如何将你的数据货币化

Idea in Brief The Problem Many companies struggle to effectively monetize their data because they lack a clear strategy, foundational infrastructure, and an understanding of how to align data initiatives with their core business. The Solution Successful organizations follow a structured framework for data monetization. They work with their established customers and suppliers to develop strong use cases; make a considered choice between direct and indirect monetization; ensure their data capabilities and organization are mature enough; and maintain strict standards around data privacy. The Three Approaches Data sellers package their information as raw data, which is unprocessed and requires the buyer to extract value; as custom insight and analytical services; or as commercially ready solutions, which are fully developed tools or platforms. What if you were responsible for analyzing album, fan, social media, and merchandise data for more than a thousand musicians? That was the task that Naras Eechambadi faced in 2021, when he joined Universal Music Group (UMG), which represents current recording stars from Lady Gaga to Eminem, legends like the Beatles, and numerous up-and-coming artists. As the company’s first chief global data and analytics officer, he needed to find a way to make UMG’s information available to its many business units and partners. So he and his team compiled data from brick-and-mortar stores, e-commerce sites, social media, marketing campaigns, emails, and a CRM system and used it to build a reporting and analytics tool called Fan Analytics, Marketing, and E-commerce (FAME), to help UMG’s partners, including labels and artists, identify growth opportunities. FAME provided granular data and insights on the behavior of every fan and automatically suggested follow-up actions for each person. Soon listener engagement and conversion rates for marketing campaigns rose significantly, driving revenue growth of more than 30% in e-commerce channels. FAME also gave UMG an edge over its competitors when it was signing new artists and labels. By packaging UMG’s far-flung and unorganized data into an integrated, easy-to-use tool, Eechambadi’s team had found a way to grow the business while remaining aligned with the company’s principal mission—to connect artists with fans. Making money by commercializing customer data (and the insights gleaned from it) isn’t a new idea. Credit-reporting agencies, which tell lenders whether a would-be borrower is likely to repay loans, have been around for more than a century, and grocery stores have sold shopping data (collected via their loyalty programs) for decades. In the digital age companies are learning more about consumers by following their online behavior—the products they buy, the websites they visit, the reviews and comments they leave, and so on. Now AI is making it easier to analyze and gain insights from that information, increasing its value even more. And in an era of slower economic growth, companies are becoming more interested in finding ways to monetize this asset. Some companies are already seeing success. Although Amazon’s retail business remains its largest source of revenue, the firm has used its deep knowledge about customers’ interests to grow its advertising business, which brought in $56 billion last year. More recently, Walmart used a similar model to launch its online ad business, which now generates $4 billion annually. Much of LinkedIn’s $16 billion in revenue is tied directly to the user data it sells to recruiters. Financial services firms like Mastercard and Visa have set up entire consulting divisions—Mastercard Advisors and Visa Advisory Services—to sell companies the insights they gather from analyzing millions of transactions. Neither company formally reports exactly how much it’s making from these ventures, but Mastercard has suggested that the annual revenue from its value-added services division is growing at a double-digit rate. Some companies are selling user data directly to gen AI companies for use in training their large language models. When Reddit licensed its user data to OpenAI in 2024, the financial terms weren’t disclosed—but Reddit’s stock jumped 12% on the news. Stories like these have made more companies think about the best way to extract profits from their data. Yet our research shows that companies still struggle to choose the right way to do that and don’t know where to begin. It’s not necessarily their fault. Data monetization isn’t as simple as emailing a spreadsheet to a paying client. Companies must know how to collect, organize, and analyze their data. They also need to determine the best use cases and understand how they should price their offerings. And too many of them create data offerings that aren’t closely related to their core business—a move that often turns into a low-profit distraction. To identify the reasons some companies struggle with data monetization—and why others succeed—we conducted in-depth case research with more than 30 organizations and interviewed 12 senior executives who are spearheading data monetization efforts across retail, media, tech, manufacturing, and marketing. Drawing on what we learned, we developed a framework for how to approach data monetization. In this article we’ll explain it and provide advice on how companies can get started on the journey. It requires asking three strategic questions. Many companies are sitting on what they think is valuable proprietary data. Potential buyers for it may include tech companies, data brokers, hedge funds, and companies in adjacent businesses. But selling it involves more than running a report. During our research we observed many instances where business leaders began the monetization process by building out their technical infrastructure. Often they spent a couple of years on it, only to realize that they didn’t know what products to develop or who might buy them. Dan Forbes/Trunk Archive The most successful organizations begin the monetization process by focusing on use cases within their core business and with existing partners, specifically their suppliers and customers. Why? First, existing partners understand the potential value of your data better than others do, because it’s specific to their industry and key goals. Second, because they already have relationships with your company it’s easier to work with them to identify good use cases for the data. Your established operations and sales relationships with them also make it easier to generate more revenue from the data and to capture and distribute the data to them once a project starts. Your sales teams and relationship managers can sell the new offering to them as a line add-on and scale it up quickly. And the final reason it’s best to work with businesses in your existing ecosystem is privacy. An organization’s proprietary data is often subject to strict sharing and custodian agreements, such as those precluding the sale to data brokers or other nonaffiliated third parties. Even companies that understand that logic may be tempted by offers from data brokers that package and sell data to hedge funds or other nonstrategic partners. On the surface these seem to be quick-and-easy deals that require little effort: You sell the raw data, you make money, and the value ends there. However, such deals may be tricky to navigate. Identifying potential customers and settling on prices across multiple parties with no partnership history is difficult. These opportunities also may present significant risks to your customers and suppliers, such as data leaks that could endanger their core business and strategic priorities. We aren’t saying that it never makes sense to do deals like this, but our research shows that they typically are riskier and create less value than deals with strategic partners. To protect yourself and your customers, you need to manage the privacy, regulatory, reputational, data security, and other risks data monetization creates from day one. Leading transaction companies, consulting firms, and tech companies, for instance, take great care to aggregate and anonymize benchmark data when sharing it with customers. But even if you do this, you should check whether the use of data in a product or service could be misconstrued by your partners. For any data-based offering, you need to work closely with legal and risk managers right from the start to assess potential problems and create mitigation plans. In parallel, and guided by the data needs of the prioritized use cases of potential buyers, you can progressively build a modern data platform and companywide data assets. Efforts to monetize data that’s poorly organized, of low quality, or incomplete will backfire. Yet many companies have a ways to go to build strong data and tech foundations: While they may already have the technology in place to collect data, they can’t transfer it into a central repository. Or they can’t organize the data or verify its quality. Or they have no easy way to run analytics or produce data visualizations. Most businesses aren’t entirely unprepared. They’ve begun to feed data from internal and external sources into data lakes and data warehouses and model how all the data will fit into cohesive data assets such as a customer 360 or a supplier 360. By leveraging flexible tools such as Databricks, Domo, and Snowflake, they can rapidly build data products to test with customers. The most straightforward approach is to directly charge customers or channel partners for the data or data-based offerings they use. This model typically works as a subscription, with a set price giving the buyer access to the data for a defined period. The seller incurs some costs to package and maintain the data, and the rest is profit. The British retailer Tesco took this approach when it collected vast amounts of point-of-sale data and used it to offer services to consumer-packaged goods (CPG) companies through a data analytics division called Dunnhumby. Over time, Dunnhumby became its own separate business, selling a variety of offerings to retailers and CPG companies. Indirect monetization is different. It involves integrating data into existing offerings and providing it to partners and customers at no additional cost. Organizations that choose this approach are often in high-margin businesses where differentiation is key. For example, the leading consulting firms bundle proprietary data insights with consulting services in order to stand out from competitors and win projects. Dan Forbes/Trunk Archive Sony Interactive Entertainment (SIE) used indirect monetization to get creators to develop new offerings for its PlayStation platform. SIE built a data platform that gave creators rich data on how gamers were using PlayStation, including gameplay and development data, as well as data on marketing and advertisement efforts. Giving the creators all that information for free made strategic sense because it allowed them to produce better games and increase engagement; SIE’s gains came from increased selection and sales of games. John Taft, a former director of analytics engineering with SIE, also noted that “when the next industry-defining innovative experience—such as Fortnite—emerges, it often happens on our ecosystem first.” While direct data monetization has direct P&L benefits, indirect data monetization can be a powerful way to increase the return on data investments. Monetizing data indirectly can help companies expand their product offerings and improve their operating efficiency and customer retention. For example, at one technology provider in our research, retention rates were highest among the customers that utilized its data offerings. A training-and-development company we spoke with expanded its services by providing customers with data benchmarks on high-demand skills and with personalized suggestions on new training programs to consider—information that its analysis showed led to higher customer satisfaction and retention. To enhance its operational efficiency, a manufacturer provided data scorecards to each of its suppliers on customer service levels and inventory availability, which reduced supply chain bottlenecks and boosted margins. Once a company has decided whom it will sell or provide its data to and whether to pursue direct or indirect monetization, the next step is to figure out the right type of offering. The three most common methods are selling it raw, selling insight services, and investing in the design and development of a full-fledged data product. Sell raw data. This is simple for sellers to do but creates substantial work for buyers, who must process and mine the data to derive value from it. With raw data, if you’re unaware of the specific downstream use cases, the sales process can be time-consuming because you must first collaborate with buyers to identify value opportunities. This sales method is most suitable for companies that lack internal data and analytics capabilities for processing and mining their data or whose data does not have a strategic fit with existing channel partners. In such cases they typically sell to data brokers, tech companies, or sophisticated users, who will further refine the information for other applications. Sell insight services based on data. Some companies analyze their data and prepare custom insights for customers, eliminating the need for them to structure the data and mine it on their own. Many custom-build products for customers’ unique needs. One big plus of this approach: It offers more security and privacy protection. Raw data often contains personally identifiable and proprietary information; selling insights instead guarantees that sensitive details don’t inadvertently get passed on. Mastercard Advisors does this in its services business for banks and retailers, providing clients with rich aggregated and anonymized insights from its transaction network. If you’re early in the data monetization process, you might consider creating a small services team to focus on supporting your channel partners’ data and analytics needs. If demand seems to be building around a common set of needs, consider developing an offering to meet them and scaling it up. Listen closely to customer feedback and watch how your competitors are extracting value from their data. Sell commercially ready solutions. The most successful sellers offer fully built products. Data dashboards, workflows, machine-learning models, and storage are packaged together with the insights. The insights help users make better decisions or form the backbone of a valuable business process. Solutions can be integrated into an existing digital product or can be stand-alone products. Companies generally charge more for data the closer it is to a finished product. Often they start by offering services on top of their raw data and then move into productizing popular use cases. For example, ADP sells a compensation benchmark product that helps employers see the average market rates for different jobs and employment categories. Buyers use it to set competitive salaries for existing and new roles and be compliant with applicable laws. Instacart has created an app that harnesses user data to help companies target ads more accurately. When shoppers are buying groceries using the service, Instacart Ads displays promotions for products they’re likely to buy, given their previous behavior. In 2024 the app generated estimated revenues of $958 million for Instacart. FordDirect, a marketing-technology company focused on automotive retail, created an indirectly monetized, commercially ready solution that’s designed to improve the sales performance of the carmaker’s dealer network. Tom Thomas, its SVP of data strategy, analytics, and AI, started by compiling raw data from various channels, including dealer websites, CRM systems, and back-office dealer-management systems, and then combined several external data sources into its customer-journey platform. With the right data and technology foundation in place, he and his team were able to turn the data into an analytics tool that helps dealers optimize their advertising spending. The tool helps drive vehicle purchases and service appointments on digital channels. Dealers can also use the data to segment customers by vehicle of interest, ownership status, and propensity to purchase, enabling them to produce targeted messaging. FordDirect has helped dealers achieve a 40% increase in sales and a 22% decrease in cost per lead for customers within targeted segments. . . . Data monetization can be a source of new revenue—and when executed correctly, it can have a big influence on the strategic direction of your organization. The journey to effective data monetization involves understanding your data’s potential, choosing the right partners, picking a type of offering, and implementing robust security measures. There’s a reason more companies are pursuing this strategy: because the rewards can be substantial. Editor’s note: We’ve updated the language describing the FordDirect program to be more precise.

如何将你的数据货币化

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