Many organizations previously viewed customer analytics as a nice-to-have or luxury. While it offered decision-making benefits, it wasn't considered essential.
That view has shifted. Businesses today increasingly understand data's role in conceptualizing products, establishing pricing, reducing costs, and attracting and retaining customers. This change has inspired companies to collect and analyze more customer data to extract value.
Yet, collecting reams of data isn’t enough. To put it to use, you need tools to analyze it—and artificial intelligence (AI) has proven powerful for exactly that task.
“In the early stages of building or leading a business, intuition plays a big role,” says Harvard Business School Professor Karim Lakhani in the online course Data Science and AI for Decision Making. “But intuition alone doesn’t scale. As data becomes available, organizations need ways to make decisions that are more precise, systematic, and scalable. Machine learning is one way to bring that discipline to decision-making.”
The guide below explores what customer analytics is, the types of analysis organizations use, and the role AI and machine learning can play in modernizing your customer data strategy.
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What Is Customer Analytics?
Customer analytics is the process of collecting and analyzing user data to inform business strategy and key decisions. This data can originate from several sources, including:
Customer purchase history
Website and search activity
Social media interactions
Customer service inquiries
Customer relationship management (CRM) systems
Feedback surveys
Third-party data aggregators
Industry data
When an organization understands its user data, it can support critical functions like customer segmentation, forecasting, predictive modeling, and customer journey mapping.
Types of Customer Analytics
There are different types of customer analytics: descriptive, diagnostic, predictive, and prescriptive.
Descriptive Analytics
Descriptive analytics leverage historical data to describe prior actions. In the context of customer data, companies use descriptive analytics to evaluate past purchases, website traffic, or other interactions to identify patterns or understand customer behavior.
Diagnostic Analytics
Diagnostic analytics move beyond the “what” in pursuit of the “why.” While descriptive analytics describes what happened, diagnostic analytics seek to explain why something happened. For example, it might uncover:
Why a customer made their initial purchase
Why that customer did or didn’t make a repeat purchase
Why that customer chose one solution over another
At the organizational level, diagnostic analytics can help determine why customer satisfaction scores have dropped or churn patterns have changed over time.
Predictive Analytics
Predictive analytics use historical data to determine what might happen next. For example, if a customer purchases a product, the business might use predictive analytics to determine the next item they’re most likely to buy.
Similarly, if a customer visits a particular website page, predictive analytics can help anticipate their next action. Consider a customer who repeatedly visits a company's pages about subscription cancellations; predictive analytics might flag them as a renewal risk.
Prescriptive Analytics
Prescriptive analytics combines historical data and predictive capabilities to recommend actions an organization should take. Continuing the example above, prescriptive analytics might recommend offering a discount to prevent the customer from canceling their subscription.
Related: Listen to HBS Professor Iav Bojinov, who co-teaches Data Science and AI for Decision Making alongside Lakhani, discuss AI adoption, trust, and decision-making on The Parlor Room podcast. You can also watch the episode on YouTube.
How AI Can Facilitate Your Customer Analytics Strategy
Identifying Patterns at Scale
One of AI's core strengths is its ability to process and analyze large amounts of data at scale—far more than a human analyst could do manually.
“During training, the model absorbs patterns across vast amounts of text, code, or data and compresses it into a compact internal representation,” Bojinov says in Data Science and AI for Decision Making. “Because the model has learned patterns across domains, it can be used to model complex systems.”
This capability provides a deeper understanding of who your customers are and their segments within your database. Ultimately, it allows you to identify patterns that can inform everything from your customers’ purchase triggers and roadblocks to their willingness to pay and marketing and communication preferences.
Powering “Next Best Experiences”
As organizations seek to maximize customer lifetime value—typically through repeat purchases, cross-sales, and recurring subscriptions—it's vital to understand which interactions best serve individuals at each customer journey stage. These interactions are often called the “next best experience,” and well-trained models can help predict them for customer segments, cohorts, and even individuals.
“Machine learning can cluster data into meaningful groups to build predictive models that classify or forecast outcomes,” Lakhani says in Data Science and AI for Decision Making. “It's based on a common structure: you give the model labeled or unlabeled data, and it learns patterns that help it sort, group, or predict something specific.”
A finely tuned “next best experience” engine can inform your email strategy and touchpoint sequence so that your outreach has the best chance of resonating with customers. It can also support product and content recommendations and identify customers most likely to churn—and who might benefit from a retention incentive.
Creating Usable Assets
AI isn’t limited to identifying patterns and surfacing insights from customer data. It can help you execute on those insights by generating assets that support sales, marketing, and customer service efforts.
“What if, instead of predicting the probability that a customer will churn, a model could predict the most likely sequence of words to form an effective retention email?” Lakhani asks in Data Science and AI for Decision Making. “That’s the promise of generative AI. These models don't just analyze existing data—they create new data. By repeatedly predicting the next word, sound, or pixel, they can write, summarize, translate, code, and even simulate reasoning.”
In practice, this might include using AI to generate:
Sales transcripts
Customer-facing FAQ documents
Content such as blog posts, images, and videos
Marketing and lead nurturing emails
Social media messaging and assets
AI chatbots for your website
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AI Still Requires a Human at the Wheel
AI is now capable of automating many of the complex and burdensome aspects of customer analytics, including data processing, correlation mapping, customer scoring, and asset generation. Yet, that doesn’t mean human expertise and intuition are no longer essential. To use these models effectively and extract value, humans must still guide and manage them along the way.
With this in mind, business professionals across industries can benefit from learning how AI and data science contribute to stronger decision-making. Experimenting with AI in everyday workflows is one way to begin developing these skills. Completing a focused online course is another option for quickly building expertise.
Are you ready to improve your ability to leverage data and artificial intelligence in your business? Explore Data Science and AI for Decision Making—one of our online digital transformation and AI courses—or download our free flowchart to choose the right course for your goals and needs.