Why not all martech integration methods are created equal — and how CDPs differ

If marketers can agree on anything it’s that there’s no such thing as a one-size-fits-all tech vendor. The average large enterprise marketing department uses dozens of technology tools, including multiple analytics solutions and multiple diversified marketing clouds, and this number is only growing annually. In fact, the growth rates have only accelerated in recent years as new platforms like mobile, OTT, and voice have reached maturity, and it doesn’t show any signs of abating.

Given that no monolithic, single vendor solution will do, cross-vendor integration is vital to maintaining a seamless customer experience and maximizing overall investment returns. But what most people don’t realize is that there’s many different flavors of integration.

…what most people don’t realize is that there’s many different flavors of integration.

Sure, we all know marketing is moving toward a state of martech vendor pluralism, and we can all agree that it’s (for the most part) a good thing, but sometimes it feels like that George Orwell quote, “All animals are created equal, but some animals are more equal than others”. Because not every martech integration approach supports the trend toward martech stack plurality to the same degree.

Here are three warning signs to look out for:

1. Vendor centricity

The first warning sign is vendor-centricity. While every vendor will tout their open architecture and prebuilt connectors to other vendors, examine these more closely and you sometimes find a bias toward lackluster, proprietary integration methods. These can serve an ulterior business motive of keeping customers locked in, or charging them extra fees, or just be an innocent byproduct of a lack of real focus on integration engineering and product management compared to other priorities.

2. Legacy data-centricity

The second warning sign is legacy data-centricity. Whether knowingly or not, many integration methods still don’t support mobile or connected device data. In other words, they don’t support the forward-looking demands of the business, or the majority of the customer journey.

3. Use-case centricity

The third warning sign is use-case centricity. Here, the problem is that many integrations, while they might work for insight-oriented use cases, fall apart when it comes to identifying customers across channels, devices and locations, and engaging them in the moment. This results in what Forrester as described as a sort of “lowest common denominator” targeting, which may have worked in the past but no longer meets customer expectations today.

How do CDP integrations differ?

More than just passing data from point A to point B, a customer data platform establish an independent, interoperability layer that provides translations between different services and platforms, without resorting to the aforementioned partial and lowest common denominator approaches.

Courtesy of mParticle

It does so in a few different ways:

1. By providing a flexible data schema that can change as needs evolve. This is particularly important for marketing data, which has idiosyncratic contexts such as privacy, identity, devices, and governance, which are changing all the time.
2. By offering a fully open and real-time architecture, so that all data that comes in, comes out as quickly as the business needs it.
3. By supporting ever-changing data endpoints, including not just insight, but also engagement applications. As new engagement models emerge, new tools are needed by marketing. Any CDP vendor worth its salt should be capable of supporting tools have not even been imagined yet.
4. And finally, CDPs like mParticle offer native platform SDKs to enable data collection from every point in the customer journey, including mobile, web, and all connected devices.

To learn more about CDPs, check out the CDP learning center on mParticle.com.

This post originally appeared as a vlog on mParticle.com.