32:Tbf8,md` **Summary:** - Pandemic-driven market uncertainty has put a premium on advertiser flexibility, agility, and the ability to drive business outcomes through media spending. - Both ad-supported video on demand (AVOD) streaming TV and data-driven linear TV offer these advantages and therefore belong at the core of advertiser and agency premium video strategies. - Based on a time-spent media metric for linear TV and AVOD, brands should spend $9 on linear TV advertising for every $1 spent on AVOD. - This is in-line with linear TV’s household penetration and available impressions advantage over AVOD: [Assuming aggressive assumptions for growth in AVOD viewership and a conservative average CPM estimates, linear TV’s impressions are still many times times higher than AVOD’s](https://observablehq.com/@simulmedia/quantifying-the-reach-of-smart-scatter-tv-advertising-data). - The best premium video alternative with the capacity to absorb dollars not spent in the TV’s Upfront is TV’s scatter market. Given massive changes in TV viewing associated with COVID-19, media planners are asking us, “What’s the optimal balance of spending on data-driven linear and AVOD?” This post will provide a rule of thumb that can help brands and agencies answer this question. In the process, we’re also going to show the readiness of data-driven linear TV as executed in the scatter market as well as AVOD to absorb dollars that are likely to be reallocated from the Upfronts, a marketplace that many expect to be smaller this year than in recent memory. Our analysis begins with a concept popularized by Mary Meeker in her annual Internet Trends reports: over time, the time users spend with media (e.g. print, radio, digital, TV) will equal the relative amount of money advertisers spend in the media. For example, if consumers next year were to spend 25% of their time watching virtual reality movies, over time advertisers would find a way to spend about 25% of their media budgets in that channel. Here's a chart that shows how advertising dollars spent on desktop and mobile caught up to time spent between 2010 and 2018.

Source: [bondcap.com](https://www.bondcap.com/report/itr19/#view/1)

According to [Nielsen](https://answers.nielsen.com/portal/workspace/US+Media+Client+Communications/US+TV+Client+Communications/2020+Client+Communications/COVID-19+Insights/Streaming+Update+-+COVID+-+week+of+051120.pdf), COVID has led to a change in viewing habits from last year. Streaming, as a percentage of total TV viewing, has risen from 16% (May 2019) to 24% post-COVID (May 2020). Now let's show the current spending breakdown in advertiser spending on linear TV and AVOD streaming for this year, as projected by eMarketer ([source 1](https://www.emarketer.com/content/us-connected-tv-advertising-2019), [source 2](https://www.emarketer.com/content/prior-assumptions-about-2020-tv-ad-spend-growth-may-shift-in-light-of-pandemic)): `33:T51e,md` There are a couple of wrinkles here that a pure time spent metric misses, though. While total streaming accounts for 24% of TV viewing time, that number includes all streaming, even those that are not ad supported. No matter how much brands may want to spend on Netflix and Amazon Prime Video, they won’t accept ad dollars and aren’t likely to start any time soon. Also, some AVOD streaming ad environments are more premium than others. YouTube, for example, features a lot of user-generated content (UGC). While that can be entertaining, in general UGC doesn’t provide the same high quality advertising environment that network-produced programs create and that TV advertisers require. We therefore need to adjust the analysis to capture TV environments that support advertising and offer premium, brand-safe inventory. Here's an estimate of the percentage of total streaming minutes by provider:

Source: [Nielsen](https://answers.nielsen.com/portal/workspace/US+Media+Client+Communications/US+TV+Client+Communications/2020+Client+Communications/COVID-19+Insights/Streaming+Update+-+COVID+-+week+of+051120.pdf)

`34:Taac,viewof inputs = { return html`

Advertising Minutes (%)

Linear TV

${selLinearAMView}

${selLinearAM}%

Netflix

${selNetflixAMView}

${selNetflixAM}%

Amazon

${selAmazonAMView}

${selAmazonAM}%

Hulu

${selHuluAMView}

${selHuluAM}%

YouTube

${selYoutubeAMView}

${selYoutubeAM}%

Other

${selOtherAMView}

${selOtherAM}%

Premium Inventory (%)

Linear TV

${selLinearPIView}

${selLinearPI}%

Netflix

${selNetflixPIView}

${selNetflixPI}%

Amazon

${selAmazonPIView}

${selAmazonPI}%

Hulu

${selHuluPIView}

${selHuluPI}%

YouTube

${selYoutubePIView}

${selYoutubePI}%

Other

${selOtherPIView}

${selOtherPI}%

`; }35:T599,viewof interactiveTable = { return table(data3, { sortable: false, paged: 200, columns: { medium: { title: 'Medium', formatter(val, i) { return html`${val}`; } }, timeSpent: { title: 'Time Spent', formatter(val, i) { return html`${d3.format( '.0%' )(val)}`; } }, weeklyMins: { title: 'Weekly Minutes', formatter(val, i) { return html`${d3 .format('.2s')(val) .replace("G", "B")}`; } }, adMinutes: { title: 'Ad Minutes', formatter(val, i) { return html`${val}%`; } }, premium: { title: 'Premium', formatter(val, i) { return html`${val}%`; } }, refactor: { title: 'Refactor', formatter(val, i) { return html`${d3.format( '.1%' )(val)}`; } } } }); }36:T4ee,md` We wrap up by answering the question: “Where will dollars once reserved for the Upfronts go?” This year, the combination of pandemic-induced network show production delays and brand uncertainty is likely to result in a smaller overall Upfront market. Networks have less to sell in advance, and many brands just aren’t ready or able to commit to anything more than a month or two in the future. This uncertainty has many marketing teams contemplating the alternative to the Upfronts. Should they shift budgets to AVOD streaming? What about the scatter market? Or should brands stop spending altogether? (There’s ample evidence from previous recessions to answer the “should I stop advertising?" question. Unless your business has been shut down due to the pandemic, the answer is a definitive “no.”) We estimated the impressions, total dollar volume ("Revenue") and CPMs associated with the Upfront, Scatter, and AVOD markets. The first two were sourced from [mediadynamics](https://www.mediadynamicsinc.com/uploads/files/UPFRONT-2019-20-PRESS-RELEASE-xro.pdf), while the latter is coming from Nielsen's "Streaming TV Update: COVID" update for May 21, 2020, which estimates that AVOD streaming has about 9% of linear TV's total impressions. `37:T4c4,md`As you can see, if advertisers were to attempt to redirect just 35% of their Upfront budget and impressions to AVOD streaming, the AVOD market would have to nearly double its available impressions. AVOD is growing fast, but no one is forecasting this sort of growth. In fact, it’s likely that AVOD will need several years of sustained high levels of growth in order to be able to absorb significant levels of budget and impressions from linear TV. By comparison, linear TV's scatter market can (and likely will) absorb nearly all the impressions that the Upfront does not secure in 2020. That’s because the Upfronts and scatter are two different markets available to transact in the same medium. **The bottom line: marketers must play the hand they've been dealt today. In this market, the best way to get flexibility, agility, precision, speed and provable business outcomes is via data-driven linear TV. While AVOD streaming belongs in the mix, the data shows that premium video advertisers should maintain a roughly 9:1 ratio of spending on linear TV and AVOD.** If you have questions or additional data you’d like to see, we’d love to hear from you. Get in touch with us via marketing@simulmedia.com.`38:T47b,data3 = { const data = [ { medium: "Linear TV", timeSpent: 0.76, weeklyMins: 447000000000, adMinutes: selLinearAM, premium: selLinearPI }, { medium: "Netflix", timeSpent: 0.08, weeklyMins: 45600000000, adMinutes: selNetflixAM, premium: selNetflixPI }, { medium: "Amazon", timeSpent: 0.02, weeklyMins: 11400000000, adMinutes: selAmazonAM, premium: selAmazonPI }, { medium: "Hulu", timeSpent: 0.03, weeklyMins: 16000000000, adMinutes: selHuluAM, premium: selHuluPI }, { medium: "Youtube", timeSpent: 0.05, weeklyMins: 29300000000, adMinutes: selYoutubeAM, premium: selYoutubePI }, { medium: "Other", timeSpent: 0.07, weeklyMins: 39300000000, adMinutes: selOtherAM, premium: selOtherPI } ]; const total = d3.sum(data, d => 100 * d.timeSpent * d.adMinutes * d.premium); data.forEach( each => (each.refactor = (100 * each.timeSpent * each.adMinutes * each.premium) / total) ); return data; }39:[{"id":"7290a76c1ca51df44c28add37a8f8e9fceda85d57bd99e1c012666375028c0639ad7349506cf303ab283abe4acde308549255806dec624eea4e01b01933ada0e","href":"https://static.observableusercontent.com/files/7290a76c1ca51df44c28add37a8f8e9fceda85d57bd99e1c012666375028c0639ad7349506cf303ab283abe4acde308549255806dec624eea4e01b01933ada0e","name":"streaming_viewing.jpg","type":"image/png","lastModified":1590644340196.684,"size":818264},{"id":"6111e1acd0a59c5c682f4776a4909bb2284ab19df532d1f73bb2fcadd4c17dfd12ab73c3163cd4548620c222390fa23bcd5b740ae4c4736abaf554722a095f45","href":"https://static.observableusercontent.com/files/6111e1acd0a59c5c682f4776a4909bb2284ab19df532d1f73bb2fcadd4c17dfd12ab73c3163cd4548620c222390fa23bcd5b740ae4c4736abaf554722a095f45","name":"mary_meeker.jpg","type":"image/png","lastModified":1590678852992.897,"size":198651}] 30:{"title":"Quantifying \"Smart Scatter\" TV Advertising, Part 3: The Optimal Balance of Linear TV and AVOD Budgeting","cells":[{"id":0,"value":"md`# Quantifying \"Smart Scatter\" TV Advertising, Part 3: The Optimal Balance of Linear TV and AVOD Budgeting`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"viewof table1 = {\n return table(data1, {\n sortable: false,\n paged: 200,\n columns: {\n medium: {\n title: 'Medium',\n formatter(val, i) {\n return html`${val}`;\n }\n },\n spend: {\n title: 'Yearly Spend',\n formatter(val, i) {\n return html`${d3\n .format('$.2s')(val)\n .replace(\"G\", \"B\")}`;\n }\n },\n percentage: {\n title: 'Percentage',\n formatter(val, i) {\n return html`${d3.format(\n '.0%'\n )(val)}`;\n }\n }\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":73,"value":"md`When we apply Meeker’s observation to TV viewing, we see that time spent with linear TV and streaming does not match media allocation. It appears that advertisers should spend considerably more on streaming. Meeker's rule of thumb suggests that spending on streaming should be 24% of the total category, not 11%, which would bring the yearly spend on streaming to $19B from $8.8B, as well as a decrease of about $11B in linear TV:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":81,"value":"viewof table2 = {\n return table(data2, {\n sortable: false,\n paged: 200,\n columns: {\n medium: {\n title: 'Medium',\n formatter(val, i) {\n return html`${val}`;\n }\n },\n spend: {\n title: 'Yearly Spend',\n formatter(val, i) {\n return html`${d3\n .format('$.2s')(val)\n .replace(\"G\", \"B\")}`;\n }\n },\n percentage: {\n title: 'Percentage',\n formatter(val, i) {\n return html`${d3.format(\n '.0%'\n )(val)}`;\n }\n }\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":120,"value":"md`With these data points in mind, we can adjust the pure time-spent viewing percentages from 24% streaming and 76% linear in order to *account only for ad viewing*. Play with some of the assumptions guiding the percentage of linear TV and AVOD that features advertising and how much of each features premium content. (Note that we have fixed linear TV's advertising minutes (%) at 100%, as well as Netflix and Amazon at 0%, so you can't adjust them.) Changing the values of the sliders will automatically update results in the table and graph below. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":368,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":201,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":279,"value":"viewof chart = {\n return percentChart;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"viewof legendInteractive = {\n return swatches({\n color: percentChartColor,\n columns: \"80px\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":726,"value":"html`

↓

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":718,"value":"md`

\n **${d3.format(\".1%\")(\n data3[0].refactor\n )}** on Linear TV & **${d3.format(\".1%\")(\n 1 - data3[0].refactor\n)}** on AVOD\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":510,"value":"md`\nAbove you can see how the allocation of budget between Linear TV and AVOD changes in response to input variations. \n\nWith the initial input configuration, we calibrated Mary Meeker's time-spent rule of thumb to focus only on premium inventory available in AVOD and linear TV, making a couple of assumptions that all err on the high side:\n- 80% of Hulu viewing is ad-supported.\n- 50% of all other streaming minutes are ad-supported.\n- 60% of the ad minutes on YouTube and other streaming are in premium, non-UGC environments.\n\nWhen we complete this part of the analysis, we see that 91% of all time spent with premium, ad supported TV happens on linear TV. That's the reality marketers need to plan for today. As a result: \n

\n we recommend that **91%** of premium video advertising dollars should flow to linear TV and **9%** should go to AVOD \n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":557,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":584,"value":"viewof table3 = {\n return table(data4, {\n sortable: false,\n paged: 200,\n columns: {\n market: {\n title: 'Market',\n formatter(val, i) {\n return html`${val}`;\n }\n },\n impressions: {\n title: 'Impressions',\n formatter(val, i) {\n return html`${d3\n .format('.2s')(val)\n .replace(\"G\", \"B\")}`;\n }\n },\n revenue: {\n title: 'Revenue',\n formatter(val, i) {\n return html`${d3\n .format('$.2s')(val)\n .replace(\"G\", \"B\")}`;\n }\n },\n cpm: {\n title: 'CPM',\n formatter(val, i) {\n return html`${d3.format(\n '$.2f'\n )(val)}`;\n }\n }\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":610,"value":"md`Now, we let you play with the assumption regarding how much the Upfront market may lose in dollar volume to see how much the AVOD market would have to grow (in impressions) in order to absorb those lost dollars and impressions. The section below the slider will update automatically as you make adjustments.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":619,"value":"viewof upfrontDrop = slider({\n min: 0,\n max: 100,\n step: 5,\n value: 20,\n description: \"Adjust The Upfront Budget Decrease (%)\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":628,"value":"viewof upfrontDecreaseOutcomes = {\n return md`

\n ${d3\n .format('$.2s')((data4[0].revenue * upfrontDrop) / 100)\n .replace(\"G\", \"B\")} Estimated Upfront Decrease
\n\n ${d3\n .format('.2s')((1000 * data4[0].revenue * upfrontDrop) / 100 / data4[0].cpm)\n .replace(\"G\", \"B\")} Associated Impressions
\n\n **${d3.format('.1%')(\n (1000 * data4[0].revenue * upfrontDrop) /\n 100 /\n data4[0].cpm /\n data4[2].impressions\n )}** AVOD Impressions Increase Required To Absorb Upfront Decline\n

\n`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":659,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":676,"value":"md`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":672,"value":"selLinearAMView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":412,"value":"selNetflixAMView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":414,"value":"selAmazonAMView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"selHuluAMView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"selYoutubeAMView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":421,"value":"selOtherAMView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":453,"value":"selLinearPIView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":451,"value":"selNetflixPIView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":455,"value":"selAmazonPIView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":457,"value":"selHuluPIView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":459,"value":"selYoutubePIView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":461,"value":"selOtherPIView = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs"},{"id":385,"value":"selLinearAM = Generators.input(selLinearAMView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":425,"value":"selNetflixAM = Generators.input(selNetflixAMView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"selAmazonAM = Generators.input(selAmazonAMView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"selHuluAM = Generators.input(selHuluAMView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":436,"value":"selYoutubeAM = Generators.input(selYoutubeAMView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":438,"value":"selOtherAM = Generators.input(selOtherAMView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":470,"value":"selLinearPI = Generators.input(selLinearPIView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":473,"value":"selNetflixPI = Generators.input(selNetflixPIView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":480,"value":"selAmazonPI = Generators.input(selAmazonPIView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":482,"value":"selHuluPI = Generators.input(selHuluPIView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":484,"value":"selYoutubePI = Generators.input(selYoutubePIView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"selOtherPI = Generators.input(selOtherPIView)","pinned":false,"hidden":false,"mode":"ojs"},{"id":44,"value":"md`### Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":48,"value":"data1 = [\n { medium: \"Linear TV\", spend: 72000000000, percentage: 0.89 },\n { medium: \"Streaming\", spend: 8800000000, percentage: 0.11 }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"data2 = [\n { medium: \"Linear TV\", spend: 61400000000, percentage: 0.76 },\n { medium: \"Streaming\", spend: 19400000000, percentage: 0.24 }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":588,"value":"data4 = [\n {\n market: \"Upfront\",\n impressions: 2542392926223,\n revenue: 21870000000,\n cpm: 8.6\n },\n {\n market: \"Scatter\",\n impressions: 8457607073777,\n revenue: 20000000000,\n cpm: 2.4\n },\n { market: \"AVOD\", impressions: 949250005116, revenue: 20000000000, cpm: 15 }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":307,"value":"media = data3.map(obj => obj.medium)","pinned":false,"hidden":false,"mode":"ojs"},{"id":273,"value":"donutData = data3\n .map(obj => ({\n name: obj.medium,\n value: obj.refactor * 100\n }))\n .filter(item => item.value > 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":291,"value":"donutData2 = {\n const data = { name: \"\" };\n donutData.forEach(each => {\n data[each.name] = each.value;\n });\n const retData = [data];\n retData.columns = Object.keys(data);\n\n return retData;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"md`### Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":305,"value":"percentChartColor = d3\n .scaleOrdinal()\n .domain(media)\n .range(d3.schemeSpectral[media.length])\n .unknown(\"#ccc\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":276,"value":"import { chart as percentChart } with {\n donutData2 as data,\n percentChartColor as color\n} from 'aeef913efce55c19'","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":362,"value":"jQuery3 = require(\"https://code.jquery.com/jquery-3.3.1.slim.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"import { slider } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"import { table } from \"@khubert/tables@522\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":317,"value":"import { legend, swatches } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":356,"value":"import { importjs, importcss } from \"@chomtana/import-external-library\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":358,"value":"BootstrapCss = importcss(\n \"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":360,"value":"BootStrap4 = require(\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W39","stdlib":"1","titling":"auto"}