Client vs Server Side Events

Apoorv Nandan
CTO & Co-founder
October 11, 2023

The importance of analytics is constantly increasing. As a result, so is the need for increasing complexity in analytics. 

As a result, your ability to capture and interpret user actions is crucial. This isn't just about amassing data points; it's about deriving meaningful insights that drive strategy. This article aims to delve deep into server-side and client-side events—understanding their mechanics, assessing their impact, and measuring them effectively. Whether you're a seasoned product manager, startup founder, or product analyst, you'll walk away with actionable insights to refine your analytics approach.

WHAT ARE SERVER AND CLIENT SIDE EVENTS?

                                                                                                 Source         

Server-side events are actions tracked straight from your servers. These actions are often captured through API calls, which are like digital handshakes between systems, and scripts, which are automated tasks running on the server.

On the other hand, client-side events are captured in the user's browser or application, often through JavaScript tags or mobile SDKs. What sets client-side events apart is the granularity of data they provide. These events offer real-time insights into user behavior by capturing specific actions like button clicks, form submissions, or even mouse movements. This granularity makes client-side events the go-to choice for tracking immediate user reactions and UI/UX interactions.

DIFFERENCES BETWEEN SERVER AND CLIENT SIDE EVENTS

1. Data Integrity and Accuracy

Server-side:

Emphasizing data integrity, server-side tracking minimizes discrepancies arising from varied browser behaviors or user settings. Imagine users with aggressive ad-blockers or rigorous privacy configurations; their actions might be invisible to client-side tools. However, server-side tracking circumvents these hurdles by directly collecting data from backend processes, ensuring it's both consistent and comprehensive. This method acts as a foundational layer, ensuring that even with the myriad of client-side variables, there's a consistent and reliable data backbone to rely on.

Client-side:

While susceptible to challenges like ad-blockers, the true strength of client-side tracking lies in its integration with user interface elements. Through event listeners and direct instrumentation of interface components, it can detect specific interactions such as hovers, clicks, scrolls, and even durations of interactions. For instance, if a new animation is introduced on a product image, client-side tools can capture how users hover over that image, providing immediate feedback on their engagement. Beyond just knowing an action occurred, it provides context, like whether users engaged more with the animation-loaded image versus a static one. This level of detail, capturing both the action and its context, defines the granularity of client-side tracking.

2.Latency and real-time analysis 

Server-side

Predominantly relying on batch processing, server-side events prioritize data aggregation over immediacy. This processing model serves a purpose: By collecting data in batches, you can process large volumes more efficiently, ensuring thorough analysis and reduced strain on resources. However, the trade-off is the latency introduced. 

While this might not be ideal for immediate tactical decisions, it's perfectly suited for strategic initiatives where comprehensive data sets are more valuable than real-time feedback. Consider long-term trend analyses, cohort studies, or monthly performance reviews—here, the completeness and integrity of data matter more than its speed of availability.

Client-side

Embodying the ethos of real-time analytics, client-side events are all about the ‘now.’ The almost instantaneous availability of data empowers teams to make agile decisions. In scenarios like flash sales, short-lived marketing campaigns, or real-time user behavior monitoring during a product launch, every minute counts. Client-side analytics, with its real-time feedback, becomes the linchpin for businesses to react, adjust, and pivot swiftly. However, it's essential to strike a balance: the rapid influx of data requires robust filtration mechanisms to ensure actionable insights aren't drowned in noise.

EXAMPLES OF KEY METRICS

Server Side:                                          

  • Conversion Rates                                                                                      
  • Customer Lifetime Value (CLV)
  • Churn Rates
  • Average Revenue Per User (ARPU)
  • Subscription Upgrades/Downgrades
  • Transaction Volume
  • Retention Rate
  • Referral Source
  • Failed Transactions
  • Revenue Attribution

Client Side: 

  • Page View Duration
  • Bounce Rate
  • Click-Through Rate (CTR)
  • Scroll Depth
  • Exit Rate
  • Interactions Per Session
  • Event Tracking
  • Heatmaps
  • Form Abandonment Rate
  • User Navigation Path

MEASURING SERVER AND CLIENT-SIDE EVENTS

Now, knowing these metrics is largely useless without also knowing how to measure them.

Let’s have a look

Server Side Measurement:

1. APIs Emitting Signals:

  • Functionality: On server-side operations, whenever state-changing actions transpire—like a transaction confirmation—APIs act as conduits, transmitting event data. The sophistication lies in the precision of these signals: they can convey nuances about the action, such as the specific transaction type, amount, or even user behavior leading up to the action.
  • Event Listeners: With advanced analytics infrastructure, servers aren't just passive receptors but are proactively monitoring these API emissions. Take, for example, a "Subscription_Upgrade" event: modern systems can parse this in real-time, potentially triggering other automated systems, like customer communication tools, to send out congrats emails or promotional offers.
  • Storing Data: Post event detection, meticulous logging procedures ensure data integrity. Advanced databases now support features like ACID compliance, ensuring data reliability and consistency even in complex operations.

2. Direct Database Interactions:

  • Direct Entry: Not all data journeys through the API landscape. Periodic server health checks, system diagnostics, or automated report generations can populate databases directly. The sophistication is in the automation of these entries, often driven by cron jobs or scheduled tasks, ensuring timely data entry without manual intervention.
  • Relational Databases: Beyond mere data storage, modern relational databases, with their normalized schemas, allow for complex query operations. They can decipher patterns, correlations, and even predictive indicators by understanding the intricate relations between diverse data points.

3. Batch Processing Approach:

  • Efficiency: The choice between real-time data processing and batch processing often hinges on specific analytical needs. Batch processing, especially with tools like Apache Kafka or Spark, allows for the handling of massive data streams, performing operations en masse, and optimizing server resource usage.
  • Organized Data Collection: Advanced analytics platforms often have built-in data wrangling and ETL (Extract, Transform, Load) capabilities. This ensures data, even when processed in large batches, adheres to predefined schemas, making subsequent analysis more streamlined and accurate.

Client Side Measurement:

1. JavaScript Event Tracking

  • Real-time Interactions: JavaScript, especially with the rise of asynchronous operations and single-page applications, captures the nuances of real-time user interactions. Libraries such as React or Vue have elevated this by offering virtual DOM diffing, which logs changes even if they're transient or rapidly alternating.
  • Granular Event Listeners: Modern front-end frameworks, coupled with analytics libraries, allow not just for generic click tracking but also for measuring nuances. For example, the duration a user hovers over a specific feature, or the speed of their scroll, might give insights into their intent or interest.
  • State Management & Data Flow: Tools like Redux or Vuex help manage state across larger applications, which becomes critical in logging user journey or preferences, especially during session-based interactions. These state management tools ensure that every micro-interaction, even if not immediately sent to the server, gets stored systematically for potential later analysis.

2. Mobile SDKs for App Analytics

  • Gestural Interactions: Mobile users don't just 'click'. They swipe, pinch, long-press, and more. Modern SDKs, tailored for mobile analytics, capture this range of interactions, offering insights into user preferences and behaviors that are unique to the mobile experience.
  • Integrative Functionality: With mobile devices brimming with sensors, SDKs go beyond touch interactions. They can measure tilt angles (useful for game developers), ambient light (for reader apps), or even geolocation pings for location-based services.
  • Lifecycle Events & User Journeys: Mobile apps have unique lifecycles - foreground, background, terminated, and more. Advanced SDKs log these transitions, which can be pivotal in understanding app performance, user return rates, or identifying potential issues in app operation.

3. Real-time Data Syncing & Local Storage

  • Efficient Data Handling: Especially in regions or conditions with spotty internet, real-time syncing might be a challenge. Advanced client-side scripts can temporarily store interactions locally, using mechanisms like IndexedDB or Local Storage, and sync when the connection is optimal, ensuring minimal data loss.
  • Bandwidth Optimization: For content-rich applications, especially ones with high media usage, client-side scripts can make intelligent decisions. They might prioritize sending text-based interaction logs first and queue heavier media files for optimal times, ensuring efficient bandwidth usage.

CONCLUSION

As server and client-side events offer distinct but complementary insights, weaving them seamlessly into your product analytics strategy can be challenging. But with the right tools and knowledge, the intricacies of these two realms can be harnessed effectively, painting a holistic picture of user behavior and intent.

Yet, even with the profound insights at hand, the true challenge often lies in the setup, maintenance, and interpretation of these analytics. This is where leveraging next-generation solutions, like Crunch, becomes invaluable. By integrating Generative AI, Crunch not only simplifies the analytics journey but makes it accessible and actionable for every stakeholder on your team.

Sign up for the waitlist here! 

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If you are looking to make better use of your product data, gain insights faster and improve decision making across teams, Crunch can help you get there.
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October 11, 2023
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Client vs Server Side Events

This article aims to delve deep into server-side and client-side events—understanding their mechanics, assessing their impact, and measuring them effectively. Whether you're a seasoned product manager, startup founder, or product analyst, you'll walk away with actionable insights to refine your analytics approach.

Client vs Server Side Events

The importance of analytics is constantly increasing. As a result, so is the need for increasing complexity in analytics. 

As a result, your ability to capture and interpret user actions is crucial. This isn't just about amassing data points; it's about deriving meaningful insights that drive strategy. This article aims to delve deep into server-side and client-side events—understanding their mechanics, assessing their impact, and measuring them effectively. Whether you're a seasoned product manager, startup founder, or product analyst, you'll walk away with actionable insights to refine your analytics approach.

WHAT ARE SERVER AND CLIENT SIDE EVENTS?

                                                                                                 Source         

Server-side events are actions tracked straight from your servers. These actions are often captured through API calls, which are like digital handshakes between systems, and scripts, which are automated tasks running on the server.

On the other hand, client-side events are captured in the user's browser or application, often through JavaScript tags or mobile SDKs. What sets client-side events apart is the granularity of data they provide. These events offer real-time insights into user behavior by capturing specific actions like button clicks, form submissions, or even mouse movements. This granularity makes client-side events the go-to choice for tracking immediate user reactions and UI/UX interactions.

DIFFERENCES BETWEEN SERVER AND CLIENT SIDE EVENTS

1. Data Integrity and Accuracy

Server-side:

Emphasizing data integrity, server-side tracking minimizes discrepancies arising from varied browser behaviors or user settings. Imagine users with aggressive ad-blockers or rigorous privacy configurations; their actions might be invisible to client-side tools. However, server-side tracking circumvents these hurdles by directly collecting data from backend processes, ensuring it's both consistent and comprehensive. This method acts as a foundational layer, ensuring that even with the myriad of client-side variables, there's a consistent and reliable data backbone to rely on.

Client-side:

While susceptible to challenges like ad-blockers, the true strength of client-side tracking lies in its integration with user interface elements. Through event listeners and direct instrumentation of interface components, it can detect specific interactions such as hovers, clicks, scrolls, and even durations of interactions. For instance, if a new animation is introduced on a product image, client-side tools can capture how users hover over that image, providing immediate feedback on their engagement. Beyond just knowing an action occurred, it provides context, like whether users engaged more with the animation-loaded image versus a static one. This level of detail, capturing both the action and its context, defines the granularity of client-side tracking.

2.Latency and real-time analysis 

Server-side

Predominantly relying on batch processing, server-side events prioritize data aggregation over immediacy. This processing model serves a purpose: By collecting data in batches, you can process large volumes more efficiently, ensuring thorough analysis and reduced strain on resources. However, the trade-off is the latency introduced. 

While this might not be ideal for immediate tactical decisions, it's perfectly suited for strategic initiatives where comprehensive data sets are more valuable than real-time feedback. Consider long-term trend analyses, cohort studies, or monthly performance reviews—here, the completeness and integrity of data matter more than its speed of availability.

Client-side

Embodying the ethos of real-time analytics, client-side events are all about the ‘now.’ The almost instantaneous availability of data empowers teams to make agile decisions. In scenarios like flash sales, short-lived marketing campaigns, or real-time user behavior monitoring during a product launch, every minute counts. Client-side analytics, with its real-time feedback, becomes the linchpin for businesses to react, adjust, and pivot swiftly. However, it's essential to strike a balance: the rapid influx of data requires robust filtration mechanisms to ensure actionable insights aren't drowned in noise.

EXAMPLES OF KEY METRICS

Server Side:                                          

  • Conversion Rates                                                                                      
  • Customer Lifetime Value (CLV)
  • Churn Rates
  • Average Revenue Per User (ARPU)
  • Subscription Upgrades/Downgrades
  • Transaction Volume
  • Retention Rate
  • Referral Source
  • Failed Transactions
  • Revenue Attribution

Client Side: 

  • Page View Duration
  • Bounce Rate
  • Click-Through Rate (CTR)
  • Scroll Depth
  • Exit Rate
  • Interactions Per Session
  • Event Tracking
  • Heatmaps
  • Form Abandonment Rate
  • User Navigation Path

MEASURING SERVER AND CLIENT-SIDE EVENTS

Now, knowing these metrics is largely useless without also knowing how to measure them.

Let’s have a look

Server Side Measurement:

1. APIs Emitting Signals:

  • Functionality: On server-side operations, whenever state-changing actions transpire—like a transaction confirmation—APIs act as conduits, transmitting event data. The sophistication lies in the precision of these signals: they can convey nuances about the action, such as the specific transaction type, amount, or even user behavior leading up to the action.
  • Event Listeners: With advanced analytics infrastructure, servers aren't just passive receptors but are proactively monitoring these API emissions. Take, for example, a "Subscription_Upgrade" event: modern systems can parse this in real-time, potentially triggering other automated systems, like customer communication tools, to send out congrats emails or promotional offers.
  • Storing Data: Post event detection, meticulous logging procedures ensure data integrity. Advanced databases now support features like ACID compliance, ensuring data reliability and consistency even in complex operations.

2. Direct Database Interactions:

  • Direct Entry: Not all data journeys through the API landscape. Periodic server health checks, system diagnostics, or automated report generations can populate databases directly. The sophistication is in the automation of these entries, often driven by cron jobs or scheduled tasks, ensuring timely data entry without manual intervention.
  • Relational Databases: Beyond mere data storage, modern relational databases, with their normalized schemas, allow for complex query operations. They can decipher patterns, correlations, and even predictive indicators by understanding the intricate relations between diverse data points.

3. Batch Processing Approach:

  • Efficiency: The choice between real-time data processing and batch processing often hinges on specific analytical needs. Batch processing, especially with tools like Apache Kafka or Spark, allows for the handling of massive data streams, performing operations en masse, and optimizing server resource usage.
  • Organized Data Collection: Advanced analytics platforms often have built-in data wrangling and ETL (Extract, Transform, Load) capabilities. This ensures data, even when processed in large batches, adheres to predefined schemas, making subsequent analysis more streamlined and accurate.

Client Side Measurement:

1. JavaScript Event Tracking

  • Real-time Interactions: JavaScript, especially with the rise of asynchronous operations and single-page applications, captures the nuances of real-time user interactions. Libraries such as React or Vue have elevated this by offering virtual DOM diffing, which logs changes even if they're transient or rapidly alternating.
  • Granular Event Listeners: Modern front-end frameworks, coupled with analytics libraries, allow not just for generic click tracking but also for measuring nuances. For example, the duration a user hovers over a specific feature, or the speed of their scroll, might give insights into their intent or interest.
  • State Management & Data Flow: Tools like Redux or Vuex help manage state across larger applications, which becomes critical in logging user journey or preferences, especially during session-based interactions. These state management tools ensure that every micro-interaction, even if not immediately sent to the server, gets stored systematically for potential later analysis.

2. Mobile SDKs for App Analytics

  • Gestural Interactions: Mobile users don't just 'click'. They swipe, pinch, long-press, and more. Modern SDKs, tailored for mobile analytics, capture this range of interactions, offering insights into user preferences and behaviors that are unique to the mobile experience.
  • Integrative Functionality: With mobile devices brimming with sensors, SDKs go beyond touch interactions. They can measure tilt angles (useful for game developers), ambient light (for reader apps), or even geolocation pings for location-based services.
  • Lifecycle Events & User Journeys: Mobile apps have unique lifecycles - foreground, background, terminated, and more. Advanced SDKs log these transitions, which can be pivotal in understanding app performance, user return rates, or identifying potential issues in app operation.

3. Real-time Data Syncing & Local Storage

  • Efficient Data Handling: Especially in regions or conditions with spotty internet, real-time syncing might be a challenge. Advanced client-side scripts can temporarily store interactions locally, using mechanisms like IndexedDB or Local Storage, and sync when the connection is optimal, ensuring minimal data loss.
  • Bandwidth Optimization: For content-rich applications, especially ones with high media usage, client-side scripts can make intelligent decisions. They might prioritize sending text-based interaction logs first and queue heavier media files for optimal times, ensuring efficient bandwidth usage.

CONCLUSION

As server and client-side events offer distinct but complementary insights, weaving them seamlessly into your product analytics strategy can be challenging. But with the right tools and knowledge, the intricacies of these two realms can be harnessed effectively, painting a holistic picture of user behavior and intent.

Yet, even with the profound insights at hand, the true challenge often lies in the setup, maintenance, and interpretation of these analytics. This is where leveraging next-generation solutions, like Crunch, becomes invaluable. By integrating Generative AI, Crunch not only simplifies the analytics journey but makes it accessible and actionable for every stakeholder on your team.

Sign up for the waitlist here! 

Customer retention is the key

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What are the most relevant factors to consider?

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Don’t overspend on growth marketing without good retention rates

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What’s the ideal customer retention rate?

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Next steps to increase your customer retention

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