How to become an analytics-driven consumer packaged goods company

How to Become an Analytics-Driven Consumer Packaged Goods Company


March 2014

Consumer packaged goods (CPG) companies realize that deep analytics capabilities can be a key differentiator in today’s competitive marketplace; accordingly, they are making analytics-related investments in talent, tools, data and systems to deliver on that promise. Yet according to recent Accenture research, many companies struggle to maximize value from these investments—in part, because of the inability to take a coordinated, enterprise-wide approach supported by a clearly defined operating model.

Most consumer packaged goods (CPG) companies are at an early stage in their analytics journey. The uptake of analytics in the industry is accelerating, to be sure, but a recent research study from Accenture—Building an Analytics-Driven Organization—has found that the majority of CPG companies have been slow to put analytics at the heart of decision making and of operations, limiting their ability to gain business advantage.

Accenture’s analysis also suggests that the problem, in many cases, is compounded by fragmented investments in narrowly defined programs that are not well coordinated and not based on a wider, enterprise-level view and operating model. This lack of an enterprise-wide analytics vision and operating model often results in pockets of unconnected analytics capabilities, redundant initiatives that do not come to fruition, underutilized analytics talent and, perhaps most important, limited returns on analytics investments.

In addition, the study reveals that the analytics functions of many CPG companies are merely descriptive in nature rather than predictive. That is, they are generating hindsight views of what has happened instead of forward-looking insights that can be used to make well-informed operational, managerial and strategic decisions.

Here are important factors for CPG executives to bear in mind as they consider how to get started down the right analytics path:

Sequence around value

Historically, companies have approached analytics from within functional silos; because of this, analytics priorities and investments have been based on specific functional needs instead of the broader needs of the organization. This would often result in wasted effort, and would eventually burn through the allocated budget—with limited returns.

By contrast, companies that are driving business value from analytics start by defining an analytics vision and strategy across the organization, and then sequencing opportunities—focusing first on high-value, high-profile, quick-win analytics opportunities that produce measurable benefits. Such projects can generate momentum and a pipeline of demand at the enterprise level, which can help to fund subsequent analytics projects.

Successful companies also tie their analytics efforts directly to business outcomes. For example, Tesco—one of the world’s largest retailers—sought to increase the efficiency of its supply chain and reduce its risk of stock-outs. An analytics team leveraged the company’s customer loyalty card program to generate customer purchasing insights, and then applied those insights toward the redesign of its internal operational processes. This has enabled Tesco to improve its ability to deliver the right type of inventory, in the right numbers, to the right store at the right time.

Organize to succeed

Once companies have an analytics vision and the value of analytics has been demonstrated, the next step is to put an organizational and governance structure in place that supports scaling analytics across the enterprise. This structure enables the company to establish a broader culture of analytics innovation, align resources with strategic goals and develop effective processes and standards.

It also enables the infusion of analytics into decision-making processes, something that is sorely lacking in most CPG companies today. Although 62 percent of companies we surveyed believe that analytics supports faster and more effective decision making, only 25 percent habitually rely on analytics findings in their decision-making processes. Properly designed, analytics can empower decision making at the point of action.

Click to Enlarge Critical to this organizational design step is working with an effective and comprehensive operating model for analytics (see chart). According to the Accenture research study, only about half (54 percent) of the CPG executives surveyed say their company has a fully defined analytics operating model. Forty percent of the respondents say that their company has only partially defined an analytics operating model, and 14 percent of those CPG executives say their company has not implemented it.

 

Such an operating model was especially important for one global apparel manufacturer that was trying to develop advanced analytics capabilities in support of an aggressive growth strategy. Executives had previously developed an enterprise analytics roadmap for North America, but they found that the company’s existing organizational model lacked a clear structure, alignment and accountability to put the roadmap into action.

The company worked with Accenture to help define the comprehensive operating model and talent blueprint needed to deliver on its enterprise analytics roadmap. The team designed a new “hub and spoke” organizational structure and governance model—including a cross-functional center of excellence—to make the analytics vision a reality and deliver the kind of talent strategy needed to scale its analytics capabilities.

As a result of the work, the company now has a prioritized path to value, greater visibility into operations to avoid unnecessary costs and a plan for quick wins that can help self-fund the journey.

Develop your talent blueprint

Implementing and sustaining an enterprise-wide analytics organization is not possible without finding and retaining superior analytics talent. Here, companies encounter two challenges. One is finding the talent in sufficient numbers. Analytics is a fairly new field and universities are not yet turning out data scientists at the rate needed. A second challenge, however, is in not knowing how to leverage analytics scientists properly. In a survey that Accenture conducted as a part of a recent analytics client engagement, we found that only 5 percent of analytics professionals across the enterprise were developing and applying advanced analytics concepts. Instead, almost two-thirds of them (62 percent) were merely creating reports and dashboards, or finding and interpreting data sources.

Not surprisingly, corporate executives are not pleased to find that their highly paid analytics talent is simply producing retrospective reports. Such a situation can undermine the longer-term prospects for analytics at a company.

Given these challenges with effectively sourcing, retaining and leveraging talent, an attractive option for many companies is to use an “analytics-as-a-service” model, securing a dedicated analytics capacity using onshore or offshore sourcing. Such a strategy gives companies access to a broad range of talent on demand and helps them quickly align resource needs with business priorities—all at a fraction of the cost of the traditional model.

The analytics journey to ROI: Driven by value

Although analytics has been at the top of many executives’ agendas in CPG for some time, most companies have yet to experience more than limited returns on their analytics investments. However, by starting with a value-based approach oriented to business outcomes, companies can deliver quick wins that can help develop analytics momentum within the company. This momentum can then be used to develop and implement an enterprise-wide analytics structure and talent management strategy to effectively harness the power of analytics—and even self-fund the overall analytics journey.

 

About the authors
Julio Hernandez is a managing director and North American practice lead for Accenture Analytics; he is also the North America practice lead for the Accenture Products operating group.

Robert Berkey, a director in Accenture Analytics, leads the analytics practice for Accenture’s Consumer Goods & Services industry group in North America.

Rahul Bhattacharya, a director in Accenture Analytics, leads offshore delivery for Accenture’s North America Consumer Goods & Services and Retail industry groups.

Chad Vaske is a consultant in Accenture Analytics, focusing on analytics strategy and operating model design.

 

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 This Article is Tagged: Analytics. Consumer Goods and Services.
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About the authors

Julio Hernandez is a managing director and North American practice lead for Accenture Analytics; he is also the North America practice lead for the Accenture Products operating group.

Robert Berkey, a director in Accenture Analytics, leads the analytics practice for Accenture’s Consumer Goods & Services industry group in North America.

Rahul Bhattacharya, a director in Accenture Analytics, leads offshore delivery for Accenture’s North America Consumer Goods & Services and Retail industry groups.

Chad Vaske is a consultant in Accenture Analytics, focusing on analytics strategy and operating model design.

 

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How to become an analytics-driven consumer packaged goods company | Accenture Outlook 
To deliver more business value from analytics, consumer packaged goods companies need to ensure they are working from an enterprise-wide operating model. Here’s how.
analytics, consumer packaged goods
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