What is Data Monetization?

Data monetization is generating revenue using data previously created for another purpose. There are a number of ways to extract information and trends from that old data. Those trends that the company did not notice before give an organization fresh information it can use to find new ways to make money.

Information about customers comes from a number of sources, starting with the data companies collect in the marketing, sales and ordering process. Once the initial order for your product or services is complete, there is more information in the data. Each customer interaction is another chance to gain information.

The traditional customer interactions to capture and analyze include sales, marketing, delivery, customer support, and social media interactions. But now, in the era of the internet of things (IoT), the product itself can be a point of interaction. The key is to collect all these data points into a single, cost effective, data repository so you can begin to perform big data analysis and monetize your data.

You can look horizontally at individual customer cycles and compare them to each other, such as attempting to correlate the entire purchasing cycle and its correlation to customer satisfaction. Are customers happier with product A if they also bought product B with it? If they bought product A and B, what other products are they likely to want? Is there any correlation between customer satisfaction levels and the service provider who delivered the product? Is it possible to correlate an entire path from initial advertising or marketing effort and eventual purchase? Compare tens of thousands of typical paths and see what you can infer there. For example, customers who first found out about you on TV are happier if they order by phone and have UPS or the postal service deliver the product. But customers who learned about you on Twitter are happier if they order online and have an Uber driver deliver the product.

You can also look at different parts of the process such as advertising or sales. Is there are a correlation between sales method and happiness? Are customers who buy by phone happier than customers who buy through your website? Is there a correlation between how the product is delivered and customer satisfaction? What about your service partners? Are customers of a particular partner happier than customers of another partner?

All of these questions and thousands more are designed to do one thing: sell more product. If we know that customers who buy A and B also buy C, then we can target advertising to them to make sure they know we sell C. If you know that customers who buy over the phone are happier than those who buy through the website, you can consider beefing up your phone sales team or increasing hours. If you can identify a mismatch in product expectations through any particular system, you can adjust your messaging in that system. If you can identify customers who are likely to be unhappy, such as customers who receive a shipping time over a certain period, you can proactively counteract that unhappiness with a refund of shipping costs or a credit toward future purchases.

The challenge with data monetization is a lot of data about various customer interactions is not available in a searchable format, making big data analysis on the data very difficult. This is yet another business case for object storage, since it is inherently more searchable than the alternatives. One could search data stored on a NAS system, of course, but the sheer volume of such data would require a very large system to store petabytes of information that has relatively low value. Such data is much more appropriate to the costs of object storage.

StorageSwiss Take

The sky’s the limit with big data analysis of unstructured data, and the amount of revenue that a company can generate is limitless as well. The the storage requirements of this data is often beyond the capabilities of most unstructured data storage systems. But the storage of many petabytes of information is perfect for object storage. Those considering monetizing data should examine using object storage to store their data.

W. Curtis Preston (aka Mr. Backup) is an expert in backup & recovery systems; a space he has been working in since 1993. He has written three books on the subject, Backup & Recovery, Using SANs and NAS, and Unix Backup & Recovery. Mr. Preston is a writer and has spoken at hundreds of seminars and conferences around the world. Preston’s mission is to arm today’s IT managers with truly unbiased information about today’s storage industry and its products.

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