31:[] 2f:{"title":"iOS 14 modelling examples","cells":[{"id":0,"value":"md`# iOS 14 modelling examples`","pinned":false,"hidden":false,"mode":"ojs"},{"id":79,"value":"md`## Mixed Media Modelling`","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"html`

installs_total = 0.24 x spend_Facebook + 1.45 x Spend_{Google Ads} + 0.01 x spend_Vungle

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":72,"value":"md`#### ${totalInstalls} = 0.24 x ${spendFacebook} + 1.45 x ${spendGoogle} + 0.01 x ${spendVungle}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":45,"value":"viewof spendFacebook = slider({\n title: \"Total ad spend on Facebook over period\",\n min: 0,\n max: 1000,\n value: 500,\n step: 100,\n description: \"How much budget allocated to Facebook\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"viewof spendGoogle = slider({\n title: \"Total ad spend on Google Ads over period\",\n min: 0,\n max: 1000,\n value: 500,\n step: 100,\n description: \"How much budget allocated to Google Ads\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"viewof spendVungle = slider({\n title: \"Total ad spend on Vungle over period\",\n min: 0,\n max: 1000,\n value: 500,\n step: 100,\n description: \"How much budget allocated to Vungle\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"md`*Mixed Media Modelling*: Takes inputs (cost, clicks, impressions) and tracks output (total installs). By checking the impact the inputs have on the output, it can calculate the number of installs per network. However, there’s a huge correlation between different networks and no way to account for organic installs so it is not a perfect approach.`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"html`

installs_total = (organics) + (0.24 x spend_Facebook + 1.45 x Spend_{Google Ads} + 0.01 x spend_Vungle)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"md`#### ${installsObfuscated} = (?????) + (0.24 x ${spendFacebook} + 1.45 x ${spendGoogle} + 0.01 x ${spendVungle})`","pinned":false,"hidden":false,"mode":"ojs"},{"id":115,"value":"md`## Relative Channel Importance`","pinned":false,"hidden":false,"mode":"ojs"},{"id":122,"value":"vl.markCircle({ size: 200 }) // Make a scatter chart\n .data(rciNetworks) \n .encode(\n vl.x().fieldQ(\"Relative_Channel_Importance\"), \n vl.y().fieldQ(\"Total_Tracked_Installs\"), \n vl.tooltip().fieldN(\"name\") // For tooltips, show the Name field\n )\n .render() // Draw the chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"viewof rSquared = slider({\n title: \"Index\",\n min: 0,\n max: 1,\n value: 0.5,\n step: 0.01,\n description: \"R^{2\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":237,"value":"md`*Relative Channel Importance*: Calculates the relative contribution of each channel towards the prediction. Assigns a score and then ranks which networks are the more important ones. Is not capable of predicting total installs and can often miss outliers.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":60,"value":"totalInstalls = ((0.24*spendFacebook) + (1.45*spendGoogle) + (0.01*spendVungle))","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"installsObfuscated = Math.round(((totalInstalls * Math.random())*10))","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"value":"import { slider } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"rciNetworks = d3.csvParse(`name,Relative_Channel_Importance,Total_Tracked_Installs\nSnapchat,1,80000\nFacebook,4,95000\nByteDance,6,80000\nApple Search Ads,10,100000\nironSource,14,180000\nAppLovin,16,200000\nVungle,17,700000\nUnity Ads,18,220000\nGoogle AdWords,20,240000\n`)","pinned":false,"hidden":false,"mode":"ojs"},{"id":123,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":208,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}}