Data Monetization is the End Goal

Business executives face tough questions every day. Many of these questions don’t always have easy or straightforward answers: Which analytical investments and strategies really increase revenue? What pilots should I run to test data monetization ideas out? What small data or big data monetization strategies should I adopt? At the root of these queries is the one billion dollar question facing organizations everywhere: How do we monetize our data?

First, it’s critical to understand what data monetization is, that is the process of converting data (raw or aggregate data) into something useful and valuable to an organization’s bottom line. Not only can data monetization help make decisions (such as predictive maintenance) based on multiple sources of insight, it also creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell).

There’s just one snag. Data monetization requires a new IT clock-speed, one that most firms are struggling to keep up with. Aberdeen Research found that, with traditional BI software, it takes IT eight days on average to complete BI support requests, such as add a column to a report, and 30 days to build a new dashboard. For an individual attempting to find an answer, make a decision or solve a problem, this is an unacceptable timeline. For an organization trying to differentiate itself with information innovation or data driven decision-making, it is a major barrier to strategy execution – and it’s bad for business.

To speed up insight generation and decision-making (all elements of data monetization), business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own questions and follow their own path to insight. Unlike traditional BI, which provides dashboards, heatmaps and canned reports, these tools offer a discovery platform rather than a pre-determined path.

What’s more, companies like Marketo, which creates marketing automation software, are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals to find future customers; to build, sustain and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the Web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. In many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across numerous fast-moving digital channels requires an engagement strategy fueled by data and analytic insights.

Analytics and Data Monetization: More Than Meets the Eye

Data Monetization is the End Goal

New Revenue Streams – Moving from Analytics to Data Monetization

Here are questions that people ask me at every meeting I attend: How do we increase revenue by leveraging analytics? What are the best practices and what are my competitors doing around this?

In every competitive industry – from retail and insurance to healthcare and financial services – companies along the value chain are racing to replace lost revenues as new regulatory environment changes, the competition heats up, and consumer choices eliminate traditional revenue sources. Rather than looking just to increase transaction volume, firms are shifting their focus to leveraging current transaction volume by mining a valuable, underleveraged asset—client/transaction data—to create new revenue streams.

The healthcare industry, for instance, is fast becoming an industry based on analytics outcomes. Within healthcare are accountable care organizations (ACOs), which are expected to connect groups of providers who are willing and able to take responsibility for improving the health status, efficiency and experience of care for a defined population. This can’t be done without a sizable investment in data, analytics and monetization capability.

Another common use case of monetization is the benefit from better inspection scheduling and preventive maintenance. The result is a huge cost savings because expensive and experienced resources are not used to respond to emergency repair calls. This was certainly the case with a large ATM manufacturer. By monitoring various assets in the ATM (cash dispensers, printers, cameras etc.) via log analysis, the manufacturer was able to substantially reduce maintenance downtimes.

As top-line growth gets tougher and cost takeout via outsourcing reaches real-world practical limits, the game is shifting to novel monetization strategies to extract new revenue from the existing customer base. Data monetization isn’t easy; it requires a sophisticated process of capturing appropriate data sources; storing and managing the data; performing analytics to identify key trends and latent themes; and presenting the insights in an accessible, easy-to-understand format.

Data Monetization analytics use cases, toolsets, skillsets and mindsets. Expanding this capability and the tools to exploit it is the new frontier. The hypothesis is that investments in technology, process and organization to build these capabilities will pay dividends not just in the ability to deliver and monetize data, but also in ancillary benefits such as faster time to market, improved organizational communication, better customer service and, ultimately, a better customer experience. The downside is that if it’s not done right, the results can hurt the company.

Data Monetization in Financial Services

So how do you monetize customer touch points via segmentation, prospect identification, campaign analysis, cross-selling and upselling, retention-lapse and lifetime value?

Here’s the thing: data monetization isn’t analytics. By themselves, analytics – predictive, descriptive or exploratory – are of limited value. Monetization comes from the downstream consumption of analytical insights to then create value. Analytics have to be consumed either by humans, machines or applications to make or facilitate decisions and create new revenue streams. The payments value chain, where issuing banks, processors and acquiring institutions all profit from data monetization, is a perfect example:

  • A VISA card issuer realized a 10-percent increase in application completion rates by better utilizing available predictive data.
  • By analyzing and improving the data used for online cross-sell, a global bank realized a 50-percent improvement in its cross-sell rates.

Effective data monetization enables large credit card acquirers to offer merchants more value-added services, such as analytics or report packages. Increasing the timeliness, accessibility, quality or completeness of data offered (by integrating external data, for example) can set an acquirer apart from its competitors and add important new revenue.

Interestingly, the growing trend is to arm the consumer with a lot of powerful data discovery tools. Credit card companies like American Express are providing plenty of information to help customers make the best possible decisions. They are letting the customer follow an information scent, rather than a pre-determined path, as they go off searching for the data that will help answer their business questions.

By providing cardholders with breakdowns of their card spend, alerts based on preset limits or data on what friends who bank with the same institution are buying, card issuers can use data to increase touch points with their customers—and that helps improve loyalty and stickiness. Leveraging the Internet and mobile devices further enhances the customer experience by enabling these updates to happen in real time.

Creating a Data Monetization Roadmap

Companies that want to maximize the benefit of their BI and Analytics investments should begin by evaluating their organization’s data monetization maturity level.

One good first step is a comprehensive, enterprise-wide assessment to establish a baseline. It’s important during this process to ask key questions: How is the organization monetizing its data? What data is currently being monetized? What is the business value of that monetization? How much money has been left on the table? The baseline assessment should identify not only what data is available, but also what data can be used from a practical and regulatory perspective.

Successful data monetization requires the ability to fully exploit data across organizational and application silos. A financial institution, for instance, will have data segmented and owned by different lines of business—commercial, consumer, retail banking or mortgage. Breaching these data barriers is essential to ensuring that the insights from analytics are extracted.

Companies must also improve and speed up access to their data. Having thousands of individual data stores around the company, worldwide, is a very inefficient way to store data, and it makes accessing the data for monetization purposes a challenge in itself. Collapsing, consolidating and rationalizing data stores are continuous must-do exercises.

Understanding use cases is crucial. Initiating a data monetization effort in, say, digital marketing is the perfect time to brainstorm and identify new target customers for marketing/selling data and related services as well as expanding the usages for data currently being sold to customers. Getting another perspective from outside the organization is especially important. Knowing what other institutions are doing in terms of data monetization can provide valuable insight and new ideas.

Monetizing Means Deciding

Finally, a business case or a cost-benefit analysis of the desired changes will be required for any major investment. This should include how much revenue those changes will deliver and what they will cost to implement.

Upsides and Downsides

Today a lot of emphasis is on infrastructure for managing big data. The next wave is delivering on the unique monetization opportunity that developing analytics and applications on top of the available data. While the revenue potential of data monetization can be significant, there are some considerations and potential roadblocks to note: organizational resistance, overly strict interpretations of regulatory requirements, inflexible data silos and an out-of-date infrastructure, to name a few.

Companies should compare anticipated revenue gains with the cost of bringing that revenue through the door, and adjust their expectations and scope accordingly. While some organizations will be able to justify and support a wholesale infrastructure upgrade (if required) to achieve their data monetization goals, others will not. In most cases, however, there are ways to incrementally improve on data monetization without emptying the bank account. But there’s no doubt that changing status quo isn’t for the faint-hearted.

Notes and References

Advanced analytics will automate many management decision-making processes, which will allow managers to focus more on strategy setting and business innovation. At the same time, developments in machine learning and in-memory computing will enable people to analyze masses of unstructured data. Finally, collaborative and social technologies will enable more employees to participate in decision making, thus improving the quality of management decisions. For more information see the March 27, 2012, Gartner report, “Information Innovation Will Revolutionize Decision Making.”

Aberdeen Research conducted a survey of 237 organizations on the topic of agile BI in March 2011. See the report, “Agile BI: Complementing Traditional BI to Address the Shrinking Decision-Window,” November 2011 (available to subscribers only).

Leave a Reply