Convert data into revenue?

There are three ways to increase revenue from data: one is based on data’s ability to add overall value to the company; two is to sell that data back to customers or interested parties, and three is the core value: data mining capabilities.

According to the Fortune 100 ranking, we can see that 4 out of 5 of the world’s most valuable companies build their business models on data mining: Apple, Alphabet (Google’s parent company); Microsoft and Facebook and Amazon. Especially; Amazon has made the leap from 19th to 9th place in the rankings just by optimizing data mining. What is most relevant among these companies is their ability to collect and mine massive amounts of data to their advantage.

1. Based on data capabilities to add overall value to the company.

Now, the value of a company can also depend on the amount of data that company currently has. Each business currently stores a lot of customer information: names; address; where you live;… All of that information can show interests; life style; their spending patterns; bring a more secure source of customers for each business. The more businesses collect such customer data, the more valuable it is because it shows the company’s performance level; as well as their reputation in the eyes of customers. Hence; increase the overall value of the business.

2. Sell that data back to customers or interested parties .

No doubt; data can be extremely valuable; so much so that it becomes the company’s greatest asset. Consider an example: British supermarket chain Tesco has a popular loyalty card program; named Clubcard; with 16 million members. This highly popular customer program helped Tesco overtake Sainsbury’s as Britain’s largest supermarket in 1999. The Clubcard allows Tesco to collect data (like who their customers are and where they live). and what to shop for), all of which help them build detailed customer profiles and create targeted offers.

The loyalty card program, with all its data and analytics, is run by a (third party) company called Dunnhumby (which also works with other retail partners like Macy’s). Dunnhumby’s volume of data and ability to extract customer insights were so valuable to Tesco, that they bought a stake in Dunnhumby in 2001. 2006, In the context of the selling industry retail in trouble in the UK and saw a sharp drop in profits; Tesco decided to sell Dunnhumby at the end of 2014 for £2 billion. If Tesco sells the company; they will become one of Dunnhumby or transfer their data elsewhere. That has caused potential buyers to skip buying the shares. After a “comprehensive strategic assessment”; Tesco decided to cancel the sale at the end of 2015. All this shows the value of the company comes from the company’s data. Without Tesco data; Dunnhumby’s value will be reduced to only human resources and technology.

3. When the value lies in the company’s data mining capabilities.

Data, in its own right; can significantly increase the value of a company, but also depends on how well the company can extract value from the data. Data is especially valuable when combined with systems; sophisticated applications and algorithms to extract insights from data. For example, pizza delivery company Domino’s collects a lot of customer data and uses that data to improve their marketing. Having such solid data at hand and being able to work with data makes a company more valuable and attractive. Domino’s value can be significantly higher than a comparable pizza delivery company, but it doesn’t use data efficiently.

Please keep in mind that; the bottom line is to focus on the data that’s right for your business; i.e. data that moves the organization closer to its long-term strategic goals. It’s not a good idea to just gather as much information as you can in the hope that it will one day become valuable. There are some companies that have had success with the “collect everything” approach, but they are usually data brokers; whose main business function is to collect data and sell it to third parties, or companies with huge budgets and manpower to handle such a large volume of data. However; the advice given to most organizations is to have a more centralized approach to data; deeper.

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