Behavioral Analytics Essentials

Fox Studios invited hundreds of moviegoers from Boston, Chicago, Philadelphia, and Washington, DC, to attend the premiere of a macabre historical revenge drama film that was set in the 1800s in the autumn of 2015. In contrast to previous premieres, however, this one included wristbands created by the bio analytics startup Lightwave for the audience.

The viewers' autonomic nervous systems took control and the wrist trackers recorded their heart rates, body temperatures, movements, and skin conductivity continuously for the two and a half hours of the video. Together, these showed their fight-or-flight reactions. The film's creators were aware of the film's suspense and potential for audience attachment, but now they had information to help them decide which scenes were compelling, which ones could use some work, and how long viewers would spend staring at 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?

The practice of gathering information on the whats and hows of user behaviour and using that information to infer the whys of user behaviour patterns is known as behavioural analytics. In the digital realm, it refers to the procedure of gathering and examining every single user interaction with a programme or website in order to decipher user behaviour. By doing this, businesses can enhance their digital goods and increase conversion rates by manipulating user behaviour.

Today's digital firms are always attempting to interpret the requirements, preferences, and activities of their customers. To do this, they utilise analytics on user behaviour. We will go into the realm of behaviour analytics in this comprehensive book, covering its uses, advantages, and tactics. We will also explore the specifics of tracking, gathering, and evaluating behavioural data, as well as other forms of user behaviour analysis.

Importance of user behavior analytics (UBA)

User Behaviour Analytics (UBA) is essential for monitoring consumer issues with software applications, user growth, and adoption of digital products. It benefits all teams in the following ways:

• Enhancement of user experience (UX): It is possible to detect usability problems in a product and enhance the user experience by comprehending the needs of the users when one knows how consumers interact with it and the reasons behind their abandonment of a specific page.
• Data-driven decision making: The product usage statistics can assist teams in making data-driven decisions about which feature upgrades or bug fixes have the greatest impact on users and how to prioritise them appropriately.
• Product personalization: Software teams can design product processes that are tailored to each user based on their personas by having a thorough understanding of the demographics, needs, motivations, and behaviour of their customers. The product's content can also be customised to the preferences of the users; one example of this is the personalisation feature on YouTube.
• Competitive advantage: Businesses can get an advantage over rivals and market trends by continuously learning about client preferences through the use of user behaviour analysis.
• Improving user security: Digital analytics teams can map out aggregate usage trends by gathering data on user behaviour. This enables them to spot any odd user behaviour patterns and take appropriate action against fraudulent transactions or potentially malicious login attempts that pretend to be someone else.
• Optimization of marketing campaigns: UBA is used by marketing and growth teams to monitor and evaluate website visitor behaviour and campaign conversions. This is made possible via UTM tracking across social media posts, advertising campaigns, etc., which provides teams with insight into the campaigns that are most successful and areas for improvement.

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

The activities, mouse movements, clicks, inputs, searches, and page visits that users make on websites or digital applications—whether they be web, tablet, or mobile—are collectively referred to as user behaviour. Through user behaviour research on digital platforms, organisations can create more user-centric experiences, make data-driven decisions while improving their apps, and gain access to actionable user insights.

To identify areas for development, digital teams must, for instance, know where customers are leaving off in the workflow in the case of an e-commerce website selling clothing online:

• There may be problems with the product listings or they may not be seeing items that are relevant to their search if users are leaving the product or search listing pages in increasing numbers.
• The user may not have had the intended intent if the drop-off increases during or after adding items to the cart.
• If the drop-offs occur during the checkout process, payment gateway issues may be the cause, which should be investigated and resolved right away. Businesses need to address these kinds of problems since they have a direct impact on income.

User data can be tracked in two different categories. The first type of data is user activity, which consists of all of the user's digital activities. The second type of data is user environment, which contains identification, environment, and demographic information about the user. The categories of different user behaviour data points that can be gathered for behavioural analytics on digital platforms are as follows:

User activity data

• Page visits: This involves monitoring each and every page URL a person accesses within a website or web application. This offers insightful information about the interests and surfing habits of users, which is useful in determining the most popular pages and drop-off points.
• Clicks: The components that grab the most attention are revealed through tracking user clicks and interactions with homepage elements, which helps with design and content placement decisions.
• Mouse movements: Due to their similar rhythms, research indicates an 84% connection between mouse and eye movements made by users. Therefore, analysing mouse movements provides a proxy for user engagement and attentiveness and highlights moments of hesitancy or problems that users encounter.
• Keyboard Inputs:  Monitoring keyboard inputs, such as search terms and form submissions, offers insight into the intents and preferences of users. If you have concerns about monitoring personally identifying information, there are numerous tracking tools available today, such as Zipy, that allow you to mask user inputs, protecting user privacy and security.
• App specific CTAs: Applications can gain valuable information about how well calls-to-action (CTAs) guide users towards desired behaviours, such as signing up, logging in, adding items to their basket, etc., by tracking the clicks on these prompts. With the help of this data, marketing teams may increase conversion rates by A/B testing different CTAs.Time spent: Tracking how long visitors spend on sites or in apps provides insight into their level of interest and interaction; longer visits are a sign of more in-depth research or user involvement.
• Frequency of usage: When tracking user loyalty and retention, identifying power users and potential attrition risks, and tracking website or app revisit frequency are all made possible by this information. Greater utilisation corresponds to greater intent to use, purchase, or upgrade.
• Usage trend: When product usage trends are tracked over time, patterns, cyclical tendencies, or aberrations in user behaviour are revealed. These insights are then used to inform strategic decisions and product roadmap course adjustments. This is where longer-term usage trends are indicated by measures such as DAU, WAU, and MAU (Daily/Weekly/Monthly Active Users).
• Feature specific usage: Analysing the uptake of particular features or functions within an application aids in identifying underutilised parts, issue regions that may require improvement, and popular features.

User environment data

• User name, email or unique ID: While protecting user privacy, tracking individual user behaviour across sessions and devices is made possible by identifying users by name or by unique user IDs such as Email ID, UUID, and other identifiers.
• Persona: Determining user personas makes it easier to customise features and information to match the roles or attributes of users, providing a more relevant experience. This makes it possible for the design and product teams to give every consumer a more customised experience.
• Age: The gathering of user age data enables demographic user segmentation, providing valuable insights for product decisions, marketing campaign planning, and the delivery of pertinent content.
• Company: Understanding the linked company of a user can help with the personalisation of offers, services, and content, especially in business-to-business (B2B) interactions. The majority of B2B SaaS solutions gather user corporate data during the registration process.
• Geography:Gathering location information such as city, state, and nation is helpful for localization efforts, which include content adaption, language preferences, and focused marketing campaigns. Additionally, patterns of app user behaviour can be examined based on the languages and geographic areas of users.
• IP address: Geolocation, security protocols, and the detection of unusual activity or repeated account logins from different places are all based on IP addresses.
• Operating system: Monitoring the user's operating system is crucial for guaranteeing interoperability and enhancing the user experience on particular platforms. Additionally, based on the OS, this aids customer support personnel in identifying and reproducing client difficulties.
• Browser: The guarantee of cross-browser compatibility and the fixing of browser-specific problems that could affect user behaviour are guided by browser data.
• Device: Understanding the type of device the user is using (tablet, smartphone, or desktop) is essential for improving user interfaces and responsive design.
• Time of the day: Examining the temporal correlations between user interactions reveals trends in user behaviour, including periods of high activity and time zone concerns. Teams from Marketing and Customer Success are now able to communicate with users according to their time zones.

In conclusion, organisations are empowered with a comprehensive understanding of user behaviour, preferences, and engagement through the tracking of user activity and user environment data. These insights constitute the cornerstone for tailored user experiences, data-driven decision-making, and the ongoing improvement of software and digital goods.

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.

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 <head> 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. 

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.

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.

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.

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.

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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 21 user behavior analytics tools
• Revolutionize UX with Zipy's User Behavior Analytics Software (UBA)
• Cracking the code: Analyzing E-commerce consumer behavior patterns
• Precision Marketing: Leveraging behavioral targeting strategies
• A comprehensive guide to customer behavior analysis
• Navigating digital journeys: Strategies for effective user behavior tracking
• Overcoming user behavior analytics challenges in the digital era
• Measuring Success: Essential user behavior metrics for SaaS platforms