38 PERSONALISATION
the smallest service that lets you learn the most about what to do next. Doing all this needs R&D teams - people
who understand the science and know how to handle complex scientific data and model the real world. It also needs people who understand the customer, the business and legal requirements. It needs people who can build the systems, software, and infrastructure to deliver the service. It needs buy-in from the top. And most of all, they need these people to be able to understand each other and work together effectively. This means creating interdisciplinary teams
to drive these projects, that build the business case and bring together all the expertise needed to build and launch the service.
Building digital services – a road to maturity This can be a new area and, like an innovative project, especially in the world of data, the best approach is to start small, and progress maturity, learning as you go. In this section we will talk through some
practical examples of how to approach increasingly sophisticated digital services, using a hypothetical example of a digital skin care service (though this could apply to many other personal care products). ■ Simple Insights You could start with an app to help consumers pick the right skin care product and regime. We are already seeing services (such as this from Nu Skin) that personalise regimes through user preference input. To this, we could add directly captured skin measurements such as smartphone photos. Technologies such as Skintuition from Cambridge Consultants are already making advances in skin image recognition, that may prove valuable to such a service. Or you may want to deploy tailored connected devices to collect data – Cutitronics have developed a device for measuring skin hydration for example. With this type of data, you can map simple parameters such as dryness to product
ranges that suit that skin type, based on your own detailed R&D data. ■ Advanced Insights and Personalisation Gradually add in new data sources to improve the model’s insights and value. By combining models of skin products with environmental data (like weather forecasts, humidity, proximity to the sea, etc) you can personalise the recommended products to where the person lives and time of year. This may need you to dig into historical R&D data to understand links between your products effect and weather conditions. Doing so will unearth useful insights for customers. ■ Capture real world data from customers By collecting real world data on how customers are using their products, feedback, and – most valuably – measurements of the skin after application, you gain a huge amount of useful data. This can be used to refine the digital service’s model and so improve recommendations. It can also be used to improve products or identify gaps in the market. ■ New As-A-Service Business Model With the above in place, you may want to start rethinking what and how you sell. You are no longer selling moisturiser, you are selling healthy-skin-as-a-service. You can move to subscriptions which automatically send the right combination of products for that customer (skin drying out, change the regime; heatwave predicted, send products with UV protection; cold snap, send lip balm). ■ New Markets Your huge collection of useful data about how people use products can now be used to explore new markets. Can you learn from your skin care models to quickly launch equally sophisticated hair care apps? Can you combine them and upsell these addition services to skin care subscribers? Can you recommend cosmetics based on people’s skin care regimes? Are there whole new markets you can enter,
alone or with partners? Skin photos could offer serious health benefits, such as detecting skin diseases – a service users may welcome. And that same data may be very valuable to
pharmaceutical companies developing topical medicines – providing privacy is respected and communicated.
The importance of digital R&D The existence of R&D data does not immediately provide all the answers, companies also need to be able to use it effectively. The better organised your R&D data and models are, the easier it is to identify valuable research that can be exploited in digital services. Harnessing R&D data to build digital services means setting up data stores, lakes, and warehouses so that data is accessible to anyone who needs it. This includes selecting tools and building integrators that would pipe data to the data science teams. It means agreeing standard processes for capturing and labelling data, so it is understood when people come to use it. Companies that have embraced digital R&D will find this easier.
Why build data driven services on scientific models? While building digital services may seem beyond the remit of many R&D departments, there is a case for them to be actively involved, and even drive it. Firstly it is clearly in their immediate
interest that they get the rich data that these services can provide. Secondly, it is in their long term interest that their company has differentiated digital products that will help the products they make succeed. The success of these products will hinge on
their expertise. Only R&D teams understand the data and models well enough to create these models. And as apps get better, the digital aspect of your product may become as significant as the physical aspects – both need good R&D behind them. They should not, of course, be alone in
this. Such products must be a collaboration between market research, marketing, IT, software, user experience and privacy experts, and so on. Experts may be brought in to guide the development process, but at the heart of it will need to be willing participants who can make sense of the data for them. Digital services associated with products
make that product more desirable, and open a channel of communication that brings companies closer to customers. By leveraging data and models from years of specialised R&D, product companies can differentiate themselves and generate new revenue streams. While the building and marketing of the
services will go beyond R&D, ensuring R&D experts shape the direction will be critical to making them effective. This creates new opportunities and improves the longevity of the products. This is surely in the interest of their creators. Tessella is a data science and AI
consultancy with a unique blend of scientific understanding, advanced data science and software services. Its wide ranging data science work has included guiding R&D focused companies across consumer and healthcare industries to build digital services.
PERSONAL CARE April 2021
PC
www.personalcaremagazine.com
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