{"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\\n\\nYou just spent $500 on a newsletter sponsorship, $2k on display ads (or multiply those figures by 100 if you’re a big enterprise).\\n\\nYou need signal on the efficacy of these campaigns as quickly as humanly possible.\\n\\n## Why SQL?\\n\\nYou’re going to test a lot of acquisition channels.\\n\\nSQL gives you a way to view all your results in one place (as opposed to copy/pasting results from various tools into spreadsheets).\\n\\n## First touch vs last touch?\\n\\nIn this template, we look at \\\"first touch\\\" attribution, i.e. giving 100% of the credit to the initial channel that acquired the user. This post from HIPB2B explains why you might want to consider \\\"last touch\\\" (e.g. you do a lot of re-targeting and want to measure how effectively you bring a user back).\\n\\nIn the query below, just change `asc` to `desc` and you'll see the UTM associated with the last touch.\\n\\n## SQL you can copy / paste\\n\\nIf you have an `events` table with a `timestamp` and `utm_source` you can use the query below with little modification.\\n\\n```sql\\nwith _events as (\\n select\\n *,\\n row_number() over (partition by distinct_id order by time asc)\\n from events\\n where time >= now() - interval '1 week' -- looking only events from the last week (you likely want to go further)\\n)\\n\\nselect\\n time at time zone 'utc' at time zone 'pst',\\n utm_source,\\n distinct_id,\\n-- any other columns you’d like\\nfrom _events\\nwhere row_number = 1\\n and utm_source = 'your_utm_source'\\norder by 1 desc;\\n```\\n\\n**Output of the CTE** `_events`**:**\\n\\n```\\n| time | name | user_id | distinct_id | utm_source | row_number |\\n|---------------------|---------------------|---------|------------------|------------|------------|\\n| 2020-03-18 11:28:42 | View Landing Page | 563714 | 0032v5yh0d39g... | google | 1 |\\n| 2020-03-18 11:44:20 | Sign Up | 563714 | 0032v5yh0d39g... | | 2 |\\n| 2020-03-18 11:44:34 | Complete Onboarding | 563714 | 0032v5yh0d39g... | | 3 |\\n| ... | ... | ... | ... | ... | ... |\\n| 2020-04-17 16:51:42 | View Landing Page | | 0055l1pg9p73h... | mailchimp | 1 |\\n| 2020-04-30 3:11:50 | View Landing Page | 574991 | 0658o9ki3b22t... | facebook | 1 |\\n| 2020-04-30 15:17:58 | Sign Up | 574991 | 0658o9ki3b22t... | | 2 |\\n| 2020-04-30 15:18:19 | Complete Onboarding | 574991 | 0658o9ki3b22t... | | 3 |\\n| ... | ... | ... | ... | ... | ... |\\n```\\n\\nThe [CTE](/learn-sql/postgresql/how-to-write-a-common-table-expression-in-postgresql) in the query above chronologically orders every event of a user. It uses a Window function to do so.\\n\\nNote how for every `distinct_id`, the Window function adds an incrementing `row_number` for every event. And at every change in `distinct_id` the `row_number` restarts.\\n\\n**Output of entire query:**\\n\\n```\\n| time | name | user_id | distinct_id | utm_source | utm_medium | row_number |\\n|---------------------|-------------------|---------|------------------|------------|------------|------------|\\n| 2020-03-18 11:28:42 | View Landing Page | 563714 | 0032v5yh0d39g... | google | cpc | 1 |\\n| 2020-04-17 16:51:42 | View Landing Page | | 0055l1pg9p73h... | mailchimp | newsletter | 1 |\\n| 2020-04-30 3:11:50 | View Landing Page | 574991 | 0658o9ki3b22t... | facebook | cpc | 1 |\\n| ... | ... | ... | ... | ... | ... | ... |\\n```\\n\\nThe latter part of the query simply grabs the first record (i.e. `where row_number = 1`), showing you the `utm_source` of the \\\"first touch\\\".\\n\\n## Try it yourself?\\n\\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## Pro Tips\\n\\n### Two steps to use this template with your own data:\\n\\n1. Use UTMs whenever you share links externally (an awesome guide on UTMs, h/t to @danielmccaw).\\n2. Get your analytics data into your database. Back in 2018, we wrote this guide on [how to export Mixpanel data into your DB](/blog/how-to-export-raw-mixpanel-data-to-your-database). Since then, Mixpanel has built a product called Data Pipeline to facilitate.\\n > Using Amplitude, Heap, etc? We’re working on those guides 🙏 (ping us if you want to help write them).\\n\\n### Understand user behavior\\n\\nBeyond speed, the other benefit of this approach is 360 visibility. This SQL-driven approach let’s you follow a user end-to-end through your product.\\n\\nPlatforms like Google Analytics aggregate/anonymize your traffic; you lose the through-line from acquisition channel to later user behavior in your product. Plus you can’t filter in Google Analytics on the internal customer attributes you’ve created, like [Lead Score](/sql-templates/sales/creating-lead-scores-in-sql). As a result, your qualified traffic gets mixed in with you unqualified traffic.\\n\\n### Automating this process\\n\\nHave you launched a campaign and written this query? With PopSQL you can [schedule this query](/docs/getting-more-out-of-popsql/scheduled-queries) to run daily so you get updates on new user sign-ups via Slack / email. Add a [query variable](/docs/getting-more-out-of-popsql/how-to-use-variables-in-a-query) to pass in a different `utm_source` per run.\\n\",\"data\":{\"title\":\"Marketing Attribution in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"announce\",\"author\":\"Bill 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