B2B Data Products - How To Monetise Your Data Assets

Data monetisation is a highly lucrative opportunity that allows organisations to leverage their data assets to generate alternate revenue streams or augment existing ones. However, more often than not, data monetisation endeavours are challenging, involving upfront investment and producing poor results. 

Data is a unique asset category; it’s more akin to a raw material than a finished product, the latter of which requires value-added refinement to make it a lucrative end product. While digital products have succeeded in becoming mainstream since the days of dot com, data products have remained the prerogative of niche providers who have been able to create compelling usage patterns in specialised spaces such as digital marketing, credit ratings, and more. 

While there are many models for achieving data monetisation, I am going to focus on one niche area: how to monetise your data assets focused on building B2B data products. The emphasis here will be on leveraging first party data for a traditional company (a financial institution, travel company, or an ecommerce marketplace).  

We’ll cover this in two parts: 

1. What is required to be successful in the B2B data monetisation space?
2. B2B product variants and the capabilities required to build them

This article will focus on the pre-requisites for an organisation to create strong B2B data products that will not only excite your clients, but also drive meaningful revenue opportunities. 

What does it take to succeed?

Many factors go into creating and launching a successful product; I am going to focus on key levers from an organisational standpoint that are critical for success.There are four key levers that carry the most significance in this context. 

1. Market Share with Robust Data 

In order to successfully create a strong B2B data product, you need access to rich data assets and a strong market presence.

• Rich Data Assets: The building blocks for data products are rich data assets. The data that is usually most helpful is first party data.Transactional data: Customer booking details, products purchased, pricing, etc.Demographic data: Customer age, interests, etc.Geo-location data: Addresses, in-geography location, etc.Reviews data: Travel reviews, blogs, etc.
• Significant Market Share: For any insights to be representative of the wider market, the company must own a sizable market share. Typical products would involve your ability to compare a client's performance with the rest of the industry or sector, allowing them to derive actionable insights. 
• Quality of Data: For products to work effectively, you need data you can trust. Trust deficit is a significant challenge across all data use cases. Poor quality of data can become a significant deterrent—even more so when it could have a bearing on a client's decision making ability.

2. Data, Analytical, and Machine Learning Platforms

• Data Platform with Strong Foundational Capabilities: Data Platforms that abstract complexity from engineers to create new propositions are key to building things at pace. The platform needs to offer elastic scale (preferably on the cloud) with robust data controls.
• Data Aggregation Engines Running at Scale: Invariably, a number of B2B data product ideas rely on pre-processed data sets or real time event processing—which requires adoption of specialist tools to process at scale and pace.
• Data Analytics with Self-Serve Dashboards: The easiest way for your clients to process insights are easy-to-read dashboards with interactive roll-up and drill down capabilities.
• ML Platform: In some product scenarios, there is heavy reliance on data science  models, requiring ML platforms that accelerate and support quick iterative development and deployment of models (for example, forecasting/pricing models).
• Data Anonymization, Security, and Privacy Management: You need capabilities that allow you to share, and in some cases, co-create products along with partners. In that context, your ability to have robust mechanisms to both anonymize and share data with your partners and clients is critical. It is also important from a data privacy standpoint to safeguard your customer data and interests. Ensuring that robust access and security controls are in place is key to preventing accidental breaches. 

3. Human Capital

Data teams in a regular organisation are inherently focussed on building products and applications that are focused on internal consumption. This places a lower threshold on the standards you need to adhere to, while building data products. For example, an MI dashboard that goes down for a day may not materially impact the organisation—-unless its a regulatory report breaking down the day before submission. Organisations are equipped to process failures and build a certain level of performance and resilience; however, the story is different with data products.

Building products in a B2B context places a higher emphasis on non-functional aspects of resilience, performance, scalability, monitoring, and incident management. This is more akin to how a SaaS company would operate. In general, organisations are attuned to this emphasis on their website or app, but often lack the maturity and investment to do this for data products. 

It’s a huge shift in the cultural mindset of product teams—a mindset change which can’t be underestimated. 

4. Scalable Product & Business Models 

The business models also need to evolve to support client requirements with more strategic, product-led thinking. 

• Product Thinking: Applying product thinking to data products yields significant benefits in terms of scaling the model—build once, and configure it for multiple clients. Data products are generally built to internal customer requirements of the organisation; teams are not practised in treating them like products with a roadmap. Inculcating product thinking is key to unlocking value for your clients. Building out an MVP with some anchor clients is a good way to trial data products—the intention is to inculcate a new paradigm of learning, both for the organisation and for clients. The ability to build products that can scale through configuration for multiple clients is key to success. Without product thinking, you risk running multiple projects with no cohesive strategy—which eventually leads to cost overruns. 

• Revenue Models: The relative infancy of this model (compared to more mature ecosystem players) makes it challenging to sell these products as value-add to your clients. This is where you need to experiment with revenue models that range from Freemium to Premium; this can entice customers to try out the product before they decide to convert to a paid client. Similar to SaaS revenue models, the organisation needs to be committed to an upfront investment that could potentially lead to recurring revenue focussed on customer Long Term Value (LTV).

• Managed as a Separate P&L: The P&L for these products need to be managed separately, but also needs to be subsidised by the company’s data assets, as there is no incremental cost associated with accessing this data. This allows for an accurate measure of revenue vs. costs associated with the new products. It also needs a gated funding approach to ensure that there is adequate financial discipline.

You cannot truly unlock the value of your data assets unless you use them with intent and keep end-use cases in mind. Raw data is your crude oil; how you add value through refining it. The refinement process determines whether or not it ends up becoming jet fuel. 

More on that in the next article.