Why not all customer data integration methods are created equal

Today we're talking about customer data integration methods. If marketers can agree on anything it's that there's no such thing as a one size fits all tech vendor. The average marketer has dozens of vendors, including multiple analytics tools and multiple diversified marketing clouds in their stack, and this number is only growing annually. The growth rates have accelerated, in recent years as new platforms like mobile, OTT, and voice have reached maturity, and it doesn't show any signs of abating. If no monolithic single vendor solution will do. That really means that customer data 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. Sure, we all know marketing is moving towards this world of vendor pluralism and we can all agree 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.

Indeed, not every integration approach supports the plurality in the same way when it comes to martech customer data integration specifically, here are three warning signs to look out for.

The first is vendor centricity. While every vendor says they have an open architecture and prebuilt integrations, you'll often find a bias toward lackluster proprietary integration methods. These conserve an ulterior business motive of keeping customers locked in or charging extra fees or just be an innocent byproduct of a lack of focus on integration integrity.

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, the bit of the business or the majority of the customer journey.

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

CDPs, like mParticle, address these gaps and more more than just integration it creates in interoperability layer that provides translation between different services and platforms without resorting to a lowest common denominator approach. At the center, the data schema is flexible and can change as needs evolve. This is particularly important for marketing data, which has idiosyncratic contexts such as privacy, identity, devices, and govern events, which are changing all the time. It also provides a fully open and real-time architecture, so all data that comes in, comes out as quickly as your business needs it. Second, it provides support for ever-changing data end points, not just insight, but also engagement applications. As new engagement models emerge, new tools are needed by marketers in a CDP can support tools we have not even imagined yet. And third, as you see at the top native platform SDKs to support data collection from every point in the customer journey. That's all for today. Thanks for joining us. To learn more, visit mparticle.com.