Sponsored by Google Cloud
Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
AI tools can carry out complex analysis to provide accurate customer insights within minutes
Recent advances in machine learning technology are transforming how businesses make use of customer information. New AI-powered tools require end users to have minimal technical expertise, enabling staff across all departments to analyze customer behavior patterns directly.
Previously, to make this data available to business users, technical teams needed to spend countless hours cleaning data – standardizing formats, removing duplicates and consolidating records. For instance, when a customer uses multiple email addresses, various phone numbers or physical addresses to purchase from the same retailer, their spending history often becomes fragmented across separate profiles. These technical teams would then need to manually write code to identify and merge these duplicates.
Without AI, the current process of customer data analysis typically involves several manual steps:
Data engineers write code to standardize varying date formats (01/01/25 vs 2025-01-01)
Teams manually verify and merge duplicate customer profiles
Analysts spend weeks building statistical models to predict purchasing patterns
New AI-driven customer data clouds, however, can complete these tasks in hours rather than weeks. For example, it previously took three data engineers at a leading U.K. retailer a fortnight to identify their most valuable customers who hadn't purchased in six months. This can now be completed in under an hour.
Marketing teams, in particular, have traditionally relied on technical colleagues to extract customer insights. A typical scenario may include a marketing manager needing to identify customers who spent over £1,000 in the past quarter but haven't made a purchase in the last 60 days. Previously, this request would require:
Writing a formal data request
Waiting for a data engineer to become available
Having the engineer write and test SQL queries
Reviewing and refining the results
With new AI tools, the same marketing manager can type: “Show me customers who spent over £1,000 last quarter but haven't purchased since November,” and receive accurate results within minutes.
This improved access to data will enable businesses to be more targeted with their communications. Rather than sending the same promotional email to thousands of customers, companies can now, for example:
Identify customers who browse winter coats online but haven't purchased
Target loyal customers who typically shop seasonal sales
Re-engage customers who have reduced their purchase frequency
The practical impact is significant. Rather than broad marketing campaigns such as “20% off all winter wear,” retailers can send personalized offers. For example: “Ms. Smith, the wool coat you viewed last week is now available in your size and preferred color.”
As these systems become more sophisticated and affordable, their applications will expand. Success, however, will depend on an organization's ability to maintain data quality and train staff to use these tools effectively. The true measure of success will be whether customers receive more relevant, timely communications that actually serve their needs.
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