{"pageProps":{"posts":[{"post_information":"{\"title\":\"Monitoring a Feature Launch with SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"inspect\",\"author\":\"Bill Babeaux\"}","slug":"monitoring-a-feature-launch-with-sql","id":"product","position":0,"isDraft":false},{"post_information":"{\"title\":\"Auditing a Customer's Usage of Your Product\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"inspect\",\"author\":\"Bill Babeaux\"}","slug":"how-has-customer-used-your-product","id":"product","position":1,"isDraft":false},{"post_information":"{\"title\":\"Ranking the Most Used Features in Your Product\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"inspect\",\"author\":\"Bill Babeaux\"}","slug":"product-most-used-features","id":"product","position":2,"isDraft":false},{"post_information":"{\"title\":\"Finding Your Product's Most Engaged Users\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"inspect\",\"author\":\"Bill Babeaux\"}","slug":"finding-your-most-engaged-users","id":"product","position":3,"isDraft":false},{"post_information":"{\"title\":\"Filtering Users by Version Number with Regex in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"schema\",\"author\":\"Bill Babeaux\"}","slug":"filtering-users-by-version-number-with-sql-regex","id":"engineering","position":4,"isDraft":false},{"post_information":"{\"title\":\"Filtering Users by Platform in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"schema\",\"author\":\"Bill Babeaux\"}","slug":"filtering-users-by-platform-in-sql","id":"engineering","position":5,"isDraft":false},{"post_information":"{\"title\":\"Calculating Daily Active Users (and digging deeper)\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"announce\",\"author\":\"Bill Babeaux\"}","slug":"calculating-daily-active-users-in-sql","id":"marketing","position":6,"isDraft":false},{"post_information":"{\"title\":\"Analyzing NPS Responses in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"troubleshooting\",\"author\":\"Bill Babeaux\"}","slug":"analyzing-nps-responses-in-sql","id":"support","position":7,"isDraft":false},{"post_information":"{\"title\":\"Finding Customers at Risk of Churning\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"troubleshooting\",\"author\":\"Bill Babeaux\"}","slug":"finding-customers-at-risk-of-churning","id":"support","position":8,"isDraft":false},{"post_information":"{\"title\":\"Detecting Spikes in Issues from Support Tickets\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"troubleshooting\",\"author\":\"Bill Babeaux\"}","slug":"detecting-spikes-in-issues-from-support-tickets","id":"support","position":9,"isDraft":false},{"post_information":"{\"title\":\"Tagging Sign Up Emails as Work vs Personal\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"grow\",\"author\":\"Bill Babeaux\"}","slug":"tagging-sign-up-emails-as-work-vs-personal","id":"sales","position":10,"isDraft":false},{"post_information":"{\"title\":\"Creating Lead Scores in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"grow\",\"author\":\"Bill Babeaux\"}","slug":"creating-lead-scores-in-sql","id":"sales","position":11,"isDraft":false},{"post_information":"{\"title\":\"Running a Funnel Analysis in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"announce\",\"author\":\"Bill Babeaux\"}","slug":"running-a-funnel-analysis","id":"marketing","position":12,"isDraft":false},{"post_information":"{\"title\":\"Marketing Attribution in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"announce\",\"author\":\"Bill Babeaux\"}","slug":"marketing-attribution-in-sql","id":"marketing","position":13,"isDraft":false},{"post_information":"{\"title\":\"Creating Histograms in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"analyze\",\"author\":\"Bill Babeaux\"}","slug":"how-to-create-histograms-in-sql","id":"analytics","position":15,"isDraft":false},{"post_information":"{\"title\":\"Detecting Skewness in a Dataset in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"analyze\",\"author\":\"Bill Babeaux\"}","slug":"detecting-skewness-in-sql","id":"analytics","position":16,"isDraft":false},{"post_information":"{\"title\":\"Calculating Linear Regression in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"analyze\",\"author\":\"Bill Babeaux\"}","slug":"linear-regression-in-sql","id":"analytics","position":17,"isDraft":false},{"post_information":"{\"title\":\"Exploring our Sample Dataset\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"analyze\",\"author\":\"Bill Babeaux\"}","slug":"exploring-sample-dataset","id":"analytics","position":18,"isDraft":false}],"data":"{\"content\":\"\\n\\nYou gotta have visibility on any newly launched features. You can't fly blind or “test on users”.\\n\\nFortunately, you can make a feature launch dashboard in PopSQL in under 20 minutes. Here's a preview:\\n\\n\\n## Why SQL?\\nOur team comes from Uber, Instacart, Segment, InVision, and Auth0. We know that even large teams face analytics challenges like:\\n - deadlines that sneak up;\\n - your team is short an analyst;\\n - the data eng team has a 2 week backlog and can’t get your (experimental) data into your BI tool (which requires heavy data instrumentation 😬 ); etc.\\n\\n## SQL you can copy / paste\\n*Note: these queries give high-level visibility. We’re writing a SQL template on how to statistically measure if a feature has met the predicted success criteria with no degradation in secondary metrics (aka the gold standard).*\\n\\n### Quick links:\\n- [Is anybody using the feature?](#is-anybody-using-the-feature-)\\n- [Who is using it?](#who-is-using-it-)\\n- [How much are they using it?](#how-much-are-they-using-it-)\\n- [How exactly are they using it?](#how-are-they-using-it-)\\n- [How does use compare to similar features?](#how-does-use-compare-to-similar-features-)\\n- [Has anything gone wrong?](#has-anything-gone-wrong-)\\n\\n---\\n### Is anybody using the feature?\\nIf you have a `users` table and an `events` table with a `timestamp` and `event_name` , you can use the queries below with little modification. \\n\\n```sql\\nselect\\n date_trunc('day',time) as day -- or week, month\\n count(*)\\nfrom events\\nwhere time > '2020-03-28' and time < '2020-05-15' --launch date\\n and name = '{{feature}}'\\ngroup by 1\\norder by 1;\\n```\\n\\n**Sample Output (visualized in PopSQL):**\\n\\n\\nGreat! People are actually using this feature.\\n\\n---\\n### Who is using it?\\nHere's a simple way to find the last 100 interactions with your feature:\\n\\n```sql\\nselect\\n distinct\\n e.time,\\n u.id as user_id,\\n concat(u.first_name, ' ', u.last_name) as user_name,\\n u.email,\\n u.created_at as user_created_at\\nfrom events e\\ninner join users u on u.id = e.user_id\\nwhere e.time >= '2020-03-28' --launch date\\n and e.name = '{{feature}}'\\norder by e.time desc\\nlimit 100;\\n```\\n\\n**Sample Output:**\\n\\n```\\n| time | user_id | name | email | user_created_at |\\n|---------------------|---------|--------------------|-----------------------|---------------------|\\n| 2020-05-15 23:55:47 | 494729 | Frederic Abernathy | frederic@craftery.com | 2019-03-04 21:25:04 |\\n| 2020-05-15 22:50:34 | 571484 | Erik Hansen | erik@packyard.com | 2020-04-17 00:12:50 |\\n| ... | ... | ... | ... | ... |\\n```\\nPro tip: add customer attributes (e.g. company size, role) to spot trends in what kind of users are adopting the feature. You can even enrich users/leads with Clearbit to go deeper (a topic for a future template!).\\n\\n---\\n### How much are they using it?\\nTo find the most engaged users of the feature:\\n\\n```sql\\nselect\\n user_id,\\n COUNT(1)\\nfrom events\\nwhere name = '{{feature}}'\\ngroup by 1\\norder by 2 desc\\nlimit 10;\\n```\\n\\nThe least engaged users of the feature:\\n\\n```sql\\nselect\\n user_id,\\n COUNT(1)\\nfrom events\\nwhere name = '{{feature}}'\\ngroup by 1\\norder by 2 asc\\nlimit 10;\\n```\\n\\nAnd to show all users (showing intensity of use too):\\n```sql\\nwith event_data as (\\n select\\n user_id as user_id,\\n min(time) as first_time,\\n max(time) as last_time,\\n round(((extract(epoch from max(time)) - extract(epoch from min(time))) / 86400)::decimal, 1) as days_elapsed,\\n count(1)\\n from events\\n where name = '{{feature}}'\\n group by 1\\n)\\n\\nselect\\n *,\\n ROUND((count / days_elapsed),1) as avg_use_per_day\\nfrom event_data\\nwhere count > 0 -- to prevent dividing by 0\\n and days_elapsed > 0 -- to prevent dividing by 0\\norder by 6 desc -- sort by avg_use_per_day;\\n```\\n\\n**Sample Outputs (visualized in PopSQL):**\\n\\n\\nTalk to the extremes. For the barely engaged with the feature, ask them what’s lacking. For the heavily engaged, ask them what they’re loving.\\n\\n---\\n### How exactly are they using it?\\nHere you can see a stream of events through your product for a particular user:\\n\\n```sql\\nselect\\n time,\\n name,\\n user_id\\nfrom events\\nwhere user_id = {{user_id}}\\norder by time desc;\\n```\\n**Sample Output:**\\n\\n\\nWhat's even better than this query? Talking to your customers! Offer a $25 gift card (Starbucks, Amazon, etc) for 25 minutes of their time. You’ll get much better insight. We've even [included a list of questions](#pro-tips) to ask.\\n\\n---\\n### How does use compare to similar features?\\nSee our post on [Comparing Feature Usage](/sql-templates/product/product-most-used-features) for the full overview.\\n\\n```sql\\nselect\\n date_trunc('week',time), -- or day, month, etc\\n name,\\n count(1)\\nfrom events\\nwhere name in ('Add Image','Delete Image','Auto-Organize Canvas','New Canvas','Share Canvas') -- here we select a few relevant features\\ngroup by 1,2;\\n```\\n\\n**Sample Output (visualized in PopSQL):**\\n\\n\\nOur new feature (line 🟣 ) is growing in usage. That dip in the latest week is curious, though...\\n\\n---\\n### Has anything gone wrong?\\nFor small startups, keep an eye on error logs, beta users, and support tickets. You'd be surprised how readily visible spikes / anomalies are when you've visualized your data.\\n\\n```sql\\nselect\\n date_trunc(day,created_at) as day,\\n count(*)\\nfrom tickets\\ngroup by 1\\norder by 1;\\n```\\n\\n**Sample Output (visualized in PopSQL):**\\n\\n\\nThat spike is anomalous and corresponds with the dip in feature usage above.\\n\\n---\\n\\n## Try it yourself?\\nRun this template against our [sample database](/sql-templates/analytics/exploring-sample-dataset) that mirrors real startup data. See the [connection credentials](/sql-templates/analytics/exploring-sample-dataset#accessing-this-database), then connect in PopSQL.\\n\\n---\\n\\n## Bonus Content\\nAs we mentioned [above](#how-are-they-using-it-), here are easy questions you can ask your users about a newly launched feature:\\n\\n- **What’s your use case for this feature?**\\n - What type of people on your team would most benefit from this feature?\\n - What roles wouldn’t benefit or use this feature?\\n- **How are you using this feature? (What is your workflow?)**\\n - What is the main benefit you receive from this feature?\\n - What do you dislike about this feature?\\n - What’s the main thing we can improve about this feature for you?\\n- **How would you feel if you could no longer use this feature? (Very disappointed, Somewhat disappointed, or Not disappointed)**\\n - What would you use instead of this feature?\\n - What other tools did you look into to solve this use case?\\n\",\"data\":{\"title\":\"Monitoring a Feature Launch with SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"inspect\",\"author\":\"Bill 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