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Section 508

March 29th, 2016

Data Rich Does Not Equal Profit: How to Do Data Monetization

Most companies today are producing and collecting massive amounts of data from various sources like enterprise systems, web and e-commerce sites, POS systems, social media, and even sensors and machines. And while it is great to have the data, many companies are finding that they may be data rich but still aren’t seeing the profit or ROI from their big data investments. Most organizations today are at the very least using data as a way to better operations, cut costs, or predict trends.

However, there is more to data then just the backend usage. The true potential lies in its ability to move top line revenue through a data monetization or commercialization model. Let’s talk about some best practices as well as how various industry players are cashing in on their data.

Examples of Data Monetization

One of the most widely used examples of data monetization is in retail. Many retailers, especially grocery store chains, have combined their transactional data with their loyalty card data in order to share insights back via a subscription-based analytics platform to Consumer Packaged Goods (CPG) companies. Consumers then receive personalized offers as a result of sharing their data, retailers improve loyalty and profits and CPG companies have more visibility so they can better their product offerings and targeting.

In the same manner, many ISO’s and payment processors monetize their data by providing it back to their merchants as a value-added service offering. With access to a broader base of their customer data, merchants can then evaluate the number of transactions, average transaction size, and how total sales are performing over time. It can even help identify marketing and promotion opportunities by showing how often customers buy, and how much they spend. As a result of this data monetization effort, processors are able to generate new revenue streams and decrease attrition. Meanwhile, merchants gain valuable insights into their business and in turn provide better products, services, and offerings. It’s a Win-Win-Win.

Case Study

Let’s look at a case study. Here is how one insurance company was able to boost revenue, adoption rates and customer satisfaction through the simple data monetization act of providing their customers more data visibility.

Faced with increasing customer attrition, one leading insurance company approached DMI for a solution. They attributed the attrition to the existing outdated infrastructure which made it difficult and time-consuming for customers to understand the key drivers of their claim costs and manage risks using the available details. The company wanted to competitively differentiate its product by bundling useful and relevant information into it.

DMI worked with the insurance company to create a risk management tool based on their customers’ profiling, exposures, behaviors and interests which allowed them to capture, analyze and structure risk information in formats that are most meaningful to their business.

This is an example of how to deliver a focused set of data points allowing customers access to the data they need quickly and effectively which clearly shows them the benefit and value of the paid subscription. The product offering, which included a premium business intelligence package, resulted in an immediate increase in user adoption rates and revenue while improving overall customer experience and acquisition.

How to Monetize Your Data

Hopefully we’ve got you thinking about the huge returns that can be had from packaging your data as an offering. However, before you go diving into a pool full of dollar bills, there are a few house rules and guidelines you need to put in place to truly be successful:

First, a business needs to figure out which information would be the most useful and/or valuable to another organization or customer. It then needs to make sure that it is equipped with the right datasets and instrumentation needed to capture, augment and enrich the base data. They may also decide that information needs to be supplemented with other data outside of what is currently available. The use of this data must, of course, comply with all the relevant regulatory requirements and privacy policies and a proper data governance plan should be in place.

Next, it’s important to understand what insights and analytics you can package along with the data that will meet your customers’ needs. Remember, data in itself provides no value – you must provide knowledge. Finally, the most successful companies often recognize that they don’t have the tools or expertise to develop this type of data ecosystem infrastructure. They therefore look to partner with solution providers that can provide the appropriate skills, analytical models and tools needed to get the job done.

Today, the opportunities for companies to cash in on their data are abundant. True transformation and growth can be found by putting the customer at the center of your strategy. Let the data show you where the opportunities are, but let the customer drive each decision. This is where data monetization becomes reality.

If you want information on how to monetize your IoT investments and competing with data, we suggest you check out this post.

Tags: analytics big data insights

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