Four Fundamentals of Data-driven Customer Experience
For your customer experience to be relevant, consistent and scalable, you need your data to be ready for four fundamental imperatives:
Omni-channel customer recognition
Activating data in the new data economy
An open approach to the single customer view
This post will take a closer look at the first of these fundamentals; omnichannel customer recognition.
Understanding Omni-Channel Identity Resolution
As we’ve explored in previous posts, the rapid adoption of new platforms and channels has led to some non-trivial customer experience issues. The most salient of these issues is the fact that you can’t recognise the individuals you’re targeting when you’re targeting them.
That’s because different channels and platforms only have their fragments of any given individual’s identity. Consider the common case of someone who saw some products online and then bought them in-store.
Your banner ads will see an anonymous user on a publisher’s site. Your website will see an unregistered user browsing product categories. Your point of sale system will see a person who bought a product. And your retargeting system will see someone who didn’t buy anything at all.
Identity Resolution: Reducing Fragmentation For Seamless Customer View
In the above example, that fragmentation is the reason that one person was treated like four different people – and it’s the reason marketers struggle to make their customer experience relevant and consistent enough to be effective.
What’s needed, therefore, is a way to reconcile all these fragments and create a single, knowledgeable view of that person in a privacy-compliant way.
More specifically, you need the ability to recognise your customers and prospects as the people they are whether they’re online, offline, mobile, buying in-store and wherever else you’re reaching them.
That calls for identity resolution – a way of matching the different IDs stored by both online and offline channels, systems and data sources so you can tie them all back to the same person, meaning you can recognise your customer.
The online and offline worlds throw up specific challenges for identity resolution so let’s look at each one in more detail.
Recognising customers offline
The data stored in your offline or CRM is hugely valuable. For instance, in the example, we just used, your offline point of sale system had arguably the most valuable insight – the fact that someone bought something.
That information will determine what your next offer to that person is and when you should make that offer and is often the richest data you’ll have.
Crucially, even when this information is stored offline, it needs to be connected to the right individual. Traditionally, companies have relied on ‘fuzzy matching’ to do this. Effectively, you’re asking a computer to decide if Mary Smith of 1 The Road, Manchester is the same person as Mary Brown of 2 New Street, Brighton based on how similar their data is. Given the only similarity is the word ‘Mary,’ you can see how difficult it can be to get this right.
On the other hand, knowledge-based or identity graph matching uses a data set of different presentations and representations of an individual over time to recognise the real person behind the data points. It’s why it’s the necessary foundation for effective, people-based marketing.
Recognising customers online
The proliferation of new online channels and marketing platforms makes the challenge of identity resolution in the digital world incredibly complex.
Not only are you dealing with a host of different IDs from different digital channels, you’re also dealing with any number of browser and device touchpoints – each one of which will likely yield another duplicated fragment of someone’s identity.
Here, you need to be able to match all these different cookies and IDs in privacy-compliant ways that protect your audience’s anonymity, while still leveraging a deterministic, identity graph approach to matching.
Crucially, you also need to account for the limitations of cookies. For instance, people clear their cookies from time to time and on mobile, there are hardly any cookies to attribute activity back to.
That calls for persistent links to online identifiers that can ensure your multitude of digital platforms and channels have a consistent version of the same person that’s de-identified to maintain their privacy.
It’s also important to understand the difference between probabilistic and deterministic matching. The former aims for reach over accuracy, relying on ‘fuzzy matching’. The latter is far tighter, relying on more data and more sophisticated techniques to tie several online representations back to the same individual just like in the offline world.
Because the identity graph enables deterministic identity resolution, you can recognise your customers in digital space and tie your data back to real people; people-based marketing.
Online or Offline, Data is Key
It’s common sense that being able to recognise the right individuals at the right times, via the right channels is integral to building positive data-driven customer experiences.
However, identity resolution is just one part of that. Only once your strategies are prepared to incorporate other data fundamentals, such as data onboarding and omnichannel customer recognition can you truly begin to build relevant experiences.