2e:null 2f:["$","title",null,{"children":"psyML Audience Exclusion | K Lindberg | Observable"}] 34:T436,{ var counter=0 const context = DOM.context2d(400, 400); const L = d3.packSiblings(d3.range(Audience).map(() => ({r: 10000/((Audience+1000)^(.001))+Math.random()}))); context.save(); context.clearRect(0, 0, 400, 400); context.translate(400 / 2, 200); L.forEach(p => { context.beginPath(); context.moveTo(p.x + p.r, p.y); context.arc(p.x, p.y, p.r, 0, 2 * Math.PI); if (counter/Audience`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":195,"value":"md `Your audience of ${numberWithCommas(Audience*1000)} is simulated below, with each yellow dot representing 1,000 individuals or 1 CPM. The average CPM bid on Twitter is $3.50 — so ${numberWithCommas(Audience)} average bids and for 1 impression on everyone in the whole audience costs $${ numberWithCommas(Math.round( Audience*3.5 * 1e2 ) / 1e2 )}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":205,"value":"{\n \n const context = DOM.context2d(400, 400);\n const L = d3.packSiblings(d3.range(Audience).map(() => ({r: 10000/((Audience+1000)^(.001))+Math.random()})));\n context.save();\n context.clearRect(0, 0, 400, 400);\n context.translate(400 / 2, 200);\n \n L.forEach(p => {\n context.beginPath();\n context.moveTo(p.x + p.r, p.y);\n context.arc(p.x, p.y, p.r, 0, 2 * Math.PI);\n context.fillStyle = \"#ffc907\";\n\tcontext.fill();\n context.strokeStyle = \"#faa51a\";\n\tcontext.stroke();\n \n });\n\n\n\n return context.canvas;\n\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"viewof Frequency_Cap = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"md`With a frequency cap for of ${Frequency_Cap} - the audience sees the ads no more than ${Frequency_Cap} times each. So, if you targeted 100% of that audience with the absolute max number of impressions you’d want to serve with the same creative - your maximum per-campaign cost is $${numberWithCommas( Math.round( Audience*Frequency_Cap*3.5 * 1e2 ) / 1e2 )}. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":253,"value":"viewof Clickthrough_Rate = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"md` Given a click through rate of ${(Clickthrough_Rate*100*1e4)/1e4}% on the ${numberWithCommas(Audience*1000)} people in the audience and ${Frequency_Cap} impressions per user the campaign will amount to an expected ${numberWithCommas(Math.round(Clickthrough_Rate*Frequency_Cap*Audience*1000))} clicks to your website. Down below we see your audience again, but now we have simulated a campaign with your given parameters and highlighted with users who clicked through to your website a deeper orange tone.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"{\n var counter=0\n const context = DOM.context2d(400, 400);\n const L = d3.packSiblings(d3.range(Audience).map(() => ({r: 10000/((Audience+1000)^(.001))+Math.random()})));\n context.save();\n context.clearRect(0, 0, 400, 400);\n context.translate(400 / 2, 200);\n \n L.forEach(p => {\n \n context.beginPath();\n context.moveTo(p.x + p.r, p.y);\n context.arc(p.x, p.y, p.r, 0, 2 * Math.PI);\n if (counter/Audience`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":298,"value":"md `With an ecommerce conversion rate of ${Math.round(Conversion_Rate*100*1e4)/1e4}% you would get ${numberWithCommas(Math.round(Clickthrough_Rate*Conversion_Rate*Frequency_Cap*Audience*1000))} purchases from the ${numberWithCommas(Math.round(Clickthrough_Rate*Frequency_Cap*Audience*1000))} visitors in your campaign. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":331,"value":"viewof Profit_per_Sale = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":324,"value":"md `With a $${numberWithCommas( Math.round( Profit_per_Sale* 1e2 ) / 1e2 )} profit per sale (not including ad costs), an annual cost of $${numberWithCommas( Math.round( Audience*3.5*Frequency_Cap* 1e2 ) / 1e2 )} for the ads for this audience and with ${numberWithCommas(Math.round(Clickthrough_Rate*Conversion_Rate*Frequency_Cap*Audience*1000))} buyers your total profit before ad costs from the annual campaign is $${numberWithCommas( Math.round( Profit_per_Sale*Clickthrough_Rate*Conversion_Rate*Frequency_Cap*Audience*1000 * 1e2 ) / 1e2 )}. Subtracting the $${numberWithCommas( Math.round( Audience*3.5*Frequency_Cap * 1e2 ) / 1e2 )} in ad costs this un-optimized audience generates $${numberWithCommas( Math.round( ((Profit_per_Sale*Clickthrough_Rate*Conversion_Rate*Frequency_Cap*Audience*1000)-(Audience*3.5*Frequency_Cap)) * 1e2 ) / 1e2 )} in revenue.\n\nBelow we have the your audience again. This time users who clicked through and purchased in the simulation are green.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"{\n var counter=0\n const context = DOM.context2d(400, 400);\n const L = d3.packSiblings(d3.range(Audience).map(() => ({r: 10000/((Audience+1000)^(.001))+Math.random()})));\n context.save();\n context.clearRect(0, 0, 400, 400);\n context.translate(400 / 2, 200);\n \n L.forEach(p => {\n \n context.beginPath();\n context.moveTo(p.x + p.r, p.y);\n context.arc(p.x, p.y, p.r, 0, 2 * Math.PI);\n if (counter/Audience`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":449,"value":"md`By eliminating ${Math.round(Percent_Dropped*100*1e1)/1e1}% of the audience, to ${numberWithCommas(Math.round(Audience*1000*(1-Percent_Dropped)))} people while still maintaining the high quality users, the spend would drop across a campaign to $${numberWithCommas( Math.round( Audience*3.5*Frequency_Cap*((1-Percent_Dropped)) * 1e2 ) / 1e2 )}, a $${numberWithCommas( Math.round(Percent_Dropped*Audience*3.5*Frequency_Cap * 1e2 ) / 1e2 )} savings.\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"md `\nBelow we can see your campaign again, after psyML's empathetic AI is done with it. By picking out and eliminating bots and emotionally non-receptive people, you can stop wasting money on extraneous profiles while protecting your true audience.`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"viewof Campaigns = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":707,"value":"\nfunction numberWithCommas(x) {\n x = x.toString();\n var pattern = /(-?\\d+)(\\d{3})/;\n while (pattern.test(x))\n x = x.replace(pattern, \"$1,$2\");\n return x;\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":710,"value":"d3 = require(\"https://d3js.org/d3.v5.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 33:"$32:body"