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How retailers can leverage data as an alternative profit source

Retail CEOs, strategy officers and marketing leaders can turn data from a marketing cost center to profit center.

In brief

  • Retailers have a vast store of data about their customers, but many need to go further to get value from this data.
  • Establishing a robust data and analytics functions is an important first step toward developing actionable insights from that data.
  • A strong business case sets the foundation for moving data from a marketing cost center to an organizational profit center.

Retailers have long collected troves of data about their customers, primarily through loyalty programs that offer consumers incentives such as discounts, experiences or other rewards. Some businesses have used this data to improve internal decisions, such as how to deploy marketing assets, create an individualized customer experience, determine supply planning and pricing. However, most retail CEOs, strategy officers and marketing leaders still have a long way to go to realize the potential value data and analytics can bring to the organization.

Developing this data function is important for effective internal decision-making and allows retailers to monetize the data externally. Turning data into profit sources has become more urgent as the COVID-19 pandemic accelerated a move to online shopping, and the rise of digitally based competitors cuts into traditional retailers’ revenue.

Retailers need to use their data to enable the type of customer-centric performance measurement that digitally native retailers have embedded in their business. They also need to find new sources of value for their real estate, including customer data collected from store trips. Monetizing this storehouse of customer data, whether collected at the store or through digital channels, is one way for retailers to both enhance the customer experience and generate more revenue.

Retail executives can often turn this marketing cost center into a profit center in several ways (see Figure 1):

  • Enhancing supplier insights to improve assortment and pricing
  • Offering more adjacent customer services, such as financing and warranties
  • Helping third-party advertisers enhance the marketing mix and measure return on investment
  • Providing marketing trends and information to other third-party service providers to support business decisions

Data monetization is growing rapidly and still has a long way to go

The global data monetization market is growing rapidly and is estimated to reach US$371b by 2023, according to an Allied Market Research report.¹ The e-commerce and retail component, which makes up about 11% of the total market, is forecast to show a compound annual growth rate of 36% from 2016 to 2023.

Developing and implementing a data and analytics function is the first step to monetizing data and should not be rushed. Companies can first focus on how data can assist internal decision-making. Using the data as a source of external revenue can be a longer-term goal.

To take advantage of this potential revenue stream, retail executives may first need to determine where their company is on the path to data monetization. They can then take steps to enhance their data’s value, both for making better internal decisions and monetizing it to outside firms. Then they can build up a data and analytics organization, either internally or through partnerships or acquisitions. That team can be organized both to meet marketing, merchandising and e-commerce needs and monetize the data with third parties.

As a retailer’s data and analytics functions evolve, their business relevance increases. Most retailers still have not captured the potential of a data organization and advanced analytics capabilities. Many may use analytics for standard reporting to view past business performance. Some use data to shape purchasing and marketing decisions. Often, analytics functions are dispersed across the organization with little collaboration, shared goals and resources. Few have reached the level of using advanced analytics and reporting, such as artificial intelligence and dynamic updating, to obtain real-time insight into the impact of any market shift.

How major retailers are monetizing data

While many retailers have a long way to go to develop their data strategy, some have shown how action to generate new data-driven revenue streams can work. They often have done so in a staggered manner, by pursuing pieces of the opportunity:

  • A US-based grocer converted a joint venture to a wholly owned subsidiary that provides insights into the retailer’s functions and supports data monetization revenue from third-party advertisers, including suppliers.
  • One global retailer made acquisitions to scale up its digital advertising business, allowing advertisers to use customer data to offer personalized, targeted ads. The retailer was able to increase the monetization rate on its website.
  • A US mass retailer brought its digital analytics operations in-house, partnered with a media company for brand attribution data and acquired a delivery service. These steps helped it to grow online ad revenue.

EY teams have helped companies along this journey, including one fast-moving national consumer goods retailer under pressure to unlock new ancillary revenue streams and build specific data monetization initiatives. EY teams analyzed existing internal initiatives and outlined options to create a new revenue stream capability; and recommended a path to a robust alternative profit platform with three main options: outsource, build or buy. EY teams also provided the client with recommended approaches on additional considerations related to scope and governance, IP and data management, and exit planning.

EY teams then assisted the client in evaluating the chosen investment to monetize loyalty data and developed an execution road map. This plan provided the client with the tools to realize their data’s value through the collaboration and by undertaking a needed organizational change.

Building a robust data monetization function

A dedicated, centralized data and analytics function is useful in developing a data monetization business. Such an organization can drive improved cross-functional decision-making and identify and grow new, profitable revenue sources. Its objectives include polling resources, software and infrastructure, establishing data quality standards and report standardization, breaking down silos, and driving the adoption of tools and analytics (see Figure 2).

One important decision involves selecting a leader in the organization who will oversee the data and analytics function, including monetization. While the data and analytics function can collaborate closely with the technology function, having its leader report to the chief information officer or CTO can limit the ability to monetize data and distance data and analytics from the business operations. Ideally, the function and its leader will report directly to the leader who is responsible for driving the business’s strategic growth decisions, which could be the CEO, chief operating officer, chief strategy officer or CFO. The data and analytics organization leader ideally would have cost and revenue targets to manage for the function and clear financial impact goals for the overall business.

Once in place, it can be important for the data and analytics leader to identify the near-term objectives and opportunities to deliver value to the business. To do so, s/he can assess the level of maturity of the retailer’s data management, talent and organization, analytics and insights capabilities, and technology platforms to decide where and how to invest in improvements first.

Sub-teams within the organization can be responsible for data tools and operations, advanced analytics and data science and market insights to serve multiple business needs across the company.

Filling gaps: buy, build or partner

Once gaps are identified, a retailer can decide whether the best way to fill them is to build the capabilities in house, acquire a third party that provides the capabilities or form a partnership. Such partnerships can include a joint venture agreement, minority investment or strategic alliance.

Each option has pros and cons (see Figure 3). Building internally can give a retailer full control over functionality and the data itself and allow for easier integration with existing applications. However, building from scratch is also time-consuming and resource-intensive.

On the other hand, an acquisition or partnership can allow for quicker access to specialized data analytics and bring in a dedicated team of data professionals. But a partnership can mean less control over back-end analytics and add privacy and security concerns, while an acquisition or JV may require a significant equity investment.

Steps to take to define the data monetization model

While different organizations are at varying maturity levels with big data initiatives, there is a common set of steps for businesses to follow as they build their data monetization business and operating model:

  1. Establish the business case: Estimate the size of the opportunity to monetize data given several factors, including the scale and scope of loyalty data, potential customers and the competitive landscape. Assess the current state of capabilities, data and analytics. Determine the use cases for the organization and third parties, with a focus first on internal needs. Then compare the current state to that of industry leaders and innovators to identify gaps and opportunities to differentiate.
  2.  Define the monetization approach: Identify potential options, such as buy, build, partner or outsource. Assess the potential risks and rewards for each and develop a road map to achieve your approach.
  3. Adopt the operating model: Define the complete operating model, including technology, infrastructure, analytics, management oversight, organizational structure, key performance indicators, profit goals and timing. Execute the governance model to ensure appropriate standards, guidelines and compliance policies across teams, including.  cybersecurity and privacy compliance. The latter action is particularly important, since data privacy and consumer control over data is becoming a regulatory priority, both in Europe through the General Data Protection Regulation and in the US through California data protection laws and other state and federal laws.
  4. Build the commercial approach: Establish the commercial strategy and the sales and marketing functions for this new revenue stream. These typically need to be distinct from the current commercial organization because the products and customers will vary significantly from the core business, and yet can  be connected as appropriate to the core business. In this way, the commercial organization can both reach new customers and capture synergies with the core business.

Turning customer data into actionable internal insights and potential external revenue streams can be essential for traditional brick-and-mortar retailers that view their business being pressured both by the pandemic and consumers’ move toward more digital shopping. Companies that put in place a robust data organization can leverage the data to grow their business, determining where to invest in their core business and building a new source of revenue.


Global data monetization is a rapidly growing market, but many retailers have more work to do to leverage the trove of data they collect every day, turning it from a marketing cost center into a profit center. A robust data and analytics organization is a first step to using data to inform strategic decision making and, later, to package the insights to monetize them externally.

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