By powering personalized marketing and better customer experiences, among other benefits, Customer Data Platforms (CDPs) have become absolutely essential to consumer businesses seeking a competitive edge. CDPs also play an important part in the AI revolution reshaping how we understand marketing.
AI goes hand in hand with CDPs, powering more effective data unification and surfacing more sophisticated insights, while the unified data of CDPs can in turn power AI initiatives with better data, for better results. When you have AI powering your CDP, and the data quality from your CDP informing generative AI, the result is a solution that can transform business outcomes.
Let’s take a closer look at the benefits of harnessing the collective power of AI and CDPs.
Using AI/Machine Learning to improve data unification and data infrastructure
While CDPs have many benefits, the scale, volume, and complexity of customer data can present challenges. CDPs start by consolidating and managing customer data from multiple sources, including mobile apps, online and in-store transactions, customer service interactions, website engagement, and CRM systems. The data is then organized, cleaned, and prepared for marketing and sales teams to develop more personalized campaigns. All of this requires sophisticated matching methods in order to properly resolve identity and be sure that records are associated with the correct customer.
Many CDPs are bound by a rigid, rules-based schema: matching up related data points is based on a set of “if-then” conditions that are challenged by the complexity and nuances of customer data.
But it doesn’t stop there, since customer data evolves as people go through life: email addresses change, people have multiple email addresses, phone numbers change, physical mailing addresses change. Sometimes people change their names or interact with brands under an alias.
This is why an extensive table of match rules struggle to accommodate frequent customer changes and updates, not to mention typos or errors in data entry, both of which are characteristic of customer data. Along with all of these vicissitudes comes changes in loyalty status, purchase cadences, online engagement, product preferences, and more. Some customers drift away and then come back; others have moved on for good.
Thanks to AI-powered pattern matching, these kinds of challenges can be overcome. Part of the unique power of AI is its ability to make connections with the nuance and understanding of a human data engineer—but at massive scale and astonishing speed, constantly. AI has the capability to:
Allow data engineering teams to do their initial data unification in a matter of days or weeks, rather than months, years—or worse, never
Keep profiles up-to-date without breaking down or losing data fidelity
Build more robust and more complete profiles by accounting for all the data available, not just some of it
How AI powers better customer insights to enhance your marketing strategy
By combining data points into unified customer profiles, AI-matched data creates more precise customer segments and yields sharper and more meaningful insights into customer journeys and more. AI algorithms excel at predictive analytics, calculating customer lifetime value and forecasting future customer behaviors based on the historical data stored in CDPs.
When the underlying data is accurate and reliable, marketing teams feel empowered rather than siloed, able to craft more effective customer-centric marketing strategies. By being able to identify the right target audience and high-value customers, marketing teams can focus budgets and bandwidth on the areas most likely to have the most impact.
Ultimately, this enables them to cultivate greater customer loyalty and retention. Because the customer experience improves significantly—with more personalization and more responsiveness to customer preferences and needs—customers are more satisfied, and thus more likely to keep coming back.
Data properly unified powers generative AI initiatives
We’ve all heard the expression “garbage in, garbage out.” Creating sophisticated generative AI projects, or AI-powered personalization campaigns, relies on an accurate data input. As CDPs consolidate data holistically on customer behaviors, preferences, and interactions, AI initiatives in turn become more effective by processing and analyzing data that’s more accurate and more up to date.
CDPs are built to gather, sort, analyze, and activate first-party data from various channels, providing AI with the necessary inputs for instant decision-making in marketing, sales, and customer service. As CDP data sets ensure that customer profiles are up-to-date and reflect real-time interactions, this allows AI to tailor messages, offers, and content more precisely.
To stand apart from the crowd, businesses must have a solid underlying data asset. Data scientists play an important role in developing and implementing CDPs, applying their expertise in both data analysis and machine learning to help businesses make the most of their customer data. Why is this so crucial? Because as the same generative AI tools for marketing personalization and content customization become available to everyone, the quality of the underlying data will help brands differentiate. Imagine two people owning the same car model, but one uses kerosene, while the other uses high-octane fuel. Suddenly, it’s as if these are two radically different cars, no longer the same model at all.
Make the most of an ideal partnership—and gain a competitive edge
As AI and CDPs become increasingly ubiquitous, shaping the future of customer-centric business strategies, it’s more essential than ever to take full advantage of their symbiotic relationship. Bringing AI and CDPs together only amplifies the collective power of both technologies, allowing businesses to respond rapidly to changing customer behaviors and ever-shifting market dynamics. The combo is a seemingly unstoppable force with endless potential. Be sure to talk with your CDP provider about how AI is part of their platform, and how their platform allows users to take full advantage of generative AI.
Read more about the difference data quality can make in your AI initiatives.