The Data Explosion, Part 2: What’s Your Data Revenue Strategy?

data revenue strategy

Data Management

In the first post in this series, we talked about the massive amount of data that companies have in their possession now, and how that is expected to increase exponentially in the coming years. Figuring out how to collect, store and analyze all of that data is one thing, but the more critical question is: how can you “mine” that data to create revenue?

Seven-hundred billion dollars. That impressive number is the value that big data can create for consumer and business end users, according to the McKinsey Global Institute. However, this depends on companies developing successful strategies to turn their data stores into valuable assets that can be licensed or sold.

Understand Your Data to See Where the Value Lies

Data alone is nothing more than numbers. It cannot create business advantage nor build revenue streams. While companies will use technology to collect and store data, and advanced analytics will make sense of the data and establish value, in order to monetize their data companies must have effective revenue strategies and sound business models.

“Understand your data to see where the value may lie…Ensure your data is structured to allow you to extract relevant, marketable insights.”

Accenture, “Creating Revenue from Customer Data”

By looking beyond the data to the economic questions it can help answer, companies can begin to identify how their data can create value for customers and the market overall.

Data Revenue Strategies that Work

At its most simplistic, data can be used to generate revenue in two basic ways:

Directly: Companies can sell or trade their data (either raw or after applying analytics and formatting)

Using direct revenue strategies, there are extensive opportunities for transforming raw data into valuable and usable formats. One option is to offer data to third parties – in the form of preassembled reports or as a raw syndication feed -- to third parties to conduct their own analytics, research or product/service development. Another revenue strategy would be to offer prepared data as an additional “premium” service to current customers via a SaaS mechanism. As an example, KPMG suggested that customers of a wearable fitness device could access aggregated health or sports performance information for an additional monthly fee.  

Indirectly: Data can be used to inform new or revised product or service offerings, enhance customer experiences, improve service quality, target marketing efforts, increase upsell opportunities, establish competitive advantage, etc.

Indirect revenue strategies are nearly limitless and restricted only by your creativity and insight. A recent article in InformationWeek explored “8 Ways to Monetize Data,” which included:

  • minimizing churn
  • stopping revenue leaks
  • inferring customer satisfaction (before the customer provides feedback!)
  • supplementing products with flexible software options and services
  • detecting fraud and piracy
  • improving marketing ROI

Other possibilities are to use data to identify customers who may be more likely to purchase more – or are at risk to leave to a competitor; create even narrower customer segmentation to enable precision tailoring of products or services; or using embedded sensors to understand customer use and better inform post-sales offerings.

Strategy First

The above possibilities all represent exciting opportunities to turn their mountains of data into new revenue streams. While that transformation – from raw data to valuable offerings – will require extensive technology and analytics, it is certainly possible. Early innovators are paving the way already. However, before you can take action and begin seeing the profit from your data, you need to understand its value and develop a thoughtful revenue strategy.

For more on this topic, read about how “One Company Used Recurring Revenue to Get a $1 Billion Valuation”.

The next post in our Data Explosion series will explore the technologies and tools that businesses will need in place to tackle all that data.

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