The common theme in this blog is to align your monetization strategy with the “horizon view” of your products and services with three essential points.
1 – Digital Services are at the heart of your customer success.
2 – The winning play is to create an Ecosystem built on these digital services.
3 – Monetization of the Ecosystem is the goal.
At the heart of success is having the data required to support the future monetization model. In my last article, I outlined the need for “transformational pricing” and suggested that it must be data-driven. Having the correct data to drive individual customer success is required.
The common theme of this blog is that to own your ecosystem, you have to move to transformational pricing, which is most likely consumption or “outcome-based” pricing. To align here, the data model must capture the outcome of the product or product system. This combines asset usage, ecosystem tool usage, and predictive analytics.
This article provides a great example of how Autodesk is evolving its product development process around this concept. This software company seeks to optimize the TCO for buildings, designing outcomes that understand TCO and require data and the ability to use it.
And “oh yeah,” if you have the correct and accurate data, AI can create a customer-specific transformation pricing model to drive predictable returns.
There are three critical steps in planning your data strategy for monetization.
Step #1 – Design Monetization into Your Products
Often overlooked is the role of your product design solutions & processes in managing the many design aspects of your products. Most Engineering teams have a firm grasp of the data strategies for connected product capabilities. The question is, have they designed the data requirements for monetization? Have they made the data accessible within the ecosystem?
A great example might be the strategy of including insurance programs as part of your ecosystem. What data does the insurance company need to provide the best consumption-based pricing? There is a good chance their needs change design requirements. If your PLM solution is prepared for inevitable requirements, reduced time to market for ecosystem services aligns well with strategic objectives.
Step #2 – Support Continual Data Capture and Analytics
Most connected product platforms plan to monitor and collect operational data. Is this the correct data for monetization? If the design supports them, data collection and analytics are the next steps. Serial data monitoring, data mediation, edge monitoring, etc., are all complicated in their own right. However, without these solutions in place, monetization goals can not be achieved.
Step #3 – Capture/Predict “Outcome” Metrics
Perhaps the hardest is to project the outcomes from the product or product system. To do this, the product system must collect data from internal and external sources. Today, most product systems collect output metrics to align with service and support. Items like mileage or hours of operation are typical. More refined data for items like hard braking, excessive acceleration, etc., might be collected in many cases. The dollar-based output of the product or product system is often overlooked.
One key hypothesis of this blog is that market dynamics within the digital ecosystem are essential determinants of where you should invest. What is the data strategy for each monetization item inside your digital ecosystem? What is the dollar value output from each monetizable aspect of your ecosystem?
Step #4 – Have a Platform where Business Users can Iterate on Pricing Options
If the foundational data is in place, then how do business users use that data to iterate on customer-driven “transformational” pricing? Consider today’s leasing model driven by Capital & risk analysis and moving that to outcome-based pricing. If business users can create individual models that show the optimal pricing and offer that, how might that transform leasing? Would traditional leasing be all but gone? What might the pricing be for digital services independent of the asset besides leasing? In this case, the capital risk might not be a factor, but the output the digital services enable would be.
I recently spent time understanding Sigma Computing, which might provide this capability.
In this simple model, let’s consider three different examples to illustrate how to participate in all high-value offerings in the digital ecosystem.
At Value Threading Solutions, we are creating a data modeling process built on the ideas of a collection of threads and how you identify the threads needed for the future monetization of your ecosystem. If you have the correct model, partnering with the Product Development and Data Science teams will be the foundation you need.
