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CPG industry guide to data analytics and AI integration

CONSUMER GOODSDATE POSTED JANUARY 14, 2025
Team collaborating on how to use artificial intelligence in their business

Table of contents 

  1. Understanding the data analytics and AI landscape in CPG

  2. Data harmonization: a key component of CPG data readiness audits

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

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

  5. Applications of machine learning models 

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

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

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. 

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. These consumer goods technologies have moved beyond buzzwords to become fundamental drivers of business innovation, allowing CPG manufacturers to gain a competitive advantage. From predictive analytics to large language models (LLMs) and rapid image recognition, the applications are vast and constantly expanding. However, harnessing these technologies effectively requires a strategic approach and a solid data foundation.

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.”

"Conducting a data readiness audit is a crucial first step for organizations when assessing their current data landscape and AI readiness."

Patrick Higgins

VP business development, data and AI services lead at TELUS Digital

Data harmonization: a key component of CPG data readiness audits

Diagram illustrating the stages of data harmonization in the CPG industry for data analytics and AI

Data harmonization involves integrating data from disparate sources, formats and systems into a cohesive, standardized framework. Data harmonization is essential because it ensures consistency and compatibility across datasets, enabling more accurate analysis and insights. During a data readiness audit, assessing the level of data harmonization helps organizations identify silos, inconsistencies and gaps in a data ecosystem. 

By addressing these issues, CPG manufacturers can create a unified data environment that supports seamless AI implementation and cross-functional analytics. 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.

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. This vision should align with overall business objectives and target specific organizational challenges or opportunities.

2. Assess data readiness

The foundation of any successful data analytics and AI strategy is high-quality, accessible data. 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

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

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

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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Woman reviewing data readiness results with team

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 machine learning application icon

Classification

Classification organizes large amounts of data into predefined categories or buckets. This technique is useful for ranking and prioritization data sets. For example, to distinguish between syndicated data or retail direct data, apply classification. Classification also detects anomalies, such as incorrect promotional pricing, allowing a field sales representative to take action quickly.

Prediction machine learning application icon

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 machine learning application icon

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 machine learning application icon

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

  • Define objectives: Clearly articulate the desired organizational outcomes for data analytics and AI

  • Conduct a data audit: Assess your current data landscape and identify gaps

  • Prioritize use cases: Identify the needs that are most critical to customers of your organization that are not currently being met. Select high-impact, feasible projects to start with

  • Build or acquire capabilities: Determine whether to build in-house capabilities or partner with external experts, such as TELUS TABS Analytics for data analysis and TELUS Digital for AI

  • Develop a pilot project: Start small with a proof of concept to demonstrate value and learn

  • Measure and iterate: Establish clear KPIs and continuously refine your approach based on results

  • Scale successful projects: Once you've proven value, expand successful initiatives across the organization

  • Foster continuous learning: Stay updated on emerging AI tools and best practices in the rapidly evolving field of AI and machine learning

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:

  • Data quality and integration: Invest in data cleansing and integration tools to ensure your AI models have high-quality inputs

  • Resource allocation: Consider partnerships, training programs and flexible work arrangements to attract and retain top data analysts and AI strategy specialists

  • Ethical concerns: Develop clear guidelines and governance structures to address ethical considerations in AI development and deployment

  • Scalability: Design your infrastructure and processes with scalability in mind from the outset

  • Change management: Implement a robust change management strategy to ensure organization-wide adoption and support for AI initiatives

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.

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