CPG data analytics for revenue growth: What you need to know

You may remember this quote from the 1980s: “We are drowning in data, but starved for knowledge” (John Naisbitt, Megatrends). Since then, there has been a real data explosion, with the volume, velocity, and variety of data increasing exponentially. But, fortunately, so has the capacity to process, analyze, and make sense of all this data. Let’s dive into how data analytics has become a cornerstone for driving revenue growth in the consumer-packaged goods (CPG) industry.  

For CPG companies, data is now clearly a business-critical asset. With the ever-increasing volume of data generated, winning CPG teams are leveraging advanced analytics to gain valuable insights and make informed decisions. As data continues to grow in importance, it's crucial to understand how to harness its power effectively. In this blog article, we'll explore: 

• What data analytics is in the CPG industry 
• The various sources of retail/CPG data 
• The challenges you face in integrating and managing all this data 
• How you can leverage data analytics to unlock actionable insights 
• How technology tools and AI can support you 

We'll delve into the world of big data analytics, predictive and prescriptive analytics, and discuss how to optimize and harmonize data for better utilization. Additionally, we'll examine CPG analytics use cases, including demand forecasting, trade promotion optimization, and inventory management, to illustrate the practical applications of data analytics in the world of consumer-packaged goods. 

Join us as we navigate through the fascinating world of CPG data analytics. Let’s get started! 

What is CPG data analytics? 

Imagine having a treasure trove of information at your fingertips, just waiting to be unlocked. That's essentially what CPG data analysis is all about. In the CPG industry, data analytics refers to the process of examining vast amounts of data to uncover hidden patterns, correlations, and insights that can drive better business decisions. 

Think of it as a powerful tool that helps you make sense of all the data generated from various sources, such as sales transactions, trade investments, and shifts in shopper behavior. By analyzing this data, you can gain a deeper understanding of performance drivers and return on investment. 

For example, data analytics can help you identify which products are flying off the shelves and which ones are lagging behind. It can reveal the effectiveness of your promotional campaigns and pinpoint areas where you can optimize your inventory management to reduce costs and avoid out-of-stocks. Essentially, it transforms raw data into actionable insights that can give you a competitive edge in the market. 

In the world of consumer-packaged goods, data analytics is not just about looking at historical data. It's also about predicting future outcomes and making proactive decisions. This is where advanced techniques like predictive analytics come into play. By using sophisticated algorithms and machine learning models, you can forecast demand, optimize inventory levels, and even determine which combinations of products and retail channels are best suited to target specific customer segments. 

In summary, CPG data analytics is your key to transform data into a revenue growth powerhouse. It empowers you to make informed decisions, improve operational efficiency, and ultimately drive business growth.  

CPG data sources 

There are several key sources of CPG manufacturer and retail data. Understanding these sources is crucial for making informed decisions and staying competitive in the market. Let's look at the three main types of CPG data sources: internal data, syndicated data, and retailer direct data. 

Internal data 

Internal data is generated within your CPG company and includes a wealth of information about your operations. This data is typically free and readily available, making it a valuable resource for analysis. Some common types of internal data include: 

• Sell-in data: This data tracks the products shipped from your warehouse to plan accounts. It includes detailed information about product types, variants, quantities, and prices. 
• Financial data: This encompasses key performance indicators (KPIs) such as revenue, profit, gross margins, and cost of goods sold (COGS). It also includes trade investments and marketing spend. 

Syndicated (Nielsen/IRI) data 

Syndicated data is provided by third-party vendors and offers a broader view of the market. This data is not specific to your company but covers general CPG market trends and competitor performance. Some common sources of syndicated data include: 

• Point of sale (POS) data: This data captures actual unit sales, sales revenue, promotion prices, base prices, and volume sales. It helps you understand your market position and how your competitors are performing. 
• Shopper or panel data: This data provides insights into consumer behavior and preferences. It helps answer questions about demographic trends, product preferences, and household penetration. 

Retailer direct data 

Retailer direct data is provided by large retail chains and offers a close-to-reality view of consumer purchases. This data is specific to individual retailers and provides detailed insights into shopper behavior. However, you won’t receive information about other banners or competing products. While this data is highly valuable, it’s not free and often requires significant effort and resources to process and analyze. Common types of retailer direct data include: 

• POS (sell-out) data: This data captures actual consumer purchases at the point of sale, providing a granular view of sales performance. 
• Shopper data: This data offers insights into shopper behavior and preferences, helping you build stronger collaborations with retailers. 

By leveraging these diverse data sources, you can gain a comprehensive understanding of your business and the market. Integrating and analyzing data from multiple sources allows you to make more informed decisions, optimize your operations, and stay ahead of the competition. 

Big data in the consumer-packaged goods industry 

Now that we've explored the various sources of data in the CPG industry, let's look at how big data analytics can transform this wealth of information into actionable insights. Big data is a game-changer for CPG companies. By processing vast amounts of data from diverse sources, CPGs can uncover valuable insights that drive strategic decision-making and operational efficiency. This capability is crucial for laying the groundwork for creating detailed revenue plans and enhancing decision-making precision. 

Big data plays a crucial role in revenue growth management (RGM) by optimizing trade spend, pricing strategies, demand forecasting, promotions, and product assortment. It allows companies to analyze sales data, market trends, and consumer behavior patterns, leading to more effective promotional campaigns and better market positioning. 

The integration of big data with AI further enhances these capabilities. AI-driven analytics can process data at a scale and speed beyond human abilities, providing reliable forecasts and identifying trends that offer a competitive edge.  

However, handling big data is fundamentally different from managing traditional data due to the sheer volume, velocity, and variety of data involved. Traditional data processing tools and architectures are often inadequate for dealing with the complexities of big data. Scalable storage solutions like distributed file systems, stream processing tools for real-time analysis, and NoSQL databases (e.g., MongoDB) and data lakes for diverse data types are essential. Advanced analytics tools and public cloud services (e.g., AWS) provide the computational power needed for complex analyses. In addition, modern data integration tools and pipelines (e.g., Bifrost) streamline the process of combining data from multiple sources, ensuring that CPG companies can fully leverage their data for business growth. 

Optimizing data utilization in the CPG industry 

Optimizing data utilization in the CPG industry involves effectively combining various data sources and implementing best practices for data integration and analysis. Here are some strategies to help you make the most of your data: 

By following these strategies and best practices, you can optimize data utilization in your CPG business, leading to improved decision-making, market responsiveness, and growth potential.  

For more detailed information, you can refer to the blog article Combining data sources in the CPG industry: A guide to optimizing data utilization. 

How to harmonize CPG data and make it ‘analysis-ready’ 

Going beyond mere integration, data harmonization is essential for making sense of diverse data sources. It involves integrating, cleansing, and standardizing data to create a unified dataset, ready for analysis. This process ensures that all data is consistent and accurate, providing a complete view of your business. 

Data harmonization involves several key steps: 

• Integration: Combining data from various sources, both internal and external. 
• Cleansing: Removing inaccuracies, duplicates, and irrelevant data. 
• Standardization: Aligning formats, units of measurement, and naming conventions. 
• Unified schema: Organizing data into a consistent structure. 
• Analysis-ready dataset: Creating a clean, organized, ‘analysis-ready’ dataset. 

Harmonizing data is crucial for CPG analytics because it ensures that data from different sources can work together seamlessly. This process enhances the quality and usability of data, enabling better decision-making and strategic planning. 

However, achieving data harmonization can be challenging due to disparate data sources, different naming conventions, data silos, and the need for high data quality. Overcoming these challenges requires robust systems and stringent quality control measures. 

The benefits of harmonized data are significant. It enhances analytics capabilities, improves market insights, optimizes trade and revenue strategies, and provides better store-level performance tracking.  

In summary, data harmonization is a foundational element that supports the integrity and reliability of data-driven insights. It fosters informed strategic planning and robust business growth. 

For more detailed information, you can refer to the article: Enhancing CPG analytics: the role of data harmonization. 

Automating CPG data integration 

Automating data integration processes is crucial for keeping information up to date and eliminating time-consuming, error-prone manual data entry. Let's explore the benefits of automating data integration: 

1. Efficiency and accuracy: Manual data entry is not only time-consuming but also prone to errors. Automation ensures that data is collected, processed, and updated accurately and efficiently, freeing up valuable resources for strategic analysis. 

2. Real-time insights: Automated data integration enables real-time data flow, providing up-to-the-minute insights that enhance decision-making and agility. This is essential for responding quickly to market changes and shifts in consumer preferences. 

3. Consistency and reliability: Automation ensures that data from various sources is consistently integrated and harmonized, maintaining the integrity and reliability of the information used for analysis and decision-making. 

4. Reduced dependency on IT: By automating data integration, CPG companies can reduce their reliance on IT resources, allowing business users to access and utilize data more independently and effectively. 

An example of streamlined data integration: Visualfabriq Bifrost 

Visualfabriq's Bifrost is a data integration suite designed specifically for the CPG industry. It offers several benefits that make data integration easier and more effective: 

• Transparency: Bifrost provides clear insights into data, making it easy for users to understand where their data is and how it’s being used. This transparency removes the "black box" element from data integration, ensuring trust and traceability. 
• Data-transfer-as-a-service: Bifrost simplifies data integration by offering data-transfer-as-a-service, eliminating the need for complex development work. This makes the process more user-friendly and accessible. 
• Ease of use: Bifrost streamlines file management and standardizes access to file storage, making data integration straightforward and efficient. 
• Data security: Visualfabriq ensures that your data remains yours and takes the responsibility of safeguarding it seriously. This trust enables flexibility in a rapidly changing data landscape. 

For more detailed information, you can refer to the blog post Visualfabriq Bifrost: Simplifying data management & integration for CPG companies. 

Predictive analytics in CPG 

Predictive analytics is a powerful tool in CPG, enabling companies to anticipate future outcomes and make proactive decisions. By analyzing historical data and identifying patterns, predictive analytics can forecast future outcomes with a high degree of accuracy.  

One of the primary applications of predictive analytics in CPG is demand forecasting. By examining past sales data, market trends, and consumer behavior, companies can predict future demand for their products. This helps in optimizing inventory levels, reducing stockouts, and minimizing excess inventory. AI-enhanced predictive analytics also plays a crucial role in promotional forecasting, allowing companies to estimate the impact of promotions on sales. This enables more effective planning and execution of promotional activities. 

In addition to predictive analytics, prescriptive analytics is gaining traction in the CPG industry. While predictive analytics tells you what is likely to happen, prescriptive analytics goes a step further by recommending actions to achieve desired outcomes. It uses advanced algorithms and machine learning models to analyze data and suggest the best course of action. For example, prescriptive analytics can recommend optimal pricing strategies, promotional tactics, and inventory management practices to maximize profitability. 

The potential benefits of prescriptive analytics in CPG are substantial. By providing actionable insights and recommendations, it empowers CPG companies to make data-driven decisions that enhance performance and drive growth. As the industry continues to evolve, the integration of AI-powered predictive and prescriptive analytics will become increasingly important for staying competitive and meeting consumer demands. 

CPG analytics use cases 

How do CPG companies use data analytics to optimize various aspects of their operations? Let's explore some real-world examples: 

Demand forecasting 

Demand forecasting is crucial in the CPG industry as it helps companies predict future demand for their products. Accurate demand forecasting allows CPGs to create a reliable baseline, which helps them maintain optimal inventory levels, reduce stockouts, and minimize excess inventory. Techniques such as time series analysis, regression models, and machine learning algorithms are commonly used for demand forecasting. 

For more information, check out: What is demand forecasting in CPG? 

AI-powered demand forecasting 

Artificial Intelligence (AI) plays a growing role in enhancing the accuracy of demand forecasting. AI-powered solutions can analyze vast amounts of data, identify patterns, and make precise predictions. For example, AI-enhanced solutions like Visualfabriq use machine learning algorithms to forecast demand based on historical sales data, market trends, and external factors. These solutions provide more reliable forecasts, enabling companies to make proactive decisions and optimize their inventory management. 

Read also: AI demand forecasting in CPG: a comprehensive guide 

Trade promotion forecasting 

Trade promotion forecasting is essential for planning and executing effective promotional campaigns. By predicting the impact of promotions on sales, companies can allocate resources efficiently and maximize the return on investment. Methods such as historical analysis, uplift modeling, and scenario planning are used for trade promotion forecasting.  

For more information, refer to: The value of trade promotion forecasting: Empowering sales teams in the CPG industry 

Trade promotion evaluation 

Evaluating trade promotions before and after execution is critical for understanding their effectiveness. Pre-evaluation helps in planning and optimizing promotions, while post-evaluation measures the actual impact. Key metrics and KPIs for trade promotion evaluation include incremental sales (additional units or extra revenue), return on investment (ROI), and promotional lift (percentage increase compared to a baseline period). By analyzing these metrics, companies can refine their promotional strategies and improve future campaigns. 

Read more about evaluating trade promotions: How to evaluate Trade Promotion ROI in the CPG industry . 

Trade promotion optimization 

CPGs are increasingly using advanced data analytics to design and execute promotions that maximize sales and profitability. This involves the use of machine learning and AI, predictive modeling, and scenario planning. Strategies for optimization include targeting the right customer segments, selecting the most effective promotional tactics, and timing promotions strategically.  

Read more: How to build an effective trade promotion optimization model for CPG. 

Trade spend analysis 

Measuring the effectiveness of trade spend is crucial for ensuring that promotional investments yield the desired outcomes. Trade spend can be categorized into two types: contractual trade spend and promotional trade spend. Contractual trade spend includes fixed costs agreed upon with retailers, such as listing fees and slotting allowances. Promotional trade spend, on the other hand, covers variable costs associated with promotional activities, such as discounts and in-store displays. Best practices for maximizing trade spend ROI include analyzing the performance of past promotions, identifying areas for improvement, and reallocating resources to high-performing activities. Data analytics tools provide insights into trade spend effectiveness, enabling companies to make data-driven decisions and optimize their promotional budgets. 

For more insights, read: Trade spend effectiveness in CPG: 5 KPIs to track. 

Trade spend optimization 

Optimizing trade spend allocation involves using data analytics to allocate resources to the most effective promotional activities. This includes both contractual trade spend and promotional trade spend. Techniques for optimization include analyzing historical data, modeling different scenarios, and using predictive analytics to forecast the impact of various strategies. Successful trade spend optimization examples include reallocating budgets to high-performing promotions and discontinuing underperforming activities. 

Read more: Exploring the next level of trade spend optimization. 

Inventory management & optimization 

Effective inventory management is vital in the CPG industry to ensure product availability and minimize costs. Strategies for optimizing inventory levels include demand forecasting, safety stock calculation, and inventory turnover analysis. Unconstrained (i.e., supply-neutral) demand forecasting is used to provide a clear picture of true market needs, enabling companies to make more informed decisions about production, inventory, and trade promotions. This approach helps maintain optimal inventory levels, reducing the risk of overstocking or stockouts. Additionally, consistently meeting customer demand leads to higher end-user satisfaction and greater leverage in negotiations with retailers. 

By leveraging data analytics, CPG companies can optimize various aspects of their operations, from demand forecasting and trade promotion management to inventory optimization. These use cases demonstrate the transformative power of data analytics in driving business growth and enhancing competitiveness in the CPG industry. 

CPG analytics trends 

The future of data analytics in the consumer goods industry is being shaped by several key trends: 

Artificial Intelligence (AI) and Machine Learning (ML) are transforming how companies analyze data, providing more accurate predictions and deeper insights. These technologies are enhancing demand forecasting, optimizing trade promotions, and taking on a role of data-informed ‘guided planning’. 

Real-time data analytics is becoming crucial for making timely decisions. Access to up-to-the-minute data allows companies to respond quickly to market changes, optimize inventory levels, and improve customer satisfaction. 

Data integration and harmonization capabilities are essential for breaking down data siloes and making data ready for analysis. It enables CPGs to create a single source of truth, giving cross-functional teams a comprehensive view of the business.  

The shift to cloud-based analytics offers scalability, flexibility, and cost-effectiveness. These tools enable companies to store and process large volumes of data and collaborate more effectively. 

Finally, there is a growing focus on data privacy and security. Ensuring the privacy and security of data is paramount, and companies are investing in robust measures to protect their data assets. 

By staying ahead of these trends, CPG companies can leverage data analytics to drive innovation, enhance decision-making, and achieve sustainable growth. 

How to select the right data analytics solution for your CPG company 

Choosing the right TPx/RGM data analytics solution is a critical decision that can significantly impact your business's success. Here are some key considerations to help you make an informed choice: 

1. Assess your company's specific needs and objectives. Identify the key areas where data analytics can drive value, such as demand forecasting, trade promotion optimization, or trade spend effectiveness. Understanding your priorities will help you narrow down the options and focus on solutions that align with your goals. 

2. Evaluate the scalability and flexibility of the analytics solution. As your business grows, your data needs will evolve. Ensure that the solution you choose can scale with your company and adapt to changing requirements. Look for true SaaS software that offers centralized tenant management, modular features, and extensive configuration. 

3. Data integration capabilities are crucial. The right solution should handle data from various sources, both internal and external, and harmonize it for analysis. This ensures that you have a comprehensive, up-to-date, and unified view of your business, enabling better decision-making. 

4. User-friendliness is another important factor. The solution should be intuitive and easy to use, allowing your team to quickly adopt and leverage its capabilities. Look for vendors that offer robust training and support to help your team get up to speed. 

5. Security and data privacy are paramount. Ensure that the solution complies with industry standards and regulations to protect your sensitive data. Look for features like data encryption, access controls, and audit trails to safeguard your information. 

6. Time to value is a critical consideration, especially for multi-market rollouts. Often, one of the biggest issues with software implementations is that they take too long. For international CPG companies, it's not just about going live in a first (pilot) market but also about the capability to rapidly replicate and adjust the solution to other markets. This goes hand in hand with choosing configuration over customization. The right type of configurable solution allows CPGs to centrally manage the application as well as adapt flexibly to new market requirements. This is typically done through a CPG’s Center of Excellence, ensuring that the tool remains a great fit for live markets over time. 

7. Consider whether the advanced analytics and AI capabilities are part of a software solution specifically designed for the CPG industry. These often come with pre-built models and features tailored to the unique needs of revenue growth management (RGM) and trade promotion (TPx). This can significantly reduce the time and effort required to implement and adopt the solution, providing faster time to value and ensuring that the analytics capabilities are aligned with industry best practices. 

8. Evaluate the total cost of ownership. Assess the pricing model, including any hidden costs for implementation, maintenance, and upgrades. Compare the value provided by different solutions to ensure you get the best return on investment. 

By carefully considering these factors, you can select a data analytics solution that meets your CPG company's needs and enhances your competitive edge. 

Would you like to see how Visualfabriq’s AI-empowered analytics could benefit you? Feel free to book a demo.