Introducing the digital twin

A new concept in computer science – the digital twin – is being used to increase business effectiveness across industries. By applying it to marketing we find it identifies potential opportunities for communicating more effectively and creating more personal brand experiences.

Industries such as manufacturing, aeronautics and medical science have for years built ‘close-to-life’ computer simulations for data modelling and predictive analytics. In these cases, the closer to reality a computer model is, the more useful it will be. In meteorology for instance, a weather report is only as accurate as the quality of data being simulated.

This concept – the so-called ‘digital twin’ – was recently named by Gartner as one of ten strategic technologies for 2019. It refers to any virtual representation of a real-world product or process, person or place – often through a real-time sensor link. Gartner states that these dynamic
data constructs allow organisations to improve their effectiveness by up to 10%, and predicts that half of large industrial companies will use them by 2021. We have found it useful to take this concept and apply it to marketing, where it demonstrates the potential for improving brand
communication and brand experiences.

In our industry, large-scale customer datasets – living in data management platforms (DMPs), proprietary machine-learned databases, or even the blockchain – are intended to reflect ‘that which is true’ about some current real-world entity or system. As marketers, we access ‘mirror worlds’ of data held on brand and publisher audiences for the purposes of planning and execution, optimisation and analysis. We do not
– often cannot – access ‘real world’ data on our audiences, particularly individuals, so we rely on the data trails left behind by users.

Our mirror worlds provide us with a ‘data shadow’, rather than a digital twin, and we use this data shadow to segment audiences, and work with the lowest common denominators of a specific set of variables: their interests, geography, and shopping habits for instance. The data shadow provides enough consumer relevance for brand communication. The better the data shadow represents those consumers who
are most receptive to our brand communications, the more effective it will be. Brands therefore need to seek out the most comprehensive and relevant data available, through direct data partnerships with GDPR-compliant third-party vendors.

But while data shadows enable brands to segment their audiences effectively, they are too broad to allow for truly personal brand experiences. For that we need to build more accurate representation of real-world audiences via their digital twins, which requires data that’s detailed and unique enough to allow us to treat consumers as individuals. The better the digital twin, the more marketers can personalise their brand experience to be more valuable to both parties.

The only data that’s good enough to allow this is first-party data, provided willingly by customers in return for something they value, such as a reward, unique content or a better customer experience. So to use digital twins to create truly personal brand experiences, brands need active consent and engagement from consumers.