# product analytics Published articles for product analytics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How we built Datadog Experiments DevFeed: [How we built Datadog Experiments]() Original publisher: [Read original article]() Author: Chas DeVeas; Aaron Silverman; Tyler Buffington; Jonathan Fulton; Taylor Overturf Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments](), [real user monitoring]() Tags: [a-b-testing](), [acquisition](), [data-analytics](), [data-engineering](), [experimentation](), [experiments](), [metrics](), [observability](), [product-analytics](), [real-user-monitoring]() ### AI overview Datadog describes rebuilding its experimentation platform to speed up confident A/B-test decisions. The article explains a flexible CUPED approach that reduces metric variance and can be applied to segments. ### Source excerpt Datadog Experiments shortens the time from result to decision with CUPED on percentiles, verifiable warehouse results, and near real-time RUM metrics. ## Coordinate product launches with Datadog DevFeed: [Coordinate product launches with Datadog]() Original publisher: [Read original article]() Author: Milene Darnis; Adam Virani Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [Instrumentation](), [experiments]() Tags: [ai](), [bits-ai](), [digital-experience-monitoring](), [experimentation](), [experiments](), [feature-flags](), [instrumentation](), [launch](), [product-analytics](), [session-replay]() ### AI overview A tutorial on using Datadog Product Analytics Launches to plan product releases, define measurement questions, create tracking plans, and identify missing events and properties before rollout. ### Source excerpt Learn how to turn a product brief and feature flag into a connected launch workflow for instrumentation, experimentation, QA, and reporting. ## Visualize how CUPED adjusts experiment results with Datadog DevFeed: [Visualize how CUPED adjusts experiment results with Datadog]() Original publisher: [Read original article]() Author: Tyler Buffington; Lukas Goetz-Weiss; Ryan Lucht Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments]() Tags: [analysis](), [digital-experience-monitoring](), [experiment](), [experiments](), [how-to](), [metrics](), [product-analytics](), [visualization]() ### AI overview This tutorial explains Datadog Experiments' CUPED adjustments visualization, which breaks the difference between raw and CUPED-adjusted experiment lift into individual covariate contributions. ### Source excerpt Learn how Datadog visualizes CUPED adjustments so you can trace which covariates change experiment lift estimates and improve precision. ## How Guideless uses Tinybird for real-time product analytics and training insights DevFeed: [How Guideless uses Tinybird for real-time product analytics and training insights]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [product analytics](), [real-time](), [Back end]() Tags: [analytics](), [backend](), [customer-stories](), [effective](), [insights](), [product-analytics](), [real-time]() ### AI overview The article describes how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend. It says the approach saved weeks of engineering work and produced engagement insights for improving training effectiveness. ### Source excerpt Learn how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend, save weeks of engineering work, and turn engagement data into insights that help users create more effective training. ## Investigate account-level churn risk with Product Analytics account segments DevFeed: [Investigate account-level churn risk with Product Analytics account segments]() Original publisher: [Read original article]() Author: Sharon Ye; Adam Virani Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Software as a service](), [data]() Tags: [analytics](), [b2b](), [churn](), [conversion](), [digital-experience-monitoring](), [product-analytics](), [real-user-monitoring](), [retention](), [saas]() ### AI overview Datadog Product Analytics account segments combine account attributes with user activity to investigate account-level churn risk. The article describes applying reusable segments to funnels, retention analysis, and Datadog Pathways, with examples involving SaaS, enterprise accounts, ARR, feature adoption, and renewal engagement. ### Source excerpt Learn how Product Analytics account segments combine business context and product behavior to identify accounts that may be at risk of churn. ## Find, analyze, and collaborate on user sessions in Datadog Session Replay DevFeed: [Find, analyze, and collaborate on user sessions in Datadog Session Replay]() Original publisher: [Read original article]() Author: Stella Ma; Abhi Motgi Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [session replay](), [real user monitoring](), [Artificial Intelligence](), [Front end](), [browser](), [Operating system]() Tags: [ai](), [browser](), [collaboration](), [digital-experience-monitoring](), [frontend](), [product-analytics](), [real-user-monitoring](), [session-replay]() ### AI overview Datadog Session Replay brings session discovery, AI-assisted investigation, and timestamped team comments into one workspace. Teams can find relevant replays through RUM or Product Analytics, identify important moments, and share findings with context attached. ### Source excerpt See how Datadog Session Replay unites session discovery, AI analysis, and team collaboration in a single workspace. ## Correlation Lied to Us: Rethinking Product Impact with Causal Inference DevFeed: [Correlation Lied to Us: Rethinking Product Impact with Causal Inference]() Original publisher: [Read original article]() Author: Enderson Santos Published: 2026-08-04T15:31:01Z Content type: article Language: en Sources: [OLX]() Topics: [data]() Tags: [causal-inference](), [data](), [data-science](), [experimentation](), [machine-learning](), [metrics](), [performance](), [product-analytics]() ### AI overview The article examines why higher-tier OLX exposure packages appeared to perform worse than cheaper packages in some cases. It explains that sellers self-select packages, making direct package-level comparisons potentially misleading, and introduces causal inference as an approach to separate correlation from causation. ### Source excerpt Introduction At OLX, professional sellers pay for higher-tier packages because they promise more exposure. More visibility, and, in theory, better results. But when we looked at the data, something unexpected happened. In some cases, ads published with premium packages appeared to perform worse than ads using cheaper packages. That raised an uncomfortable question: If higher-tier packages provide more exposure, shouldn't they consistently perform better? At first glance, there were several possible explanations. Perhaps the extra visibility weren't creating as much value as we expected. Perhaps ranking dynamics were offsetting the additional exposure. Or perhaps the package itself wasn't the real driver of performance. It was then that we started asking a different question: Were we measuring this correctly? More specifically, were the ads across different packages actually comparable in the first place? Answering that question turned out to be far more important than comparing package-level metrics. It forced us to rethink how we measure product impact in a marketplace environment and ultimately led us to a causal inference approach designed to separate correlation from causation. In this article, I'll walk through how we approached that problem, what we learned, and how comparing similar ads changed our understanding of the true value created by exposure products. Problem Definition To understand the challenge, it's important to first understand how package exposure works at OLX. Professional sellers self select into a package when publishing their ads. The difference between packages is largely defined by how many boosts an ad receives during its lifetime. For example, in the picture below we can see that Package 1 includes 1 boost on the period of 30 days, package 2 includes 2 boosts, package 3 includes 3 boosts, and package 4 includes 4 boosts all in the same period of 30 days. The business expectation is straightforward: more boosts should create more visibili ## Why Most Single Source of Truth Initiatives Fail (And What Successful Teams Do Differently) DevFeed: [Why Most Single Source of Truth Initiatives Fail (And What Successful Teams Do Differently)]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-07-20T10:07:52Z Content type: article Language: en Sources: [Housing.com]() Topics: [data](), [product analytics]() Tags: [analytics](), [dashboards](), [data](), [data-architecture](), [data-engineering](), [data-strategy](), [databricks](), [governance](), [leadership](), [product-analytics](), [sql](), [technical]() ### AI overview This article argues that Single Source of Truth initiatives often fail because teams use different definitions for shared business metrics. It presents governance, business ownership of KPI definitions, and alignment between teams as more important than centralizing tables, pipelines, or dashboards. ### Source excerpt "We have multiple dashboards showing different numbers. Which one is correct?" If you've worked in data long enough, you've probably heard this question more times than you'd like. Sales reports one revenue figure. Finance reports another. Product Analytics has a third. Executives spend more time debating whose dashboard is correct than discussing what action to take. The natural response is often: "Let's build a Single Source of Truth." Sounds simple. Build a few centralized tables. Move everyone onto the same dashboards. Problem solved. Except...it rarely is. After leading an enterprise-wide Single Source of Truth (SSOT) initiative, I learned an important lesson: The hardest part wasn't building pipelines or writing SQL. It was aligning people. Technology was the easy part. Changing how the organization thought about data was the real challenge. The Biggest Myth About Single Source of Truth Many organizations believe an SSOT is simply a technical project. The thinking usually goes like this: Collect Data ↓ Transform Data ↓ Build Gold Tables ↓ Everyone Uses Them Unfortunately, reality looks more like this: Different Teams ↓ Different Definitions ↓ Different Dashboards ↓ Different Decisions ↓ Lost Trust The problem isn't that data lives in different places. The problem is that different teams define the same business metrics differently. Figure 1: Moving from fragmented metric definitions to a trusted Single Source of Truth is as much about standardization and governance as it is about technology. A table cannot solve that. Only governance can. Technology Doesn't Create Trust Imagine a metric as simple as Revenue. Ask five departments what "Revenue" means, and you might receive five different answers. Finance may recognize revenue after invoicing. Sales may count closed deals. Marketing may include projected pipeline. Product Analytics may track subscription purchases. Customer Success may exclude refunds. None of them are necessarily wrong. They're answering differen ## Appcues delivers personalized customer engagement with ClickHouse Cloud DevFeed: [Appcues delivers personalized customer engagement with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-18T16:21:05Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [dashboards](), [SDKs]() Tags: [airflow](), [analytics](), [apis](), [aws](), [batch](), [clickhouse](), [cloud](), [dashboards](), [data](), [databases](), [latency](), [observability](), [product-analytics](), [real-time](), [saas](), [sdks](), [streams]() ### AI overview Appcues moved its real-time customer-facing analytics and segmentation workload to ClickHouse Cloud on AWS. The article reports lower query and ingestion latency alongside reduced analytics spend at a scale of 1.31 PB and 410 billion events. ### Source excerpt Appcues cut P95 query times 90%, ingestion latency 99%, and analytics spend 23% by migrating from Snowflake and Airflow to ClickHouse Cloud for real-time segmentation across 1.31 PB of data. ## AI-Driven Development Should Focus on Product Value, Not Just Faster Shipping DevFeed: [AI-Driven Development Should Focus on Product Value, Not Just Faster Shipping]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-06-10T12:27:59Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [AI Development](), [product analytics](), [session replay](), [experiments](), [datadog](), [Software Engineering](), [ITMO]() Tags: [agentic-ai](), [ai](), [analytics](), [datadog](), [product-analytics](), [session-replay](), [software-engineering]() ### AI overview The article argues that AI-assisted development should be judged by the value it creates for customers and businesses, not simply by shipping speed. It contrasts new products with existing products that have customers, legacy constraints, churn concerns, and growth goals, and suggests combining product analytics, session replay, and experiments to inform roadmaps. ### Source excerpt The world is getting weird. ## Monitor critical user journeys with Datadog Journey Monitoring DevFeed: [Monitor critical user journeys with Datadog Journey Monitoring]() Original publisher: [Read original article]() Author: Maël Lilensten; Arti Arutiunov; Younes Berradia; Lauren Zuniga Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [real user monitoring](), [dashboards]() Tags: [availability](), [dashboards](), [digital-experience-monitoring](), [digital-experiences](), [metrics](), [monitoring](), [performance](), [product-analytics](), [real-user-monitoring](), [synthetic-monitoring]() ### AI overview Datadog Journey Monitoring centralizes user behavior, technical performance, availability SLOs, and synthetic test results to help teams assess critical digital journeys and investigate issues in context. ### Source excerpt See how teams can monitor critical digital experiences from a single hub. ## Using Sentry Telemetry for Product Analytics DevFeed: [Using Sentry Telemetry for Product Analytics]() Original publisher: [Read original article]() Author: Rahul Chhabria Published: 2026-05-21T09:00:00Z Content type: tutorial Language: en Sources: [Sentry Blog]() Topics: [product analytics](), [telemetry](), [Traces](), [SIEM, Security, Observability]() Tags: [logs](), [metrics](), [product-analytics](), [sentry](), [telemetry](), [traces]() ### AI overview The article explains how developers can use Sentry traces, structured logs, and application metrics to answer product analytics questions such as onboarding completion, checkout latency, and signup changes. It emphasizes that telemetry can connect product findings to operational context and the code responsible. ### Source excerpt Your Sentry traces, logs, and metrics already answer most product analytics questions. Learn how to query existing telemetry for product insights. ## Mintlify boosts NPS 30% and saves 60% with real-time analytics on ClickHouse Cloud DevFeed: [Mintlify boosts NPS 30% and saves 60% with real-time analytics on ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-04-14T12:05:27Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [dashboards](), [product analytics](), [Software as a service]() Tags: [agents](), [analytics](), [architecture](), [clickhouse](), [cloud](), [cost](), [customer](), [errors](), [product-analytics](), [real-time](), [saas](), [scale]() ### AI overview Mintlify replaced PostHog with ClickHouse Cloud to provide real-time analytics for how humans and AI agents engage with developer documentation. The migration reduced dashboard load times from tens of seconds to under one second, improved NPS by an estimated 30%, eliminated rate-limit errors, and lowered costs by 60%. ### Source excerpt Mintlify replaced PostHog with ClickHouse Cloud, cutting dashboard load times from tens of seconds to under a second, eliminating rate limit errors, and achieving a 30% NPS improvement at 60% lower cost. ## Run Product Analytics on Your Neon Data Using Fabi.ai DevFeed: [Run Product Analytics on Your Neon Data Using Fabi.ai]() Original publisher: [Read original article]() Author: Marc Dupuis Published: 2025-11-14T18:24:08Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [Data analysis](), [Databases](), [AI Chat]() Tags: [ai](), [business-intelligence](), [community](), [dashboards](), [data](), [database](), [how-to](), [product-analytics](), [sql](), [workflows]() ### AI overview A tutorial showing how to connect a Neon Postgres database to Fabi.ai, use its AI Analyst Agent to query product data in plain English, and turn the results into dashboards and automated workflows. ### Source excerpt You're already using Neon, so chances are you've got valuable application data sitting in your Postgres database that reflects how people interact with your product. This data can tell you a lot about your customers and help guide product decisions, whether you're an engineer, a... ## How GitLab serves sub-second analytics to 50 million users DevFeed: [How GitLab serves sub-second analytics to 50 million users]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-10-21T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [GitLab](), [product analytics](), [Scalability](), [real-time](), [Database](), [sdlc]() Tags: [ai](), [clickhouse](), [database](), [gitlab](), [product-analytics](), [real-time](), [scalability](), [sdlc]() ### AI overview GitLab built its product analytics platform on ClickHouse to support real-time, scalable analytics for 50 million registered users. The article describes how queries over 100 million rows were reduced from 30-40 seconds to under one second, with ClickHouse now powering Contribution Analytics, GitLab Duo, and SDLC trends. ### Source excerpt Discover how GitLab transformed its analytics stack with ClickHouse - turning 30-second queries into sub-second insights and powering real-time visibility across GitLab.com. ## Databuddy Is Open-Sourcing Privacy-First Analytics, Built on Neon DevFeed: [Databuddy Is Open-Sourcing Privacy-First Analytics, Built on Neon]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-08-28T15:50:25Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [product analytics](), [Open Source](), [Next.js](), [Tailwind CSS](), [Bun](), [Front end](), [Supabase](), [Back end](), [Prisma]() Tags: [analytics](), [backend](), [bun](), [case-studies](), [frontend](), [next-js](), [open](), [open-source](), [privacy](), [product-analytics](), [self-hosted](), [web]() ### AI overview The article introduces Databuddy, a privacy-first web and product analytics platform that is open source and available through a free plan. It describes the project's preference for a composable stack and explains why its creator chose Neon for serverless Postgres, citing simplicity, performance, and reduced maintenance. The supplied text also identifies a frontend built with Next.js, Tailwind CSS, and Bun, while the API-layer description is truncated. ### Source excerpt "I was surprised by how fast Neon was. It was faster than my self-hosted setup and Prisma Postgres. This plus the convenience of the Free Plan makes it a no-brainer for building your projects." (Issa Nassar, founder of Databuddy) Databuddy is a new privacy-first web and product a... ## How Windsurf writes docs DevFeed: [How Windsurf writes docs]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-05-27T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [product analytics](), [ai-coding](), [human review]() Tags: [agent](), [ai-coding](), [ai-trends](), [documentation](), [human-review](), [product-analytics]() ### AI overview Windsurf treats documentation as a core product component, with marketing leading the process and shipping docs alongside every release. The team uses product analytics to monitor adoption and guide improvements, focuses pages on user value, and applies AI tools with human review to maintain consistency and quality. ### Source excerpt At Windsurf, a leading player in the AI coding space, documentation is integrated in how they build, ship, and market their product. ## How Inbox Zero uses Tinybird for real-time analytics DevFeed: [How Inbox Zero uses Tinybird for real-time analytics]() Original publisher: [Read original article]() Author: Elie Steinbock Published: 2025-04-04T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [product analytics](), [real-time](), [dashboards]() Tags: [analytics](), [dashboards](), [i-built-this](), [product-analytics](), [real-time]() ### AI overview The article explains how Inbox Zero uses Tinybird for internal product analytics and user-facing real-time dashboards. ### Source excerpt Here's how we use Tinybird at Inbox Zero to power both our own internal product analytics and user-facing, real-time dashboards. ## LocalStack scales product analytics with Tinybird DevFeed: [LocalStack scales product analytics with Tinybird]() Original publisher: [Read original article]() Author: Tinybird Published: 2024-06-25T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [product analytics](), [MySQL](), [real-time](), [Cloud]() Tags: [customer-stories](), [developers](), [mysql](), [platform](), [product-analytics](), [real-time]() ### AI overview LocalStack migrated from MySQL to Tinybird to support real-time product analytics for more than 10,000 developers and 100,000 instances. ### Source excerpt Learn how the cloud emulation platform migrated from MySQL to Tinybird to support real-time analytics for 10,000+ developers and 100,000+ instances. ## What is a Full Stack Data Scientist? DevFeed: [What is a Full Stack Data Scientist?]() Original publisher: [Read original article]() Author: Micayla Wood Published: 2022-09-15T13:35:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Data Science](), [data-modeling](), [product analytics](), [engineering-culture]() Tags: [data-modeling](), [data-science](), [engineering](), [machine-learning](), [pipeline](), [product-analytics](), [production](), [shopify](), [streaming]() ### AI overview Shopify defines a full stack data scientist as someone who takes ownership of a data science project across its entire lifecycle, from discovery and analysis through data acquisition, modeling, pipeline development, and production delivery. The article emphasizes communication with stakeholders, engineering practices, product analytics, and proactively using data to solve business problems. ### Source excerpt At Shopify, we've embraced full stack data science, so we chatted with our data scientists to share what it means to be a full stack data scientist. ## Success Metrics for Product Analytics DevFeed: [Success Metrics for Product Analytics]() Original publisher: [Read original article]() Author: Daeus Jorento Published: 2022-07-06T19:00:00Z Content type: tutorial Language: en Sources: [Square Corner Blog](), [Square Corner Blog RSS Feed]() Topics: [product analytics](), [A/B Testing](), [Testing]() Tags: [a-b-testing](), [analytics](), [data-science](), [product-analytics](), [statistical-significance](), [strategy]() ### AI overview This article explains how primary and secondary success metrics support product launches and product analytics. It argues that metrics should confirm whether a strategy was executed successfully, while predefined plans guide decisions when experiment results are negative, positive, or neutral. ### Source excerpt Metrics are not a replacement for strategy ## CX90: Rethinking, Redesigning and Reimplementing the Groupon User Experience DevFeed: [CX90: Rethinking, Redesigning and Reimplementing the Groupon User Experience]() Original publisher: [Read original article]() Author: Avleen Kaur Published: 2021-12-22T16:57:17Z Content type: opinion Language: en Sources: [Groupon Engineering -- Medium]() Topics: [User Experience](), [Web](), [browsers](), [Mobile](), [modules]() Tags: [agile](), [browsers](), [interface](), [mobile](), [modules](), [product](), [product-analytics](), [user-experience](), [user-interface-design](), [web]() ### AI overview This article describes Groupon's CX90 project, a 90-day effort to redesign and reimplement the user experience. The team changed the interface from a deal-focused feed to a category-focused experience across the Home, Browse, and Search pages for desktop and mobile browsers. ### Source excerpt All Grouponers who haven't been living under a rock this past year probably know what CX90 is. But for the few that missed out on this extraordinary project and the rest of the world, CX90 was a huge effort across the Product and Engineering Teams at Groupon where all of us came together to redesign our user experience and interface within a period of 90 days. It was an "all hands on deck" situation and probably one of the greatest collaborations we've seen across the organization where people from different backgrounds and areas of expertise joined forces to transform a simple vision into reality. Why? The old Groupon interface focused primarily on a deal feed that allowed our users to scroll through the entire depth of our inventory either for all types of deals or for specific categories of interest. We soon realized that our users were missing out on all the wonderful opportunities we had to offer. We wanted to help them discover new experiences and "Grab Life by the Groupon" by going out of their comfort zones. Our new interface solved exactly this problem. We changed our feed from being deal focused to category focused in order to understand user intent and preferences, expose them to the breadth of the inventory that we had to offer, and to help them explore experiences they could potentially enjoy if only they took a chance. And to be honest, we were also long overdue for a design refresh. 😅 What? Since I'm a part of Web Search and Discovery team, also internally known as Ion, I'll focus on my own experiences and that of my team. We were responsible for reimplementing the Groupon Home, Browse and Search pages both for desktop and mobile browsers. For the Homepage, we transitioned from a plain deal feed and deal carousels to bigger and bolder modules with engaging text and images that highlighted the different categories from our inventory, allowing users to explore specific deals that might interest them instead of aimlessly scrolling through a generic feed. ## How to Make Dashboards Using a Product Thinking Approach DevFeed: [How to Make Dashboards Using a Product Thinking Approach]() Original publisher: [Read original article]() Author: Lin Taylor Published: 2021-01-28T20:45:00Z Content type: tutorial Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [dashboards](), [product analytics](), [Data Science](), [User Experience](), [Shopify]() Tags: [analytics](), [dashboards](), [data-science](), [guide](), [how-to](), [metrics](), [product-analytics](), [shopify](), [user-experience]() ### AI overview A step-by-step guide to creating user-centred, impact-driven dashboards by applying product thinking. It explains when dashboards are appropriate, how they support monitoring and data-informed decisions, and why designers should consider users' needs and the dashboard's intended impact. ### Source excerpt A step-by-step guide to how you can create dashboards that are user-centred and impact-driven. ## The Value in Early Product Analytics DevFeed: [The Value in Early Product Analytics]() Original publisher: [Read original article]() Author: Claire Meyer Published: 2019-07-31T19:00:00Z Content type: article Language: en Sources: [Square Corner Blog](), [Square Corner Blog RSS Feed]() Topics: [product analytics](), [data](), [User experience (UX)](), [Testing](), [Logging](), [configuration]() Tags: [configuration](), [data](), [data-science](), [development](), [logging](), [product-analytics](), [testing](), [ux](), [visibility]() ### AI overview The article explains how Square engaged Product Analytics from the early stages of developing Square for Restaurants. It describes using data-backed decision-making, customer research, testing, event logging, and existing merchant data to guide strategy, validate insights, and support iteration before and after launch. ### Source excerpt Power in having visibility from the beginning [Next page]()