Many of us have heard the saying “Data is the new oil,” and for good reason. As data tools have greatly improved throughout the 2010s, we’ve seen our clients make better and faster decisions with the data they produce.
Thanks to their high margin, data sales can generate free cash flow to be reinvested in the business. Some companies have even retooled their go-to-market strategy to materially fuel their bottom-line via data sales and not their core profit offering.
For starters, we are not advocating the sale of consumer data, and certainly not if such a transfer is not completely transparent. This piece is about extracting differentiated insights from analytics, using the data a business itself generates and unquestionably owns.
Innovative uses of data like these beg the question: how can organizations get started? This blog is the first in a series that will translate the buzz surrounding data monetization into actionable steps and considerations so that you can unlock the potential of this growing trend. Along the way, we’ll not only explore the importance of data monetization, but also offer firsthand insight into successes, failures, and the importance of organizational alignment.
As excitement around the topic of data monetization continues to intensify, many of our clients are interested in understanding how to capitalize on this trend. There are a variety of ways that companies can extract value from their data. They can better understand their business performance, improve operational efficiencies, avoid costs, target customers, and the list goes on. The topic we want to explore is the concept of data monetization and the financial value that businesses are extracting from it. There is even a growing number of companies that exist not for the core product they deliver, but for the sole purpose of monetizing data. Take, for example, Loverly – a start-up that designed its bridal collection (e.g. styles, fabrics, colors) based on product performance analytics (e.g. searches and clicks) from over 150 partner sites, such as David’s Bridal. Data has truly made the transition from a tool to help guide us in our business decisions into an actual product that we can take to market. It needs all of the tender loving care any other product needs.
Here’s the challenge: Data monetization is becoming increasingly prevalent as companies’ data organizations mature, but very few are doing it well. It takes the combination of mature organizations like legal, sales, strategy, product, IT, analytics, marketing, etc. that can all work collaboratively towards a common goal. Organizations need to truly treat data like a product they are taking to market while remaining agile and nimble.
How does solving the challenge look in practice?
- Focus first on the customer: A true understanding of customer pain points and the data they would write a check for must be the foundation of data monetization.
- Think outside your industry: It’s also important to think from the perspective of customers inside and outside of your industry. How would Disney be interested in our automotive client’s data? How would financial services data be interesting to customers in the retail space?
- Ideate, identify, prioritize: We like to take our clients through a framework of rapid ideation, identification, and prioritization on new data monetization opportunities to quickly build a backlog of ideas that can be stack ranked based on business value. This can only be done effectively with a highly cross-functional group of individuals that know the customers, the business, the data, data science, etc.
- Rapidly prototype: We then quickly work to take the best ideas through a rapid prototyping process to get data products in the hands of sales teams and paying clients to quickly validate the market interest in the product.
In getting started, it’s also critically important to revisit the oil analogy, exploring the concept of raw vs. refined. There is tremendous value creation in the refinement process from oil to gasoline. A customer’s willingness to pay for oil changes drastically as you move from selling a piece of land with access to oil, to selling a barrel of crude oil, to selling a gallon of gas for a passenger car, and finally to highly refined fuels like jet fuel and racing fuels. But the ROI needs to be there.
The same concept applies to the data monetization space. Too many clients are looking to get a “quick win” by selling their raw data and letting another company or competitor turn that data into insights and real value. Selling your raw data has a big risk of undermining your own business model. The real value in data monetization comes from combining your raw data with other internal or external data sources to refine it and create additional value. Putting this advice into practice, we push our clients to look for ways to package their data, applying data science, data visualization, and automation to create true business insights that they can sell.
Data is achieving unprecedented levels of value. According to Gartner, “by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset…” In zeroing in on data monetization as a key component of this opportunity, it’s a matter of moving from a “regular” gasoline way of thinking to a “premium” one – and we’ve observed that the investment in premium is well worth it when it comes to high-octane business performance.
Keiylene Strickland, David Healy, and Charlie Morn also contributed to this blog.
Andrew Watson is a Master Practitioner with North Highland and is the Client Lead for several large accounts. He is passionate about using data and technology to achieve meaningful business impact and ROI. Andrew utilizes a business first approach to help clients fulfill their strategies, and pulls from his 13+ years of experience to do so. He brings finance, marketing and strategy knowledge to drive value in the media & communications, financial services, non-profit and energy industries.