Following on from the first in the series of Big Data articles, this article focuses on how big data can actually generate revenue.  The sheer volume and depth of data that is available and the technological capabilities of processing and analysing it, coupled with low storage costs, has led to a boom in the commercialisation of data.

From where does the data originate?

Taking a media outlet as an example, the plethora of products and services offered (e.g. news, shopping, health and fitness, travel segments, competitions to enter, vouchers to collect etc…) means that users are providing more and more data in return for accessing relevant content.

As such, the outlet operators have the advantage of micro-segmentation of the consumers’ habits. They can determine where they are physically, the web sites (and micro-sites) they visit, the shopping they conduct, the food they like, the apps they purchase, and much more through a combination of GPS location services, WiFi hotspots, and mobile internet via their mobile devices.

This provides the outlet with a wealth of user-specific data which has the potential to be very valuable to the operator and third parties.

But how to actually make money from this…

There are many ways in which the data is manipulated and for many reasons. Here we have divided it up into two categories for simplicity; internal opportunities and external opportunities.

Internally: Customer-centric marketing is a core tenet of big data.  Its uses and data intelligence, which relate to a business being able to understand what the customer wants and what he or she will spend money on, can greatly increase a company’s return on assets and evolve new revenue streams. 

Operators will make use of the data collected not only in marketing but also in customer services and network management applications.  This may help an operator create efficiencies and improve the operator’s infrastructure management. This can be done through multi-dimensional analysis of the operator’s data in relation to specific pre-determined queries. Multiple types of reports generated can then show managers where profits may lie in the business or where losses are occurring, allowing managers to act accordingly.

Through data exploration, and more accurately, through cluster analysis, operators are able to segment the users according to what data they provide (such as, at a basic level, those who visit the travel section of a website, and those who visit the motoring section). However, some segments or user groups may not have even been thought of by the website operator, yet.

Once these ‘new’ groups can be discovered, targeted actions such as user-centric marketing messages, and cross-selling or up-selling to each user group can be better achieved. The opportunity to learn what any particular user may like and then direct targeted and specific individual user-based content  provides the operators with the opportunity to maintain user interest continually, predict those groups that are at risk of leaving the site and proactively coordinate strategies to retain the custom of those groups.

However, it is also externally where operators can maximise the value of their data effectively.

Externally: If we take the example of mobile phone operators (or even media outlets by way of mobile applications), we can see that they can add value to a huge range of industries from retail, food and drink, gaming, financial services, healthcare and many more. At one end of the value chain is the raw data that can be sold “in bulk” and unprocessed. This is then processed by the purchaser of such data for their own use. Geo-location services data is often sold in this way for traffic management and route guidance operators.

As the data is processed, the value of it increases both in terms of the information it can provide and the price one can charge for it. Processed data feeds value added services and platforms for data-driven transactions such as ad targeting, retail payments and the provision of customer insights and predictive modelling.

Accordingly, further up the value chain operators may interact directly with the end-user for the collection of data to obtain bespoke and tailored data (provided with the consumer’s consent). This data driven interaction with the consumer can be the most valuable to third parties, presenting insight in intelligible formats enabling improved decision making.

Big Data as a “commodity” is growing quickly. The advent of wearable technology and the internet of things will serve to provide even more data from different parts of our lives when consumers become almost entirely inseparable from their gadgetry. As the Big Data boom accelerates, companies of all shapes and sizes need to be wary of their data protection and other legal obligations in how they collect, store and process the data they harvest, as well as their own policies and governance procedures.