Leading data initiatives at companies like dunnhumby and Reward, where we tracked billions of transactions worth billions of pounds, I’ve observed a common mistake about data monetization: the belief that selling raw data is a great way to create value.
This is not just wrong – it’s potentially damaging to your long-term business prospects. Here’s why, and a better approach.
The Raw Data Trap
Many companies sitting on valuable data assets immediately think about selling that data to interested parties. It’s an understandable – you have something others want, why not sell it directly? But this approach has several important flaws:
- Value Quantification Challenge One of the most significant challenges with raw data sales is determining the right price of sale. How do you value something whose ultimate utility to the buyer (even worse, to the seller, you!) is unknown? This often leads to either underpricing valuable assets or pricing yourself out of the market. In contrast, when you focus on specific use cases and deliver insights or actions, you can directly measure the value created for customers – whether it’s increased revenue, reduced costs, or improved efficiency. This makes pricing discussions more straightforward and value-based, and creates a healthy framework of shared success, and shared risks. Not easy, but very rewarding.
- Value Control and Capture Challenges When you sell raw data, whether as batch exports or continuous streams, you’re trading away control of a strategic asset. Even with streaming data that remains fresh and current, you’re still limiting your ability to capture its full value. While data streams can provide recurring revenue, you’re essentially selling the ingredients rather than the finished course – and any decent chef knows he can make more money selling meals than the raw ingredients. At dunnhumby, we found that the same real-time transaction data that might be sold as a stream could instead support multiple high-value use cases, for example personalization engines, each generating significantly more value than the raw feed.
- Limited Price Power Raw data is increasingly commoditized. With the explosion of data sources and collection methods, buyers have more options than ever. This puts downward pressure on prices and makes it harder to maintain margins. Your data might not be that unique after all, or their signal might be obtainable from other sources.
- Regulatory and Reputational Risks Selling raw data, especially when it contains any form of personal (PII) or sensitive information, exposes you to significant regulatory risks under frameworks like GDPR. Even with proper anonymization, public perception of data selling can damage your brand.
The Power of Insights
The first step up the value chain is transforming data into insights. This approach offers several advantages:
- Higher Value Proposition When you transform data into insights, you’re solving specific business problems. You’re not just providing information – you’re delivering understanding. At dunnhumby, we transformed retail transaction data into deep customer behaviour insights that helped retailers and CPG companies make better decisions. This is worth 10x the raw data underpinning it. Of course, you need domain expertise, and customer intimacy.
- Multiple Use Cases The same dataset can generate different insights for different customers or use cases. For example, at Reward, we used transaction data to:
- Help retailers understand customer loyalty patterns
- Enable banks to increase customer engagement and retention
- Support marketing teams in targeting their campaigns more effectively
- Retained Control By providing insights rather than raw data, you maintain control of your core asset while still delivering value to customers, and keep your edge secret. This creates a more sustainable business model and protects your competitive advantage, and increase customer stickiness.
The Ultimate Goal: Actionable Outcomes
The highest form of data monetization is turning insights into actions. This is where the real value multiplication happens:
- Direct Business Impact Instead of giving customers data or insights they need to act on themselves, you’re providing ready-to-implement solutions. At Reward, we didn’t just tell retailers about customer behaviour – we helped them execute targeted marketing campaigns that delivered measurable ROI.
- Higher Price Points The closer you get to actual business outcomes, the more you can charge. A solution that delivers £1 million in incremental revenue is worth far more than the raw data that helped identify the opportunity. Far more.
- Recurring Revenue Action-oriented solutions often create ongoing relationships rather than one-time sales. This leads to more predictable revenue streams and higher customer lifetime value. Again, more customer stickiness and stronger relationships and bonds.
Building a Sustainable Data Business
To successfully monetize data through insights and actions:
- Start with the Problem Don’t begin with what data you have – always start with what problems your customers need to solve. At dunnhumby, our success came from deeply understanding retailer and CPG challenges, then working backward to create solutions. Domain expertise is really important.
- Invest in Analytics Capabilities Raw data rarely speaks for itself. You need robust analytics capabilities to transform data into meaningful insights. This includes both technology and talent investments.
- Build Scalable Products Turn your insights and solutions into standardized, repeatable products. A well-designed data product can serve multiple customers with minimal customization, creating economies of scale. This is one of the priorities we focused on at dunnhumby with significant success: turning insights from a consulting exercise into a self-service product that retailers could use independently, dramatically increasing our reach while reducing delivery costs. Customizations kill data monetisation economics.
- Maintain Data Quality The foundation of any data monetization strategy is high-quality data. As I’ve written before, investing in data quality is not a cost – it’s an investment in your future ability to create value.
The Multiplication Effect
Perhaps the most compelling argument for this approach is the multiplication effect. A single dataset, properly leveraged, can power multiple products serving different use cases at different price points. Each step up the value chain – from data to insights to actions – multiplies your potential revenue. This again was one of our ‘killer’ apps at dunnhumby: ‘recycling’ data for multiple use cases and customers.
Think about it: would you rather sell your customer data once for £X, or build a sustainable business that generates multiples of &X by solving various high-value problems with that same dataset?
The key is understanding that data’s true value lies not in the data itself, but in its application to solve real business problems. Focus on turning your data into solutions that deliver clear business outcomes, and you’ll build a more valuable, sustainable business.
What’s your experience with data monetization? Have you seen companies succeed with raw data sales, or do you agree that insights and actions are the way to go?
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