Customer ID Graphs: The Cornerstone of Customer Intelligence & Personalisation

by Rob McLaughlin

Customer identity is the highest priority on any list of prerequisites for delivering a data-driven customer experience. Without a comprehensive identity mechanism to combine customer data from its many and varied sources, there is no realistic hope for creating customer intelligence nor driving personalisation. Therefore, it’s essential to establish and maintain a register of the many customer identifiers (IDs) to recognise the customer, and this is why an ID graph is so relevant and powerful.

Typically, large organisations will have amassed a range of IDs by which a customer is known. Sometimes the creation of multiple IDs has been by design – perhaps to support certain operational, technical or regulatory needs. A customer entity may also be known via multiple IDs due to mergers and acquisitions from the past which customer groups and business units have stitched together. For these reasons, customer ID graphs are not a new concept. The omnichannel nature of customer relationships today offer a new and exciting opportunity to extend the ID graph concept.

There are some very valuable use cases for harnessing the power of customer ‘ID graphs’ within the digital realm (including multi- and omni-channel customer relationships). As customers step across owned, paid and influenced channels, through a variety of channels, in and outbound relevance, effectiveness and efficiency can really only be attained through the use of a device graph. The ability to recognise a customer and then deliver a relevant experience enables the automated delivery of personalised experiences and allows brands to deliver a more human touch at a larger scale. As discussed here, one of the foundations of relationship-building is to recognise and remember a customer. An ID graph supports this concept in the omnichannel and high frequency interactions which customers and prospects experience with brands today.

Some examples of experiences which necessitate ID graph capabilities include:

  • Behaviourally-driven triggers which push direct communications such as basket abandonment or product/category interest campaigns
  • Paid digital media targeting of audience and individuals for precision marketing
  • Bid management and/or frequency capping across multiple paid media channels for effectiveness and efficiency
  • Optimisation of contact centre & digital activities to be reactive to customer behaviours and reduce call volumes or handling time (and increase NPS/CSAT)
  • Personalising experiences prior or without the need for an explicit customer login, thereby driving increased relevance in high volume touchpoints such as home and other landing pages

These kinds of experiences can only be delivered through a continued ability to identify the customer, even as they shape-shift across the omnichannel ecosystem. To enable these types of use cases, ID graphs connect our customers’ known and anonymous profiles and leverage the various identifiers used across platforms. By ‘graphing’ the hierarchical relationship between these identifiers, we then have an ID graph for each customer, and a mechanism for recognising them wherever we might find them.

Figure 1: A typical household includes multiple users on multiple devices

Since ID graphs power customer intelligence and personalised experience, they need to be maintained both within your enterprise data environment and available ‘on the edge’ for real-time recognition of customers. Within your enterprise data environment, you will be able to create analysis and insight which reflects the many behaviours of your customers across the vast array of channels you interact with them on. Crucially, delivering APIs to identify customers in the field sees you leverage ID graph for driving experience. Marketing technology vendors have offered solutions from several different angles from Adobe’s Experience Cloud ID through to Google’s Audience 360 capabilities. These capabilities enable the delivery of an actionable and unified customer profile for use across on and offline channels.

In a somewhat counter-intuitive twist, ID graphs also offer the ability to deliver against elements of the user privacy and GDPR standards. To live up to the promise of ‘do not track’ in a persistent manner, ID graphs offer a non-intrusive method to ‘remember not to track’ by simply keeping true on not measuring or personalising a user’s experience by recognising them as someone who has opted-out such activities in the past.

As a prerequisite for customer intelligence and personalisation, many organisations are looking to build ID graph approaches into their enterprise data. Syntasa’s Synthesiser capabilities enable organisations to quickly construct bespoke ID graphs at the point at which data is ingested and integrated, enabling immediate benefits for business performance and use case delivery. This is how leading brands such as RS Components, Dixons Carphone, Sky, and The Telegraph have been advancing their customer-centric strategies at speed by embracing ID graph as the number one priority in their roadmaps.

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Tags:  Personalization

Rob McLaughlin

Co-Founder of Loop Horizon

Rob is an experienced digital marketing professional and a proven leader in the field of data & analytics. He most recently served as the Head of Digital Decisioning and Analytics at Sky. Prior to that, he held analytics roles at Digitas, Barclays, Canon, and Oracle.

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