{"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\":\"![](/static/images/templates/covers/cover_funnel_analysis.png)\\n\\nYou’ve spent a ton of effort / money to get users to explore your product. Are there chasms in your product where visitors simply fall off?\\n\\nA funnel analysis gives clarity.\\n\\n\\n## Why SQL?\\nWe initially used Google Analytics for funnel analyses. It was... lacking.\\n\\nWe wanted to talk to users who fell off at a particular step... *but we couldn’t since Google Analytics aggregates and anonymizes.* We wanted to see a funnel for our most qualified users by passing in an internally calculated metric like a [Lead Score](/sql-templates/sales/creating-lead-scores-in-sql). *No luck.*\\n\\nSQL empowers you to get a 360° understanding of how customers use your product.\\n\\n## SQL you can copy / paste\\n### First step: define the stages of your funnel\\nInspired by the Pirate Metrics framework, in this example, we’ll segment our funnel steps as:\\n![](/static/images/templates/funnel_steps.png)\\n\\n### Second step: follow the *same* users through your funnel\\nMany other funnel analysis templates make a big mistake: they independently calculate each step, then lump them all together.\\n\\nTo illustrate the problem, you could have *activations* in July 2020 who first visited and signed up in *September 2019*.\\n![](/static/images/templates/broken_funnel_steps.png)\\n\\nThese 2019 users experienced a totally different landing page or product -- a different funnel altogether! If you include these historical user actions, you run the risk of artificially inflating all your funnel conversion metrics. That can be a big problem if you’re using these conversion metrics in any future models.\\n\\n### Here's the query\\nIf you have an `events` table with a `timestamp`, `event_name`, and `user_id` you can use the queries below with little modification. \\n\\nThis query looks complex, but we break it down inline:\\n\\n```sql\\n-- VISITORS (DEFINES THE GROUP WE FOLLOW THROUGH THE FUNNEL)\\nwith visitors as (\\n select\\n distinct_id, -- effectively a user_id\\n min(time) as min_time -- gets the earliest Visit for each person\\n from events\\n where name = 'View Landing Page'\\n group by 1\\n having min(time) between '2020-04-01' and '2020-05-31' -- selects people whose first visit is in this time range\\n),\\n\\n-- SIGN-UPS (FROM THE VISITORS ABOVE)\\nsign_ups as (\\n select\\n distinct e.distinct_id\\n from visitors v -- ensures we only look at the Visitors defined above\\n inner join events e on e.distinct_id = v.distinct_id\\n where e.name = 'Sign Up' -- an internal event that defines sign-up\\n),\\n\\n-- ACTIVATIONS (FROM THE SIGN-UPS ABOVE)\\nactivations as (\\n select\\n distinct e.distinct_id\\n from sign_ups s -- ensures we only look at the Signups defined above\\n inner join events e on e.distinct_id = s.distinct_id\\n where e.name = 'New Canvas'\\n),\\n\\n-- PURCHASES (FROM THE ACTIVATIONS ABOVE)\\npurchases as (\\n select\\n distinct e.distinct_id\\n from activations a -- ensures we only look at the Activations defined above\\n inner join events e on e.distinct_id = a.distinct_id\\n where e.name = 'Start Subscription' \\n)\\n\\nselect 'Visit' as step, COUNT(*) from visitors\\n union -- joins the output of queries together (as long as they have the same columns)\\nselect 'Sign Up' as step, COUNT(*) from sign_ups\\n union\\nselect 'Activate' as step, COUNT(*) from activations\\n union\\nselect 'Purchase' as step, COUNT(*) from purchases\\norder by count desc -- applies to the whole result set\\n;\\n```\\n\\n**Output:**\\n```\\n| Step | Count |\\n|-------------|-------|\\n| Visit | 593 |\\n| Sign Up | 142 |\\n| Activations | 127 |\\n| Purchases | 6 |\\n```\\n\\n### Calculating drop-off at each step\\nYou’ll likely want to know where the drop-off between steps is the worst. You can use the `lag()` function (docs) to compare the output of each step against the one prior:\\n\\n```sql\\n-- query is identical to that above. We add a CTE called steps so we can do one further analysis\\nsteps as (\\n select 'Visit' as step, COUNT(*) from visitors\\n union\\n select 'Sign Up' as step, COUNT(*) from sign_ups\\n union\\n select 'Activate' as step, COUNT(*) from activations\\n union\\n select 'Purchase' as step, COUNT(*) from purchases\\n order by count desc\\n)\\nselect\\n step,\\n count,\\n lag(count, 1) over (),\\nfrom steps;\\n```\\n**Output:**\\n```\\n| Step | Count | lag |\\n|----------|-------|-----|\\n| Visit | 593 | |\\n| Sign Up | 142 | 593 |\\n| Activate | 127 | 142 |\\n| Purchase | 6 | 127 |\\n```\\n\\nWe add a line below to calculate 1 - (`Count` / `lag`) for each step to get `drop_off`. To illustrate, for the Sign Up step: `1 - (142 / 593) = 0.76`, i.e. a 76% drop off.\\n\\n```sql\\n-- query is identical to above, just adding one extra line to calculate ∆ between Lag and Count columns of each step\\nselect\\n step,\\n count,\\n lag(count, 1) over (),\\n round((1.0 - count::numeric/lag(count, 1) over ()),2) as drop_off\\nfrom steps;\\n```\\n**Output:**\\n```\\n| Step | Count | lag | drop_off |\\n|----------|-------|-----|----------|\\n| Visit | 593 | | |\\n| Sign Up | 142 | 593 | 0.76 |\\n| Activate | 127 | 142 | 0.11 |\\n| Purchase | 6 | 127 | 0.95 |\\n```\\n\\nThis query suggests we focus on the drop-off from Visit to Sign Up. The drop-off is substantial and it's near the top of our funnel. Any improvement at the top of the funnel positively impacts all down-funnel metrics.\\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\",\"data\":{\"title\":\"Running a Funnel Analysis in SQL\",\"date\":\"2020-07-30T00:00:00.000Z\",\"draft\":false,\"icon\":\"announce\",\"author\":\"Bill 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