惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
Recorded Future
Recorded Future
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
S
SegmentFault 最新的问题
博客园 - 司徒正美
Last Week in AI
Last Week in AI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
博客园 - 叶小钗
The GitHub Blog
The GitHub Blog
MyScale Blog
MyScale Blog
腾讯CDC
博客园 - 聂微东
D
DataBreaches.Net
博客园 - Franky
人人都是产品经理
人人都是产品经理
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 【当耐特】
量子位
宝玉的分享
宝玉的分享
D
Docker
T
Tailwind CSS Blog
IT之家
IT之家
Engineering at Meta
Engineering at Meta
P
Proofpoint News Feed
C
CERT Recently Published Vulnerability Notes
Scott Helme
Scott Helme
Project Zero
Project Zero
Microsoft Azure Blog
Microsoft Azure Blog
AWS News Blog
AWS News Blog
Google DeepMind News
Google DeepMind News
H
Heimdal Security Blog
W
WeLiveSecurity
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
有赞技术团队
有赞技术团队
Simon Willison's Weblog
Simon Willison's Weblog
NISL@THU
NISL@THU
C
Cybersecurity and Infrastructure Security Agency CISA
Google DeepMind News
Google DeepMind News
T
Threatpost
TaoSecurity Blog
TaoSecurity Blog
N
News and Events Feed by Topic
aimingoo的专栏
aimingoo的专栏
Recent Commits to openclaw:main
Recent Commits to openclaw:main
www.infosecurity-magazine.com
www.infosecurity-magazine.com
SecWiki News
SecWiki News
S
Securelist

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Investigate funnel drop-offs with Product Analytics
Adam Virani, Sharon Ye · 2026-05-27 · via Datadog | The Monitor blog
Adam Virani

Adam Virani

Product Marketing Manager

Sharon Ye

Sharon Ye

Senior Product Manager

For most product teams, funnels are a staple of the analytics toolkit despite a frustrating limitation. You can see which step users are dropping off at, but understanding why requires hours of manual slicing across segments, separate comparison views, and a lot of trial and error before you land on a useful hypothesis. And even when you find something meaningful, taking action typically means jumping to another tool, building a new segment, or filing a request with a data team.

Datadog Product Analytics funnels now close that gap. Our new Conversion Analysis panel uses statistical analysis to automatically surface the user attributes and behaviors most correlated with conversions and drop-offs at any step. Journey Paths visualize where users actually go after they leave your funnel, and side-by-side comparisons let you benchmark across segments or time periods in a single chart. Across these tools, direct shortcuts let you create a segment or launch Session Replay without leaving the funnel view.

In this post, we’ll cover how to:

  • Surface the drivers behind conversions and drop-offs at any funnel step

  • Explore where users drop off with Journey Paths

  • Benchmark segments and time periods with side-by-side comparisons

Instantly surface the drivers behind conversions and drop-offs

When a step in your funnel has a low conversion rate, you immediately want to know why. Historically, answering that meant manually hypothesizing which dimensions to slice by (e.g., plan tier, browser, region, signup cohort) and then building each comparison one at a time. By the time you’d tested a few hypotheses, you might have exhausted the better part of an afternoon and still weren’t certain you’d found the right signal.

The new Conversion Analysis panel does this work for you. You can click on any step in your funnel, and Datadog automatically ranks the user attributes and behavioral segments most correlated with conversion or drop-off at that step. From the panel, you can:

  • See the ranked list of attributes driving conversion or drop-off at any funnel step

  • Drill into any driver to see the full segment breakdown

  • Create a targeted segment of converters or drop-offs directly from the panel

  • Launch Session Replay for users who converted or dropped off without leaving the funnel

Conversion Analysis side panel showing any funnel step and the most statistically significant drivers of conversion and drop-off associated with it.

To open the panel, click any step or drop-off in the funnel chart and select Conversion analysis. From there, you can filter, drill in, or jump straight to a segment or replay without leaving the page.

Explore user drop-offs with Journey Paths

Knowing where users drop off is only the beginning. For users who converted, what did they do to get there? And for users who dropped off, where did they go instead? Journey Paths answer both questions by visualizing the actual sequences of events users take through your product, ordered by session frequency.

A Product Analytics view visualizing the actual sequences of events users take through your product, ordered by frequency.

Click on any step or drop-off in the funnel chart and select View journeys to open the visualization. Toggle between Converted and Dropped off to see the most popular paths for each cohort. Common behaviors surface at the top, so you can quickly spot patterns such as a detour through a help article before a successful conversion, a second attempt at an onboarding step, or an exit from your app entirely after a failed action. Each of these patterns is a hypothesis you can take directly into a segment or a Session Replay session for further investigation.

Benchmark segments and time periods side by side

Funnels become especially useful when you can compare them. For example, you can see whether mobile users are dropping off at a higher rate than desktop users or whether this month’s signup cohort is converting better than last month’s. Or, did the redesign of your onboarding flow improve completion rates or just shift where users get stuck?

A Product Analytics funnel view showing the side-by-side comparison of cart conversions for desktop, mobile, and bot device types.

Side-by-side comparisons let you answer these questions in a single chart. Select Compare in the funnel editor, then add the segments or time periods you want to benchmark. The visualization updates in real time, with each group rendered as a parallel funnel so you can read step-by-step conversion rates against each other at a glance. From the comparison view, the same shortcuts apply: Create a segment from any group or launch Session Replay for users in a specific cohort.

Get started with upgraded funnels in Product Analytics

The upgraded Product Analytics funnels give teams a faster path from data to understanding the drivers behind funnel drop-offs and conversions. Whether you’re optimizing a signup flow, tracking feature adoption, or trying to reduce churn, the Conversion Analysis panel, Journey Paths, and side-by-side comparisons provide the context you need to act on drop-offs without leaving the tool. To get started, open any existing funnel in Product Analytics or create a new one. Check out our Product Analytics documentation to learn more, or if you don’t already have a Datadog account, you can sign up for a 14-day free trial.