The INSIGHT Group, an executive management consulting firm, conducted a study in partnership with Arizona State University’s Center for Services Leadership (ASU CSL) to understand how senior executives are prioritizing, managing and optimizing their data monetization efforts. Surveying the executives of 200 global organizations across twelve industries, the study found that when companies align their efforts with strategic customer priorities and establish the culture, skills and systems to support, they are able to effectively convert data into sustainable sources of customer revenue growth and competitive advantage.

One participant of the study that redefined their service value through innovative data application is Vixxo. A facilities management company overseeing more than 65,000 supermarket, retail, and convenience store locations, Vixxo embedded sensors into their customers’ store assets in order to provide a real-time window into operational performance. Delivered via mobile app, the insights allow customers to rapidly deploy technicians to identified issues and make better product investment decisions. From monitoring coffee brewing machines at Starbucks to baking ovens at grocery stores, Vixxo is enabling their customers to improve their operations and reduce service interruption. Now, the company is collaborating with the product manufacturers themselves, helping them embed IoT (Internet of Things) capabilities into their products so that they too can access the performance analytics.

But how can a CPG manufacturer translate the success story of Vixxo into one of their own? According to the study, while a growing number of organizations are talking about monetizing data and analytics, less than half feel they have a clear understanding of how to get started. Below are three practices that manufacturers in the early stages can apply to steer their business onto a path towards data-driven commercial growth:

1. Evaluate the Market Opportunity 

Perform a market assessment to understand what data has value— and to whom. Doing so gives your data investment strategic focus and creates a targeted customer model and value proposition to drive capabilities development. Market validation should be conducted by assessing customer ROI and identifying top customers. In-person meetings with the key decision makers of these identified retailers should then be arranged in an effort to understand their needs and ways in which you can help operationalize their data or enhance it in order to meet a business objective. Data opportunities should be viewed and designed as an integrated extension of the existing services you offer as a supplier, working to optimize your pre-established value and expertise.

2. Embed into Account Management Model

Account managers are normally provided with generalized category insights that they are unable to connect to customer-specific needs and performance, but in deploying a custom analytics solutions, account reps. must evolve into informed data consultants. This involves training account teams on how to embed customer-specific analytics into planning process in order to continually evidence service value. To support this training, a cross-functional team should be established to oversee the customer analytics initiative and drive, collect and disseminate learnings. This team should not just include data scientists and systems analysts, but key members of marketing, finance, sales and supply chain. It is also critical to get senior management on board. In establishing company-wide governance, the service is viewed less as a functional solution and more as a holistic driver of commercial growth that can be scaled and implemented across other customers.

3. Define & Measure Performance Metrics

The participants of the study in the mature phases of  their data monetization efforts reported to have aligned their service innovation with one of three goals: deliver incremental revenue, increase profit, and drive customer acquisition & market expansion. They then developed metrics that demonstrated progress across these goals and used the performance insights to help direct better investments and customer recommendations. In addition, they established a set of early indicators of market traction, which in the case of a manufacturer, could include increased retailer program participation, incremental sales lift, basket value or online conversion.

Deepening Partnership with Data-Based Services

Consumer goods manufacturers are increasingly leveraging data to optimize their customer experience, operational efficiency and cost effectiveness. As significant strides are made in these areas, CPGs are beginning to center their data efforts on a new objective—market expansion. These forward-lookers have the opportunity to expand retail partnership by converting shopper, trade and retail execution analytics into customer-specific revenue growth strategies. With opportunities ranging from customized product assortments to custom SKUs, packaging, and shopper marketing solutions, the potential is extensive, but can only be tapped with an evolved business model that puts retail customers, as opposed to products and categories, at the heart of operations and sales strategy.