Data monetization


Data monetization

Data Monetization, a form of monetization, involves maximizing the revenue potential from available data by institutionalizing the capture, storage, analysis, effective dissemination, and application of that data. Said differently, it is the process by which corporations, large and small, leverage data to increase profit and efficiency, improve customer experience and build customer loyalty. The practice, although common since 2000, is now getting increasing focus as regulatory (for instance in the financial services industry Gramm–Leach–Bliley Act and Dodd-Frank) and economic pressures increase on businesses in the United States from 2008-2011.

Financial services companies are a relatively good example of an industry focused on replacing lost revenue by leveraging data. Credit card issuers and retail banks are using customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time.

Contents

Data Monetization Steps

  1. Identification of available data sources – this includes data currently available for monetization as well as other external data sources that may enhance the value of what’s currently available to the corporation
  2. Storage and access to the data – many global corporations have inefficient data storage infrastructures, which stymies centralized, efficient access to required data
  3. Business intelligence and analytics – drawing predictive insights from existing data becomes the basis for using data for profit with enhancing product extensions or customer experience improvements
  4. Presentment and monetization – the last phase of monetizing data includes determining best delivery vehicle to promote new data-based products or ideas to existing or potential customers. Examples may include new or enhanced channels such as web, mobile or response mechanisms for offers.

Benefits of Data Monetization

  • Improved decision-making that leads to improved profits, decreased costs, reduced risk and improved compliance
  • More frequent decisions (e.g., make decisions daily versus monthly)
  • More timely (lower latency) decisions (e.g., make purchase recommendations while the customer is still on the phone or in the store)
  • More granular decisions (e.g., localized pricing decisions at the store level versus the city level or seasonality-based supply chain and procurement decisions).

Examples of Data Monetization

  • Packaging of data (with analytics) to be resold to customers for things such as wallet share, market share and benchmarking
  • Integration of data (with analytics) into new products as a value-added differentiator such as On-Star for General Motors cars
  • GPS enabled smartphones
  • Geolocation-based offers and location discounts, such as those offered by Facebook[1] and Groupon [2] are other prime examples of data monetization leveraging new emerging channels

See also

References


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