31:T4f5,table( d3 .groups(spot_metrics, d => d.spot_id) .filter(([, spot_metrics]) => spot_metrics.some(d => d.total_spend)) .map(([key, spot_metrics]) => ({ "Spot ID": key, "Revenue trend": area(spot_metrics.map(d => Math.max(0, d.total_spend)).reverse(), { width: 120, height: 15, color: { line: "green", fill: "#d0f0c0" }, highlights: { max: "blue", end: "black" }, margin: { vertical: 0.1, horizontal: 0.01 }, scaling: "min" }), "Monthly revenue": spot_metrics[0].total_spend, "Max monthly revenue": d3.max(spot_metrics, d => d.total_spend), "# of trans.": spot_metrics[0].num_transactions, "\u0394 in # of trans.": spot_metrics[0].num_transactions_change, "Average spend per trans.": spot_metrics[0].avg_spend_per_trans, "~\u0394 in avg. spend per trans.": spot_metrics[0].avg_spend_per_trans_change })), { width: "800px", layout: "fixed", widths: ["50px", "150px", "100px", "100px", "100px", "100px", "100px", "100px"], formats: [null, null, "$,", "$,", ",", "+,", "$,", "+$,.1s"], align: ["left", "right", "right", "right", "right", "right", "right", "right"], color: [null, null, null, "blue", null, null, null, null] } )32:T52f,The three metrics chosen for this exercise were (1) *monthly revenue*, (2) *average spend per transaction*, and (3) *monthly transactions*. When considering KPIs for a company, it is important to choose metrics that are important indicators of overall health of the business and to adopt metrics that are mutually exclusive, but collectively exhaustive to allow for clear actionability. In this scenario, monthly revenue was chosen as a priority KPI as it is indicative of overall business health, yet monthly revenue alone is not very actionable. By decomposing revenue into its component parts, we see that average spend per transactions and the number of transactions considered together can explain any fluctuations in total revenue. This means that if revenue, let's say, decreases next month, an analyst can look to see if the decrease is driven by lower spending or lower usage. These same metrics may be tracked at a different unit of aggregation as well. For example, we can assess revenue and its component parts for each individual spot. If additional data were made available, each metric could be broken down even further to arrive at more specific metrics. In this example, the number of transactions can be boiled down to usage by user and the number of users. And so on, as illustrated by the graphic below.33:[{"id":"56c1a232dbf73daf940af0a62d6f3cdc37d018e784f24751f2faad6804427fc6f2ed48435ce3082b45195ba4abe0de67bb6ddc0eaafb882a1ee359fccae13ca7","href":"https://static.observableusercontent.com/files/56c1a232dbf73daf940af0a62d6f3cdc37d018e784f24751f2faad6804427fc6f2ed48435ce3082b45195ba4abe0de67bb6ddc0eaafb882a1ee359fccae13ca7","name":"revenue-diagram.PNG","type":"image/png","lastModified":1640765898604.96,"size":23883}] 2f:{"title":"Dashboard for [Anonymous Challenge]","cells":[{"id":0,"value":"# Dashboard for [Anonymous Challenge]\n\n#### Author: [Justin Ferrara](https://github.com/jcferrara?tab=repositories)\n\n

","pinned":false,"hidden":false,"mode":"md"},{"id":590,"value":"### Summary","pinned":false,"hidden":false,"mode":"md"},{"id":595,"value":"In the most recent month of ${revenue_metrics.slice(-1).map(d => d.year_month)[0].toLocaleString()}, the company recorded a 3-month moving average revenue figure of **$${Math.round(revenue_metrics.slice(-1).map(d => d.total_spend_3ma)[0])}**, which represented a **${revenue_metrics.slice(-1).map(d => d.total_spend_3ma_growth_perc)[0].toLocaleString()*100}%** month-over-month change.\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":648,"value":"The revenue change was driven by fluctuations in (1) *average spend per transaction* and (2) *number of transactions*.\n\n3-month moving average spend per transaction has moved **${Math.round(revenue_metrics.slice(-1).map(d => d.avg_spend_per_trans_3ma_growth_perc)[0].toLocaleString()*100)}%** to **$${Math.round(revenue_metrics.slice(-1).map(d => d.avg_spend_per_trans_3ma)[0].toLocaleString())}** since last month while the 3-month moving average number of transactions has moved **${Math.round(revenue_metrics.slice(-1).map(d => d.num_transactions_3ma_growth_perc)[0].toLocaleString()*100)}%** to **~${Math.round(revenue_metrics.slice(-1).map(d => d.num_transactions_3ma)[0].toLocaleString())}** since last month.","pinned":false,"hidden":false,"mode":"md"},{"id":405,"value":"### Monthly revenue","pinned":false,"hidden":false,"mode":"md"},{"id":16,"value":"Plot.plot({\n height: 300,\n width: 1000,\n x: {\n label: \"Month\"\n },\n y: {\n grid: true,\n label: \"↑ Revenue ($), 3-month moving average\",\n domain: [0,1600]\n },\n marks: [\n Plot.areaY(revenue_metrics.slice(3,), {x: \"year_month\", y: \"total_spend_3ma\", fill: \"#d0f0c0\"}),\n Plot.lineY(revenue_metrics.slice(3,), {x: \"year_month\", y: \"total_spend_3ma\", stroke: \"green\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"Plot.plot({\n height: 200,\n width: 1000,\n x: {\n padding: 0,\n label: \"Month\"\n },\n y: {\n grid: true,\n label: \"↑ MoM growth in 3-month moving average revenue\",\n tickFormat: \"+%\"\n },\n color: {\n range: [\"#ed2939\", \"#138808\"]\n },\n marks: [\n Plot.barY(revenue_metrics.slice(3,), {x: \"year_month\", \n y: \"total_spend_3ma_growth_perc\", \n fill: d => Math.sign(d.total_spend_3ma_growth_perc - 0),\n insetLeft: 0.5, \n insetRight: 0.5}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":399,"value":"### Spend per transaction by month","pinned":false,"hidden":false,"mode":"md"},{"id":162,"value":"Plot.plot({\n height: 300,\n width: 1000,\n y: {\n label: \"↑ Amount paid per transaction ($)\",\n domain: [0,120],\n inset: 6\n },\n x: {\n label: \"Month\"\n },\n marks: [\n boxY(trans_data_edited, {x: \"year_month\", y: \"amount_paid_dollars\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":415,"value":"### Transactions by month\n3-month moving average","pinned":false,"hidden":false,"mode":"md"},{"id":434,"value":"Plot.plot({\n width: 1000,\n x: {\n round: false,\n label: \"Month\",\n align: 10,\n inset: 10\n },\n color: {\n type: \"linear\",\n scheme: \"greens\"\n },\n marks: [\n Plot.cell(revenue_metrics.slice(3,), {x: \"year_month\", fill: \"num_transactions_3ma\"}),\n Plot.text(revenue_metrics.slice(3,), {x: \"year_month\", text: d => d.num_transactions_3ma.toFixed(1)}),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":842,"value":"### Current month performance by spot","pinned":false,"hidden":false,"mode":"md"},{"id":699,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":945,"value":"Note: Change in the table above represents a current month vs. prior month difference.","pinned":false,"hidden":false,"mode":"md"},{"id":429,"value":"md`\n

\n### Rationale\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":678,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":419,"value":"md`${await FileAttachment(\"revenue-diagram.PNG\").image()}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":906,"value":"Note: Figures were smoothed with a 3-month moving average to ensure metrics reflect sustained changes in underlying business performance not just short-term fluctuations.","pinned":false,"hidden":false,"mode":"md"},{"id":17,"value":"md`\n

\n### Data Prep\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":547,"value":"trans_data_edited = trans_data\n.filter(d => d[\"year_month\"] !== \"202011\")\n.filter(d => d[\"year_month\"] !== \"202012\")\n.filter(d => d[\"year_month\"] !== \"202101\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":701,"value":"spot_metrics = d3.json(\"https://raw.githubusercontent.com/jcferrara/anonymous-challenge/main/spot_metrics.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"revenue_metrics = d3.json(\"https://raw.githubusercontent.com/jcferrara/anonymous-challenge/main/revenue_metrics.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"trans_data = d3.json(\"https://raw.githubusercontent.com/jcferrara/anonymous-challenge/main/transaction_data.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":690,"value":"

\n### Packages","pinned":false,"hidden":false,"mode":"md"},{"id":178,"value":"import {boxY, outliers, iqr1, iqr2, quartile1, quartile3} from \"@observablehq/plot-box\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"import {table, area} from \"@hamzaamjad/tiny-charts\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}