{"_id":"57c7238180cbfa0e0070653c","user":"560c92f2ac2859170013faa3","version":{"_id":"560c93ae7e9b9d0d00ca81a5","project":"560c93ad7e9b9d0d00ca81a2","__v":9,"createdAt":"2015-10-01T02:00:14.709Z","releaseDate":"2015-10-01T02:00:14.709Z","categories":["560c93af7e9b9d0d00ca81a6","560c9d9399bb5a0d0044f220","560d76d899bb5a0d0044f307","560d76ee1ec45619006069ed","560d86e099bb5a0d0044f32e","560dba80373c0e0d0024ff3b","57c722ecdf19130e001fba5d","57c743d1b6f94a2200659903","58995ec083f743190077bbe2"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"category":{"_id":"57c722ecdf19130e001fba5d","project":"560c93ad7e9b9d0d00ca81a2","version":"560c93ae7e9b9d0d00ca81a5","__v":0,"sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-08-31T18:33:16.326Z","from_sync":false,"order":4,"slug":"attribution-model-examples","title":"Attribution Model Examples"},"parentDoc":null,"project":"560c93ad7e9b9d0d00ca81a2","__v":0,"updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-08-31T18:35:45.975Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"settings":"","results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":2,"body":"One of the reasons that people have trouble understanding attribution models is that there are a lot of rules to follow, and it's almost impossible for a human to keep track of everything for many users and many visits.  The best way to understand how Attribution works is to look at one user with multiple conversions over a timeline.\n\nConsider this scenario, where a user has 6 visits over 12 days and 2 conversions.  How would you allocate the conversions to each of these channels in each Attribution Model?\n[block:parameters]\n{\n  \"data\": {\n    \"0-0\": \"Date\",\n    \"0-1\": \"Channel (visit)\",\n    \"0-2\": \"Action\",\n    \"1-0\": \"5/1\",\n    \"2-0\": \"5/4\",\n    \"3-0\": \"5/5\",\n    \"4-0\": \"5/7\",\n    \"5-0\": \"5/9\",\n    \"6-0\": \"5/11\",\n    \"7-0\": \"5/12\",\n    \"1-1\": \"Facebook\",\n    \"2-1\": \"Adwords\",\n    \"3-1\": \"Direct\",\n    \"4-1\": \"Facebook\",\n    \"5-1\": \"Direct\",\n    \"6-1\": \"Adwords\",\n    \"7-2\": \"Conversion $5\",\n    \"5-2\": \"Conversion $10\"\n  },\n  \"cols\": 3,\n  \"rows\": 8\n}\n[/block]\n\n[block:callout]\n{\n  \"type\": \"warning\",\n  \"title\": \"Understanding Date Ranges\",\n  \"body\": \"When you select a date range in Attribution, the results shown are the visits that happened in that date range and the resulting conversions that came from those visits, even if they happen in the future.\"\n}\n[/block]\n\n[block:api-header]\n{\n  \"type\": \"basic\",\n  \"title\": \"Linear Attribution (Direct Traffic Included) 5/1 - 5/12\"\n}\n[/block]\nFirst we will look at the results of the model, and then explain the math below.\n[block:parameters]\n{\n  \"data\": {\n    \"0-0\": \"Source\",\n    \"0-1\": \"Visits\",\n    \"0-2\": \"Conversions\",\n    \"0-3\": \"Conversion Revenue\",\n    \"1-0\": \"Facebook\",\n    \"1-1\": \"2\",\n    \"2-0\": \"Adwords\",\n    \"2-1\": \"2\",\n    \"3-0\": \"Direct\",\n    \"3-1\": \"2\",\n    \"1-2\": \".73\",\n    \"2-2\": \".53\",\n    \"3-2\": \".73\",\n    \"1-3\": \"$5.66\",\n    \"2-3\": \"$3.66\",\n    \"3-3\": \"$5.66\"\n  },\n  \"cols\": 4,\n  \"rows\": 4\n}\n[/block]\nCalculation for the 1st linear model conversion, which happened on 5/9:\n[block:parameters]\n{\n  \"data\": {\n    \"0-0\": \"Source\",\n    \"0-1\": \"Visits\",\n    \"0-2\": \"Conversions\",\n    \"0-3\": \"Conversion Revenue\",\n    \"1-0\": \"Facebook\",\n    \"1-1\": \"2\",\n    \"2-0\": \"Adwords\",\n    \"2-1\": \"1\",\n    \"3-0\": \"Direct\",\n    \"3-1\": \"2\",\n    \"1-2\": \".2 * 2 = .4\",\n    \"2-2\": \".2 * 1 = .2\",\n    \"3-2\": \".2 * 2 = .4\",\n    \"1-3\": \"$10 * .4 = $4\",\n    \"2-3\": \"$10 * .2 = $2\",\n    \"3-3\": \"$10 * .4 = $4\"\n  },\n  \"cols\": 4,\n  \"rows\": 4\n}\n[/block]\nCalculation for the 2nd linear model conversion, which happened on 5/12:\n[block:parameters]\n{\n  \"data\": {\n    \"0-0\": \"Source\",\n    \"0-1\": \"Vistis\",\n    \"0-2\": \"Conversions\",\n    \"0-3\": \"Conversion Revenue\",\n    \"1-0\": \"Facebook\",\n    \"1-1\": \"2\",\n    \"1-2\": \".1667 * 2 = .33\",\n    \"2-0\": \"Adwords\",\n    \"2-1\": \"2\",\n    \"3-0\": \"Direct\",\n    \"3-1\": \"2\",\n    \"2-2\": \".1667 * 2 = .33\",\n    \"3-2\": \".1667 * 2 = .33\",\n    \"1-3\": \"$5 * .33 = 1.65\",\n    \"2-3\": \"$5 * .33 = 1.65\",\n    \"3-3\": \"$5 * .33 = 1.65\"\n  },\n  \"cols\": 4,\n  \"rows\": 4\n}\n[/block]\nWhen you add the values from the 1st conversion and the 2nd conversion, you get the total!","excerpt":"","slug":"linear-attribution-example-direct-included","type":"basic","title":"Linear Attribution Example"}

Linear Attribution Example


One of the reasons that people have trouble understanding attribution models is that there are a lot of rules to follow, and it's almost impossible for a human to keep track of everything for many users and many visits. The best way to understand how Attribution works is to look at one user with multiple conversions over a timeline. Consider this scenario, where a user has 6 visits over 12 days and 2 conversions. How would you allocate the conversions to each of these channels in each Attribution Model? [block:parameters] { "data": { "0-0": "Date", "0-1": "Channel (visit)", "0-2": "Action", "1-0": "5/1", "2-0": "5/4", "3-0": "5/5", "4-0": "5/7", "5-0": "5/9", "6-0": "5/11", "7-0": "5/12", "1-1": "Facebook", "2-1": "Adwords", "3-1": "Direct", "4-1": "Facebook", "5-1": "Direct", "6-1": "Adwords", "7-2": "Conversion $5", "5-2": "Conversion $10" }, "cols": 3, "rows": 8 } [/block] [block:callout] { "type": "warning", "title": "Understanding Date Ranges", "body": "When you select a date range in Attribution, the results shown are the visits that happened in that date range and the resulting conversions that came from those visits, even if they happen in the future." } [/block] [block:api-header] { "type": "basic", "title": "Linear Attribution (Direct Traffic Included) 5/1 - 5/12" } [/block] First we will look at the results of the model, and then explain the math below. [block:parameters] { "data": { "0-0": "Source", "0-1": "Visits", "0-2": "Conversions", "0-3": "Conversion Revenue", "1-0": "Facebook", "1-1": "2", "2-0": "Adwords", "2-1": "2", "3-0": "Direct", "3-1": "2", "1-2": ".73", "2-2": ".53", "3-2": ".73", "1-3": "$5.66", "2-3": "$3.66", "3-3": "$5.66" }, "cols": 4, "rows": 4 } [/block] Calculation for the 1st linear model conversion, which happened on 5/9: [block:parameters] { "data": { "0-0": "Source", "0-1": "Visits", "0-2": "Conversions", "0-3": "Conversion Revenue", "1-0": "Facebook", "1-1": "2", "2-0": "Adwords", "2-1": "1", "3-0": "Direct", "3-1": "2", "1-2": ".2 * 2 = .4", "2-2": ".2 * 1 = .2", "3-2": ".2 * 2 = .4", "1-3": "$10 * .4 = $4", "2-3": "$10 * .2 = $2", "3-3": "$10 * .4 = $4" }, "cols": 4, "rows": 4 } [/block] Calculation for the 2nd linear model conversion, which happened on 5/12: [block:parameters] { "data": { "0-0": "Source", "0-1": "Vistis", "0-2": "Conversions", "0-3": "Conversion Revenue", "1-0": "Facebook", "1-1": "2", "1-2": ".1667 * 2 = .33", "2-0": "Adwords", "2-1": "2", "3-0": "Direct", "3-1": "2", "2-2": ".1667 * 2 = .33", "3-2": ".1667 * 2 = .33", "1-3": "$5 * .33 = 1.65", "2-3": "$5 * .33 = 1.65", "3-3": "$5 * .33 = 1.65" }, "cols": 4, "rows": 4 } [/block] When you add the values from the 1st conversion and the 2nd conversion, you get the total!