A CDP’s Savvy 7: Customer Data Integrations

May 18, 2018 | By

Customer Data Integrations

He did it again, that Scott Brinker. The 2018 edition of the Marketing Technology Supergraphic outdid the year prior with a staggering 6,829 companies represented:

In the speech introducing this year’s edition at the MarTech Conference, as well as the accompanying blog post, Brinker observes that we are in:

post-platform era in martech. The dream of one-suite-to-rule-them-all has been superseded by a vision of many open platform-like tools being woven together in a more dynamic fashion. Some will be big, foundational platforms; many will be small, specialized platforms. All of them will give more power to marketers to shape their craft in a digital everything world.

Just recently, I had the opportunity to visit the Vatican, and I (and thousands of other tourists) walked through a gallery of centuries-old tapestries that I think are an apt reference point to what Scott’s talking about when he says “woven together.”

The monumental shift that the explosion of martech solutions represents isn’t just sheer volume of options, but rather the fact that marketers are now able to build essentially martech engines, powered by their own, first-party data. We’re talking about the assembly of what are essentially completely bespoke stacks with incredible diversity, complexity, and scope. If these stacks, like the Vatican’s tapestries, are going to stand up to the rigors of time, the strength of the weaving is critical.

But buyer beware: not all integrations are created equal – in capacity, in speed, or in usability.

Customer data platforms (CDPs) are designed to give the marketer the breadth of integration options she needs to liberate the relevant, first-party data that exists in different parts of her marketing stack and also make it usable in other places. What kind of integration is required, however, depends on a number of factors, most noteworthy, the use case the integration supports. In BlueConic’s case, there are seven different options with out-of-the-box support for connecting data to support the full range of marketer needs.

We put together the table below to explain why there’s no one-size-fits-all model for integration but also to point out that you don’t need to custom-build every integration you’re looking for, at a high cost in dollars and time. The CDP is marketing’s gateway to benefiting from the bounty of martech riches available, while right-sizing the perfect stack for you.

The out-of-the-box integration methods you should expect from CDPs:

CDP Integration Method Benefits Caveats Sample Use Case

Server-to-server integration

  • Examples: Salesforce Marketing Cloud, Google AdWords, Facebook Custom Audiences
  • Creates a deep integration between systems for extensive data transfer, which supports the broadest set of use cases
  • Stable and typically certified by the  other system that the CDP is integrating with
  • Can be unavailable or unsupported (i.e. walled gardens)
  • Scalability depends on the external system*Requires unique identifier
  • Send customer profile attributes and segments to an ESP, as well as remove individuals from lists
  • Customize email campaigns to web visitors with copy/creative that reflects their on-site interests and preferences, and customize their site experience based on which email campaigns they have interacted with

Client-side integration

  • Examples: Universal Analytics, Krux, BlueKai, LiveRamp
  • Lighter weight to develop
  • Close-to-real-time data exchange
  • Doesn’t required a unique identifier
  • High scalability
  • Limited to in-browser activities
  • Exposes profile data that is sent to other system
  • Can be blocked by the visitor using browser extensions
  • Send segments in your CDP based on predictive behavioral data to web analytics solution
CSV and Batch
  • Widely available and highly scalable regardless of the external system providing the data to the CDP
  • Easily managed by the marketer
  • Supports frequent but not necessarily real-time import/export
  • Manual set up of fields
  • Requires unique identifier
  • Import data to your CDP from systems without an API or an initial upload of tens of millions of records that will be updated continuously via an API or server-to-server connection on an ongoing basis
  • Import data from  in-house data warehouses/custom systems that you have built to your CDP
  • Target visitors on-site who were flagged in your database as high value prospects by your sales organization

Webhook

  • Example: Slack
  • Widely available and used by different systems
  • Easy to test & implement
  • Close-to-real-time data exchange
  • Typically highly scalable
  • Limited to in-browser activities
  • Typically requires unique identifier
  • Update/retrieve data based on something that’s happening in the browser
  • Process newsletter subscriptions
  • Enrich profile with firmographic data based on IP address
  • Slack: announcing what someone is doing on the website in Slack

Data layer connection

  • Examples: Adobe Analytics, Google Tag Manager, BlueConic Data Layer
  • Use the data taxonomy and structure you’ve already got
  • Pre-packaged solutions to pick up data from those systems
  • Limited to the data in the data layer, which may not include offline or other relevant sources
  • Read and write data from/to any data layer
  • Surface information about product pages to determine individual customer affinity for brand and category interest level

Embedded connections

  • Examples: iOS, Android
  • Visibility into paid channels
  • Support attribution and customer journey analytics
  • Requires developers to place the script and/or add the SDK to apps
  • URL and pixel have a narrow scope of use cases
  • Place a tracking URL in your emails so that when a user clicks through to your website, their profile is linked
DIY
  • Custom built to be exactly what you need
  • Internal resources that are often hard to come by or creates a project in a long queue
  • Connect the CDP proprietary system with a lot of custom features

 

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