Recommendation and Conclusion 1

1) Few CPG Companies Have the Required Analytical Foundation in Place

Current-State:  Over the last decade, the majority of firms in the CPG industry have not progressed beyond localized analytics and are still challenged to capitalize on their “small” existing data (internal and structured).  Conversely, a small handful of CPG firms have improved their analytical maturity to become strong analytical companies and therefore have the required foundation to capitalize on new emerging sources of “Big Data”.

The research suggests that the majority of CPG companies do not currently have the required analytical maturity or full set of foundational analytical capabilities to fully capitalize on the last two decades of “small” or existing data assets (e.g., ERP, TPM, EDI, POS, Syndicated), let alone new and emerging forms of Big Data (See 2x2).

This study found that most companies in the CPG industry can be characterized as being at a Stage 2 analytical maturity namely having strong localized analytics in each business function. This stage of analytical maturity is characterized by each business function maintaining their own reporting and analytical capability that allows the function to make basic decisions based on historical data. Examples of Stage 2 localized analytics are, trade promotion analytics, category management analytics, or demand planning analytics. 

The research also suggests that, for most CPG companies, tools and systems are not the biggest challenge.  Many CPG companies find it difficult to move beyond Stage 2 as they have yet to establish the proper information management approaches, decision-making processes, organizational structures, talent models, analytical culture, and leadership characteristics required to evolve into higher levels of analytical maturity.

A common sentiment voiced by industry executives as part of the research interviews was:

“After over a decade of investing in tools for data management, reporting, and analytics, I feel we have evolved from ‘Information Poor’ to ‘Information Rich’, however it does not feel like we have dramatically improved the competency level of the organization – our ability to consistently use our information assets and related knowledge to make timely and effective cross-functional business decisions.”

This statement was found to be generally reflective of organizations that have historically focused more on the tools and techniques for analytics rather than the other areas needed to improve analytical maturity discussed in this section.

The Analytical Maturity Model illustration below provides a high level overview of the required capabilities that define an organization’s progression from Stage 1 to Stage 5.  Within each of these five categories, the model illustrates the difference between the least mature analytical companies (Stage 1-2) and those who have developed notable analytical maturity (Stage 4-5).

The research suggests that companies that have made progress on two or three of the required categories often feel they have adequate analytical capabilities to drive operational efficiency.  However, the companies that have taken a more comprehensive approach, focusing broadly on each of the five categories, are the companies positioned to use analytics as a competitive advantage.  Companies looking to capitalize on new emerging forms of Big Data will find that in order to partake in the new opportunities, they will need to have made significant strides along each of the five categories.

The following section further expands on the Analytical Maturity Model and provides an overview of the current state of the CPG industry.  The section provides a high level contrast between the analytically impaired (Stage 1) and analytical competitors (Stage 5).  This high level current-state assessment is intended to be a general gauge for the industry to better understand the overall preparedness to capitalize on Big Data.

To summarize the overall maturity model, most CPG companies are still focused on developing analytical capabilities using existing data assets the industry has had for more than a decade. As we move into an era of rapid innovation and resulting Big Data, it will be a challenge for the less analytically mature companies to keep pace. Conversely, the companies with the required foundational capabilities – across all five areas – are in a position to leverage Big Data to gain competitive advantage. This analytical “maturity gap” within the industry may have existed amongst competitors for many years; however, the implications will likely become increasingly visible in this new era of rapid technological and digital innovation which will be outlined further in the following conclusions and recommendations.

Tools and Techniques

The analytical tools and techniques that demonstrate analytical maturity in the CPG and retail industry.

Information and Insights

The way in which analytically mature companies deliver information and insights to decision makers.

Decision-Making Processes

Measuring analytical maturity by evaluating key decisions in a cross-functional context.

Talent and Organization

The ways analytically mature competitors structure their organizations and hire and retain analytical talent.

Leadership and Culture

Leadership and culture can help organizations embrace data driven decision making and evolve to become more analytically mature.