For a better understanding of this strategic approach, this article is a complete information package that informs behavioral analysis definition to its practical examples and some prominent tools to regenerate business performance.
Behavioral analytics definition
Behavioral analytics is a sub-field of data analytics that provides insights into people’s actions. Business can uses behavioral analytics to optimize opportunities and achieve specific business outcomes and goals. It uses large amounts of raw data from users when they visit social networking sites, game applications, marketing, and retail websites. It requires a much deeper examination of user behavior than basic metrics such as the number of users or pages viewed.
Some examples of digital users can be used for behavior analytics
- Making an account
- Filling in a form
- Performing a song
- Cart adjustment
- Buying a subscription
- Typing in search engine
- Scrolling time
These behaviors are event-based analytics to reveal user preferences, intentions, and habits. This algorithm helped increase engagement and customer retention, as well as offer customer better value.
How does user behavioral analytics work
The foundation of behavioral analytics is hard data. It uses the massive amounts of raw data which people generate while using social media, gaming applications, marketing, retail sites, or applications. This data is collected and analyzed before being used to make certain decisions, such as how to determine future trends, business activity, or ad placement.
Your analytics platform should collect user behaviors from your product and a data warehouse or CDP, and it should integrate with other tools such as social and email messaging systems. The behavioral data can then be visualized using a dashboard or reporting system.
After gathering behavioral data, you must define the specific business goals and objectives you wish to achieve. These objectives are specific to your company and product, but retention, conversion, and engagement are almost universal across industries. It will be much more difficult to draw insights from behavioral data if you do not have specific short- and long-term business goals.
Finally, your primary goal should be to learn as much as possible about your customers: what they want, what they don’t like, and what keeps them engaged.
When certain groups’ behaviors are examined, user patterns and tendencies emerge. Cohorts can be segmented on specific behaviors to discover opportunities to improve the entire customer journey.
Significant benefits of behavioral analytics for your business
Target the right type of user
If user behavior is unclear, such as users not converting or engaging, you’re most likely targeting the wrong audience. User behavior informs you about the types of visitors who transform the most and those who convert the least; understanding this distinction can help determine who you should market to. This observation should be made over a long enough period to determine which actions are habitual and which are meaningless variables that have no bearing on the overall evaluation.
Save time and budget
Once you have data on user behavior, you won’t have to waste time assuming which features on your site will attract and persuade visitors to convert. Through this powerful and well-researched data, you know exactly what users want. User behavior can also suggest where you should conduct A/B Testing, which we’ll discuss in the following section of the article.
Furthermore, you can contact users directly and learn about their experiences with your product through interviews, identifying their challenges and problems. Combining A/B testing insights with qualitative data allows you to make better product decisions.
Influence future behavior
Knowing your current users’ behavior provides insights that will help you create the right products and influence more future users. You can quickly identify users’ roadblocks that prevent them from converting or returning to your service. For example, if you notice that people are abandoning the lead gen form after they reach the physical address field, you should either make that field optional or remove it for future users.
In a nutshell, by identifying key challenges in design sprints and speeding up the iterative process of creating something useful for your audience, you will be able to make better product decisions.
Know your best marketing sources
Understanding how users find your site to improve marketing and conversions is critical. When you know where most of your users go and convert, you can prioritize that source to increase sales and revenue significantly. On the other hand, you must identify the source from which the user never converts so that marketing efforts can be directed elsewhere for better results. At this point, the various geographical and cultural factors will assist you in determining the correct type of service and product that customers require to capitalize on the opportunity.
Who should use user behavioral analytics
The new currency of modern business is data. Everyone should be able to use behavioral analytics to drive their business, regardless of industry. When marketers, product managers, and data analysts use behavioral analysis tools, they can begin to see their customers as people rather than a set of data points.
Marketing team
Marketers can use behavioral data and analytics to maximize campaign effectiveness, improve customer acquisition, and increase customer lifetime value (LTV). It is essential for marketing teams to understand which campaigns are successfully increasing engagement and revenue. A marketing team, for example, that sends out an email campaign to increase blog traffic can track the success of the email, the number of visitors to the blog, and which blogs are getting the most views.
Marketing teams can use behavioral analytics’ outcomes to:
- Optimize customer acquisition by comparing and focusing on the most beneficial channels and campaigns.
- Increase customer LTV by identifying the common characteristics and behaviors of the most loyal customers.
- Increase customer retention by understanding where, how, and why they engage throughout their lifecycle.
- Create dashboards on the cloud to track and share important KPIs throughout the customer journey.
Customer service team
Similar to Marketing team, the Customer Service team could harness the full potential of behavioral analytics. The team stands at the forefront of delivering exceptional customer experiences.
Here is how customer service team could leverage behavioral analytics to:
- Provide personalize service, tailored interactions and product recommendations by understanding each customer’s preferences, past interactions and even browsing history on the web.
- Get more predictive issue resolution by deeply analyzing customer behavior. For example, if a customer repeatedly visits the FAQ section on the website but hasn’t found a solution, the team can reach out with additional assistance before the customer contacts them with a problem.
- Allocate resources effectively for quicker responses during peak hours based on the ability to to analyze the customer behavior patterns. The team could determine better when customers are most likely to come to the store, allowing for well-organized staffing to enhance customer service quality.
- Support the marketing team to work on more targeted marketing campaigns. Behavioral customer data can be used to segment customers into different categories, based on their behavior, such as frequent buyers, occasional visitors, or those who abandon their shopping carts. And the customer service might be the last check for any targeted campaigns that marketing team proposes, either it is true or feasible for further actions.
- Identify which areas they need to improve. Without behavioral analytics, it is not easy to identify areas of improvement, most of them are normally based on gut instincts, or personal references. With the assistance of customer feedback and sentiment analysis, the team can readily pinpoint areas that require enhancement and take actionable steps to improve their service.
Product team
Product team can use behavioral data and analytics to create a product roadmap, improve user engagement, and decrease churn. For example, suppose you’re a product manager at an app developer who wants to track the performance of a new feature on an app. In that case, behavioral analytics can help you isolate users who have used the new feature that led them to use it. If the new feature fails to engage users, the product team may be able to identify friction points.
Product teams can use behavioral analytics’ outcomes to:
- Understand and map user behavior throughout the entire customer journey.
- Iterate quickly on product implementations by measuring real-time user engagement.
- Proactively isolate and identify user behavior that results in higher retention and lower churn.
- Create dashboards on the cloud to track and share important KPIs throughout the customer journey.
Data team
Data analysts can use behavioral data to perform complex analytics without having to create complex SQL queries or analysis capabilities that SQL does not provide. This frees up time for business users to work on other projects and streamlines their workflows. For example, if you want to create a report on conversion time versus new features, you can use a conversion over time funnel and mark the dates when new features were rolled out.
Data teams can use behavioral analytics’ outcomes to:
- Conduct real-time behavioral analyses that SQL cannot.
- Break down data silos and analyze the entire customer journey in context.
- Allow your organization to incorporate data-driven decisions into their daily operations, allowing you to focus on other projects.
- Create dashboards on the cloud to track and share important KPIs throughout the customer journey.
Here are some prominent advantages for these authorized people in summary.
Tools For User Behavioral Analytics
1. A/B Testing
A/B testing tools enable you to test changes to your website or app to determine which variation performs the better conversion, resulting in more clicks, signups, sales, and so on. It is also useful when testing a hypothesis developed based on previous user data or insights.
For example, suppose you want more users to upgrade their subscription to your product and believe that a shorter form will remove barriers and result in more upgrades. To determine whether your hypothesis is correct, you split the current version with eight fields against a test variation with five fields.
2. Session replay tool
Session replay tools display actions taken by real visitors while browsing your site or app, such as mouse movements, clicks, taps, and scrolling.
Viewing session recordings is similar to peering over your users’ shoulders as they interact with various pages, CTA, headlines, copy and images. You can see what they do before leaving your site or app, and what they pay attention to or ignore.
For instance, suppose you have a page with a low conversion rate, but high traffic and time spent on the page. Session replays will reveal whether users scroll up and down looking for something, rage-click on an element, or attempt (and fail) to open the interactive menu that would provide them with the information they need to take action.
3. Heatmap tool
Heatmap tools provide a visual, color-coded representation of the website or product pages and elements with which users interact the most in the rank of rating correspond. The more engagement is paid to the area, the hotter color is presented.
You’ll be able to see whether users, or as a group, reach vital content, click on links and buttons, or are distracted by non-clickable elements. To get the information you need, use scroll heatmaps, click heatmaps, and move heatmaps.
For example, suppose you’re using your CTA to present crucial content and want to know if people are clicking on it. A heatmap of clicks can reveal the harsh reality: only 0.04% of users on that page clicked that button. With that information, should you remove the button or optimize the content of your CTA? Even though the decision is all up to you, acknowledging your button needs adjustment is better than believing it works perfectly.
You can also use heat map tools to track customer behaviors while shopping in-store. With the data collected, you can identify which products attract the most customers and which are less likely to.
4. Feedback analytics tool
Feedback tools are a top priority for online businesses, as they have become essential to customer experience initiatives. These customer feedback tools enable visitors to communicate directly about their customer experience while avoiding interruptions to the online journey. They are also excellent for gathering ‘in-the-moment’ feedback. Customers can tell you why they’re abandoning their cart, leaving your site, or what is preventing them from acting.
Depending on the context, you can ask your users closed- or open-ended questions, and even request a rating with surveys such as Net Promoter Score or Customer Satisfaction.
For example, you want to improve your homepage but are unsure of the problems. You may display a mini on-page survey asking visitors to take part in a stress-free multiple-choice question about their surfing experiences.
5. Cohort analytics tool
Behavioral Cohort is a subset of behavioral analytics that analyzes all data within an eCommerce platform, web application, or online game rather than looking at a single unit of mentioned platforms. Cohort helps businesses groups their customers and users by identifying the sharing characteristics or experiences. With the data given, decision-makers can easily offer attractive incentives for each group to keep their stay.
Thus, cohort analysis is a powerful tool to enhance user retention. This enables you to create more accurate profiles of your users’ interests and needs.
For example, all users who read reviews before purchasing a product. This act can answer intriguing questions such as:
- Do users who read reviews have a higher conversion rate than users who don’t read good reviews?
- Are users more engaged in longer sessions and spend more time in the app with fewer skips?
5 User behavioral analytics examples from top brands
1. NBC increases retention with behavioral cohorts
For NCB Universal’s media and entertainment manager, behavioral cohorts were a beneficial tool to enhance user experience, especially on streaming applications.
By analyzing customers’ groups, they quickly make critical adjustment where it is needed. NBC discovered that by displaying a user’s personal watch history on the app’s homepage, they could personalize the streaming engagement. NBC reminds their users about unfinished shows, offers them instant access instead of name searching or simply suggests a rewatch of users’ favorite shows.
This enhancement doubled retention on the customer journey’s Day 7 benchmark. NBC also used cohorts to test changes to their homepage as part of a product experiment. They began the test by only introducing changes to a small segmented cohort. NBC updated the homepage for all users after noticing that the changes resulted in a 10% increase in viewership.
2. Seamless product management with Apple
Apple is a leading technology company that offers its customers a wide range of products. Despite this, the company faces many technical issues and relies on customer behavior analytics to solve them and develop new products.
First, it determines its target audiences through various surveys, websites, and other methods. They also identify the user, their needs, and why they choose specific products. It also assists the company in detecting any issues, pain points, or bottlenecks that may impede a user’s experience. As a result, they obtain this information and address the problems contributing to customer loyalty.
Apple also employs customer behavior analytics to create new products and close the gap between users and their needs.
3. How Amazon accelerates sales with behavioral analytics
Amazon is a multinational corporation that provides a wide range of products, services, and solutions in various fields. Furthermore, it is a widely known customer-focused e-commerce platform that provides personalized experiences to its shoppers. One of their strongest aspects is the algorithms and machine learning that recommends high-quality and approved products to customers.
The company employs customer behavior analytics to identify user intent based on the search, view, add to cart, and purchase. By doing this, they suggest the right product to the right customer and sometimes at the right time. Amazon also allows customers to compare different products in terms of similar price ranges. This only helps their personalized recommendations work better. The behavioral analytics tools will analyze the users’ budget and suggest the most feasible price range for their next shopping.
The end purpose of Amazon when using behavior analytics is to boost sales. A good product suggestion makes a shopper think, “Well, I might need this.” An approachable price makes a shopper less reluctant to buy. Behavioral Analytics is a valuable solution for e-commerce or retail businesses to push up appropriate products to meet customer needs.
4. Enhance user experiences with behavioral analytics from Netflix
Netflix is a data-driven company that forecasts and analyzes its customers using various metrics and insights. The company provides personalized services to its customers through its platform by making recommendations based on their Wishlist or stopping-by movie. Not only does it employ data to identify the most current trends, but it also tailors its content accordingly.
Netflix did not just present themself as cinematographers, but they did produce their shows and series to engage the platform’s viewers. Their service users span many ages, and most are young people, so they are regularly active on various social media platforms such as YouTube, Instagram, and Twitter. As a result, the company is increasingly creating personalized advertisements for audiences who prefer specific genres.
5. Under armour successfully increases user engagement
Under Armour wanted to learn how their mobile experience can help users achieve their health goals. Yet, their product analysts were required to go through a time-consuming, repetitive process that drained company funds while failing to achieve the desired effect.
With the user behavior analytics solution, Under Armour can now quickly test assumptions, access data, and respond. They discovered that their race training plan received low user engagement. To remedy the situation, they modified the plans to include a variety of goals, ranging from basic running to cardiovascular fitness, to appeal to a wide range of audiences. These changes have greatly pleased users, increased free-to-paid conversions, and improved user retention. Compared to before, the training plan features used by paid users have tripled.
Behavioral analytics vs. Business analytics
The two terms “behavioral analytics”, “business analytics” might give you a little bit confusion. Business analytics has a broader focus on various aspects of business intelligence, such as who, what, where, and when. In contrast, behavioral analytics narrows its scope, enabling the extraction, prediction, and determination of errors and future trends from seemingly unrelated data points.
Here is the table summarizing the key differences between behavioral analytics and business analytics:
| Behavioral Analytics | Business Analytics | |
|---|---|---|
| Purpose | Gains customers’ insights into their preferences, intentions and habits for better understanding customer behavior. | Provides insights for every aspect of the business including business performance, customer behavior, market trends and operation efficiency. |
| Focus | Concentrates mostly on understanding individual and group behaviors, in both offline and digital environments | Encompasses a wide range of business data, from different sources, to answer “who, what, where, when” questons. |
| Data Sources | Data mostly comes from users’ interactions and touch points, such as clicks, page views,… | Data draws from a wide variety of sources, such as customer databases, spreadsheets, financial reports, and market research data,… |
| Applications | Often used in Marketing and Customer Service departments. | Utilized across different functions, including finance, marketing, op |
Wrapping up
In a digitally driven world, businesses must build customer loyalty and identify their needs in order to provide more solutions. Behavioral analytics data should be used to improve your product, satisfy customers, and improve key performance indicators. The purpose of using behavioral analytics depends on your objectives, but conversion, retention, and engagement factors are extremely crucial to increase business efficiency. Having provided some prominent real case examples, you should examine and learn from that experience for your own company to adjust your customer engagement strategy. To save time and enhance productivity, Synodus, a dedicated technology partner, is willing to offer a wide range of advanced and professional technology solutions for your business steps.
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