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October 20, 2015

Author: HIR Staff

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Infonomics is the emerging discipline of managing and accounting for information with the same or similar rigour and formality as other traditional assets (e.g., financial, physical, intangible, human capital). Infonomics posits that information itself meets all the criteria of formal company assets and, although not yet recognized by generally accepted accounting practices, increasingly, it is incumbent on organizations to behave as if it were to optimize information’s ability to generate business value. (Source: Gartner, Inc.)

Many organizations do not manage customer data as a corporate asset, and the monetary value of that data is often ignored, according to Gartner, Inc.

“Digital business is having a significant impact on customer data,” says Douglas Laney, vice-president and analyst with the company. “The growing wealth of information – from social media, location and context-sensitive data collected from mobile devices and the Internet of Things – is increasing the volume, velocity, and variety of that information, radically expanding the scope of the 360-degree customer profile.”

Lack Business Models

Although some organizations have begun to invest in big data technologies in relation to their customers, with a view to direct or indirect monetization, many organizations lack business models to monetize their customer data.

Gartner recommends that organizations apply ‘infonomics’ principles to their customer data by managing it with the same discipline as any other corporate asset. “Organizations should use valuations of their customer data as the basis for prioritizing investments in technologies that help them acquire, maintain, enrich, archive, and apply information. They should also calculate thorough business cases when designing monetization products,” says Laney.

By doing so, they will be able to create the necessary transparency for their stakeholders regarding the profitability of monetization initiatives. It will also enable them to present proof of the valuation of their information assets during any merger or acquisition, or in an initial public offering scenario.

Organizations should also learn how to monetize their customer data from industry examples and various business models currently in use. For example, banks and credit card companies provide payment information value-added services (PIVAS) to retailers and merchant partners. Banks use PIVAS to understand customers better and to help them build stronger relationships with retailer and merchant communities. Large retailers put their point-of-sale and other store promotion data online for their business partners to subscribe to. Some offer such data free to their suppliers; others monetize it by charging an access fee.

Evaluate The Risks

There are, of course, obstacles to the monetization of customer data, notably complex data privacy legislation and the public's concerns. Organizations must evaluate the associated risks in relation to data ownership and data privacy when developing a business model to monetize customer data. Products should then be designed on the basis of the organization’s level of risk tolerance.

Companies have to determine their risk tolerance in relation to how they want to monetize their customer data – for example, whether they want to design information products using anonymized or aggregated personal data.

Retailers also have the option of partnering with one of several companies available that specialize in data monetization.

Creating Revenue From Customer Data

Companies such as Accenture and Information Builders can help retailers understand the value of their enterprise data to realize revenue opportunities.

Accenture says companies are becoming increasingly aware that they are sitting on huge amounts of under-utilized data and looking for ways to increase its value. The conditions for data monetization are ripe: massive volumes of structure and unstructured data; decreasing storage costs; data-driven marketing campaigns that create relevant customer experiences; and improving business intelligence and processes by applying data analytics.

Call centre logs, data stored as text, or social media posts exist in abundance. But unless data is easily accessible in a scalable format, it’s of little use. These companies can help ensure your data is structured to allow you to extract relevant, marketable insights.

According to Accenture, data monetization alliances are gathering momentum. For example, retailers are collaborating with wireless carriers to gain insights into geo-location data, tracking customer movements at shopping malls or in-store, and using that data to devise targeted marketing campaigns and design loyalty programs based on relevance and frequency.

And, as the volume and variety of data continues to rise, so do the opportunities to find value in it. But to identify data monetization opportunities in an informed and results-driven manner, companies must assess the value of enterprise data, determine how best to maximize its potential, and figure out how to get the data to the market efficiently.

Organization, Governance, And Sharing

Regardless of the approach organizations take to data monetization, a successful initiative is dependent on three key areas – organization, governance, and sharing, says Information Builders. It’s critical that companies are equipped to handle data from a variety of sources and formats; ensure its quality and accuracy; and customize the delivery of the resulting insights to the preferences of all audience segments.

Research conducted by Analysys Mason for AsiaInfo, a China-based telecoms IT software and services company, highlights the gap between mobile operators and Large Internet Players (LIP) in their capabilities to monetize their customer insights.

The research covered 50 operator respondents drawn from across the world and focused on the challenges facing operators looking to successfully monetize customer data in a similar way to LIPs such as Google, Amazon, Facebook, and Apple (GAFA).

Although two-thirds of the operators surveyed placed high importance on personalized, contextual marketing based on customer insights, almost as many, 62 per cent, felt that they were currently at least two years behind the GAFA group. Among European operators, nearly 60 per cent also saw regulation as the main barrier to their ability to compete successfully.

The operators’ responses were analyzed to assess both their vision and their readiness to compete with the LIPs and the GAFA group in particular. Based on the analysis, operators were categorized into four distinct groups:

  • Transformers: those displaying both the vision and developing the IT tools required to create new business models based on their customer insights (20 per cent).

  • Head-downers: those who have the tools but are focusing their capabilities on maximizing their existing business, rather than applying them to new business models (12 per cent).

  • Worriers: those with the vision but without the tools and data (30 per cent). 

  • Unbelievers: those lacking both the vision and the required tools (the largest group at 38 per cent).

Perhaps surprisingly, the survey also revealed that fear of losing customer trust and loyalty is perceived as a relatively minor barrier for operators seeking to monetize their customer data, with lack of access to data and the technology to process it ranking as much higher concerns.

The results also showed that operators need to make changes to some of their established business processes if they are to generate more value from their customer insights.

In many cases the data they need to access and analyze is siloed in different departments, and that makes it more difficult to share and use the information. However, the survey shows there is widespread and growing recognition among operators worldwide that they are some way behind the GAFA group in their use of customer data, and that they are placing a higher priority on the need to improve that capability both for themselves and for their partners.

More detailed information is available in the report ‘How Organizations Can Best Monetize Customer Data’ at; ‘Monetizing Customer Insights’ at; and at; and

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