Imagine you have a small business selling tote bags on Etsy. While selling a good quality product is a major aspect of building your brand, another vital component is understanding your customers. Through conducting customer behavior analysis, businesses can adapt their products, price points, and marketing campaigns to increase customer satisfaction and drive success.

But what exactly is a customer behavior analysis? Learning about customer analytics allows you to understand who your customers are (age, gender, location), how they shop (when, how often, what platforms they browse on), how they think about your product or business (reviews, responses to marketing campaigns), and any pain points they encounter (negative or annoying experiences with the brand). 

Overall, this valuable knowledge allows you to make data-driven decisions about how to adapt your business and product. Let’s take a look at how different insights can be leveraged to drive results:

• If you found out most of your customers were female, you may conduct further research to understand how to appeal to your female demographic and adjust your tote designs and marketing campaigns accordingly.
• If the data reveals that most of your customers buy your products seasonally, you might introduce seasonal designs, or put your items on sale during busy periods. Not only do these adjustments meet customer expectations but could also drive larger orders.
• What if the report suggests that the bulk of your customers arrive at your Etsy website when browsing Instagram on their phones? In this case, you could focus on social media campaigns and ensure that your UX design is friendly for people browsing on their cells.

E-commerce is just one area where customer behavior analysis is useful—it’s also important in the healthcare, finance, and hospitality sectors. In today’s rapidly evolving landscape, with a plethora of choices for consumers to decide between, customers expect personalized experiences that cater to their needs. By understanding behavior patterns, businesses can anticipate customer needs, tailor their offerings, and build stronger relationships, ultimately improving satisfaction and driving loyalty.

Customer segmentation: the process of grouping your customers based on common characteristics, such as demographics, location, or purchasing behavior.

Customer journey mapping: creating a visual representation of a customer’s interactions with an organization to help understand their journey from start to finish to identify which parts a client is enjoying and if there are any pain points.

Purchase behavior: a specific category of customer behavior analysis, referring to the actions taken and thoughts before investing in a product or service.

User experience (UX): the overall quality of a user's interactions with a product, service, or system, focusing on ease of use, efficiency, and satisfaction, often used to discuss digital design and experiences. 

Member experience: refers to the overall satisfaction and engagement a member has with an organization, encompassing all interactions, from the first point of contact to the end of their journey map.

Feedback loops: a closed system where the outputs are fed back and used as inputs in a circuit-like design. In terms of customer behavior analysis, businesses gather feedback from customers about their experience and use this feedback to make improvements to their products or service.

Emotional analytics:  an emerging area that focuses on uncovering emotional responses customers have during their journey with a product. Using tools like facial emotion recognition and conducting sentiment analysis, businesses can gain a deeper understanding of how these emotions impact consumer purchasing decisions.²

Sentiment Analysis: Analyzes text data from customer reviews, social media posts, and customer support communications to detect sentiments such as happiness, frustration, or disappointment.

In the 18th and 19th centuries, economic theory was dominated by the homo economicus: the belief that humans are rational decision-makers that act in self-interest to maximize desired outcomes and minimize cost. Based on this understanding of human economic choices, the general consensus was that customers would buy products that fulfilled their needs and priced to match the value it would bring them. Little effort was put into how to market to customers or build relationships. 

In the late 1920s to the 1940s, the U.S. experienced the deepest economic downturn in history,  which led to customers being more careful with their finances and resistant to buying products. That forced businesses to invest in advertising and sales tactics to try and entice customers. However, there was still little focus on customer needs and preferences—the message was push push push, regardless of whether consumers wanted their products. 

This shifted in the 1950s when businesses started to take time to understand and address customer needs. Businesses started to implement market research and customer segmentation—components of customer behavior analysis—to inform product development and targeted advertising. Researchers leveraged both quantitative research methods, analyzing numerical data related to sales, purchasing behavior, and customer demographics, and qualitative research methods, such as surveys and focus groups to create a feedback loop with their customers. 

By the 1990s, customers were getting used to this kind of customized unique experience and also became more concerned with ensuring that businesses and their products aligned with their values. It was no longer enough for a business to create a product that fulfilled their needs—the business needed to resonate with who they were.³ 

During this time, customer behavior analysis expanded beyond understanding the market and the demographic of consumers, to mapping out their entire customer journey. Businesses started to focus on creating a relationship with consumers, realizing that long-term engagement and loyalty were imperative to success. Emotional analytics began to be more important so that businesses could understand how customers responded to their products and brands.

This also led to a shift from mass marketing techniques to targeted marketing. Instead of businesses trying to reach a large audience with a generalized message, trying to attract a large customer base, they began to focus on a target audience and tailor their products and messages accordingly. With vast competition and increased access to information, personalized experiences are necessary to stand out. The shift to targeted marketing has been made possible thanks to technological advances, and the rise of the internet and digital platforms that give businesses access to vast amounts of data on their customers.⁴

Today, most businesses have a customer relationship management (CRM) system that tracks all interactions current and potential customers have with their company, including data from websites, social media, and phone calls. This data can be shared with everyone at the company, with real-time updates, to inform the company what changes they can make to increase customer satisfaction.⁵ 

In the digital era, customer behavior analysis has become an integral part of business strategy. Businesses have access to vast amounts of data about customer behavior and more advanced methods and technologies to analyze them. And this can happen in real time—ever felt like you were just talking about wanting new golf clubs and all of a sudden, you see a set for sale as you’re scrolling Instagram? That’s thanks to customer behavior analysis!

Philip Kotler 

Often referred to as the Father of modern marketing, Kotler is one of the most influential marketing experts of all-time. With a background in economics, Kotler recognized that people were not swayed to buy products just because of their quality or price, and argued that businesses needed to put greater focus on the customer. His book, Marketing Management: Analysis, Planning, and Control” published in 1967, explained how businesses could implement a customer-centric approach to drive success.⁶ 

Daniel Kahneman

Known as the Father of behavioral science, Kahneman was very influential in moving people and businesses away from the theory of homo economicus. He demonstrated that many factors go into decision-making and that humans are susceptible to biases that can cause some irrational choices. In his influential book Thinking Fast and Slow, Kahneman showed that reference points influence our economic decisions by introducing theories like prospect theory and loss aversion. Kahneman’s deeper understanding of decision-making has been leveraged in business settings where product managers and marketers learn about biases to better reach their consumers. 

Richard Thaler

One of the most prominent figures of behavioral economics, Thaler is an American economist and 2017 Nobel Memorial Prize winner. Thaler argued that the environment or context in which someone makes a decision influences their behavior, contributing to nudge theory that showed businesses ways that they could make it more likely for consumers to buy their product or service. One of his popular books, Nudge: Improving Decisions About Health, Wealth, and Happiness, showed how choice architecture can influence our decision-making. 

Byron Sharp

Author of How Brands Grow, Sharp is an influential marketing scientist and professor of marketing in Australia. Sharp advocates for a scientific marketing approach, urging professionals to conduct data analysis to better understand the market and their customers. He emphasizes the importance of hyper-target marketing, expanding brand awareness to also consider how accessible products are, and suggests that focusing on both loyal and new customers is vital to a business’s success.⁷ 

An in-depth understanding of customer behavior allows companies to design more personalized and engaging experiences. This benefits both businesses, who can remain competitive in rapidly evolving markets, and customers, who better have their needs addressed. 

Product and Marketing Optimization

Customer behavior analysis allows businesses to put customer's needs first. By analyzing how customers interact with products and services, businesses can create or adapt products so that they are more appealing to their customer base. The data-driven approach of customer behavior analysis allows businesses to create products that solve actual pain points or needs, which will lead to greater customer satisfaction. Additionally, customer insights enable more targeted marketing campaigns, as businesses can segment their audiences more effectively, personalize messaging, and allocate resources to channels that generate the highest returns. 

Enhanced Customer Experience

As customer behavior analysis informs both who your customers are, but also how they behave, it can lead to enhanced customer experience. If a business realizes that customers are browsing their website, but end up dropping off after they get to the shipping page, that reveals a pain point that the business can target: they may add on a limited offer for free shipping, or follow up with customers that dropped off the website via a email to ask if they need help completing their order. 

Customer behavior analysis also informs businesses how their customers prefer to be communicated with. In 2022, HubSpot asked 1,000 consumers how they prefer to receive customer service. They found that Gen Z and Gen X customers preferred service over the phone, while Millennials preferred email. This information allows businesses to tailor their customer service channels depending on the age demographic of their consumers.⁸

Tracking Economic and Societal Trends

While customer behavior analysis has important implications for individual businesses, it can also offer valuable insights into broader economic and societal shifts. By analyzing vast amounts of data, we can discover large-scale patterns in consumer spending, preferences, and responses to economic conditions.  These insights not only influence how businesses adapt but also help governments and organizations forecast economic changes and adjust policies to better serve society. For example, if the data reveals that customers are beginning to focus on sustainable options, policymakers can give businesses incentives to reduce their carbon emissions.

What makes customer behavior analysis so useful—providing businesses with in-depth information about their customers—is also what makes it dangerous. While leveraging personalized marketing can improve customer experience, it also has the potential to manipulate or sway people into making certain decisions and poses threats to individual privacy. 

The Danger of Personalized Ads

Personalized experiences make customers feel that their needs are being met and that companies care about them. However,  it can also make us feel a bit uncomfortable and we can fall into the “creepiness ditch.” When done incorrectly, personalization can make people feel that they are being stereotyped based on their gender, age, or location, and it can also raise concerns about how their data is being collected and used.⁹ It's creepy to think our phones are constantly “listening” to us—even if that leads to better customer experiences!

It shifts from being uncomfortable to dangerous when we consider how personalized advertisements are used to influence or reinforce our political beliefs.  If people are only fed information that supports their preexisting political beliefs, they become reinforced, and it can be difficult for them to understand another point of view, which increases polarization. 

In 2018, it was revealed that Cambridge Analytica had gathered data from 87 million Facebook profiles to target key voters to vote for Donald Trump with online ads.¹⁰ This scandal brought to light the power of personalized ads in shaping political outcomes and influencing democratic processes and raised privacy concerns about who has access to our data.

Ethical Considerations and Consumer Privacy

For customer behavior analysis to be effective, businesses need to collect and analyze vast amounts of data. This includes browsing history (page views, click patterns, search history), purchase history (when, how, and what products you have purchased in the past), and even social media activity. While this helps a business better target and serve their customers, it also means they have access to personal and private data, which raises ethical concerns about how and by who it is being used, and who owns the data. There is an increased need for transparency around how data is collected and used and stronger regulatory policies to protect privacy.

Bias and Discrimination

Although advanced computer technology is usually used to conduct customer behavior analysis, that is not to say that they are objective tools free of bias. These computers build algorithms based on historical data that may contain bias related to gender, race, or socioeconomic factors. 

For example, if a bank conducts customer behavior analysis to see which customers have defaulted on a loan, it may flag a particular region or zip code as an area to avoid offering loans to. This could result in the bank offering less favorable loan terms or higher interest rates to individuals from these areas, regardless of individual financial status.

Inspire Fitness 

With health and fitness becoming increasingly important, there are a lot of different companies and apps with similar offerings, making it a very saturated market. In order to stand out and offer a product that addresses consumer needs, businesses need to conduct customer behavior analysis. 

Inspire Fitness sells fitness equipment and offers its users online fitness classes and tips. To know what products and classes they should offer customers, they conducted research to find out what were the most popular classes and instructors, analyzed heatmaps to see how customers were engaging with various features on their app, and tracked key metrics such as the top visited pages and which pages customers were browsing before leaving the app. 

Through this research, Inspire Fitness was able to offer fitness classes that customers were interested in, promote the most popular instructors, and adapt features on their app based on which ones were popular with their customers.¹¹ 

Netflix

Netflix is now a household name, with 277.65 million subscribers as of 2024.¹² When Netflix first started offering its streaming service, it was one of the first companies of its kind, but today, there are many competitors: Prime Video, Disney+, Hulu, Crave, just to name a few. To maintain its success, Netflix conducts customer behavior analysis to ensure it remains competitive.

Netflix’s personalized recommendations are an example of how they use customer behavior analysis. By tracking what shows and movies you watch, how much time you spend on them, and how you rate them, Netflix is able to optimize content delivery and provide you with suggestions that fit your preferences. By making it easy to find something you want to watch, which is an increased challenge as we face choice overload with so many streaming platforms, Netflix ensures that its customers will continue to use their platform.¹³

How to Create Journey Maps to Improve EX  (Employee Experience)

Journey mapping isn’t only useful for customers – it can also improve employee experience. Oftentimes, leaders focus solely on financial perks, but in this article, our writers Lindsey Turk and Dr. Sekoul Krastev show how other factors are important to the overall experience. They explore how journey mapping can help leaders segment their employees and improve management decisions, and provide insight into how to create an employee journey map.

Modern CX is Behavioral: How the Peak-End Rule Can Revolutionize Customer Experience

These days, consumer and employee needs and preferences change so rapidly that it’s difficult to keep up. It can be difficult to tackle every aspect of a customer or employee’s journey, so in this article, our authors Sarah Chudleigh and Dr. Sekoul Krastev explain how businesses can leverage the Peak-End rule. The Peak-End rule is a behavioral science principle that suggests people judge their overall experience based on the most intense moment (the peak) and the end. 

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