From 6af70560ef70c87e46e8071afcccf53aaef8cfb0 Mon Sep 17 00:00:00 2001 From: Mano Toth Date: Wed, 21 May 2025 12:05:30 +0200 Subject: [PATCH 1/7] Add AI and Product use cases --- docs.json | 2 + getting-started-guide/ai-machine-learning.mdx | 54 +++++++++++++++++++ getting-started-guide/event-data.mdx | 7 ++- getting-started-guide/product-analytics.mdx | 44 +++++++++++++++ 4 files changed, 103 insertions(+), 4 deletions(-) create mode 100644 getting-started-guide/ai-machine-learning.mdx create mode 100644 getting-started-guide/product-analytics.mdx diff --git a/docs.json b/docs.json index ab941da5..442812fd 100644 --- a/docs.json +++ b/docs.json @@ -26,6 +26,8 @@ "pages": [ "getting-started-guide/event-data", "getting-started-guide/observability", + "getting-started-guide/ai-machine-learning", + "getting-started-guide/product-analytics", "getting-started-guide/feature-states", "getting-started-guide/glossary" ] diff --git a/getting-started-guide/ai-machine-learning.mdx b/getting-started-guide/ai-machine-learning.mdx new file mode 100644 index 00000000..36651690 --- /dev/null +++ b/getting-started-guide/ai-machine-learning.mdx @@ -0,0 +1,54 @@ +--- +title: "Axiom for AI and machine learning" +description: "This page explains how Axiom helps you leverage timestamped event data for AI and machine learning purposes." +sidebarTitle: AI and ML +--- + +Axiom allows you to monitor, evaluate, and iterate on AI models in production. + +As machine learning (ML) becomes central to user-facing applications, the challenge is no longer just building and deploying models, but improving them in production. + +Axiom enables ML and product engineering teams to continuously monitor and improve AI models in production by closing the loop between real-world data, model behavior, and iteration workflows. We integrate with your existing ML stack—such as Databricks, SageMaker, or Modal to surface failures, detect drift, and validate improvements without adding unnecessary complexity. + +## Why event data matters in AI and machine learning + +Shipping an AI model is only the beginning. The real challenge starts when it’s live and interacting with real users. Issues like hallucinations, bias, or degraded accuracy can damage user trust or experience. While many teams can detect that a model failed, very few can easily isolate the root cause, test a fix using production data, and confidently re-ship without days of manual effort. + +## Key features of Axiom for AI and machine learning + +With Axiom, you can solve the issues mentioned above: + +- **Capture and replay real-world inference data:** Automatically sample or flag production inputs and outputs to create test scenarios based on actual usage, not synthetic data. +- **Compare model versions at scale:** Run side-by-side tests between current and proposed model versions using real production slices. Visualize regressions, improvements, and unintended changes. +- **Orchestrate model updates confidently:** Integrate with your model orchestrator to re-deploy tested improvements with a few clicks, without pipeline rewrites required. +- **Shorten iteration cycles:** Reduce iteration time from days to hours by eliminating manual steps like downloading logs, writing scripts, or rebuilding test cases. + +## Important use cases + +| Role | Problem | What Axiom offers | +|--------------------|------------------------------------------------------------------------|-------------------------------------------------------------------------| +| ML engineers | Debugging and testing are slow and manual | Automate capture and replay of inference data for quick validation | +| Applied AI teams | Hard to measure impact of model changes in production | Run version comparisons with real production examples | +| Product engineers | Limited visibility into model behavior post-deployment | See and test how real users are affected in production | + +## How Axiom works for AI and machine learning + +Axiom introduces a closed-loop workflow that integrates with your current ML infrastructure: + +1. **Detect:** Automatically surface anomalous, drifting, or low-confidence outputs in production. +1. **Sample:** Capture relevant inputs and outputs to build test cases from real-world examples. +1. **Validate:** Run tests against current and new model versions. Inspect where improvements or regressions occur. +1. **Ship:** Re-deploy the improved model confidently using built-in orchestration tools. + +This process lets you maintain a rapid feedback loop between observe, refine, re-ship. + +## Why choose Axiom for AI and machine learning + +Axiom is perfect for iteration on live usage data. It fits naturally into modern stacks without forcing major changes. + +You’ll benefit most if you: + +- Already have models in production. +- Discover issues late via user complaints or delayed log review. +- Manually gather logs and write ad hoc scripts to test model fixes. +- Want to tighten the iteration loop for faster, more confident updates. diff --git a/getting-started-guide/event-data.mdx b/getting-started-guide/event-data.mdx index 8692f6e1..2f25f969 100644 --- a/getting-started-guide/event-data.mdx +++ b/getting-started-guide/event-data.mdx @@ -19,7 +19,6 @@ Each event is simply a structured record—composed of key-value pairs—that ca Event data, understood as the atomic unit of digital activity, is the lifeblood of modern businesses. Leveraging the power of event data is essential in the following areas, among others: - [Observability](/getting-started-guide/observability) -- Security -- Product analytics -- Business intelligence -- AI and machine learning \ No newline at end of file +- [AI and machine learning](/getting-started-guide/ai-machine-learning) +- [Product analytics](/getting-started-guide/product-analytics) +- Business intelligence \ No newline at end of file diff --git a/getting-started-guide/product-analytics.mdx b/getting-started-guide/product-analytics.mdx new file mode 100644 index 00000000..c91167d7 --- /dev/null +++ b/getting-started-guide/product-analytics.mdx @@ -0,0 +1,44 @@ +--- +title: "Axiom for product analytics" +description: "This page explains how Axiom helps you leverage timestamped event data for product analytics purposes." +sidebarTitle: Product analytics +--- + +Axiom helps you leverage the power of timestamped event data. Axiom believes that event data reflects a broad range of interactions, crossing boundaries from engineering to product management, security, and beyond. For a more general explanation of event data in Axiom, see [Events](/getting-started-guide/event-data). + +This page explains how you can leverage the power of event data for the product analytics use case. + +In product analytics, the ability to harness and interpret data effectively can determine the success of a product. Axiom offers a new approach to product analytics by leveraging the power of timestamped event data. This unique capability enables organizations to gain actionable insights, optimize user experiences, and drive product innovation. + +## Why event data matters in product analytics + +Event data captures the actions and interactions users have with a product over time. From button clicks and page views to error events and feature usage, every timestamped event tells a story about user behavior. Axiom’s platform is specifically designed to process and analyze these granular datasets, making it an indispensable tool for product teams aiming to do the following: + +- **Understand user behavior:** By tracking and analyzing event streams, Axiom provides a clear picture of how users engage with your product. +- **Identify trends and patterns:** Time-series analysis reveals emerging trends, helping teams anticipate user needs and adjust strategies proactively. +- **Optimize product features:** Pinpoint which features drive the most value and identify friction points that need improvement. + +## Key features of Axiom for product analytics + +The following key features make Axiom perfect for product analytics: + +- **Real-time event monitoring:** Axiom’s ability to ingest and process data in real-time means you can monitor user activity as it happens. This empowers product managers to act quickly in response to anomalies or unexpected usage patterns, reducing downtime and improving user satisfaction. +- **Unified data platform:** Axiom eliminates data silos by integrating event data from diverse sources into a single, cohesive platform. Whether your data originates from web applications, mobile apps, or third-party tools, Axiom ensures seamless access and analysis. +- **Advanced query capabilities:** With a robust query language, Axiom enables product teams to dive deep into data analysis. Perform detailed segmentation, drill down into specific user journeys, and uncover insights that would otherwise remain hidden. +- **Custom dashboards and visualizations:** Intuitive dashboards and customizable visualizations make it easy for product managers to communicate insights to stakeholders. Axiom’s visual tools enhance collaboration and decision-making. +- **Scalable infrastructure:** As your product grows, so does the volume of event data. Axiom’s architecture is built to scale effortlessly, ensuring that your analytics remain robust and reliable, even with massive datasets. + +## Enhance product analytics with Axiom + +Axiom helps you make the most of your event data in the following product analytics use cases, among others: + +- **Feature adoption analysis:** Understand how new features are adopted by users and identify which aspects require improvement to maximize engagement. +- **Retention and churn analysis:** Leverage event data to identify patterns in user retention and predict potential churn, enabling proactive interventions. +- **Funnel optimization:** Analyze user journeys through critical funnels, such as sign-ups or purchases, to pinpoint drop-off points and optimize conversion rates. +- **A/B testing:** Compare user interactions across different test groups to validate hypotheses and make data-driven decisions. + +## Why choose Axiom for product analytics + +Axiom’s focus on timestamped event data makes it perfect for product analytics. By crossing boundaries from engineering to product management and security, Axiom empowers cross-functional teams to collaborate effectively. Its comprehensive feature set ensures that organizations can unlock the full potential of their data, driving smarter decisions and fostering innovation. + +In competitive markets, understanding your users is paramount. With Axiom, you gain a trusted partner in turning event data into actionable insights that propel your product to new heights. Experience the future of product analytics with Axiom and transform how you build, analyze, and optimize your product. \ No newline at end of file From 14ecfe083798c5f21b3b94937c60ebbd1a137643 Mon Sep 17 00:00:00 2001 From: Mano Toth Date: Wed, 21 May 2025 12:54:25 +0200 Subject: [PATCH 2/7] Update ai-machine-learning.mdx --- getting-started-guide/ai-machine-learning.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/getting-started-guide/ai-machine-learning.mdx b/getting-started-guide/ai-machine-learning.mdx index 36651690..df604e7e 100644 --- a/getting-started-guide/ai-machine-learning.mdx +++ b/getting-started-guide/ai-machine-learning.mdx @@ -8,7 +8,7 @@ Axiom allows you to monitor, evaluate, and iterate on AI models in production. As machine learning (ML) becomes central to user-facing applications, the challenge is no longer just building and deploying models, but improving them in production. -Axiom enables ML and product engineering teams to continuously monitor and improve AI models in production by closing the loop between real-world data, model behavior, and iteration workflows. We integrate with your existing ML stack—such as Databricks, SageMaker, or Modal to surface failures, detect drift, and validate improvements without adding unnecessary complexity. +Axiom enables ML and product engineering teams to continuously monitor and improve AI models in production by closing the loop between real-world data, model behavior, and iteration workflows. We integrate with your existing ML stack, such as Databricks, SageMaker, or Modal to surface failures, detect drift, and validate improvements without adding unnecessary complexity. ## Why event data matters in AI and machine learning From 97b8511b721990192b0c61a95b670fdc36221350 Mon Sep 17 00:00:00 2001 From: Mano Toth Date: Mon, 16 Jun 2025 10:52:33 +0200 Subject: [PATCH 3/7] First fixes --- docs.json | 1 - getting-started-guide/ai-machine-learning.mdx | 54 ------------------- getting-started-guide/event-data.mdx | 2 +- getting-started-guide/observability.mdx | 2 +- 4 files changed, 2 insertions(+), 57 deletions(-) delete mode 100644 getting-started-guide/ai-machine-learning.mdx diff --git a/docs.json b/docs.json index 442812fd..faa938f9 100644 --- a/docs.json +++ b/docs.json @@ -26,7 +26,6 @@ "pages": [ "getting-started-guide/event-data", "getting-started-guide/observability", - "getting-started-guide/ai-machine-learning", "getting-started-guide/product-analytics", "getting-started-guide/feature-states", "getting-started-guide/glossary" diff --git a/getting-started-guide/ai-machine-learning.mdx b/getting-started-guide/ai-machine-learning.mdx deleted file mode 100644 index df604e7e..00000000 --- a/getting-started-guide/ai-machine-learning.mdx +++ /dev/null @@ -1,54 +0,0 @@ ---- -title: "Axiom for AI and machine learning" -description: "This page explains how Axiom helps you leverage timestamped event data for AI and machine learning purposes." -sidebarTitle: AI and ML ---- - -Axiom allows you to monitor, evaluate, and iterate on AI models in production. - -As machine learning (ML) becomes central to user-facing applications, the challenge is no longer just building and deploying models, but improving them in production. - -Axiom enables ML and product engineering teams to continuously monitor and improve AI models in production by closing the loop between real-world data, model behavior, and iteration workflows. We integrate with your existing ML stack, such as Databricks, SageMaker, or Modal to surface failures, detect drift, and validate improvements without adding unnecessary complexity. - -## Why event data matters in AI and machine learning - -Shipping an AI model is only the beginning. The real challenge starts when it’s live and interacting with real users. Issues like hallucinations, bias, or degraded accuracy can damage user trust or experience. While many teams can detect that a model failed, very few can easily isolate the root cause, test a fix using production data, and confidently re-ship without days of manual effort. - -## Key features of Axiom for AI and machine learning - -With Axiom, you can solve the issues mentioned above: - -- **Capture and replay real-world inference data:** Automatically sample or flag production inputs and outputs to create test scenarios based on actual usage, not synthetic data. -- **Compare model versions at scale:** Run side-by-side tests between current and proposed model versions using real production slices. Visualize regressions, improvements, and unintended changes. -- **Orchestrate model updates confidently:** Integrate with your model orchestrator to re-deploy tested improvements with a few clicks, without pipeline rewrites required. -- **Shorten iteration cycles:** Reduce iteration time from days to hours by eliminating manual steps like downloading logs, writing scripts, or rebuilding test cases. - -## Important use cases - -| Role | Problem | What Axiom offers | -|--------------------|------------------------------------------------------------------------|-------------------------------------------------------------------------| -| ML engineers | Debugging and testing are slow and manual | Automate capture and replay of inference data for quick validation | -| Applied AI teams | Hard to measure impact of model changes in production | Run version comparisons with real production examples | -| Product engineers | Limited visibility into model behavior post-deployment | See and test how real users are affected in production | - -## How Axiom works for AI and machine learning - -Axiom introduces a closed-loop workflow that integrates with your current ML infrastructure: - -1. **Detect:** Automatically surface anomalous, drifting, or low-confidence outputs in production. -1. **Sample:** Capture relevant inputs and outputs to build test cases from real-world examples. -1. **Validate:** Run tests against current and new model versions. Inspect where improvements or regressions occur. -1. **Ship:** Re-deploy the improved model confidently using built-in orchestration tools. - -This process lets you maintain a rapid feedback loop between observe, refine, re-ship. - -## Why choose Axiom for AI and machine learning - -Axiom is perfect for iteration on live usage data. It fits naturally into modern stacks without forcing major changes. - -You’ll benefit most if you: - -- Already have models in production. -- Discover issues late via user complaints or delayed log review. -- Manually gather logs and write ad hoc scripts to test model fixes. -- Want to tighten the iteration loop for faster, more confident updates. diff --git a/getting-started-guide/event-data.mdx b/getting-started-guide/event-data.mdx index 2f25f969..dda3f2d2 100644 --- a/getting-started-guide/event-data.mdx +++ b/getting-started-guide/event-data.mdx @@ -19,6 +19,6 @@ Each event is simply a structured record—composed of key-value pairs—that ca Event data, understood as the atomic unit of digital activity, is the lifeblood of modern businesses. Leveraging the power of event data is essential in the following areas, among others: - [Observability](/getting-started-guide/observability) -- [AI and machine learning](/getting-started-guide/ai-machine-learning) +- AI - [Product analytics](/getting-started-guide/product-analytics) - Business intelligence \ No newline at end of file diff --git a/getting-started-guide/observability.mdx b/getting-started-guide/observability.mdx index f6fe712f..899bb7e1 100644 --- a/getting-started-guide/observability.mdx +++ b/getting-started-guide/observability.mdx @@ -54,4 +54,4 @@ Axiom’s support for metrics data currently comes with the following limitation - Axiom doesn’t support pre-aggregated metrics such as scrape samples. - Axiom isn’t optimized for high-dimensional metric time series with a very large number of metric/label combinations. -Support for these types of metrics data is coming soon in the first half of 2025. \ No newline at end of file +Support for these types of metrics data is coming soon. If you’re interested, [contact Axiom](https://axiom.co/contact). \ No newline at end of file From a4006f09b80c0805257637afca9040cc90ec6932 Mon Sep 17 00:00:00 2001 From: Mano Toth Date: Mon, 16 Jun 2025 11:55:06 +0200 Subject: [PATCH 4/7] Update learn-about-axiom.mdx --- getting-started-guide/learn-about-axiom.mdx | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/getting-started-guide/learn-about-axiom.mdx b/getting-started-guide/learn-about-axiom.mdx index cc4d88f7..ea7f331f 100644 --- a/getting-started-guide/learn-about-axiom.mdx +++ b/getting-started-guide/learn-about-axiom.mdx @@ -20,6 +20,13 @@ Learn about the fundamentals of timestamped event data in Axiom Learn about how Axiom helps you leverage timestamped event data for observability purposes + +Learn about how Axiom helps you leverage timestamped event data for product analytics purposes + + Date: Wed, 2 Jul 2025 13:40:05 +0200 Subject: [PATCH 5/7] Address feedback --- getting-started-guide/product-analytics.mdx | 82 ++++++++++++++++++++- 1 file changed, 79 insertions(+), 3 deletions(-) diff --git a/getting-started-guide/product-analytics.mdx b/getting-started-guide/product-analytics.mdx index c91167d7..63d402db 100644 --- a/getting-started-guide/product-analytics.mdx +++ b/getting-started-guide/product-analytics.mdx @@ -8,7 +8,7 @@ Axiom helps you leverage the power of timestamped event data. Axiom believes tha This page explains how you can leverage the power of event data for the product analytics use case. -In product analytics, the ability to harness and interpret data effectively can determine the success of a product. Axiom offers a new approach to product analytics by leveraging the power of timestamped event data. This unique capability enables organizations to gain actionable insights, optimize user experiences, and drive product innovation. +In product analytics, the ability to harness and interpret data effectively can determine the success of a product. Axiom allows product analytics to leverage the power of timestamped event data and easily read every single event. This unique capability enables organizations to gain actionable insights, optimize user experiences, and drive product innovation. ## Why event data matters in product analytics @@ -22,12 +22,42 @@ Event data captures the actions and interactions users have with a product over The following key features make Axiom perfect for product analytics: -- **Real-time event monitoring:** Axiom’s ability to ingest and process data in real-time means you can monitor user activity as it happens. This empowers product managers to act quickly in response to anomalies or unexpected usage patterns, reducing downtime and improving user satisfaction. -- **Unified data platform:** Axiom eliminates data silos by integrating event data from diverse sources into a single, cohesive platform. Whether your data originates from web applications, mobile apps, or third-party tools, Axiom ensures seamless access and analysis. +- **Real-time event monitoring:** Axiom’s ability to ingest and process data in real-time means you can monitor user activity as it happens. This empowers product managers to act quickly in response to anomalies or unexpected usage patterns, reducing downtime and improving user satisfaction. For example: + + - Track whether feature flags are toggling as expected. + - Watch for broken signup flows or onboarding drop-offs immediately after a deploy. + - Monitor if newly launched features are generating engagement. + +- **Unified data platform:** Axiom eliminates data silos by integrating event data from diverse sources into a single, cohesive platform. Axiom stores system telemetry alongside product analytics data. This eliminates the traditional separation between “user behavior” tools and “engineering” tools, and this convergence unlocks powerful debugging and insight scenarios: + + - Correlate frontend feature usage with backend latency. + - View conversion funnel stages alongside HTTP error logs. + - Link user drop-off to infrastructure anomalies. + - **Advanced query capabilities:** With a robust query language, Axiom enables product teams to dive deep into data analysis. Perform detailed segmentation, drill down into specific user journeys, and uncover insights that would otherwise remain hidden. - **Custom dashboards and visualizations:** Intuitive dashboards and customizable visualizations make it easy for product managers to communicate insights to stakeholders. Axiom’s visual tools enhance collaboration and decision-making. - **Scalable infrastructure:** As your product grows, so does the volume of event data. Axiom’s architecture is built to scale effortlessly, ensuring that your analytics remain robust and reliable, even with massive datasets. +## Standard patterns for product data: Segment compatibility + +Axiom supports event ingestion via widely adopted patterns such as the [Segment specification](https://segment.com/docs/connections/spec/): + +- **`identify`** associates events with known users. +- **`track`** records user interactions like button clicks or page views. +- **`group`** associates users with organizations or accounts. + +Axiom is compatible with these conventions used by popular tools like Mixpanel, Amplitude, June, and RudderStack. + +This approach gives product teams a familiar and low-friction way to onboard their analytics events, and the foundation for querying user behavior via APL (Axiom Processing Language). + +For example, the APL query below filters tracked events by name: + +```kusto +['segment-frontend-prod'] +| where event == "Button Clicked" +| summarize count() by userId, bin(_time, 1h) +``` + ## Enhance product analytics with Axiom Axiom helps you make the most of your event data in the following product analytics use cases, among others: @@ -37,6 +67,52 @@ Axiom helps you make the most of your event data in the following product analyt - **Funnel optimization:** Analyze user journeys through critical funnels, such as sign-ups or purchases, to pinpoint drop-off points and optimize conversion rates. - **A/B testing:** Compare user interactions across different test groups to validate hypotheses and make data-driven decisions. +## Use cases: from funnels to retention + +Here are some ways product teams use Axiom: + +### Feature adoption tracking + +Measure which users are engaging with newly released features. + +```kusto +['segment-frontend-prod'] +| where event == "Feature Used" and properties.featureName == "AI Chat" +| summarize count() by userId, bin(_time, 1h) +``` + +### Retention and churn analysis + +Analyze returning users over time: + +```kusto +['segment-frontend-prod'] +| where event == "Logged In" +| summarize sessions = count(), users = dcount(userId) by bin(_time, 1w) +``` + +### Funnel diagnostics + +Trace where users drop off between signup, onboarding, and first value. + +```kusto +['segment-frontend-prod'] +| where event in ("Signed Up", "Completed Onboarding", "Created Project") +| project userId, event, _time +| sort by userId, _time +``` + +### A/B test measurement + +Compare experiment cohorts based on downstream engagement: + +```kusto +['segment-frontend-prod'] +| where properties.experimentGroup == "variant_a" +| where event == "Clicked Upgrade" +| summarize conversions = dcount(userId) +``` + ## Why choose Axiom for product analytics Axiom’s focus on timestamped event data makes it perfect for product analytics. By crossing boundaries from engineering to product management and security, Axiom empowers cross-functional teams to collaborate effectively. Its comprehensive feature set ensures that organizations can unlock the full potential of their data, driving smarter decisions and fostering innovation. From a9ee4ffcafaf03fb1ac5a21dae946f5a227bcb02 Mon Sep 17 00:00:00 2001 From: Mano Toth Date: Tue, 8 Jul 2025 12:03:14 +0200 Subject: [PATCH 6/7] Implement feedback --- getting-started-guide/product-analytics.mdx | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/getting-started-guide/product-analytics.mdx b/getting-started-guide/product-analytics.mdx index 63d402db..84c1d908 100644 --- a/getting-started-guide/product-analytics.mdx +++ b/getting-started-guide/product-analytics.mdx @@ -46,11 +46,18 @@ Axiom supports event ingestion via widely adopted patterns such as the [Segment - **`track`** records user interactions like button clicks or page views. - **`group`** associates users with organizations or accounts. -Axiom is compatible with these conventions used by popular tools like Mixpanel, Amplitude, June, and RudderStack. +These event types are foundational to many analytics workflows and are supported by tools like Mixpanel, Amplitude, June, and RudderStack. Axiom’s compatibility with this ecosystem enables product teams to reuse existing instrumentation patterns and schemas with minimal changes. -This approach gives product teams a familiar and low-friction way to onboard their analytics events, and the foundation for querying user behavior via APL (Axiom Processing Language). +### How Axiom receives Segment events -For example, the APL query below filters tracked events by name: +To send Segment data to Axiom: + +1. Create an [HTTP destionation](/process-data/destinations/http) in Axiom. +1. Add a webhook destination in Segment. For more information, see the [Segment documentation](https://segment.com/docs/connections/destinations/catalog/webhooks/). + +Segment sends event payloads to Axiom in JSON format. Axiom stores each incoming payload as a structured event, preserving keys such as `event`, `userId`, `traits`, `properties`, and `_time` (automatically inferred or provided). No custom transformation is required. Segment’s default schema maps naturally into Axiom’s JSON ingestion pipeline. + +After sending events to Axiom, query them using APL. For example, to count “Button Clicked” events per user by hour: ```kusto ['segment-frontend-prod'] @@ -91,7 +98,7 @@ Analyze returning users over time: | summarize sessions = count(), users = dcount(userId) by bin(_time, 1w) ``` -### Funnel diagnostics +### Data for funnel diagnostics Trace where users drop off between signup, onboarding, and first value. From 140049c84af0a6d77defa9a936a1046fb639ec0f Mon Sep 17 00:00:00 2001 From: Mano Toth Date: Tue, 8 Jul 2025 12:05:59 +0200 Subject: [PATCH 7/7] Update product-analytics.mdx --- getting-started-guide/product-analytics.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/getting-started-guide/product-analytics.mdx b/getting-started-guide/product-analytics.mdx index 84c1d908..16321271 100644 --- a/getting-started-guide/product-analytics.mdx +++ b/getting-started-guide/product-analytics.mdx @@ -8,7 +8,7 @@ Axiom helps you leverage the power of timestamped event data. Axiom believes tha This page explains how you can leverage the power of event data for the product analytics use case. -In product analytics, the ability to harness and interpret data effectively can determine the success of a product. Axiom allows product analytics to leverage the power of timestamped event data and easily read every single event. This unique capability enables organizations to gain actionable insights, optimize user experiences, and drive product innovation. +In product analytics, the ability to harness and interpret data effectively can determine the success of a product. Axiom allows product analytics to leverage the power of timestamped event data and easily read every single event. Your organization can gain actionable insights, optimize user experiences, and drive product innovation. ## Why event data matters in product analytics