Identity resolution and cross-device identity management
mParticle's CEO & Co-founder, Michael Katz, explains why identity resolution needs to be coupled with data capture in order to yield the best results for today's brands in this MarTech Advisor piece.
Are you treating one customer as one single individual (irrespective of where you meet them) or as multiple different individuals? Is their experience seamless and consistent no matter what platform or device they interact with your brand over; or is their experience more like the film ’50 First Dates’, where the protagonist must begin each day afresh, without any memories of what happened the day before? (Which one of us hasn’t experienced the trauma of having to repeat our ‘problem’ to 4 different agents at a‘ service’ center? In more evolved times, why should I have to be a card-carrying Loyalty Program member for your on-ground outlets to recognize my preferences?)
Enter Identity Resolution. A unified view of the customer is the core of CDPs, but a critical subset of creating a unified view is matching the identity of customers across diverse devices, platforms and locations to help resolve multiple interactions as coming from the same individual. Identity resolution has, off late, grown into an independent solution category within Customer Data Management. It aims to help marketers arrive at a probabilistic or deterministic match of their customers across devices, platforms and even online and offline. The goal of Identity Resolution is to get a holistic view of the customer’s interactions with the brand in an omnichannel environment. Easier said than done, as Christine Frohlich Vice President , Experian Marketing Services, observes in this article on MTA, “The challenge for many brand advertisers is the sheer volume of data. There is a seemingly infinite volume of offline and online attributes, such as name, address, email address, cookies, date of birth, transaction history, mobile device identifiers and the list goes on. Each one of these attributes alone gives you a glimpse into the customer, but the ability to connect these disparate data sets in a privacy compliant manner, can create a 360-degree view of the customer and help marketers make the right decisions. And it all starts with the right resolution and matching process.”
Founder of CDP Institute, and MTA Category Expert David Raab, observes 3 trends related to the growth of identity resolution as a practice:
- “Cookies – the last-gen ‘identifiers’ of digital individuals - are on their last legs. So many Cookies are blocked, that they are no longer common enough to be useful. I’m not entirely clear what will replace them - although device tracking is the most likely winner, considering that people have relatively few devices and they’re often linked.
- The receding availability of passive identifiers (like Cookies), and people’s growing sensitivity to using them is prompting a greater reliance on first party identifiers (account numbers, logins, etc.)
- Blockchain-enabled methods for safe data sharing, which enable consumers or other data owners to tightly control what is shared – are making it easier and safer to comply with privacy regulations while still doing some data sharing.”
Latest from mParticle
Get your flywheel in motion with Data Master
Learn how mParticle's Data Master enables you to increase data quality throughout the customer data pipeline, allowing insights to compound, and making every campaign and product launch better than the last.
GOAT: Lifecycle marketing for scalable growth
Learn how GOAT uses mParticle to streamline their data pipeline and increase Customer Lifetime Value.
mParticle launches new features to help brands create ‘data flywheel’
New features for seamless data quality management, and transformation to serve as a foundation for improved customer experience and better insights.
Better data, better insights, better results: Helping brands create a data flywheel
Introduce total quality management and enforcement into your customer data pipeline with new Data Master features and Calculated Attributes. With Data Master and Calculated Attributes, establishing a source of reliable customer data that will create a customer data flywheel, where the data quality and data’s impact on the product cycle will continuously improve over time.