Part 2: Advanced Data Analytics in Consumer-Packaged Goods companies

In this series of articles, we explore the significant role of advanced analytics within the consumer-packaged goods industry.
In the first blog post (see Advanced Data Analytics in Consumer-Packaged Goods companies – part 1), we examined why the industry has seized the opportunity to advance in data and analytics maturity, showcasing how businesses are transformed by harnessing data insights to innovate, plan, decide, and act. This week, we will cover best practices and discuss the potential financial impacts of this analytical revolution.

Best Practices: The 'How' of Advanced Analytics in CPG

Implementing an advanced analytics program in the consumer-packaged goods (CPG) sector requires a detailed and methodical approach. Through comprehensive analysis of our top customers' practices and extensive market research, we have identified these best practices to guide effective implementation strategies:

1. Establish a Bold Strategy with a Realistic Roadmap

Developing an advanced analytics strategy requires a balance between ambition and feasibility. The long-range strategy should be bold, aiming for substantial business impact through clearly defined pillars that outline specific opportunities, solutions, and capabilities. These ambitions should be rooted in a detailed understanding of the potential of analytics to transform the performance of key elements of the business and operating model.

In the next installment of this series we will present and discuss a “reference” D&A strategy for the sector; for a conceptual introduction to the building blocks see Anatomy of a Data and Analytics Strategy.

Simultaneously, the strategy must be supported by a realistic roadmap that sets out gradual and achievable goals and milestones, ensuring that the organization can sustain momentum and achieve continuous improvement in its analytics capabilities. Utilizing the years of experience in descriptive and diagnostic analytics that most organizations have accumulated—or rapidly advancing knowledge in areas where business intelligence is less established—to jumpstart and accelerate predictive analytics projects exemplifies this pragmatic and organic approach.

2. Engage Top Management

It's essential to secure the commitment and active participation of top management, including the CEO and C-suite executives. Their involvement not only boosts the program's visibility and strategic alignment but also helps in shaping the analytics strategy to reflect the organization’s opportunities, priorities and challenges.

Successfully engaging these leaders starts with insightful communication of the analytics benefits and strategic positioning that emphasizes data-driven decision-making and actions as a core component of the business strategy. Implementing the federated operating model as discussed below will take it to the next level, ensuring that the strategic engagement is matched with day-to-day ownership of goals, initiatives, and products.

3. Transform Business Domains Holistically

To fully leverage data and analytics, it's crucial to apply these technologies and roll-out carefully connected products across entire business domains, not just isolated cases.

For instance, in revenue growth management, as we discussed in our previous article, it’s essential to imagine the products supporting assortment, pricing, promotions, and trade, eventually interconnected with channel optimization, product recommendations, and sales execution, as a cohesive portfolio that enables orchestrated and efficient growth.

Start by selecting areas identified in your strategic vision where quick wins are possible through leveraging existing capabilities or those that can be developed within a reasonable timeframe. Use these initial cases to kickstart your D&A program, drive momentum and develop underlying capabilities. Then, broaden the portfolio to include clusters of related products (some of which may have been initially implemented by business teams before the companywide D&A initiative) reflecting the interconnected nature of the business model and data. This modular approach ensures a robust foundation and scalable growth of the analytics initiatives.

4. Adopt a Flexible and Scalable D&A Operating Model

In most cases, this means a federated operating or service delivery model. It starts with a central D&A team that sets strategies and leads the development of analytics capabilities while monitoring execution and impact. This central unit breeds and orchestrates distributed teams located within various business, regional or functional organizations, which are responsible for leading and managing the day-to-day initiatives and projects aligned with the overarching analytics strategy.

This model not only fosters a culture of data-driven decision-making but also ensures that insights and solutions are closely tailored to specific business needs, enhancing the relevance and impact of the initiatives.

5. Design a Robust Data Architecture

TOGAF, the gold standard when it comes to Enterprise Architecture, defines Data Architecture as “a description of the structure and interaction of the enterprise's major types and sources of data, logical data assets, physical data assets, and data management resources.”

Designing this architecture involves not only the analytics components but also all applications and platforms across the technology stack. Be prepared to encounter several function-specific tools that business teams have independently acquired and managed. These must now be gradually integrated into a cohesive end-to-end architecture or replaced if they duplicate functionality or are too complex to connect.

The convergence of the architecture should be seen as a long-term project that will evolve through a mix of proactive investments and organic changes over several years. However, a critical and urgent first step is to develop a reference architecture vision and start making all technology acquisitions and implementation decisions with this end goal in mind.

A note of caution on cloud solutions: while they bring substantial benefits in terms of practicality, speed, cost, and risk management, they also introduce challenges in data architecture design and data management. Modern SaaS applications, including core systems like ERPs and CRMs, as well as specialized tools such as those used for RGM and IBP, feature cloud-based data repositories with bespoke structures and standards. This complexity requires carefully crafted integration and transformation mechanisms to ensure optimal performance, functionality, and data consistency across processes and platforms.

Build, groom, and leverage a rich ecosystem of data sources to complement and enhance your internal data. Utilizing a diverse array of data sources is crucial for gaining a comprehensive view of the market. These sources should include everything from market trends and consumer behaviors to detailed retail-level transactions, thus providing a detailed landscape that informs strategic decisions. While designing standards and processes to normalize and ingest external data is challenging and time-consuming, doing so is pivotal for effectively innovating in business domains like product portfolio, pricing, and supply chain, which are heavily influenced by market dynamics.

6. Establish a Practical Data Governance Program

Data governance can quickly become intricate. The second edition of the Data Management Body of Knowledge by DAMA International ², a comprehensive reference, spans nearly 600 pages. However, in practice, data governance can be condensed into essential principles and processes that, if rigorously applied, can get any organization to the level required to support ambitious D&A initiatives. At ixpantia, we designed our own model, tailored to the needs of mid-sized businesses, simplifying established standards for practical and cost-effective application, see table 3.

Data governance should in most cases be orchestrated by a centralized team but involve leaders from relevant business functions, with local stewards and guardians. This setup must balance consistency, compliance, and security with the need for innovation and flexibility. Well-designed data governance maximizes empowerment and value while controlling risks.

7. Get the Talent Strategy Right

Perfecting the talent strategy is crucial for integrating all elements necessary to drive change. This is vital in all data-driven transformations—see our previous post on the ixpantia framework—but distinct characteristics of consumer product companies significantly heighten the importance of the talent factor.

One aspect is the array of business processes and practices that need reengineering to maximize impact. From the top, structural approaches like channel segmentation, pricing management, and marketing initiative portfolios must be adjusted to capitalize on the refined accuracy of advanced data analytic models. Engaging top management and assigning clear ownership of transformation efforts to business leaders will speed up both change and impact.

Another distinction in this sector—compared to others like Banking or Insurance—is how data products must be seamlessly integrated with operational processes to make an impactful difference at scale. For instance, lower down in the operating model, long-established practices in sales orchestration, assortment management, and customer relationship development must evolve to realign the contributions of people and technology.

This necessitates a meticulous alignment of business, digital, and data-driven transformation, focusing on improving core performance indicators such as customer profitability, internal market share, and frontline productivity. Adoption should be seen as a means to an end; change management needs to focus on innovating models, practices, and processes, with data-driven insights serving as enablers. This holistic approach preempts pushback, accelerates adoption, and organically changes habits and culture.

8. Use a Flexible Sourcing Model

Adopt a flexible approach to sourcing talent by tapping into existing internal expertise, strategic recruitment to diversify the skill set, and partnering with specialized external vendors. This strategy allows the organization to scale its analytics capabilities dynamically and responsively, ensuring access to top talent and cutting-edge know-how as needed.

Engage external partners with a robust model that ensures compliance with architecture, governance and IT management practices, while providing the necessary guidance, training and support to do so. It’s crucial to ensure that critical knowledge remains in-house, preserving core competencies and intellectual property.

The Reward: Impact of Effective Application of Data and Analytics

The impact of deploying D&A technology in CPG companies can be examined from two perspectives: micro and macro. From a case-specific viewpoint, substantial value creation has been measured by innovating around key aspects of the business and operating models; while the aggregate impact of comprehensively transforming end-to-end processes with advanced data analytics is profound.

Colleagues at global consulting firms have analyzed their clients and cases, compiling reports on the overarching impact. These findings support the notion that even partial realization of these benefits substantiates the aggregate experiences from individual cases, demonstrating extensive operational and financial improvements across the sector.

A comprehensive Boston Consulting Group report on unlocking growth in the CPG sector with AI and advanced analytics ³  highlights several key impact areas:

• Revenue Uplift: AI and analytics can drive a revenue uplift of 2% to 10% by enhancing pricing strategies, optimizing promotions, and improving product assortments.
• Margin Improvement: Margins can be improved by 1% to 3% through cost reductions and efficiency gains in production, supply chain, and logistics.
• Marketing ROI: Advanced analytics increases the return on marketing investments by up to 15%, enabling more precise targeting and personalized campaigns.
• Customer Retention: Analytics-driven customer insights lead to higher retention rates and increased customer lifetime value.
• Operational Efficiency: Implementing AI and analytics in operations can reduce supply chain costs by 20% and inventory levels by 15%, leading to significant cost savings and improved cash flow.

McKinsey reports detailed findings ⁴ that, while not specifically looking at consumer companies, highlights key impacts of data analytics that are applicable:

• Top-line Growth: Up to 15% revenue increase by enhancing customer-facing activities such as pricing, churn prevention, cross- and upselling, and promotion optimization.
• Bottom-line Improvement: Up to 20% cost reduction through optimizing internal processes like predictive maintenance, supply chain optimization, and fraud prevention.
• Assortment Optimization: Achieving a 2-4% increase in sales by better aligning product offerings with consumer preferences.
• Pricing: Up to 5-10% revenue uplift through dynamic pricing models responsive to market conditions.
• Promotion Optimization: Enhancing promotional strategies, resulting in a 10-20% increase in marketing ROI by targeting the right customers with the right offers.

Accenture found comparable results in a similar report ⁵, which provides additional insights into the economic impact of data analytics and AI in the CPG sector:

• Marketing Expense Reduction: By optimizing marketing spend through data-driven insights, companies can cut marketing expenses by 15% to 30%.
• Faster Speed to Market: Analytical products achieve faster speed to market, with some companies reporting up to 80% quicker deployment compared to traditional methods.
• E-commerce Revenue Growth: Cloud-enabled analytics and AI can boost e-commerce revenue by 8% to 14%, enhancing online sales performance and customer experience.
• Higher ROI: CPG leaders who scale data, analytics, and AI capabilities strategically report three times higher ROI compared to companies with siloed approaches.

Overall, the deployment of advanced analytics in the CPG sector presents a substantial opportunity. By focusing on both micro and macro impacts, CPG companies can transform their operations, driving significant economic benefits and maintaining a competitive edge in the market

Conclusion: Aim High, Relentlessly Focus on Value

To sum up, the use of advanced data analytics in the consumer-packaged goods industry offers a considerable potential for growth, productivity, and value generation. By following successful practices, companies can leverage data and analytics to innovate and gain an edge over the competition.

The potential financial impacts of this analytical revolution are substantial, and by relentlessly focusing on value, companies can achieve significant economic benefits.

References

1.  The TOGAF® Standard, Version 9.2
2. DAMA-DMBOK: Data Management Body of Knowledge, Second Edition, DAMA International
3. Unlocking Growth in CPG with AI and Advanced Analytics, Boston Consulting Group, 2018
4.  Achieving business impact with data, McKinsey & Company, 2018
5. The Insight Track: Five no regret capabilities to be a data-and analytics-driven CPG business, Accenture, 2021