There’s nothing more personal than skin. Equally, there’s nothing currently more appealing to consumers than personalized products and services that truly reflect their own uniqueness. The question is, what’s stopping skincare brands really stealing a march by harnessing tech to take hyper-personalization to the next level?

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In my view, it’s a case of watch this space… but don’t wait around too long. Innovative techniques such as digital twinning – proven in other sectors – have the potential to enable unprecedented personalization as part of a wider, joined-up approach. As our Experiential Sensing concept shows, there are ways for brands to uncover previously unattainable consumer insights from data by adapting existing technologies to enable novel sensing techniques.

By combining the ability to generate ultra-personal data with the creation of a digital skin twin, the skincare industry could get closer to its customers than ever before. Digital twinning is the process of creating a computer simulation of a complex system and progressively adding real-world information to refine the model. Eventually it becomes a simulated reflection, a digital twin, of the real thing. The process suits systems that are so complex and subtle that producing a straightforward model that is detailed enough to be useful is almost impossible.

It’s a highly appropriate approach because there are few more structures more complex than skin. Modelling for analytical purposes is incredibly challenging technically – not least because of the complex interaction between the light that falls on the skin and its various constituents. Our light-tissue interaction model whitepaper explains the intricate path we negotiated to develop a tool for consumer device applications. Another reason for its suitability for beauty, cosmetics and skincare is that the digital twin can be used to predict outcomes from a given set of inputs – and potentially to model multiple scenarios leading to optimized consumer recommendations.

Digital twinning is making an impact

Digital twinning has already made its presence felt in the aerospace industry. It is tough to produce a complete and useful model of an aircraft engine with its hundreds and thousands of parts and countless sensors measuring temperatures, pressures, flow rates and dozens more parameters. Coarser models are possible but can only usually predict broad trends in engine behaviour. They lack detail.

But imagine if the manufacturer decided to build a coarse model and then apply the vast volume of information on the engine’s condition that it acquires every working hour. Gradually, incrementally, the model will improve until it becomes an extremely accurate digital twin. The manufacturer has a hugely powerful tool to test ideas that would be difficult, expensive and downright risky to try on a real engine. One company that invested in the approach to better understand its engines made a step change in maintenance planning that saved hundreds of millions of dollars every year.

The crucial factor was that the digital twins they built weren’t just a single simulation of any engine of the same design, but a series of simulations each individually created to replicate every single engine they had in service. Every engine was different, so every twin was different. The digital twinning process makes the simulation personal – which brings us back to skincare.

As I’ve already pointed out, skin is notoriously hard to model. Often the results are too superficial to provide parametric information, or so detailed that they become too large, overly cumbersome, very hard to use and extremely difficult to understand. They are certainly not personal. But digital twinning offers brands an approach that could take an existing skin model and – with the right data and the right analytics – create the digital twin of any individual customer.

The opportunity to get ultra-personal

Digital twinning would allow the accurate simulation of how any given product would look and feel for that customer. It would allow the customer to say, “I want my face to look like this,” and be supplied with the exact product to make it happen. It would allow the manufacturer to produce a custom product for a specific individual. Digital twinning would let the skincare provider understand how that individual’s skin changes as they become older, as they travel from an urban to a rural environment, as they move from summer to winter. It would let the provider know the skin of the individual consumer intimately. It would let that provider be ultra-personal.

The key to success in digital twinning is being gradual – with each new piece of data adding to the richness and accuracy of the simulation. It becomes more personal over time. And that will mean doing new things, because people aren’t routinely instrumented like an aeroplane engine is.

The novelty applies just as equally to skincare providers as it does consumers. I think there are plenty of reasons why companies are being cautious about making a concerted leap into such new and exciting territory. But there are also plenty of encouraging parallels. Technology is now entrenched in the sports and fitness industry, driving those with the ‘just do it’ mindset to optimal levels of performance – and bringing them back for more. But a decade ago, similar caution prevailed with fear of the unknown and worries over ROI hampering project development.

Feeding the digital skin ecosystem

A gradual, incremental approach is important to what I refer to as the digital skin ecosystem. The digital twin becomes increasingly effective as the model that sits at its heart is fed with regular information from the skin of the individual. The better the information, the more personal the twin becomes. A digital skin will rely on, and flourish with, an ecosystem fed by information collected on the go, at home or in the salon.

On the go – the mobile phone. The consumer uses their phone to take everyday selfies which are uploaded to the digital skin model. A combination of image processing, data analytics and artificial intelligence is used to extract maximum information on skin condition from the imagery. The information from multiple images over time will allow trends to be established and predictions made about future skin condition.

At home – affordable beauty technology. While a phone camera is a useful and ubiquitous source, better information on skin condition could be derived from dedicated technology products built to sense more than visual image. Infra-red, ultra-violet, moisture and pH sensors would all be useful. Some early versions are emerging for home use, but there’s plenty of room for superior products specifically designed to fit into a digital skin ecosystem.  Another approach is represented by our Skintuition concept platform, which illustrates the potential to bring affordable, smartphone-based skincare personalization to a mass audience.

In the salon or retail store – high-end technology. Gaining even more comprehensive information could demand sensor technology at a level that is unlikely to be viable for an at-home product. This more expensive technology would be salon or store-based in the digital twin ecosystem. The individual customer would receive a relatively infrequent but comprehensive ‘scan’ during a visit.

As I see it, a skincare brand could create truly engaging online service with the digital skin, making it as easy as possible for consumers to upload information from phone pictures or beauty tech. It would put that company as close to an individual consumer as its possible to get without them being in the room. If you’d like to explore these themes in more detail, don’t hesitate to drop me an email. I’d be delighted to talk further.

Author
Bill Hodson
Consulting director

Bill has more than 30 years' experience in technology strategy consulting to government and industry around the world. He's worked across a range of markets including energy, transport, defence, security, aerospace, healthcare, manufacturing and construction. His focus is on helping clients achieve technology-driven change.