AI Could Transform the Way Businesses Analyze Customer BehaviorAI Could Transform the Way Businesses Analyze Customer Behavior

AI tools can carry out complex analysis to provide accurate customer insights within minutes

Caleb Benningfield, Caleb Benningfield, field CTO, Amperity

January 31, 2025

3 Min Read
A marketing meeting
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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.

Faster Analysis Through Automation

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.

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Simplified Data Access

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:

  1. Writing a formal data request

  2. Waiting for a data engineer to become available

  3. Having the engineer write and test SQL queries

  4. 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.

Personalized Customer Communications

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

Related:A Cure for AI FOMO

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.

About the Author

Caleb Benningfield

Caleb Benningfield, field CTO, Amperity, Amperity

Caleb Benningfield is a field CTO at Amperity.

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