Applied Value: A Quick Guide to Data Monetization

A glimpse into how companies are transforming internal data into new products, services and revenue streams

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Data is more valuable than ever. While enterprise data will always be used to enhance decision making and monitor performance, leading-edge organizations want more.  In addition to traditional applications, they’re looking to transform internal data into new revenue streams. This process is known as “data monetization”. 

As defined by Gartner, data monetization is the “process of using data to obtain quantifiable economic benefit.” Generally speaking, there are two ways companies typically monetize data:

  • Indirect Data Monetization - using data to develop new business models and/or boosting operational performance 
  • Direct Data Monetization - the sale of data to 3rd parties

According to data monetization solution provider Aspire, data monetization depends on six main capabilities:

  • Data acquisition and processing (Eg: MDM, Meta Data management, etc.)
  • Data platform-tools (Hadoop, Cloud, Edge server, etc.)
  • Data science activities (Data exploration, Build Models, train and test models, finding the insights, visualize the data, etc.)
  • Understanding customer needs, behavior providing better customer experience (Eg: Collect the inputs from the sales team about customer needs, analyze the social media data to understand the behavior of the customer)
  • Use of data in terms of meeting all the concerns. (Eg; regulations and compliance)
  • Building data-oriented decision management culture at all levels in the organization  

 

*Data monetization explainer video sourced from https://www.youtube.com/watch?v=LIctFNfz0jA 

 

Indirect Data Monetization

Indirect data monetization is the most common approach to applied value and it too can be broken down into two subsets:

  • Data-Driven Optimization –  Using data and analytics to improve business performance and profitability. 
  • Data-Driven Business Models – Data is used to uncover additional revenue channels such as new products or services.

 

Data-Driven Optimization

The goal of indirect data-driven optimization is to leverage data-driven insights to increase operational efficiency, cost-savings and productivity. Suffice to say, many organizations are already employing this type of data monetization without even knowing it. 

Using data to analyze, automate and re-engineer business processes would fall under this category. As would leveraging predictive analytics to more effectively predict and plan for call volume in a call center environment. Another example would be using industrial IoT or sensor data to analyze and increase the efficiency of manufacturing operations. 

Consider UPS. Leveraging data gathered from IoT sensors in parcel processing facilities and delivery vehicles, UPS was able to reduce average delivery time by an average of one day in 63% of the zip codes it serves. Not only do such efficiency gains reduce costs incrementally over the long-term, it also staves off the competition as UPS now delivers packages faster than anyone else. 

 

Data-Driven Business Models 

This refers to the use of data to identify and capitalize on new areas of revenue or service offerings. 

For decades, companies have relied on surveys, focus groups and traditional market research to better understand and satisfy “the customer.” Not only is this process very slow and costly, the data gathered was typically qualitative, unstructured and almost entirely divorced from the digital ecosystem most businesses exist in. 

However, as organizations have digitized their internal systems, a new era of customer insights has been unleashed. Now, companies have access to mountains of customer data such as contact details, demographics, customer service requests, browsing history, past purchases and website clicks. 

Using this data, can create new products or services based on real-world customer trends. For example, retailers such as Zappos and Nike use customer data to develop personalized product recommendations based on individual shopping behaviors as well as hyper-localized in-store experiences such as popups and marketing campaigns. Luxury car manufacturer Aston Martin uses customer data to personalize marketing content based on an individual’s passions (i.e. fine wine, travel, etc.) and create interactive user interfaces that help potential customers engage with the company in a deeper way than just browsing the website. It’s personalisation service ‘Q’ not only allows users to customize their vehicles to suit their liking, but provides Aston Martin insight into evolving consumer tastes. 

Companies can even turn customer data and analytics themselves into a product or service, a process known as data wrapping. In other words, data analytics and dashboard are “wrapped” around a product or service to help customers better utilize or gain value from it. By adding embedded analytics features to existing products, businesses can not only help delight customers, but deliver real, tangible value.

For example, GM has leveraged data wrapping techniques to create analytics dashboards for fleet operators based on real-time telematics data. This information helps operators optimize travel routes based on time, mileage and fuel efficiency. For GM vehicle lessees, GM has created a tool for monitoring and managing mileage. If a customer is at risk for exceeding their mileage limits, they will be alerted and provided with mileage extension options.  

Another example is how BBVA used machine learning algorithms to sort customer transactions into common budgeting categories (i.e. rent, food, and entertainment). This information was then shared with customers using a simple, easy to read dashboard embedded in their personal finance app. Not only did this new tool help customers make more educated financial decisions, in less than two years the tool became the second most popular feature on the BBVA website following funds transfer.

According to the experts at MIT, effective data wraps do four things:

  • Anticipate by intuiting customer needs. Anticipatory wraps offer predictive and proactive features.
  • Advise through the use of evidence-based decision making. Wraps with advisory features provide data and insights that inform a customer’s decisions.
  • Adapt by meeting customer needs in a tailored way across different environments and contexts.
  • Act, which means that the wrap performs an action to benefit the customer. Wraps that act are integrated into customer processes or behaviors, or they trigger behavior automatically on the customer’s behalf. For example, a bank app that automatically transfers funds to help a customer avoid overdraft fees.

 

Embedded Analytics Monetization Wrapping

 

Direct Data Monetization

Last but certainly not least is direct data monetization, the sale of internal data to outside parties. Though most people associate this with the sale of customer data (i.e. email address, locations, spending habits, etc.), companies can sell other types of data as well. For example, a car company might purchase accident data from an insurer to help them design better cars. 

Though companies sometimes contact each other directly regarding the sale of proprietary data, for the most part, organizations sell data to data brokerage companies that specialize in collecting and packaging corporate data for re-sale. 

Given the rising value of data (a single email is worth $89 to the average company) companies are willing to pay handsomely for any data that might help them better target or engage with potential customers. As a result, data brokerage has blossomed into a $200 billion industry. 

As for tech companies such as Facebook, Google and APP providers, though they don’t outright sell customer data to 3rd parties, they do “share” user data with the companies that pay to advertise on their platforms. Using this information, advertisers can more effectively reach and engage with their target audience. Unlike data wrapping where the analytics components are really just an extra perk, the customer analytics are the core draw to these products. 

However, selling company data to third parties is not without its risks. To start, there are numerous regulations in place governing what type of data can be sold and to whom that must be adhered to. In February 2020, four major telecom companies were even fined over $200 million by the FCC for illegally selling real-time customer location data to 3rd parties. 

Secondly, selling data to third parties could be considered bad PR. As the general public grows increasingly concerned about privacy issues, companies that sell customer data risk losing their trust. 

Last but not least, most companies opt not to sell data because it is so valuable. As most of the data bought and sold through third parties is used for marketing purposes, why would a company want its competitors to have access to it? 

 


 

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