blog | 6 min read

What is the Ultimate Customer 360 View?

July 22, 2021

Graphic of digital image.

There are many names for customer data that have been aggregated at the individual level: unified customer record, single customer view, customer 360-degree profile. Consumer brands urgently need this view for creating personalized marketing and customer experiences. But are all customer profiles created equal? No.

In this blog post we explore the unique characteristics of the ultimate Customer 360 view.

Characteristics of the ultimate Customer 360 view

Customer views are not as simple as they sound, and there are a number of factors brands should consider. The ideal Customer 360 view: 

  • is built using all the data you need that is compliant with regulations, customer permissions and your brand’s governance policies

  • is built on reliable customer identities with full transparency so that users can trust the data

  • includes and maintains context from each data source

  • is easy to access and use for different teams depending on their needs

  • supports real-time use cases so data is available when and where it’s needed

  • has the flexibility to connect with any tool or system to import and export data seamlessly

To get a view like this, brands must focus on three areas: completeness of the data, precision of identity resolution, and timeliness of the ability to use the data.

Completeness of Data

You need all the data you need.

It may not be necessary to incorporate every last scrap of data you have, depending on the use cases you want to bring to life. The key is to have a Customer 360 that powers the use cases you need now and is flexible enough to incorporate new data sources as you grow.

Here is a check-list of all the types of data that can be included in a comprehensive Customer 360 View:

  • Personally Identifiable Information (PII): email addresses, current home address, first name, last name, former last names, phone number, birthdate

  • Transactions: all online and offline purchases ‒ historical and recent

  • Preferences: opt settings, preferred channels, interaction frequency

  • Clickstream: where, how, and when a customer interacts with your site and mobile apps

  • Geographic: where customers live, shop, and travel

  • Social: handles, interactions, likes, shares

  • Privacy compliant second- and third-party data: demographic, occupational, lifestyle, and buying intent

  • Custom attributes: attributes derived through data modeling techniques, including propensity to buy, predicted LTV, customer and engagement scoring, etc.

As you work toward incorporating all your data, it’s important to think about what you’re trying to accomplish and start from there. For example, to personalize marketing campaigns you’ll need to incorporate categories like digital and campaign interaction data, in-store purchases, and loyalty status. Without these your personalized marketing initiatives will likely be off-target — you could end up marketing products or services to people after they’ve already bought them, or recommending things that have nothing to do with an individual’s current interests. Once you’ve launched initiatives making use of these data sources and have that routine down, you can add additional data sources for more nuanced insights and sophisticated use cases. 

Precision of Connections

Customer data doesn’t neatly fit together.

It’s clear that including all the data you need to form a complete Customer 360 view is important, but how should you bring it all together? Simply getting it all in the same place isn’t enough to make it into a usable profile. Data must be organized around individuals, even when the sources have diverse formats and lack unifying keys — this process is called identity resolution.

There are more and less sophisticated approaches to identity resolution. When the process is primarily rules-based or deterministic (i.e. based on exact matches), it doesn’t account for changes in customer data over time or for any data that doesn’t fit neatly into the ruleset. When identity resolution is powered by machine learning algorithms it can make judgments that two slightly different records belong to the same individual, just like a human being would be able to, but at a massive scale. This results in more reliable customer identities to form the foundation of the Customer 360.

When identities are accurate and reliable, it improves the efficacy of everything else that happens downstream: the forming of the Customer 360, the derivation of insights and intelligence, and launching campaigns targeted to the right people with relevant messaging. 

As we approach a time where brands can no longer expect to rely on third-party data to supplement identity, it will become even more important to have state-of-the art identity resolution so that they can accurately connect their first-party data as the basis for an effective Customer 360 and take advantage of the potential that opens up. 

Timely Use of Data

Timing is everything.

Customers are constantly changing, whether it be the purchases they make, their preferences, or their buying intent. To create relevant offers, marketers need to use their customer data while it's still fresh. But at the volume, variety, and velocity of today’s customer data production, making data usable in a timely manner is not as easy as it may sound. 

To quickly stand up a Customer 360 using all the necessary data, brands need an advanced system with a distributed infrastructure. A complete, unified view should be created in minutes or hours, not days or weeks, in order to fuel effective personalization. It must also be refreshed continuously as new data enters the system, so the view is constantly as accurate and complete as possible.

For some use cases, customer data must be seconds or milliseconds fresh. This means your ultimate Customer 360 view must also offer you the option of streaming customer data directly from a source to a destination. This unlocks valuable use cases such as real-time site and app personalization, and push notifications. These two methods of connecting customer data must both be available for you to have a truly complete Customer 360 view.

A Note About Scale

If your Customer 360 View can’t be built at scale, then it isn’t complete. When you have trillions of records spanning terabytes of data, a 360 view must be able to handle them all. This requires intelligent data ingestion that can rapidly bring in, unify, and store huge amounts of data.

Roll Up

Building the zenith of Customer 360 Views is not as simple as bringing customer data together into one system. It involves scalable, probabilistic identity resolution using a distributed data infrastructure, performed rapidly across all your data. And it results in a complete, nuanced customer view that can be pivoted, depending on the use case, for optimal timeliness and precision. 

For this, brands need an AI-powered Customer Data Platform that will work progressively with what you have to bring together an increasingly complete set of data while lighting up key use cases along the way. Built in the post-big data era, they came about for the express purpose of giving brands the ultimate Customer 360 View. To learn more about what a Customer 360 can do for your business, check out our use case overview.