CPG industry guide to data analytics and AI integration

Organizations across industries are increasingly recognizing the power of data analytics and artificial intelligence (AI). As the consumer packaged goods (CPG) industry navigates this new frontier, it's crucial to develop a strategic approach that maximizes the potential of these technologies while ensuring responsible and effective implementation across multiple markets. In the $2.4 trillion US CPG market, where companies often manage operations across dozens of countries, the ability to harness data and AI effectively can determine market leadership. 

Understanding the data analytics and AI landscape in CPG

Before diving into strategies, it's important to understand the current state of data analytics and AI in the CPG industry. These consumer goods technologies have moved beyond buzzwords to become fundamental drivers of business innovation, allowing CPG manufacturers to gain a competitive advantage across international markets. From predictive analytics to large language models (LLMs) and rapid image recognition, the applications are vast and constantly expanding. However, harnessing these technologies effectively across global operations requires a strategic approach and a solid data foundation that can accommodate multiple currencies, regulatory environments, and market dynamics.

Patrick Higgins, VP business development, data and AI services lead at TELUS Digital, emphasizes the importance of a strong data foundation: "Conducting a data readiness audit is a crucial first step for organizations when assessing their current data landscape and AI readiness. This process helps companies identify areas for improvement and ensure that they build AI initiatives on a robust data infrastructure.”

Data harmonization: a key component of CPG data readiness audits

Data harmonization involves integrating data from disparate sources, formats and systems into a cohesive, standardized framework that works across international operations. Data harmonization is essential because it ensures consistency and compatibility across datasets from different markets, enabling more accurate analysis and insights for global decision-making. During a data readiness audit for global CPG operations, assessing the level of data harmonization helps organizations identify silos, inconsistencies and gaps in their international data ecosystem.

By addressing these issues, CPG manufacturers can create a unified data environment that supports seamless AI implementation and cross-functional analytics across all markets. This is particularly important for companies operating in North America, where they must integrate data from major retailers like Walmart, Target, and Kroger, while also managing international operations with different retail formats and data standards. Moreover, harmonized data facilitates better decision making, improves data quality, and enhances the overall reliability of AI models and predictive analytics.

Ultimately, prioritizing data harmonization within the audit process lays a solid foundation for successful AI adoption and more data-driven strategies that can scale across global operations while maintaining compliance with varying international regulations.

From analysis to action: data analytics and AI integration in CPG

Once a solid data foundation is established, data analytics and AI work together to help drive innovation. While analytics uncovers insights, AI creates systems that learn and make smart decisions automatically. This powerful combination can enable organizations to quickly transform data into practical insights at scale.

How to identify, prioritize and execute AI use cases in the CPG sector 

1. Start with a clear vision

Successful data analytics and AI initiatives start with a clear, needs-based vision that accounts for both domestic and international market requirements. This vision should align with overall business objectives and target specific organizational challenges or opportunities across all operating markets, from the mature US retail landscape to emerging international markets.

2. Assess data readiness across all markets

The foundation of any successful global data analytics and AI strategy is high-quality, accessible data from all operating regions. Conducting a data readiness audit is a critical first step. The data harmonization capabilities of TELUS RGM Analytics help CPG manufacturers bring disparate data points together, like point of sale and syndicated data, to see how pricing, promotions and distribution differ across retailers, regions and beyond. 

3. Build the right team and culture for global operations

Successful implementation of data analytics and AI strategy requires more than just technology – it requires the right people and organizational culture. Consider identifying a dedicated AI subject matter expert within your organization or partnering with someone outside your organization, like TELUS Digital. To minimize risks, TELUS recommends developing specific frameworks, based on the National Institute of Standards and Technology (NIST) best practices. 

4. Choose the right use cases for your market portfolio

Not all problems require AI solutions. It's important to identify use cases where data analytics and AI can provide significant value. One application of AI within TELUS RGM Analytics is Trade Promotion Management (TPM) machine learning. The TPM tool evolves by continuously learning organizational planning processes, enhancing its capability to drive CPG sales growth over time.

5. Implement responsible AI practices across all operations

As AI becomes more prevalent, ensuring its responsible and ethical use is paramount. Taha Shaikh, commercial director of data and AI at TELUS Digital, stresses this best practice: "At TELUS Digital, we lead and advise on complex AI projects with a commitment to responsible AI and data use."

Power your AI journey: start with TELUS RGM Analytics' data readiness audit

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Applications of machine learning models

As the excitement around generative AI continues to grow, it's important to remember that there's a broad range of machine learning applications that can provide significant value to organizations. To fully appreciate these applications, it’s helpful to know how machine learning fits into AI.

According to Higgins, "when we consider machine learning, it's important to understand its place within the broader context of AI. AI, in its simplest terms, is the application of software to mimic human intelligence. Machine learning, a subset of AI, focuses on analyzing large datasets and building algorithms that extract insights from this data. Generative AI, which has gained significant attention recently, is just one form of machine learning."

With this framework in mind, let's explore four key categories of applied machine learning that are driving innovation and helping solve various business challenges across industries:

Classification

Classification organizes large amounts of data into predefined categories or buckets, which is particularly useful for companies managing diverse international datasets. This technique is useful for ranking and prioritization data sets across different markets. For example, to distinguish between syndicated data from Nielsen or IRI in the US market versus local market research data from international markets, apply classification. Classification also detects anomalies, such as incorrect promotional pricing across different currencies or markets, allowing a field sales representative to take action quickly whether they're managing accounts in North America or international markets.

Prediction

Perhaps the most well-known traditional machine learning approach, prediction, uses historical data to forecast future outcomes. This category includes various forecasting techniques like regression analysis, which helps understand how different inputs affect outcomes. Predictive analytics can be particularly valuable in trade promotion management, where understanding how promotions perform over time is crucial.

Clustering 

While similar to classification, clustering is unique in that the algorithms determine the categories themselves, rather than using predefined ones. This approach allows for discovering previously unknown patterns and connections within data. Clustering is a powerful exploratory tool for uncovering insights that human analysis might miss. For example, popular music streaming services commonly use it in recommendation systems to group users with similar preferences.

Generation 

The fourth category, generation, encompasses the rapidly evolving field of generative AI. This includes applications like data summarization, where analysts condense large datasets into clear, concise summaries. Content generation is another key area, covering text, image, video and audio creation. As these technologies advance, they're opening up new possibilities for natural language interfaces and conversational AI.

Implementing a data analytics and AI use case: a step-by-step approach

Overcoming common data analytics and AI challenges

As you embark on your data analytics and AI journey, be prepared to face and overcome several common challenges:

Getting started: a strategic guide for CPG data analytics and AI

Leveraging data analytics and artificial intelligence is no longer optional for CPG manufacturers seeking to remain competitive in the digital age. By developing a strategic approach that aligns with business objectives, builds on data assets and prioritizes responsible implementation, it’s possible to unlock the transformative potential of these technologies.

Mark Gozzo, senior manager, client strategy at TELUS Digital, emphasizes the importance of identifying opportunities: “Remember, the journey to becoming a data-driven, AI-enabled organization is ongoing. It requires commitment, flexibility and a willingness to learn and adapt. Start with clear objectives, build on early successes and continuously refine your approach. With the right strategy and execution, data analytics and AI can become powerful drivers of innovation, efficiency and growth for your organization.”

As you embark on this journey, consider partnering with experts who can guide you through the complexities of data analytics and AI implementation. Whether it's harmonizing data or developing custom AI solutions, the right partners can accelerate your progress and help navigate common pitfalls.

Key takeaways for effective CPG AI implementation

Essential Foundation

Strategic Approach

Core ML Applications

Success Factors