How To Conduct a Customer Behavior Analysis

Chances are, you don’t have a crystal ball that lets you peek into the secret desires of your customers. Customer behavior analysis may just be the next best thing. Swapping mysticism for data, this strategy can help you understand what drives your customers to make purchases, so you can better meet their needs and preferences.

In this article, we’ll define consumer behavior, explain why it’s valuable to study, and guide you through performing your own customer behavior analysis.

What is customer behavior?

Customer behavior is the set of actions consumers take as they shop for, purchase, and use products or services. It is shaped by the individual’s personal characteristics, social influences, and environment.

Here are some various internal and external factors influencing consumer behavior:

• Psychological traits. Risk tolerance and decision-making style shape how customers evaluate and select products.

• Personal values. Core beliefs, ethical standards, and individual priorities can guide purchasing decisions and brand loyalty.

• Social and cultural context. Social trends, peer influence, and cultural norms impact shopping choices.

• Economic circumstances. Income level, financial stability, and perceived value affect spending patterns and price sensitivity.

• Lifestyle factors. Daily routines, hobbies, and life stages influence product preferences and shopping habits.

• Technology adoption. Digital literacy can affect how customers interact with online platforms and digital products.

What is customer behavior analysis?

Customer behavior analysis, or consumer behavior analysis, is the examination of how customers interact with your brand throughout the entire customer journey, from initial discovery to post-purchase.

Analyzing customer behavior involves studying both quantitative and qualitative data. The former includes customer data such as purchase history and average order value. The latter focuses on the motivations behind the behavior using customer feedback, reviews, and social media sentiment.

This behavioral data enables you to identify customer behavior patterns, predict future behavior, and adjust your offerings and marketing strategies accordingly. Understanding customer behavior can help you enhance customer experiences and improve customer retention, which will ultimately increase customer lifetime value (CLV) and provide sustainable growth.

How to conduct a customer behavior analysis

1. Gather data
2. Segment your audience
3. Identify selling points for each segment
4. Find and implement actionable insights

To better understand your customers’ behavior and use that knowledge to improve your marketing strategies, use this guide to customer behavior analysis as a starting point:

1. Gather data

The foundation of customer behavior analytics is collecting data about how customers interact with your brand. This should involve tracking quantitative metrics (such as purchase history, cart abandonment rates, average order value, and click-through rates on emails and ads) and qualitative insights (such as customer feedback, support ticket themes, and social media comments).

To conduct a customer behavior analysis effectively, draw from multiple data sources, such as:

• Website analytics. Website analytics tools like Google Analytics can track browsing patterns and help you map the customer journey toward a purchase.

• Customer relationship management (CRM). CRM systems document customer interactions and purchase and support history so you can build customer profiles.

• Post-purchase surveys. With a simple link to a survey in a post-purchase email, you can start gathering direct feedback from customers when your brand is top of mind.

• Ads. Digital and social media advertising metrics can offer valuable insights into what types of messaging are most effective.

• Social media. Social listening tools can give you a better idea of customer sentiment and how your brand is coming across on social media platforms.

• Customer service. Customer service data can help you identify pain points with your products, accepted payment methods, shipping options, and more.

By analyzing customer interactions across these touchpoints, you’ll be able to gain a holistic view of your audience’s behavior and preferences.

2. Segment your audience

Next, break your customer base into distinct, manageable customer segments based on shared characteristics. Customer segmentation can be demographic (age, location, income) or behavioral (purchase frequency, customer loyalty, average order value).

Demographic segmentation can give you a better understanding of your target audience. For instance, you may find that younger shoppers respond better to social media campaigns or customers in different regions prefer different products.

Behavioral segmentation helps you identify high-value customers. It can also offer customer lifetime value predictions and customer churn risk assessments. You can even create buyer personas to represent each segment’s key traits and behaviors.

Here’s an example of how a business might segment its customers based on behavioral data:

• Loyalists. They’re your customer base and more engaged than any other group. Maybe they’ve purchased within the past 30 days, placed six or more orders per year, or spent more than $500 this year alone. This customer is likely to try new products and is responsive to early access promotions.

• Active enthusiasts. Slightly less engaged than your loyalists, perhaps they’ve purchased within the past 90 days, tend to order three to five times per year, and spend $200 to $499 annually. This customer is a consistent buyer who is price-conscious and quality-focused.

• Occasional buyer. A seasonal or sale shopper, this type of customer has purchased within the past 180 days, orders once or twice a year, and spends $50 to $199 annually.

• At-risk customer. This customer is not particularly engaged and at risk of churning. Their last purchase was more than 180 days ago, they order infrequently, and their annual spending is highly variable.

Shopify’s segmentation tools can help you collect this data from your online store and provide default customer segments.

3. Identify selling points for each segment

Once segments have been established, identify unique selling points for each customer segment so you can address varying customer preferences, needs, and concerns. Look at purchase patterns and advertisement engagement for each group. Customer feedback and focus groups can also give you a clearer picture of what drives buying decisions for each segment. This can help you tailor your messaging and offering to meet each group’s desires.

Here’s an example of factors that may resonate with the previously identified segments:

• Loyalists. These customers value exclusivity and early access.

• Active enthusiasts. These customers appreciate value and product quality.

• Occasional buyer. Price and timing drive these customers’ purchases.

• At-risk customers. These customers need to be convinced of your brand’s value.

4. Find and implement actionable insights

The final step is to use your analysis for action. Now that you have an idea of what motivates each of your customer segments to make a purchase, develop strategies to prove those hypotheses. The goal is to refine messaging and tactics that will resonate with each group, encouraging them to engage with your brand and make more frequent purchases.

Segment-specific action items could look like this:

• Loyalists. Focus on premium features, VIP experiences, and first looks at new products. For example, you could offer these customers early access to limited-edition product launches to make them feel special and offer a sense of exclusivity.

• Active enthusiasts. When targeting these customers, emphasize quality, product specifications, and competitive pricing. Share detailed product comparisons and positive reviews to support their research-driven buying habits.

• Occasional buyer. Focus on seasonal promotions and limited-time offers to create a sense of urgency and make them feel like they’re getting a good deal.

• At-risk customers. To prevent these customers from churning, address pain points from past experiences and showcase product improvements or new features.

Remember that customer behavior analysis is an ongoing process. Pay attention to how your customer segments respond to marketing campaigns and adjust and optimize your strategies accordingly.

An example of customer behavior analysis

Island Creek Oysters sells fresh oysters online and ships them overnight to just about every state in the US. Early on in the brand’s ecommerce journey, CEO Chris Sherman noticed some attention-worthy customer behavior.

“When we first switched to Shopify, we all of a sudden had access to some analytics which we hadn’t had previously,” he says on an episode of the Shopify Masters podcast. “One of the things we saw right out of the gate is that we had a high cart abandonment rate.”

Chris noticed that, although customers didn’t have a problem with a relatively high sticker price for fresh oysters, they balked at the cost of shipping. Based on that analysis of customer behavior, Island Creek adjusted its pricing model.

“We made the simple decision to embed shipping into the cost. And that overnight drove sales,” Chris says. “We eliminated all but a small percentage of those cart abandonments, and our revenue that year, like, tripled.”

Offering free shipping reduced cart abandonment, and it improved another customer behavior metric: loyalty.

“We have an astoundingly high return customer rate, and that obviously blends into looking at the lifetime value of those customers.”

A high customer lifetime value (CLV) then justifies higher marketing costs to acquire a customer. The cycle repeats itself: customer behavioral analysis, adjustments to sales and marketing strategy, and so on.