by Data and intelligence is a hot topic. When b2b subscription leaders discuss data, intelligence, data as a service, and the like, what they think about is: customer value; use cases; defendable positions; and more. Over a series of Substribe ‘subsclub’ meet ups, here are just a few of the talk tracks outlined below.
It’s tempting to think technology (AI etc) will magic up the best data solution (hence the pun in the title), but as you’ll see, the key to unlock value is to start with the end in mind and intensively test for value with use cases and a focus on the ideal customers (not the total addressable market). Then get the technology, product and people pointed in the right direction.
When it’s sketched on a board, the different stages look a little like this:
Breaking it down, the 4 stages are:
1. Emerging data
– Basic data collection and aggregation
– Example: Entry-level benchmarking (e.g., salary data)
– Focus: Niche audience information
2. Established data
– More purposeful data collection
– Example: Gender pay gap analysis
– Focus: Strategic insights for internal use (e.g., balanced scorecard approach)
3. Mature data and intelligence
– Advanced data collection and analysis
– Example: Price reporting agencies
– Characteristics:
– Brings together multiple market participants (buy side, sell side, markets)
– Critical for trading
– Difficult to replace
– Takes time to achieve this level of criticality and scale.
This example is arguable – PRAs can be optimised. But I use it deliberately to a) provoke b) illustrate one key point – whilst PRAs can pull the pricing lever with huge amounts of confidence, the customers of PRAs can feel like they are held hostage.
4. Optimised data and intelligence
– Data as a service
– Embedded in client organisations
– Example: RELX (Reed Elsevier) in healthcare
– Transition from print to digital (or from magazine in medical professionals hands to advanced diagnostic embedded into machinery in a healthcare setting)
– Built AI capabilities into the digital realm
– Improved disease identification in healthcare settings = big value / big price tag
Substribe partnered with a brand who had wanted to “get into data” for 10 years. They perceived various obstacles blocking their way, including not being data people and not having the funding to build the UX around the data. By testing their idea with target customers, it soon became clear that their customers didn’t need, want, or expect them to create a user experience. 10 years of calcified opinion were blasted away. They could have been collecting this data years ago, and monetising it in some way. But the real challenge wasn’t the data or the UX, it is about understanding what the data will be used for and how that connects into measurable results for their customers. The journey to must have.
Teasing out insights and experiences from b2b subscription leaders, and the pitfalls to avoid, we would draw something on the board that looks like this:
What it says on the sketch above:
Beware / avoid commoditisation
Maintain strong customer connections
Aim for platform status
– Push customer data back to iterate on solutions
– Build more value over time
Value Propositions for customers:
What is the value in the data you have and the transformation into intelligence and insights? is it:
– Better investment decisions
– Improved product launches
– Fewer failures
– Regulatory compliance
Getting it right:
Leaders advise:
1. Understand the value first – start with the end in mind
2. Identify use cases for priority customers – and prioritise customers
3. Determine required data and its value – test it before building it
What investors look at:
Key / red (hot) yellow (lukewarm) blue (cold)
1. Data Supply
– Hot: Proprietary data supply, collection, and real-time updates
– Lukewarm: Unique approach, frequent updates, basic needs met
– Cold: No proprietary data, infrequent updates
2. Data Quality
– Hot: Must Have data, across functions, several formats
– Lukewarm: Recurring need, no visualisation, not used by multiple teams
– Cold: Ad hoc use, raw unformatted data
3. Data Defences
– Hot: Customer-generated data (interactions, preferences, behaviours)
– Lukewarm: Transactional data (sales, financial transactions)
– Cold: Publicly available data
The need for intensely understanding customers as a collective team always comes up in discussions. Data in isolation is not intelligence. Teams pointing in different directions may get you to where you want to be…but wow that’s making it hard for yourself. I like to think of my career in 5 year chapters. Don’t make 80% of that chapter about getting people to point in the same direction. Make it 20% of the chapter.
DaaS discussions at the Substribe meetups (“Subsclub”)
Taking you through the DaaS diagram above:
The trap
“Injecting” your data into a client organisation is one thing. A big thing. Once you get past the protective barrier, there is a real risk of your data becoming a commodity (“the commodity trap”) as you lose connection with your customers. This is one reason why leaders focus on use cases by role, to keep a grip on the value exchange.
The holy grail
The holy grail for data providers is achieving “platform status” – where your data solution becomes an integral part of your customers’ operations, deeply embedded in their decision-making processes. You want to get to a stage where your customers can’t imagine making decisions without your information.
Must have solutions
What platform status means: Your data platform is so deeply embedded in client organisations that it becomes painful to remove and continuously valuable. You’re not just providing data; you’re providing an ecosystem that enhances every aspect of your clients’ data-related operations.
Using customer data to improve customer data:
Create feedback loops where customer usage data informs and improve your data. This iterative process builds more value over time. For example, if you’re providing financial data, track how clients use this data, what you’re getting right, where the gaps are.
How far can you take it? Can you measure the results and what is impacting them? Or, can you begin to predict better actions to take and the likely impact it will have on your customer’s performance?
“Achieving platform status isn’t just about providing great data. It’s about creating an ecosystem where our clients’ use of our platform makes the platform smarter and more valuable for everyone. It’s a virtuous cycle of continuous improvement and deepening integration.” Substribe expert network / chief data officer
The Ongoing Nature of Data Innovation
Staying advanced is not a one-time achievement – it’s an ongoing process. You need to cultivate a culture of continuous innovation, always looking for the next breakthrough that will deliver unprecedented value to your clients.
This might mean exploring cutting-edge technologies like AI and machine learning to extract deeper insights from your data. It could involve developing new data visualisation tools that make complex information more accessible and actionable ( if your clients need it!). Maybe it is about involving your analysts and content experts to add their perspective.
“In the world of data, standing still is moving backwards. We need to be constantly pushing the boundaries, not just of what data we can provide, but how we can make that data indispensable to our clients’ success.”
Are you ready to take your data business to the next level? Substribe’s performance trackers and team workshops can help you avoid traps, strengthen customer relationships, and point your teams in the same direction to be force multipliers. Powered by insights from the Substribe expert network.
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