Data Monetization Frameworks
In its most basic form, data monetization is all about converting data into money. There can be a few challenges while doing this so we’ve answered a few questions you may have about the process.
Top tech companies like Google and Facebook make a lot of their money from using their data stores effectively. These companies have learned how to and mastered the data monetization process and now effectively turn their data assets into major revenue for the company. Their services are free and widely used and in exchange for using their services, they get to harvest the data of their users. They then sell their data for a price to advertisers to enable them to create personalized marketing experiences for their target audience.
The above is essentially what data monetization is in its base form. However, this is not the only way data can be used to the advantage of an enterprise looking to monetize its data. Data can also be used to find new ways of optimizing their operations which will result in reduced expenses and increased revenue. Below are a few ways a company can monetize its data.
- Improve the impact of its marketing via personalization
- Spotting more opportunities for revenue like customer segments, products, and markets
- Improving customer retention and reducing customer churn
- Spotting and responding to customer satisfaction levels as appropriate
- Sharing data with partners to optimize supply chain activities
- Detection and prevention of piracy and fraud
- Locating leaks in revenue and putting corrective measures in place.
All of the above activities are ways of monetizing data because they constribute to the economic value of the company by using data assets. As most industries are looking to make a profit from their operations, it’s exciting to know that data can be used to meet or even exceed a companies profit targets. We’ve outlined some of the factors that you should consider when contemplating how you can benefit from monetizing your data assets.
Which data is valuable
Before you begin, the first step is to take inventory and identify which of your data assets is valuable enough to generate revenue or can be used to cut costs. While doing this, many companies discover that not all of their data actually has the same value. A lot of the time, companies get caught in what data dictionaries’ technical definition is that it can be easy to lose sight of what the ultimate value of their inventory is. Even though knowing what the technical description of the data you have is, as well as what systems they live in is still beneficial, the actual value of your data dictionary lies in documenting which of your data elements can be leveraged and used to derive economic value in addition to the cleanliness of the data and how complete it is as well.
Identify your audience
Having identified your valuable data assets, the next step is to identify the potential receivers of the data you possess. These people do not necessarily have to be a part of your organization, they can be external or internal audiences like your marketing and sales teams who will translate the insights got from your data into opportunities to either cut costs for the company or generate more revenue. What you want to do is get the correct information to the right people who will make the decisions that transform it into something of economic value.
How you intend to deliver the data and when
After establishing the valuable data and who your audience is, the next step is to establish how and in what format you will deliver the information to them. After deciding on what your delivery mechanism is, you must decide what your economic value-driving behaviors are. Whichever dashboard or report medium facilitates and supports this behavior best should be employed. If reports and dashboards aren’t looked at effectively, you may have to consider alternative methods of getting the information to the final users at the perfect time. There is no need to use only graphs, charts, or reports as sometimes information can be delivered in an entirely different way. A good instance is how facebook generates revenue. The kind of data with the most value for an advertiser would not be a report of information on a demographic of users. Instead, the data advertisers find important is the real-time pairing opportunities to place ads before a consumer considers making a purchase by giving them information on user segmentation. While you find the perfect format and media channel for presenting your data, there is also a need to look at the timelines you use to pair your monetization opportunities and data. In areas like financial transactions, the difference between having useless and useful information could be as small as a millisecond. When it comes to marketing, there could be longer opportunities to target a specific segment of users who exhibit similar behavior. For example, when you look at a product on an eCommerce website and suddenly notice that ads for the particular product seem to be following you around the internet, what you are experiencing is a longer paced time span. Sometimes you do not start seeing the ads immediately after looking at the item as they can be strategically spaced out so you are reminded of the product for as long as days or weeks.
How to process data and add value
To round off, we must state that in its raw format, data isn’t usually very effective or valuable. This is why most of the time a combination of elements of data from multiple systems is done to increase the value of data. When a profile gets analyzed by tech gainst like Google, it is usually not based on solitary clicks or likes. They use aggregated information based on activities a user typically engages in. Their interests, demographics, buying habits, and more are examined to create a complete profile on each user which makes their data more valuable to marketers to use them to target audiences whose behavior expresses interest in their products or services. Going forward, as you identify valuable data, also look out for information that could be gotten from data elements as well as tools that can help you improve your data. With high frequency or big data, extracting them and crunching the numbers in an excel spreadsheet will not be sufficient. You’re going to need tools like automated ETL [Extract, Transform and Load] big data tools and forecasting and prediction models, algorithmic statistical process, Massive Parrel Processing [MPP] should also be used to check data timelines and ensure they are consistent across tons of records.
Monetizing your data can add tremendous value to any business but in order to fully maximize your data capabilities, you must meet the above-mentioned criteria. Start off by identifying the data you have, who your consumers are, how you will get the data to them in an effective manner, and identify what needs to be done for your data to make it more impactful. When you have all of this down there is no limit to how much economic value you can generate for your business through data monetization.