The promises of artificial intelligence (AI) are extensive, especially for marketers.
AI can be used to automate marketing workflows, optimize operations, and deliver personalized experiences. In the future, AI has even more potential to deliver better predictive analytics, real-time decision making, and personalization at scale.
But, while marketers agree that AI is critical to their future success, only 12 percent of leaders report having a mature AI strategy, and only 9 percent are confident in their AI governance. Concerns for getting AI up and running include a lack of quality data, privacy violations, security risks, and future regulations.
As AI continues to evolve, the big question is: how can you prepare your organization to maximize the potential of new technology?
Creating Centralized Data Foundation
Many companies are deploying customer data platforms (CDPs) to get all their data, people, and processes in place for enterprise-wide AI.
With a CDP, you can establish the data foundation needed to set your AI initiatives up for success. CDPs give you the ability to gather first, second and third-party data from multiple channels, cleanse that data, and integrate it into unified profiles to be sent to other tools, like personalization engines, for activation. They also have data privacy capabilities, so data can be protected, secured, and remain in compliance with regulatory requirements.
The huge advantage here is that the unified profiles that you have in your CDP have attributes that can be enriched as it moves across your MarTech stack.
As your primary data platform, in which all your other data platforms, like your CRM and DMP, feed into to inform and enrich the unified profiles, CDPs become the glue that enables your company to work together more closely with data-driven insights and actions.
This type of customer data infrastructure is just what is needed for companies to deploy AI at scale across the enterprise.
Keeping Data Clean, Safe and Secure
This type of data democratization is a key step when implementing AI across a business. But, data needs to be clean for AI to deliver insights that are useful, valuable, and relevant at scale.
Data cleansing and validation are time-consuming and resource-heavy tasks. A CDP with AI capabilities can help automate the data cleansing process, and provide the security and data governance controls needed for compliance.
To develop the right models, AI needs training – and lots of it – to make its responses accurate and human-like. AI/ML models are really good at detecting patterns, trends, and correlations within a data set – especially ones that deviate from the norm.
A CDP equipped with AI/ML can identify common sources of errors or patterns that may contribute to data inconsistencies across customer data sets. This gives companies the information they need to improve their data collection processes, update data entry guidelines, identify training requirements for employees, and improve the quality of their unified customer profiles.
AI-powered data cleansing isn’t just about saving time and manual effort – it’s also about reducing human error and speeding up the data preparation process. When you start to continuously monitor data quality metrics, you can detect potential issues before they become more severe.
AI and Customer Data Platforms
The type of CDP your company chooses will affect your ability to do data cleansing, so it’s important to look for features that will enable you to automate the process. The higher level of data quality you can achieve before data goes into other platforms will lead to better results, decision-making, and outcomes.
Some enterprise-grade CDPs offer content affinity engines, which allow you to enrich your customer data based on customer web behaviors. Other AI-powered CDPs come with predictive customer scoring, which helps detect high-value and high-potential-value customers.
Some CDPs also allow you to run your own SQL queries to build prediction models, or come equipped with variety of built-in AI/ML models that allow you to build your own predictive models, multi-touch attribution models, and next-best action modeling.
By combining the enterprise-grade data management capabilities a CDP can offer, with the actionable functionality of AI, brands can keep their data clean and accurate, with the ability to personalize the customer experience at scale.
Learn more about how you can use AI with a customer data platform here.