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A maturity model for building R&D-based digital services


Previously, we discussed how companies can build unique digital services using R&D data, and why doing so gives them an edge on the tech giants. Here, we discuss how it does.

We must be realistic. Building unique digital services using R&D data requires a degree of data maturity that many organizations are still working towards. But rather than waiting until everything is ready, there is value in starting small and learning as you go.

We propose a model building simple digital offers, and gradually progressing the level where they can incorporate detailed R&D data to create high-value services. Our Capgemini Digital Service Maturity Model is based on our experience delivering R&D-powered digital services with our clients.

1. Develop simple insights

Use your unique knowledge and data to generate initial insights that offer value to customers, and package them as a new service used in conjunction with your products.

For example, Oral-B Genius toothbrushes use accelerometers to measure how someone brushes their teeth, then offer feedback to improve brushing technique based on R&D on toothbrush design and use.

2. Add advanced insights and personalization

Combine your data and expertise with public (free or purchasable) data to generate more personalized insights. For example, Capgemini worked with an agrichemicals business to combine R&D models of probiotic effectiveness with data on diseases prevalent on individual farms. This created a sales tool that can make recommendations of the right probiotics to mix into feed blends for any specific farm.

Capgemini partnered with AkzoNobel to combine their coatings R&D data, acquired vessel activity data, and custom-developed hydrodynamic models, to create Intertrac Vision, a tool that provides accurate cost -benefit predictions of different coatings. This allowed sales teams to back up coatings advice with scientific evidence, leading to millions of dollars in increased sales.

3. Build in real-world data from customers

Use your deployed digital services to capture new data from your customers and improve the underpinning models and hyper-personalization.

For example, once customers saw the value of the coatings predictions delivered by Intertrac Vision, they were happy to share in-service performance data to get even more personalized predictions. This in turn allowed individual guarantees on coating performance for vessels, backed by the new data. This is a significant differentiator and a valuable source of data to further refine models to improve predictions.

4. Develop new as-a-Service business models

With digital services capturing data on product use, you have the infrastructure to offer new as-a-service business models.

Many manufacturers have shifted from selling bits of kit, to selling services (kit rental, parts, maintenance, etc.), and eventually to selling outcomes. Rolls Royce offers Jet-propulsion-as-a-Service, using data from engines to sell propulsion on a per-flying-hour basis, which is what airlines want.

Could shampoo companies stop selling bottles and start selling hair glossiness? Could pesticide companies sell per-hectare crop yield? If you have faith in your R&D data, and the models build on them, and you collect data to validate your claims, there is no reason why not.

5. Consider new markets

Finally, think outside the box. The new data, and new customer relationships may create new opportunities in new markets. Data from lifestyle apps may be useful for healthcare research (your own or others’). Crop models may be useful for supply chain planners.

Intertrac Vision was designed to support decisions on hull coatings. But the data and models produced have created valuable insights that can be sold elsewhere, for example through a new service for ports advising on chance of invasive species being attached to ships hulls.

Done right, R&D data offers a whole world of digital opportunity, from highly personalized consumer apps to highly bespoke decision support. It can open new revenue streams and insulate against digital disruption from the tech industry, in an area where they will struggle to compete.

To do all this, you need people to craft these models. Not just people who can spot correlations, but people with deep understanding of data and what it represents, knowledge of the available tools, software engineering, and user experience. You need to do this in teams that bring together scientists, data experts, digital experts, and the business.

Capgemini can support with data curation and management, modelling, internal structures, and digital transformation roadmaps to advance your journey to R&D-powered digital services. Contact us to discuss how we can help.

About the author:

Jamie Downing

James Downing has 15 years’ experience leading the design and implementation of data-driven digital services at some of the world’s largest product organizations.

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