Behavioral Analytics 101: A beginner’s handbook to user behavior analytics (UBA)

Karthik MSN

In the fall of 2015, Fox Studios called hundred movie buffs across the cities of Boston, Chicago, Philly, and DC for a premiere show of a grotesque period revenge drama film set in the 1800s. But unlike other premiere shows, the audience here was given wristbands made by a bio analytics company ‘Lightwave’.

All through the two and a half hours of watch time, the wrist trackers continuously monitored the viewers’ heart rates, body temperatures, movements, and conductivity of their skin as their autonomic nervous systems took over. All these together indicated their fight-or-flight responses. Makers of the film knew that it was gripping and had the potential to win people’s hearts, but now they had data to suggest which parts were engaging, which ones needed an improvement, and how long the audience were glued to the screen.

The film was “The Revenant” by Alejandro Iñárritu and it went on to win the long pending Oscar for Leonardo DiCaprio. That, in a gist, is how behavioral analytics and big data have changed films forever, and can also benefit all kinds of digital software products in the market. Above is a screenshot of the biometric insights collected during the screening.‍

What is behavioral analytics?

Behavioral analytics is the process of collecting data on the what’s and how’s of user actions, and then deduce the why’s of user behavior patterns from it. In the digital world, it is the process of collecting and analyzing each and every interaction of the users with an application or a website to understand why they behaved in a certain way. This helps companies improve their digital products, so that they can sway the user behavior to their advantage and drive more conversions.

Digital businesses today are constantly trying to decode their customers’ actions, preferences, and needs. They turn to user behavior analytics in order to achieve this. In this extensive guide, we will explore the world of behavior analytics, its applications, benefits, and strategies. Furthermore, we shall delve into the nitty-gritties of monitoring, collecting, and analyzing behavioral data, types of user behavior analysis, and more.‍

Importance of user behavior analytics (UBA)

User Behavior Analytics (UBA) plays a very crucial part in tracking digital product adoption, user growth, and problems faced by customers while using software applications. It helps teams across the board in the following:

• Enhancement of user experience (UX): When one understands how users interact with a product and why they drop-off from a particular page, this intelligence can be used to identify usability issues in the product and improve the user experience, by understanding the needs of the users.
• Data-driven decision making: While prioritizing feature enhancements or fixing bugs, the product usage data can help teams in making data-driven decisions on which ones have the most impact on users, and to prioritize accordingly.
• Product personalization: Understanding users’ demography, needs, motivations, and behavior allows software teams to create product workflows that are personalized to each user, based on their personas. The content of the product can also be personalized to the liking of the users, for example, Youtube personalization.
• Competitive advantage: By leveraging user behavior analysis, businesses can constantly learn about customer preferences, which gives them an edge to stay ahead of competitors and market trends.
• Improving user security: Collecting user behavior data allows digital analytics teams to map out patterns of usage at an aggregate level. This allows them to identify any kind of unusual user behavior patterns and take action against transactions that are fraudulent or malicious login attempts that might be impersonating someone else.
• Optimization of marketing campaigns: Marketing and growth teams use UBA to track and analyze website visitor behavior and conversions from various campaigns. This is achieved by UTM tracking across social media posts, ad campaigns, etc., and gives the teams a visibility on which campaigns are working for them and how they can be improved.‍

What is user behavior, and what kind of behavioral analytics data can be collected?

User behavior includes all the actions, mouse movements, clicks, inputs, searches, and page visits that users perform on websites or digital applications, either web, tablet or mobile. User behavior analysis on digital platforms enables businesses to have access to actionable user insights, make data-backed decisions while working on enhancing their apps, and build more user-centric experiences.

For example, in the case of an e-commerce website which sells clothes online, understanding where users are dropping off in the workflow is very important for the digital teams to understand the areas of improvement:

• If more and more people drop-off from the product or search listing pages, there might be issues with the product listings, or they might not be seeing items relevant to their search.
• If the drop-off is higher while or after adding items to the cart, the user intent might not be right.
• If the drop-offs happen during the checkout process, there might be problems related to payment gateways which need to be addressed and fixed as soon as possible. These types of issues are mission critical for businesses, as they impact revenue directly.

There are two categories of user data that can be tracked. One is the user activity data, which includes all the digital actions of the user, and second is the user environment data, which comprises the user’s demographic, environment, and identity data. Following is the classification of various user behavior data points that can be collected on digital platforms for behavioral analytics:

User activity data

• Page visits: This comprises tracking all the page URLs that a user visits in a website or web application. This provides valuable insights into users’ browsing patterns and interests, which can be used to understand the most visited pages and the drop-offs.
• Clicks: Monitoring user clicks and interactions with webpage elements unveils the components that draw the most attention, informing decisions on design and content placement.
• Mouse movements: Research shows that there is an 84% correlation between user eye and mouse movements, as they follow similar rhythms. Hence, delving into user mouse movements shows instances of hesitation or issues that users face and serves as a proxy for user engagement and attentiveness.
• Keyboard Inputs: Keeping tabs on keyboard inputs, like search queries and form submissions, provides a window into user intentions and inclinations. If you are skeptical about tracking PII (personally identifiable data), many tracking applications today like Zipy, offer ways to mask these user inputs, hence taking care of the privacy and security of user data.
• App specific CTAs: Tracking of calls-to-actions or CTA clicks within applications yields insights into the efficacy of these prompts in steering users toward desired actions like sign up, login, add to cart, etc. Marketing teams can use this data to A/B test various CTAs and improve conversion rates.
• Time spent: Measuring the time users spend on pages or within applications serves as a gauge of their interest and engagement, with prolonged stays indicative of deeper exploration or user engagement.
• Frequency of usage: The frequency with which users revisit websites or apps is instrumental in tracking user loyalty and retention, pinpointing power users and potential attrition risks. More the usage, more is the intent to use, buy, or upgrade.
• Usage trend: Monitoring product usage trends over time unveils patterns, cyclical trends, or deviations in user behavior, giving invaluable inputs for strategic decision-making and course corrections in the product roadmap. This is where metrics like DAU, WAU, MAU (Daily/Weekly/Monthly active users) come into play in indicating the usage trends over longer periods of time.
• Feature specific usage: Evaluating the adoption of specific features or functionalities within an application helps understand feature popularity, under-utilized components, and potential problem areas needing enhancements.

User environment data

• User name, email or unique ID: Identifying users by their names or unique user ID’s like Email ID, UUID, and other identifiers enables the tracking of individual user behavior across sessions and devices, all the while upholding user privacy.
• Persona: Identifying user personas facilitates the tailoring of content and features in line with user roles or characteristics, delivering a more relevant experience. This enables design and product teams to create a more personalized user experience for all users.
• Age: Collection of user age data allows user segmentation based on demographics, offering important insights for serving relevant content, planning marketing campaigns, and taking product decisions.
• Company: Knowledge of a user’s affiliated company can inform the customization of content, offers, and services, particularly for business-to-business (B2B) engagements. In most of the B2B SaaS applications, user company data is collected during the sign up process.
• Geography: Collection of geographic data points like city, state, and country aids in localization endeavors, encompassing language preferences, content adaptation, and also in targeted marketing initiatives. One can also analyze patterns of app user behavior based on the geographic locations of users and their languages.
• IP address: IP addresses serve as a basis for geolocation, security measures, and the identification of anomalous activities or multiple account logins from separate locations.
• Operating system: Tracking the user’s operating system is essential for ensuring compatibility and optimizing user experiences on specific platforms. Also, this helps customer support teams identify and reproduce customer issues based on the OS.
• Browser: Browser data guides the assurance of cross-browser compatibility and the resolution of browser-specific issues that may impact user behavior.
• Device: Awareness of the user’s device type (e.g., desktop, mobile, tablet) is instrumental for responsive design and the refinement of user interfaces.
• Time of the day: Analyzing the timing of user interactions unveils patterns in user behavior, encompassing peak activity periods and considerations related to time zones. This allows Marketing and Customer Success teams to reach out to users in their time zones.

In summary, the tracking of user activity and user environment metrics empowers organizations with a holistic grasp of user behavior, preferences, and engagement. These insights serve as the bedrock for data-informed decision-making, personalized user experiences, and the continual enhancement of digital products and software applications.

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How does user behavior analytics work?

Now that you know the kind of user behavior data points that can be tracked, let’s move on to the inner workings of behavioral analytics. The image below shows the components of a User Behavior Analytics (UBA) Engine.

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While collecting all the user behavior data discussed in the previous section, it’s important to comply with the privacy and security requirements of such data. Being GDPR compliant and SOC2 Type2 certified, a tool like Zipy helps you collect user behavior data in a safe manner, such that none of the personally identifiable information (PII) of the users is tracked. There are ways to mask out these details, even before capture, which keeps the data anonymous and secure.

Once the data is collected, it is then processed and stored in data warehouses for future analytics. Data warehouses or databases can be internal or external depending upon the way one collects the data. However, maintaining your own data lake for user behavior data adds an additional overhead for product teams, as they’d end up spending more time on internal plumbing, rather than building innovative features for customers. Hence new age cloud-based solutions are increasingly popular for storing and managing behavioral data because of their scalability and flexibility.

The most important thing in behavioral analytics is the actionable insights that you can derive out of the data that is collected. Different types of data visualizations such as user flows, feature adoption trends, conversion funnels can be created and analyzed in order to stay on top of the user engagement in the product. The key to successfully implementing User Behavior Analytics (UBA) is in constantly taking actionable feedback from the user behavior data that is collected and applying it to enhance the product experiences. Some of the common examples of metrics that can be tracked using UBA are as follows:

• How do users navigate through your website?
• What are some most common user conversion paths or workflows?
• Which are the features that are most frequently used?
• What factors influence user retention or customer churn?‍

How to collect data for user behavior analytics (UBA)?

The sheer amount of behavioral data types discussed in the previous sections might overwhelm you to an extent, but fear not, the following framework will help you understand the approach towards collecting user data.

Step 1: Define your objectives

Start by clearly defining your goals and objectives with User Behavior Analytics (UBA). It is recommended that you detail out all kinds of user metrics you want to track. What are those top 10 business questions you are trying to answer and what kind of insights are you looking to get?

Step 2: Identify key metrics to track

The next step is to identify and define your team’s KPIs (Key Performance Indicators) that could be tracked using behavioral analytics. These can be things like product or feature adoption, time spent by users on a weekly/monthly basis, user retention, or customer churn. For each of these metrics, you can set a benchmark to be achieved and keep working towards getting there.

Step 3: Select right tools

Broadly speaking, there are two varieties of tools to measure user behavior. The first kind is the quantitative data collection tools while the second is the qualitative analysis tools. Both these tools come with their individual pros and cons. While the quantitative tools are event based and allow you to track the number of users performing various actions, the qualitative tools help you understand the why behind certain user actions. Qualitative tools such as session recording tools help you go back in time and replay user actions and analyze why they did the things they did. Combining both these approaches helps you start with the metrics, measure the aggregate counts and then drill down to the qualitative view to figure out the problem areas.

Step 4: Instrumentation and tracking

These days, if you’re using a third party UBA tool, the instrumentation has become very simple. All you need to do is embed a small piece of code in the

tag of your website or application and these tools start capturing all the user data. One thing to be careful about here is the performance degradation any of these tools can add to your application. At Zipy, we have made sure, that all the user data collected is sent to the backend at optimum intervals of time and the entire processing happens on Zipy servers, instead of on the client browser, so that Zipy code doesn’t interfere with the UI main thread of your application. You can read about it more here.

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Types of behavior analytics tools

• Web and app analytics tools: These are the basic analytics tools designed to capture and track website or mobile app user behavior data and give you metrics such as clicks, page views, user conversions, etc. Some examples of these tools include Google Analytics and Adobe Analytics.

• Event tracking tools: These are the next set of tools in the hierarchy which allow you to track custom events performed by users on your product. This means you can now track custom user actions such as a ‘user sign up’, ‘checkout success’, ‘video play’, etc., which can be triggered based on the success of the user action. The main disadvantage of this approach is that you need to depend on your development team to put in some extra code for every user event that has to be tracked, which adds more complexity to the tracking process. Some examples of these tools include Mixpanel, Amplitude, and Heap Analytics.
• Session replay tools: The next set of tools are user session recording tools which capture all the actions performed by the users on a digital application, along with the clickstream data, the mouse movements, and the user interface changes, and replay them as they happened in a video-like format. These tools provide a more clearer and qualitative picture of why certain actions are being performed by the users and why certain users are dropping off from the app. Some examples of these tools include Zipy, Hotjar, and Fullstory.

• Heatmap and clickmap tools: These kinds of tools help you generate a visual representation of the click density and scroll depth of any particular URL of your website or application. This summarized the areas of most attention, clicks, or interaction, on a particular page. You can consider Zipy, Hotjar, and Lucky Orange as your go to heatmap tool.

• User surveys and feedback tools: Feedback and survey tools help companies collect direct feedback from customers through feedback forms, polls, and NPS (Net Promoter Score) surveys. This too gives qualitative insights on what users actually think about the product or a particular feature. Some examples of these tools include Hotjar and Qualtrics.
• Customer journey analytics tools: With customer journey tools, you can map the entire workflow or the steps taken by a user through the product. At an aggregate level, this helps in identifying key stages where users fumble, which in turn informs you about the areas of product improvement. Mixing a session replay approach with this gives a better qualitative picture. Some examples of these tools include Zipy, Mixpanel, and Amplitude.‍

• A/B testing and experimentation tools: This kind of software allows businesses in running experiments between two or more variations of a page, an app, or a feature, and compare them to determine which option performs better in terms of user engagement and conversions. Some examples of these tools include VWO and Optimizely.
• Cohort analysis tools: Cohorts are nothing but the groups or segments of users who share a common characteristic. It can be anything like user geography, browsers used, pages navigated, products bought, or signed up on the same day. Once cohorts are created, these tools help companies track a particular behavior of the same cohort over time, hence measuring the product retention rates. Some examples of these tools include Mixpanel, Zipy, and Amplitude.
• Error monitoring tools: Error monitoring tools help you catch frontend and network errors that occur in the user’s browser, due to which glitches happen in the user experience. Catching and solving these errors proactively indirectly helps you reduce customer churn. Some examples of these tools include Zipy, Sentry, and Bugsnag.

You might have noticed that Zipy has been mentioned in the examples of many of these categories. The reason for this is that, with Zipy, we have taken a more holistic approach towards building a comprehensive user behavior analytics software. From a product perspective, today, there are two kinds of needs that Zipy solves:

• First is the user behavior understanding which is derived from the out-of-the-box product analytics, user session replay, no-code event tracking, heatmaps and customer journey segmentation. These approaches allow product and design teams to get both quantitative and qualitative pictures of how and why users behave in a certain way digitally. The insights derived from this impact product strategy in a data-backed manner.
• Second is the proactive customer issue resolution aspect, which is taken care of by the error monitoring piece. This allows product, engineering, and support teams to stay on top of customer issues, even before someone reports it to them. These can be usability issues, code issues which result in frontend errors, or API failures that result in users having a bad experience on the digital product.

Also, since Zipy tracks all the user actions, mouse movements, and events by default via the session recording, there is no need for an additional code implementation to track custom events. These can be achieved in a no-code manner within Zipy.

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Who uses behavioral analytics?

Behavioral Analytics can be used by a varied set of teams to achieve various goals in a company. Following are some examples of how various teams use it:

• Product and design teams: With the help of behavioral analytics, product managers and designers can proactively identify, isolate, and fix bad user experience that can lead to increased user retention and reduced churn. This also helps them map user behavior across the full customer journey so that they can quickly iterate on enhancing the product by measuring user engagement in near real-time. The product teams would also be able to identify the points of friction that cause problems with new feature engagement.
• Engineering and customer support: Research shows that more than 95% of users silently churn away when they face an issue with a website or a digital product. Even in the rest of the cases where they report the issue, more often than not, it takes hours to replicate the issue on the support side. The reason for this is that a lot of time is lost in the back and forth with the customer to understand what went wrong, in which browser and OS, from which geography and device. Then this info is passed on to the tech support or engineering teams to resolve, who use their own logging systems to decipher the issue. This is exactly like Chinese whispers, where a lot of context is lost in translation. With the help of user behavior analytics tools, combined with session replay and error monitoring, all such issues can be caught and fixed proactively, even before the customer reports it to you, thus saving a lot of customer churn and revenue.
• Marketing and sales teams: Marketers can use behavioral analytics to optimize customer acquisition by comparing and honing in on the most valuable campaigns or channels, increase customer LTV (Lifetime Value) by identifying shared behaviors of most loyal users and maximize conversions by understanding how people navigate through the website. Sales and Pre-sales teams can use UBA to track how their trial customers are being onboarded onto their product. The better the proactive support, the higher would be the trial to paid contract conversions.
• Data analysis teams: Data analysts can use UBA to break down silos between the teams and analyze the customer journeys with a complete context. They empower organizations to make data-driven decisions based on real-time behavioral analysis.

In a nutshell, user behavior analytics can provide real-time user data that helps teams answer questions such as:

• Where do users click within the product?
• Where are users getting stuck? Are there any technical problems there to fix?
• How long do users take from first click to conversion?
• How do users react to new feature changes?
• How can product teams nudge users to be more successful in what they’re trying to accomplish?
• How do users react to marketing messages? Which ads are the most effective?‍

Behavioral analytics strategies

Once the behavioral data of users is gathered and the business specific goals and objectives are defined, the idea is to start measuring the KPIs using the insights you can generate from the user behavior data. The most common framework for product and acquisition metrics is AARRR , which is the short form for Acquisition, Activation, Retention, Revenue and Referral. With the help of behavioral analytics, these can be measured by answering the following questions:

• How many people are landing on your website?
• How many of them sign up for the product?
• How many of them are highly engaged with your key features?
• How many of them keep coming back to the product month over month?
• How many of them are ready to pay you for the service?
• How many of them have become so loyal that they refer your product to others?

All of these questions can be answered using the following the following methods:

• Cohorts analysis: This helps you analyze user behavior by grouping users into cohorts based on common traits or the time of sign up or acquisition of the users. This helps you track how different user groups behave and evolve over time.
• Funnel analysis: If your goal is to optimize for better conversion rates, funnel analysis helps you understand the steps users take in the conversion process. For example, you can define your funnels steps like ‘Step 1: website visit’, ‘Step 2: sign up success’, ‘Step 3: onboarding complete’, ‘Step 4: plan upgrade’, and measure how many users are moving through the steps 1 to 4. You can further measure the drop-offs in each step and deep dive into the reasons for these users dropping off. This helps you identify the bottlenecks in critical product workflows .
• User segmentation: User segmentation is similar to cohorts, in the sense, you can create various groups of users based on their characteristics such as persona, geography, time of sign up, etc., and measure their feature adoption across time. This helps you understand and prioritize the user groups which are performing well in terms of user engagement.
• User flows: Flows allow you to visualize the paths users take across your product from their first visit till they bounce-off. Seen at an aggregate level, this gives a better picture of the most optimum paths users are taking on your platform and benchmark it with your hypothesis. This helps you figure out issues in certain areas that can be prioritized and fixed .
• Heatmaps: Heatmaps and clickmaps provide visual representations of user behavior. Heatmaps show which areas of a page receive the most attention, while click maps display where users click most frequently.
• User sessions: All the above strategies give you a quantitative picture of the number of users following a particular flow or performing a particular task. But if you have to further drill down and see what each individual user has been doing on the platform, User Session recordings help you do that. This provides a more qualitative picture of the user behavior through the replays of user actions and mouse movements. Instead of having to talk to your end users and conduct user interviews to understand their behavior, you can simply watch their video replays to figure out the gaps in the system. This is both time saving and has no loss in translation of insights.

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User behavior metrics to track for behavioral analytics

Some of the examples of key metrics that can be constantly measured using user behavior data are are follows:

• Sign ups: The number of users signing up on the product on a weekly and monthly basis, categorized by the channels from which they are being acquired.
• Activation rate: Activation is the measure of how quickly you can get your customers to the ‘Aha-Moment’, post which they’re likely to keep coming back to the product. It tracks the number of people who cross the activation threshold, which is nothing but the minimum number of actions to be performed by users so that they can be considered as activated. Activation rate is the percentage of activated users amongst the total users who sign up.
• Adoption: Adoption signifies the embrace of a product, service, or feature by users. It encapsulates the process of users incorporating and utilizing the offering into their routines or workflows .
• Stickiness and retention: Stickiness and retention measure the degree to which users engage with and remain loyal to a product or service over time. Stickiness reflects the frequency of user interactions, while retention gauges the ability to keep users coming back.
• Funnel drop-offs: Funnel drop-offs pinpoint stages in a user journey where individuals disengage or abandon the desired conversion path. It’s a crucial metric for identifying bottlenecks and optimizing the user experience to minimize abandonment.
• Time series analysis: Time series analysis involves scrutinizing user behavior data points collected over successive intervals to unveil trends, patterns, or fluctuations over time.
• Conversion rates: Conversion rates quantify the percentage of users who take a desired action, such as making a purchase, completing a form, or signing up. High conversion rates indicate effective user journeys, while low rates may highlight areas for improvements.
• Bounce rates: Bounce rates measure the percentage of users who navigate away from a webpage after viewing only one page. Elevated bounce rates often suggest a disconnect between user expectations and the webpage’s content or functionality.
• Cart abandonment rates: Cart abandonment rates gauge the proportion of users who place items in an online shopping cart but abandon the process before completing the purchase. Understanding and mitigating this behavior is crucial for optimizing ecommerce conversions.
• Rage clicks/dead clicks: Rage clicks, or dead clicks, refer to instances where users repeatedly click on an element, expecting a response that doesn’t occur. This behavior signals frustration or confusion, highlighting potential issues with website functionality or user interface design.
• Average time spent on page: This gives us the average time spent on any given page URL, aggregated across all the users on the product. This can give you a statistical picture of the most engaging pages of the product or website.
• Session duration: Once a user is on the product, session duration is the measure of how long he/she is spending on the product before dropping off. Usually, the more the session duration, the higher is the engagement rate of the users.
• Scroll depth: This indicates how deep are the users scrolling on a given page. Are they sticking just to the first fold of the page, or do they scroll to see the content below the first fold? In the case of e-commerce, this is very helpful in deciding the placement of high value items on the page to boost sales conversions.‍

Examples of user behavior analytics (UBA)

Some of the example scenarios where behavioral analytics can come in handy are as follows:

• Shopping cart abandonment: User behavior analytics data helps e-commerce businesses identify users who abandon their shopping carts. By analyzing the behavior leading up to abandonment, businesses can implement targeted remarketing strategies to recover lost sales.
• Product recommendations: Behavioral analytics algorithms analyze user browsing and purchase history to provide personalized product recommendations. This increases the likelihood of cross-selling and upselling.

Software development and product improvement:

• Feature adoption: User behavior analytics tracks user interactions within software applications to identify which features are most and least used. Developers can prioritize feature enhancements or removal based on user behavior.
• Bug detection: Behavioral analytics data when coupled with error monitoring can help detect anomalies in software behavior, such as unexpected errors or crashes. This aids in identifying and addressing bugs promptly.

Marketing and advertising:

• Ad campaign optimization: Marketers use UBA to track user engagement with online advertisements. Analyzing which ad creatives and targeting strategies result in higher click-through rates and conversions allows for campaign optimization.
• Content personalization: Behavioral analytics helps tailor content recommendations on websites, apps, or email marketing campaigns based on users’ past behavior and preferences.

Healthcare and patient monitoring:

• Patient adherence: User behavior analytics in healthcare tracks patient behavior related to medication adherence. It alerts healthcare providers when patients deviate from prescribed routines, improving patient outcomes.
• Early disease detection: By analyzing patient behavior, such as changes in sleep patterns or physical activity, behavioral analytics data can help detect early signs of certain medical conditions, facilitating timely intervention.

Financial services and fraud detection:

• Transaction monitoring: Behavioral analytics data tools analyze transactional behavior to detect fraudulent activities. Unusual spending patterns, location discrepancies, or multiple large transfers can trigger alerts.
• Credit risk assessment: User behavior analytics can be used to assess credit risk by analyzing borrowers’ financial behavior and payment histories. Lenders can make more informed lending decisions.

Education and student engagement:

• Online learning effectiveness: User behavior analytics in education assesses how students engage with online courses. It helps educators identify struggling students and tailor interventions.
• Course content enhancement: Analyzing user interactions with course materials can guide the improvement of content, quizzes, and assignments to enhance learning outcomes.

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What are the benefits of behavioral analytics?

Behavioral analytics offers a wide range of benefits to businesses and organizations across various industries. Here are some key advantages of implementing behavioral analytics:

• Improved decision-making: Behavioral analytics provides data-driven insights that empower organizations to make informed decisions. These insights help identify trends, preferences, and opportunities for improvement.
• Enhanced user experience: By understanding how users interact with digital products or services, businesses can optimize user experiences. Tailored recommendations, personalized content, and smoother user journeys lead to increased user satisfaction.
• Increased conversion rates: Behavioral analytics identifies bottlenecks and drop-off points in user journeys, enabling businesses to make targeted improvements. This often results in higher conversion rates, such as more sign-ups, purchases, or form submissions.
• Precise marketing strategies: Marketers can use behavioral data to segment audiences, create targeted campaigns, and deliver personalized content. This leads to more effective marketing strategies and improved customer engagement.
• Fraud detection and security: Behavioral analytics helps detect anomalies in user behavior, which is crucial for identifying security threats and fraudulent activities. It allows companies to take proactive measures to protect data and assets.
• Cost reduction: By identifying inefficiencies and areas for optimization, behavioral analytics can lead to cost reductions. For example, it can help you reduce customer support costs by proactively addressing and fixing the common user issues.
• Customer retention: Behavioral analytics helps predict user churn by identifying signs of disengagement. Startups today can then implement retention strategies to keep customers loyal.
• Increased revenue: Through improved user experiences, targeted marketing, and better product offerings, businesses often see an increase in revenue and profitability.
• Data-driven culture: Implementing behavioral analytics encourages a data-driven culture within organizations. Decision-makers rely on data and evidence rather than intuition or assumptions.
• Competitive advantage: Organizations that effectively leverage behavioral analytics gain a competitive advantage by staying ahead of industry trends and meeting customer demands more effectively.
• Customization and personalization: Behavioral analytics enables businesses to deliver customized and personalized experiences, products, and recommendations, which can boost customer loyalty.
• Compliance and privacy: Organizations can use behavioral analytics to ensure compliance with data protection regulations by tracking and protecting sensitive user data appropriately.

It is important to understand that behavioral analytics is an iterative process that fosters continuous improvement. Organizations can adapt and evolve their strategies based on ongoing analysis and user feedback. In summary, it has become an essential tool for organizations looking to stay competitive and data-driven in today’s digital landscape.‍

How to choose a user behavior analytics (UBA) tool?

The article here lists down all types of user behavior analytics tools to choose from. But since the list is too long, to make the decision easier for you, let’s take the analogy of Maslow’s hierarchy of needs to explain the minimum requirements to look for when choosing the right behavioral analytics tool for your organization. Following picture should help you in giving a better clarity on the benefits of each.‍

As discussed in earlier sections, the most basic are the web tracking tools which can give you web page or click tracking. Then come the event tracking tools, which allow you to track custom events or actions performed by users on your product. The third set are the session recording tools, which give you a qualitative picture of what the user has done. Then comes the most evolved breed of tools , which not only tell you why a user did something, but also tell you what went wrong behind the scenes and how to fix the issue.

Zipy belongs to this fourth category of the most evolved tools, which combines the worlds of session replay, no-code event tracking, and proactive customer issue fixing, nestled with custom product analytics to make better product and business decisions, based on user behavior insights.

More resources on Behvioural Analytics

• Top 21user behavior analytics tools
• Revolutionize UX with Zipy’sUser Behavior Analytics Software (UBA)
• Cracking the code: AnalyzingE-commerce consumer behavior patterns
• Precision Marketing: Leveragingbehavioral targeting strategies
• A comprehensive guide tocustomer behavior analysis
• Navigating digital journeys: Strategies for effectiveuser behavior tracking
• Overcominguser behavior analytics challenges in the digital era
• Measuring Success: Essentialuser behavior metrics for SaaS platforms