Strategies to Choosing the Right Customer Data Platform

Businesses all over the world are looking for ways to boost operating performance, improve customer service, and maximize marketing spend by using data and analytics. As a result, CDPs have become increasingly important components of the martech stack in enterprise organizations.

Nothing seems to be slowing down the growth of Customer Data Platforms (CDPs). Not even the year 2020. According to the CDP Institute’s Customer Data Platform Industry Update, the industry added 13 vendors, 800 staff, and US$ 250 million in funding in the last six months of 2020. According to the CDP Institute, industry sales will hit US $1.55 billion in 2021, up 20% from 2020.

So, if an organization wants to monitor and nurture their consumer and prospect data, the bigger question is what kind of CDP is right for them. Since not all CDPs are created equal, it’s worth looking into how they’re classified and the market segments CDP vendors cater to.

Can the CDP make better data-driven activation?

Knowing how — and whether — a CDP would work into the marketing technology equation is the first step toward finding the right CDP match.

Selecting the right CDP is a long-term commitment, and it can be difficult since all of them provide excellent services for brands looking to invest or maximize their data strategy. One factor to look at is the CDP’s ability to collaborate with a number of tech vendors that could help businesses create a better data-driven activation, quicker and more precise audience segmentation, and a more comprehensive understanding of the customer now and in the future. The right CDP will be able to assist in evaluating and identifying places where businesses might be missing out on useful insights and opportunities.

Capabilities in-house and with service partners

Understanding the organization’s existing and future strengths, both in-house and by service providers, is essential to finding the best-fit CDP. Any CDP’s functionality, whether bare-bones or fully-featured, should be evaluated in light of the involvement or absence of human or technical resources that provide some or all of this functionality for the company today.

It all comes down to the added value that any form of Customer Data Platform scan brings to an organization’s current capabilities. A CDP that focuses on the core task of building cohesive customer profiles could fill a void in data engineering, DevOps, and other capabilities. This form of CDP may be a great solution for enterprises that have the necessary business intelligence, data science, and campaign activation capital to deliver high-value use cases using a centralized customer data asset, but lack the technical capabilities to build this asset in the first place.

Meanwhile, fully functional CDPs will complement or improve the work of teams working downstream of the integrated customer data asset. Turnkey modelling and ML features provided by fully-featured CDPs can increase the impact of an organization’s consumer segmentation beyond a rules-based approach and can enhance the campaign success of various end users through marketing, distribution, supply chain, and other areas for companies where data science expertise is already over-allocated.

Detecting dynamic data signals

The Customer Data Platforms a company picks needs to act as a source of truth for interpreting historical patterns as well as reading complex data signals and generating instantaneous consumer profiles. These profiles continuously adjust depending on what’s happening online and offline.

It’s important that all activity is tracked in real time; otherwise, one might be looking at data from a month ago and lose track of where consumers are, in the buying cycle.

Since marketers are focused on delivering excellent consumer interactions, choosing a CDP that uses data to orchestrate experiences based on the behaviors of audience segments and profiles is critical.

Creates and manages machine learning models

As the CDP empowers marketers to develop and operate machine learning models, they gain the opportunity to democratize AI and build analytics on the fly rather than waiting for IT assistance. They won’t have to worry about responding too late to consumer needs if they act quickly on these models.

The best CDPs also have user interfaces that are simple to use even for non-technical people. This method makes creating a machine learning model a much more streamlined and seamless operation. After a model is in place, it will automatically launch experiences based on customer behavior and data, similar to having a marketing manager on staff 24/7.