Customer Behavior Analysis: Uncovering Customer Insights Through Behavioral Data

Customer behavior analysis answers two critical questions: What actions do customers take relative to your business, and what motivations drive those actions? Customer behavior analytics provides a window into customer expectations, preferences and pain points, helping companies understand what changes are needed to better meet customer needs, strengthen loyalty and drive revenue growth. In this article, we’ll explore what customer behavior analytics is, why it's important and how to get started.

What Is Customer Behavior Analytics?

Customer behavior analysis is the process of collecting, analyzing and interpreting the behavioral data of customers as they move through various touchpoints in the buyer’s journey. Behavioral data may include interaction-based data such as engagement with a business’ website or mobile app, content consumption, social engagement, searches, purchasing patterns, and responses to marketing campaigns. By tracking these actions, teams can uncover patterns and insights that inform how customer journeys and experiences can be optimized to achieve desired outcomes.

Why Conduct Customer Behavior Analysis?

Understanding what actions customers choose to take, and why, is essential to attracting, engaging and retaining customers. Customer behavior analysis helps businesses make decisions that build customer loyalty and sustain long-term growth.

Developing a deeper understanding of customers

Customer behavior analytics enables businesses to develop a more accurate picture of each customer. The process helps answer critical questions about customers, such as which segments are most profitable and which marketing campaigns show the strongest performance — and why. Behavioral data can also help businesses identify their “super consumers,” those who consistently spend large amounts and advocate for the company on social channels. Using insights gleaned from customer data analysis, businesses can fine-tune their marketing and promotional strategies to maximize consumer spend and deliver experiences that customers truly value.

Improving customer satisfaction and retention

Retaining existing customers is far more cost-effective than obtaining new ones. Analyzing data from customer relationship management (CRM) platforms, social listening campaigns and other sources can help businesses uncover the primary factors driving customer churn, such as product issues, poor customer service and inconsistent outreach. 

Growing revenue through personalization

Consumers expect brands to understand their needs and preferences and respond in ways that meet or exceed them. With detailed data about demographics, purchase history, personal and professional backgrounds, customer support histories, and patterns of engagement on the business’s social channels, mobile apps and website, businesses can develop a more accurate and nuanced understanding of each customer, and individualize promotional outreach, rewards program incentives, product recommendations and customer experiences.

More accurately predicting future demand

Businesses use predictive insights to adjust their inventory, promotions, staffing and other resources to satisfy future demand. Purchasing trends and patterns, browsing behaviors, and customer responses to marketing campaigns and product pricing can identify likely customer needs, providing opportunities to increase sales and reduce costs.

Developing and improving products 

Unmet needs and pain points are valuable signals for businesses committed to ongoing product improvement and innovation. Companies can mine behavioral data such as search queries on ecommerce websites and mobile apps, product reviews, and customer support interactions. This information can reveal how customers use specific products and services, which features they value most, and what are the most common sources of frustration. These insights can then be used to prioritize existing product improvements and identify new offerings that could satisfy unmet customer needs.

How to Conduct a Customer Behavior Analysis

Customer behavior analysis provides a continuous, repeatable cycle of analysis and action. This process serves as a blueprint for converting behavioral data into insights that drive improvements in how brands serve and interact with their customers.

Segment your audience 

Customer behavior analysis starts with audience segmentation. Although customers can be categorized in various ways, it’s important to prioritize the identification of customer segments that consistently deliver the highest value. Applying metrics such as customer lifetime value (CLV), average order value (AOV) and purchase frequency can help identify these groups. At the same time, lower-performing customer segments also deserve attention. Behavioral data can explain why potentially promising customer segments fail to perform. A poor user experience or failure to effectively communicate the unique value proposition of a product or service may be preventing these groups from converting. 

Compile your behavioral data

The most successful customer data analytics programs incorporate data from both qualitative and quantitative sources. Combining data from qualitative sources — such as online reviews, customer service feedback, responses from open-ended survey questions and sentiment analysis with quantitative sources, including purchase histories and engagement patterns across touchpoints — provides a more complete picture of the customer journey. 

Analyze your data for insights 

Analyzing this behavioral data can help businesses answer essential questions about their customers. Examples include what factors motivate customers to make their first purchase and what motivates them to buy again, when and how customers choose to interact with the brand, which marketing and sales campaigns were most effective for each audience segment, and what causes some customers to leave the brand for a competitor. 

Optimize the customer journey and experience

The answers to these questions help businesses tailor their products and services to meet customer expectations. By understanding why some customers choose certain behaviors, such as abandoning a shopping cart or not responding to a promotional offer, customer behavioral analysis can inform which changes to the customer journey or experience are required to boost conversion, improve retention and drive revenue growth. 

Continuously analyze and improve

Customer behavior analysis is an iterative process that keeps businesses in step with the dynamic nature of customer behaviors and purchasing decisions. New sources of behavioral data and advances in technologies such as AI and machine learning (ML) are unlocking new opportunities for businesses to better understand and respond to customers' needs and preferences.

Maximize the Value of Your Behavioral Data with Snowflake 

Customer behavior analysis enables businesses to adapt to a shifting consumer landscape. With the Snowflake Data Cloud, businesses can streamline their data architectures and apply the power of data and AI/ML to discover insights for personalizing experiences, optimizing customer journeys and driving revenue growth across channels. Snowflake enables companies to break down data silos, ensure governance and security, and enable seamless data collaboration across teams and partners.