficiency, Tellius offers a solution to become more data-driven with fewer resources.

To get started, check out a free trial or request a demo of Tellius.

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