A Complete Guide of Customer Behavior Analysis for the Retail Industry

Even predicting our own buying habits is not always an easy task: one day, you might make a perfectly rational choice, and another you might make an impulsive, seemingly needless purchase.

No wonder, getting into your customers’ minds and understanding the nature of their decisions is even harder. We’d like to believe that our clients base their decisions on rational criteria, but as behavioristic economics shows, this is not often the case.

To have a firmer grasp on customers’ buying preferences, businesses shouldn’t solely rely on their experience and intuition — purchase choices are highly personal and depend on many subjective characteristics. Instead, let the tangible data and customer behavior analytics guide you to your customers.

What Is Customer Behavior Analysis?

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The customer behavior analysis definition is as follows: a study of qualitative and quantitative characteristics of how clients interact with your brand and website and analysis of what makes them make their buying choices.

The benefit of the consumer behavior analysis method is that it can be used to solve many business needs, from helping you to distinguish your brand from competitors to analyzing what customers think about the quality of your services. 

What Are the Advantages of Customer Behavior Analytics?

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Customer analysis is a tried-and-proven practice for understanding clients and connecting them to the brand. Even before e-commerce ever existed, businesses wanted to understand their customers’ interactions by observing focus groups, putting respondents on their sales points, and tracking their actions.

Now, with online marketing and versatile monitoring tools, predictive analytics of customer behavior became easier than ever — and these innovations offer many additional benefits.

Precise segmentation based on customer analysis

Today, customers aren’t just thankful to brands who recognize them, they assume this is common sense for all retailers. According to Accenture’s research, about 65% of buyers want to interact with brands that know their purchase history.

They also prefer brands that address them by name and offer relevant product suggestions. Customer behavior analysis pinpoints users’ specific needs and allows businesses to incorporate these insights into their offers, website interfaces, and features, as well as marketing campaigns. 

Understand customer habits and motivation

Analyzing customer behavior provides businesses with a unique out-of-the-box perspective on their online stores. Too often, business owners and developers have a vague idea of how clients actually interact with their websites. They see a logical path, which seems to be the shortest and the most accessible, but customers may find alternative ways to navigate. Customer behavior analysis provides a real picture of how shoppers see your website and highlight unique or hidden patterns. 

To offer a better shopping experience by figuring out where customers face problems

Understanding that customers’ thoughts and behavior patterns differ from what you’ve envisioned can make-or-break an organization’s approach to website development and optimization. But there is more to it. Business owners may notice regularly emergent technical issues, design failures, and functionality problems that were never tested or can’t be detected otherwise. 

An uncomfortable check-out form may lead customers to abandon their carts without completing the purchase, while a clumsy search function may prevent a user from finding a needed product. These small issues make noticeable differences in conversions and lead to revenue losses. 

To adjust existing marketing campaigns and start new ones according to better data analysis

Customer behavior analysis helps with retaining existing customers, but it also assists in generating new ones. By analyzing what current clients like, brands can model their potential customers, and target them in advertising. 

The analysis will show recurring patterns on the timing of shopping transactions, locations, devices, and favorite products: marketing managers can use this information for precise audience targeting. This way, advertising will appeal to users who match existing customers, and because of these shared similarities, it’s more likely that they would be interested in the purchase, too. 

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6 Steps to Analyze Customer Behavior

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Customer behavior analysis consists of six easy steps that can be easily automated with corresponding tools (Google Analytics, Quest Back, etc), big data algorithms and even Artificial Intelligence. Let’s take a look at these essential process components and how they are used in practice. 

Divide customers by segments

It’s easier to get an exact evaluation if you segment your target audience into groups by the characteristics that are likely to have the biggest impact on their shopping patterns. These criteria include location, gender, age, status, education, and occupation, to name a few. 

Among all the groups, we choose 2-3 main ones — the customers that buy the most in dollars and those who purchase the most frequently. This can be done by analyzing their purchase value, frequency, and lifespan. Here’s a simple customer behavior analysis example: brands use a feedback form on their websites where clients are asked to provide basic personal information and give answers on their relationships with the brand. 

Understanding main motivations

Each of your target audience segments is guided by different motivations and values. Some customers put quality above price, whereas the price will be the main criteria for others. You need to use feedback forms and big data analytics tools to determine average search history, purchase frequency, value, urgency, and shoppers’ online shopping experiences.

Collect quantitative data on predictive analytics customer behavior

With the two previous stages, businesses can get qualitative data on their customers — what they think and how they base their choices; however, for objective analysis, you need tangible numbers to back up your claims. 

The easiest and most transparent way to get numeric data is by analyzing your website visits and views, social media activity, conversion reports, and updates on favorite products. You can use big data and machine learning tools to allocate this information and convert it to visual reports. 

Unite qualitative and quantitative data 

Often, you’ll notice that numeric and qualitative insights won’t match on all points. For instance, customers will indicate that their favorite way to get updates about the company is Instagram, but your statistics show a higher engagement through email marketing campaigns.

Similarly, some people will say that delivery costs or prices are their main decision factors, but statistics will show that customers prefer certain product characteristics, even if they increase the product price. 

You need to align these differences by contacting your customers directly. Send emails and make phone calls to interview them about their shopping experience to get more context. The respondent group can be a small one (5-10 people) for tasks like this.

Analyze the results of retail customer behavior analysis

Equipped with this data, you need to establish buying behavior patterns that are typical to your target audience. Generalize the data by making unified reports that answer the following questions:

  • How does a customer access the store?
  • What is the most common shopping time?
  • What are the main obstacles when completing an order?
  • What aspects of functionality and design were misunderstood by the user?
  • What areas should you target to bring tangible improvement to your customers?

The answers to these questions will help you get crucial insights that can then be used on your website when tweaking the interface and in your communication strategy or marketing efforts.

Our Experience

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Customer behavior analysis is a lot easier if you use automation tools to collect, process, and structure data. At bvblogic, we build custom solutions for customer research that are based on big data, machine learning, and Artificial Intelligence. Big data technology assists brands with collecting information on their customers’ interactions, and machine learning categorizes and organizes this data while AI processes the raw data into actionable insights.

Conclusion 

Performing customer behavior analysis allows business managers to get a long-term perspective on average purchase value, customer lifetime, main audience segments, and their needs.

Having objective data and smart insights makes further development, marketing, and scaling much easier. You know what niches to explore, how to appeal to potential customers, and what functionality to improve.

Finally, AI and machine learning make the process much faster — you can have complete customer behavior reports in a couple of minutes, and see real-time updates as the business matures along with your audience. 

As a team that specializes in e-commerce, we know common customer behavior patterns and know where and when to start the analysis.

Contact us if you want to know your customers’ behaviors and thought processes, and get actionable insights.

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