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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-7d44deb0c3","quickstartHeroTitle":{"lines":["Reach and Frequency queries for advertising 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advertising campaign. Advertisers typically pay for \u003Cem\u003Eimpressions\u003C/em\u003E, which is simply the number of times your ad is shown. Reach, on the other hand, tells you the total number of \u003Cem\u003Epeople\u003C/em\u003E who see your ad, while \u003Cem\u003Efrequency\u003C/em\u003E refers to the number of times people see your ad.\u003C/p\u003E\n","\u003Cp\u003EAs such:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-math\"\u003Eimpressions = reach * frequency\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBecause of the speed and scale of Snowflake, many companies store ad impression data in Snowflake. This Quickstart will show you how to do common types of Reach and Frequency queries in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a Snowflake account or use of the \u003Ca href=\"https://trial.snowflake.com\"\u003ESnowflake free 30-day trial\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL, database concepts, and objects\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to generate example impression data\u003C/li\u003E\u003Cli\u003EHow to query the data to give important reach and frequency results\u003C/li\u003E\u003Cli\u003EHow to use Snowsight to visualize some of the data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESQL functions used to generate Reach and Frequency measures\u003C/li\u003E\u003Cli\u003EVisualizations of the results\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Your Environment\u003C/h2\u003E\n","\u003Cp\u003EIf you do not have a Snowflake account, you can register for a \u003Ca href=\"https://trial.snowflake.com\"\u003ESnowflake free 30-day trial\u003C/a\u003E. The cloud provider (AWS, Azure, Google Cloud), and Region (US East, EU, e.g.) do \u003Cem\u003Enot\u003C/em\u003E matter for this lab. However, we suggest you select the region which is physically closest to you.\u003C/p\u003E\n","\u003Cp\u003ETo easily follow the instructions, resize your browser windows so you can view this Quickstart and your Snowflake environment side-by-side. If possible, even better is to use a secondary display dedicated to the Quickstart.\u003C/p\u003E\n","\u003Cp\u003EWhile commands from this Quickstart can be copy/pasted to a worksheet in your Snowflake account, you can optionally \u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples\"\u003Edownload the script as a SQL file to your local machine from Github\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ENavigating to Snowsight\u003C/h3\u003E\n","\u003Cp\u003EFor this lab, you will use the latest Snowflake web interface, Snowsight.  If you have not enabled Snowsight previously, please do the following.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog into your Snowflake account\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003ESnowsight\u003C/strong\u003E Worksheets tab. The new web interface opens in a separate tab or window.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EWorksheets\u003C/strong\u003E in the left-hand navigation bar. The \u003Cstrong\u003EReady to Start Using Worksheets and Dashboards\u003C/strong\u003E dialog opens.\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EEnable Worksheets and Dashboards button\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/enable_worksheets.png\" alt=\"Enable Worksheets and Dashboards\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate a Warehouse, Database, Schemas\u003C/h3\u003E\n","\u003Cp\u003ECreate a warehouse, database, and schema that will be used for loading, storing, processing, and querying data for this Quickstart. We will use the UI within the Worksheets tab to run the DDL that creates these objects.\u003C/p\u003E\n","\u003Cp\u003ECopy the commands below into your trial environment. To execute a single statement, just position the cursor anywhere within the statement and click the Run button. To execute several statements, they must be highlighted through the final semi-colon prior to clicking the Run button.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- setup warehouse, database and schema\nUSE ROLE ACCOUNTADMIN;\nCREATE WAREHOUSE R_F_DEMO_WH WITH WAREHOUSE_SIZE='XSmall'  STATEMENT_TIMEOUT_IN_SECONDS=15 STATEMENT_QUEUED_TIMEOUT_IN_SECONDS=15;\nUSE WAREHOUSE R_F_DEMO_WH;\nCREATE DATABASE R_F_DEMO;\nCREATE SCHEMA R_F_DEMO.DEMO;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the table and generate test data\u003C/h3\u003E\n","\u003Cp\u003EWe create a table to store our impression data.  Here, we use a campaign ID, a placement ID, and a creative ID.  However, in reality, we may have things like site, line item, etc.  Also, we use a generic User ID column here for illustration purposes.  This could hold any form of identity, including email, cookie ID, Mobile Advertising Identifier (MAID), or identity provided by a 3rd party identity provider.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- create table to hold impressions\nuse R_F_DEMO.DEMO;\ncreate or replace table IMPRESSIONS(event_time TIMESTAMP_TZ,\n                                    campaign_id VARCHAR(10),\n                                    placement_id VARCHAR(10),\n                                    creative_id VARCHAR(10),\n                                    user_id VARCHAR(40));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we run SQL to populate the table with realistic-looking data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- generate test data\ninsert into IMPRESSIONS\nselect \n   dateadd(second, uniform(1, 2592000, random(1)), ('2022-09-01'::timestamp)) as event_time,\n   '174631' as campaign_id\n  ,uniform(200001,202000,random(2))::varchar(10) as placement_id\n  ,uniform(300001,301000,random(3))::varchar(10) as creative_id\n  ,sha1(uniform(1, 3500000, random(4)))::varchar(40) as user_id\nfrom table(generator(rowcount=>20000000));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere we generate data for a single example campaign that runs for one month from September 1st, 2022. The campaign has 20,000,000 impressions across 2,000 placements and 1,000 creatives.  The impressions are spread over up to 3,500,000 people.\u003C/p\u003E\n","\u003Ch3\u003EVerify the data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect count(1) from IMPRESSIONS where campaign_id='174631';\nselect count(distinct(placement_id)) from IMPRESSIONS where campaign_id='174631';\nselect count(distinct(user_id)) from IMPRESSIONS where campaign_id='174631';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe first shows that 20,000,000 were, in fact, generated.  The second shows that these rows contain 2,000 distinct placement IDs.  The final shows 3,488,543 distinct user IDs.\u003C/p\u003E\n","\u003Ch2\u003ECalculating reach\u003C/h2\u003E\n","\u003Cp\u003EWhen a campaign first starts, it reaches almost entirely new people. As it goes on, however, it reaches more people who have seen the campaign already. It gets harder and harder for the campaign to reach people the longer it goes on.\u003C/p\u003E\n","\u003Cp\u003EIn advertising, this is often visualized as a "reach curve."  In the reach curve, the X axis is the number of impressions shown, and the Y axis is the reach of the campaign.  This shows the shape of the reach curve, so the advertiser can determine when the point of diminishing returns has been reached.\u003C/p\u003E\n","\u003Ch3\u003ECalculating cumulative impressions\u003C/h3\u003E\n","\u003Cp\u003EThe first step is to write a query that calculates the cumulative impressions served by day.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- calculate cumulative impressions by day\nWITH DAILY_IMPRESSIONS AS\n(\nSELECT TO_DATE(event_time) as day,\n       COUNT(1) as day_impressions\nFROM \n  IMPRESSIONS\nWHERE campaign_id='174631'\nGROUP BY 1\n)\nselect\n  day,\n  sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\nfrom DAILY_IMPRESSIONS\norder by 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe first step (in the with clause), is to get the impressions on each day.  Then, we query over this and sum over a window function to get the cumulative number of impressions.\u003C/p\u003E\n","\u003Cp\u003ERunning this, you see each day of the campaign in order.  We serve 666,622 impressions on the first day of the campaign, and on 2022-10-01 we reach the total of 20m impressions for the campaign.\u003C/p\u003E\n","\u003Ch3\u003ECalculating cumulative uniques per day\u003C/h3\u003E\n","\u003Cp\u003EThe next step is to calculative uniques per day.  What is tricky here is that uniques, unlike impressions, do not sum.  If I have 10 impressions on the first day, and 10 impressions on the second day, then I had 20 impressions across those two days combined.  However, if I had 10 \u003Cem\u003Euniques\u003C/em\u003E on the first day, and 10 \u003Cem\u003Euniques\u003C/em\u003E on the second day, then I \u003Cstrong\u003Edid not necessarily\u003C/strong\u003E have 20 uniques across the two days combined.\u003C/p\u003E\n","\u003Cp\u003EOn day one, we would expect nearly all of our impressions to go to unique people.  On the second day, we reach some new people, but some of them were people who saw the ad on day one.  And so forth, and so on.  So let's see what this looks like.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- calculate cumulative uniques by day\nWITH firstseen AS (\n  SELECT user_id, MIN(TO_DATE(event_time)) firstday\n  FROM IMPRESSIONS\n  WHERE campaign_id='174631'\n  GROUP BY 1\n)\nSELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) daily_cumulative_uniques\nFROM firstseen\nORDER BY 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFirst, inside the WITH clause, we select the first date on which each user gets an impression.  This is the only day on which they would be considered to be a unique.  Then, we count the number of new uniques per day, using the window function.\u003C/p\u003E\n","\u003Cp\u003EWhen we run this, we see that on day one, we reach 606,839 unique users.  On the last day of the campaign, this is up to the full 3,488,543 unique users reached by the campaign.\u003C/p\u003E\n","\u003Ch3\u003ECombining to one query and visualizing the result\u003C/h3\u003E\n","\u003Cp\u003ETo make it easy to visualize these together, we can query the two WITH clauses used in previous sections and join them together.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- show cumulative impressions and uniques side-by-side to understand diminishing returns\nWITH DAILY_CUMULATIVE_IMPRESSIONS AS (\n    WITH DAILY_IMPRESSIONS AS\n    (\n    SELECT TO_DATE(event_time) as day,\n       COUNT(1) as day_impressions\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    )\n    select\n      day,\n      sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\n    from DAILY_IMPRESSIONS\n),\nDAILY_CUMULATIVE_UNIQUES AS\n(\n    WITH firstseen AS\n    (\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    )\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\n    FROM firstseen\n    ORDER BY 1\n)\nSELECT dci.DAY, dci.CUMULATIVE_IMPRESSIONS, dcu.CUMULATIVE_UNIQUES\nFROM DAILY_CUMULATIVE_IMPRESSIONS dci\n JOIN DAILY_CUMULATIVE_UNIQUES dcu on dcu.day=dci.day\nORDER BY 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhere this gets really interesting is when we can visualize the results as a classic "reach curve."  Click the "Chart" option in Snowsight.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/results-chart-click.png\" alt=\"Click Chart to visualize data\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, for the X-Axis, select "CUMULATIVE_IMPRESSIONS" and for the Y-Axis (above) select "CUMULATIVE_UNIQUES" and select "None" for aggregation.  Now you have the classic reach curve you want to see.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/reach-curve.png\" alt=\"View reach curve\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EReach curve given minimum frequency\u003C/h3\u003E\n","\u003Cp\u003EYou might not think that a single impression is impactful enough to drive the outcomes you are looking for with your advertising.  So your reach curve is misleading - your uniques are flattening out, but many of those consumers may have only seen the ad once.  You are willing to spend more money to reach consumers up to what you consider to be the minimum effective frequency.  To implement this, we can change our uniques query to only consider unique consumers who have had 3 or more impressions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- show cumulative impressions and uniques side-by-side to understand diminishing returns, with minimum of 3 impressions\nWITH DAILY_CUMULATIVE_IMPRESSIONS AS (\n    WITH DAILY_IMPRESSIONS AS\n    (\n    SELECT TO_DATE(event_time) as day,\n       COUNT(1) as day_impressions\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    )\n    select\n      day,\n      sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\n    from DAILY_IMPRESSIONS\n),\nDAILY_CUMULATIVE_UNIQUES_MIN_3 AS\n(\n    WITH firstseen AS\n    (\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    HAVING count(1) > 2\n    )\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\n    FROM firstseen\n    ORDER BY 1\n)\nSELECT dci.DAY, dci.CUMULATIVE_IMPRESSIONS, dcu.CUMULATIVE_UNIQUES\nFROM DAILY_CUMULATIVE_IMPRESSIONS dci\n JOIN DAILY_CUMULATIVE_UNIQUES_MIN_3 dcu on dcu.day=dci.day\nORDER BY 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you want to compare the slopes of these curves, you can view it all together.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- show cumulative impressions and uniques, comparing to minimum of 3 impressions\nWITH DAILY_CUMULATIVE_IMPRESSIONS AS (\n    WITH DAILY_IMPRESSIONS AS\n    (\n    SELECT TO_DATE(event_time) as day,\n       COUNT(1) as day_impressions\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    )\n    select\n      day,\n      sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\n    from DAILY_IMPRESSIONS\n),\nDAILY_CUMULATIVE_UNIQUES_MIN_3 AS\n(\n    WITH firstseen AS\n    (\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    HAVING count(1) > 2\n    )\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\n    FROM firstseen\n    ORDER BY 1\n),\nDAILY_CUMULATIVE_UNIQUES AS\n(\n    WITH firstseen AS\n    (\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\n    FROM IMPRESSIONS\n    WHERE campaign_id='174631'\n    GROUP BY 1\n    )\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\n    FROM firstseen\n    ORDER BY 1\n)\nSELECT dci.DAY, dci.CUMULATIVE_IMPRESSIONS, dcu.CUMULATIVE_UNIQUES, dcu3.CUMULATIVE_UNIQUES as "UNIQUES MIN 3 IMPRESSIONS"\nFROM DAILY_CUMULATIVE_IMPRESSIONS dci\n JOIN DAILY_CUMULATIVE_UNIQUES dcu on dcu.day=dci.day\n JOIN DAILY_CUMULATIVE_UNIQUES_MIN_3 dcu3 on dcu3.day=dci.day\nORDER BY 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EViewing the chart, and adding a second line, you can see the following.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/two-curves-uniques.png\" alt=\"Two curves at different frequency\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ECalculating frequency\u003C/h2\u003E\n","\u003Cp\u003EFrequency management is very important in advertising.  On the one hand, a single impression may not be enough to drive effectiveness.  On the other hand, deliver too many impressions to the same people, and they can get "creative wear-out" and KPIs will likely decline.  In addition, although it is common to look at average frequency, frequency is often not normally distributed, so looking at the distribution of frequency is also important.  For more detail on why distribution of ad frequency is important, see \u003Ca href=\"https://www.adexchanger.com/data-driven-thinking/the-seventh-deadly-sin-of-digital-reach-and-frequency/\"\u003Ethis AdExchanger article\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EBasic frequency\u003C/h3\u003E\n","\u003Cp\u003ETo get the average frequency of the campaign, run the following statement.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- basic frequency query\nWITH impressions_by_user AS\n (SELECT user_id, count(1) as num_impressions\n  FROM IMPRESSIONS\n  WHERE campaign_id='174631'\n  GROUP BY 1)\nselect avg(num_impressions)\nfrom impressions_by_user;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that the inner query in the WITH clause selects the count of impressions for each user, and the outer averages those to get average frequency.\u003C/p\u003E\n","\u003Ch3\u003EFrequency distribution\u003C/h3\u003E\n","\u003Cp\u003ETo get the distribtuion of frequency, we start with the same inner query, but then we group on the frequency per user and count the number of consumers with that frequency to build a histogram.  Run the following SQL.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- see distributions of frequency\nWITH impressions_by_user AS\n (SELECT user_id, count(1) AS num_impressions\n  FROM IMPRESSIONS\n  WHERE campaign_id='174631'\n  GROUP BY 1)\nSELECT num_impressions, count(1) AS num_users\nFROM impressions_by_user\nGROUP BY 1\nORDER BY 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo visualize this data as a histogram, click "Chart" as before.  Choose "NUM_IMPRESSIONS" for the X-Axis, and "NUM_USERS" with no aggregate for the Y-Axis.  The "Appearance" should be "Fill area." This generates the following.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/frequency-histogram.png\" alt=\"Frequency histogram\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENotice that this histogram looks ideal - this is because the data is generated randomly.  With real data, it may not be so clean.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIncremental reach\u003C/h2\u003E\n","\u003Cp\u003EUnderstanding how many people your campaign reached is important, but when planning the next campaign, it is common to think about \u003Cem\u003Eincremental reach\u003C/em\u003E. Incremental reach tells you how many unique people a site, publisher, platform or placement reached that could not be reached elsewhere.\u003C/p\u003E\n","\u003Ch3\u003EQueries for overlaps\u003C/h3\u003E\n","\u003Cp\u003EImagine I am analyzing two placements, placement ID 201571 and placement ID 200420.  I want to understand how many people each placement reaches, but I want to understand how many people are reached by \u003Cem\u003Eboth\u003C/em\u003E placements.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- only only both\nselect count(1), count(distinct user_id) from impressions where campaign_id='174631' and placement_id='201571';\nselect count(1), count(distinct user_id) from impressions where campaign_id='174631' and placement_id='200420';\nselect count(1), count(distinct user_id) from impressions where campaign_id='174631' and placement_id in ('200420','201571');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERunning the first query, we see that placement 201571 reached 9,939 unique users, across 9,953 impressions.  Plaement 200420 reached 10,143 unique users across 10,167 impressions.  Running the third, we see that 20,056 users were reached across 20,120 impressions.  The impressions is just the sum of the two placements.  However, the uniques is not - summing the uniques of each placement gives 20,082, but the actual number of uniques is 20,056.\u003C/p\u003E\n","\u003Ch3\u003ESimple incremental reach\u003C/h3\u003E\n","\u003Cp\u003EContinuing with the two placements of interest above, we can run the following query to understand the number of users reached by placement 201571 which are not reached by placement 200420.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- how many people does this placement have that the other doesnt\nselect count(distinct i.user_id)\nfrom impressions i\nwhere i.campaign_id='174631' and i.placement_id='201571'\n AND i.user_id not in (select user_id from impressions i2 where i2.campaign_id='174631' and i2.placement_id='200420');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESo we see that there are 9,913 such users.  However, if we are trying to judge the overlap importance of this placement to our media plan, we may want to understand how many users this placement reaches that \u003Cem\u003Eno other\u003C/em\u003E placement in the campaign reaches.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- how many users does this placement have that no other placement does?\nselect count(distinct i.user_id)\nfrom impressions i\nwhere i.campaign_id='174631' and i.placement_id='201571'\nAND i.user_id not in (select user_id from impressions i2 where i2.campaign_id='174631' and i2.placement_id<>'201571');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESo we see that this placement reaches 35 unique users that no other placement reaches. So, if we had removed this placement from the media plan, the campaign would have reached 35 fewer users.\u003C/p\u003E\n","\u003Ch3\u003EIncremental reach for all placements\u003C/h3\u003E\n","\u003Cp\u003EWhen planning my next campaign, I may want to reduce the number of placements in order to trim my overall campaign size and spend.  To do this, I may want to cut the placement that reaches the fewest unique users, so I can reduce impressions while reducing uniques as little as possible.  We can run this query to get an ordered list.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- ordered placements with least unique people\nselect i.placement_id, count(distinct i.user_id)\nfrom impressions i\nwhere i.campaign_id='174631'\n AND i.user_id not in (select user_id from impressions i2 where i2.campaign_id='174631' and i2.placement_id<>i.placement_id)\ngroup by 1\norder by 2 asc;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations, you have completed this Quickstart for a quick overview of writing queries for Reach and Frequency on Snowflake and visualizing the results in Snowsight.\u003C/p\u003E\n","\u003Ch3\u003EWhat We've Covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create test impression data.\u003C/li\u003E\u003Cli\u003EHow to write reach queries and visualize reach curves.\u003C/li\u003E\u003Cli\u003EHow to query for both average frequency and frequency distribution.\u003C/li\u003E\u003Cli\u003EHow to query for incremental reach.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/build-an-ad-tag-in-python-on-aws/\"\u003EQuickstart: Build an Ad Tag in Python on AWS\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Reach and Frequency queries for advertising measurement",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nReach and Frequency are two of the most simple, but also most important, measures for an advertising campaign. Advertisers typically pay for *impressions*, which is simply the number of times your ad is shown. Reach, on the other hand, tells you the total number of *people* who see your ad, while *frequency* refers to the number of times people see your ad.\r\n\r\nAs such:\r\n```math\r\nimpressions = reach * frequency\r\n```\r\n\r\nBecause of the speed and scale of Snowflake, many companies store ad impression data in Snowflake. This Quickstart will show you how to do common types of Reach and Frequency queries in Snowflake.\r\n\r\n### Prerequisites\r\n- Access to a Snowflake account or use of the [Snowflake free 30-day trial](https://trial.snowflake.com)\r\n- Basic knowledge of SQL, database concepts, and objects\r\n\r\n### What You’ll Learn \r\n- How to generate example impression data\r\n- How to query the data to give important reach and frequency results\r\n- How to use Snowsight to visualize some of the data\r\n\r\n### What You’ll Build \r\n- SQL functions used to generate Reach and Frequency measures\r\n- Visualizations of the results\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Prepare Your Environment\r\n\r\nIf you do not have a Snowflake account, you can register for a [Snowflake free 30-day trial](https://trial.snowflake.com). The cloud provider (AWS, Azure, Google Cloud), and Region (US East, EU, e.g.) do _not_ matter for this lab. However, we suggest you select the region which is physically closest to you.\r\n\r\nTo easily follow the instructions, resize your browser windows so you can view this Quickstart and your Snowflake environment side-by-side. If possible, even better is to use a secondary display dedicated to the Quickstart.\r\n\r\nWhile commands from this Quickstart can be copy/pasted to a worksheet in your Snowflake account, you can optionally [download the script as a SQL file to your local machine from Github](https://github.com/Snowflake-Labs/sf-samples).\r\n\r\n### Navigating to Snowsight\r\nFor this lab, you will use the latest Snowflake web interface, Snowsight.  If you have not enabled Snowsight previously, please do the following.\r\n1. Log into your Snowflake account\r\n2. Click on __Snowsight__ Worksheets tab. The new web interface opens in a separate tab or window.\r\n3. Click __Worksheets__ in the left-hand navigation bar. The __Ready to Start Using Worksheets and Dashboards__ dialog opens.\r\n4. Click the __Enable Worksheets and Dashboards button__.\r\n\r\n![Enable Worksheets and Dashboards](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/enable_worksheets.png)\r\n\r\n### Create a Warehouse, Database, Schemas\r\nCreate a warehouse, database, and schema that will be used for loading, storing, processing, and querying data for this Quickstart. We will use the UI within the Worksheets tab to run the DDL that creates these objects.\r\n\r\nCopy the commands below into your trial environment. To execute a single statement, just position the cursor anywhere within the statement and click the Run button. To execute several statements, they must be highlighted through the final semi-colon prior to clicking the Run button.\r\n\r\n```sql\r\n-- setup warehouse, database and schema\r\nUSE ROLE ACCOUNTADMIN;\r\nCREATE WAREHOUSE R_F_DEMO_WH WITH WAREHOUSE_SIZE='XSmall'  STATEMENT_TIMEOUT_IN_SECONDS=15 STATEMENT_QUEUED_TIMEOUT_IN_SECONDS=15;\r\nUSE WAREHOUSE R_F_DEMO_WH;\r\nCREATE DATABASE R_F_DEMO;\r\nCREATE SCHEMA R_F_DEMO.DEMO;\r\n```\r\n\r\n### Create the table and generate test data\r\nWe create a table to store our impression data.  Here, we use a campaign ID, a placement ID, and a creative ID.  However, in reality, we may have things like site, line item, etc.  Also, we use a generic User ID column here for illustration purposes.  This could hold any form of identity, including email, cookie ID, Mobile Advertising Identifier (MAID), or identity provided by a 3rd party identity provider.\r\n\r\n```sql\r\n-- create table to hold impressions\r\nuse R_F_DEMO.DEMO;\r\ncreate or replace table IMPRESSIONS(event_time TIMESTAMP_TZ,\r\n                                    campaign_id VARCHAR(10),\r\n                                    placement_id VARCHAR(10),\r\n                                    creative_id VARCHAR(10),\r\n                                    user_id VARCHAR(40));\r\n```\r\n\r\nNow we run SQL to populate the table with realistic-looking data.\r\n\r\n```sql\r\n-- generate test data\r\ninsert into IMPRESSIONS\r\nselect \r\n   dateadd(second, uniform(1, 2592000, random(1)), ('2022-09-01'::timestamp)) as event_time,\r\n   '174631' as campaign_id\r\n  ,uniform(200001,202000,random(2))::varchar(10) as placement_id\r\n  ,uniform(300001,301000,random(3))::varchar(10) as creative_id\r\n  ,sha1(uniform(1, 3500000, random(4)))::varchar(40) as user_id\r\nfrom table(generator(rowcount=\u003E20000000));\r\n```\r\n\r\nHere we generate data for a single example campaign that runs for one month from September 1st, 2022. The campaign has 20,000,000 impressions across 2,000 placements and 1,000 creatives.  The impressions are spread over up to 3,500,000 people.\r\n\r\n### Verify the data\r\n\r\n```sql\r\nselect count(1) from IMPRESSIONS where campaign_id='174631';\r\nselect count(distinct(placement_id)) from IMPRESSIONS where campaign_id='174631';\r\nselect count(distinct(user_id)) from IMPRESSIONS where campaign_id='174631';\r\n```\r\n\r\nThe first shows that 20,000,000 were, in fact, generated.  The second shows that these rows contain 2,000 distinct placement IDs.  The final shows 3,488,543 distinct user IDs.\r\n\r\n## Calculating reach\r\n\r\nWhen a campaign first starts, it reaches almost entirely new people. As it goes on, however, it reaches more people who have seen the campaign already. It gets harder and harder for the campaign to reach people the longer it goes on.\r\n\r\nIn advertising, this is often visualized as a \"reach curve.\"  In the reach curve, the X axis is the number of impressions shown, and the Y axis is the reach of the campaign.  This shows the shape of the reach curve, so the advertiser can determine when the point of diminishing returns has been reached.\r\n\r\n### Calculating cumulative impressions\r\nThe first step is to write a query that calculates the cumulative impressions served by day.\r\n\r\n```sql\r\n-- calculate cumulative impressions by day\r\nWITH DAILY_IMPRESSIONS AS\r\n(\r\nSELECT TO_DATE(event_time) as day,\r\n       COUNT(1) as day_impressions\r\nFROM \r\n  IMPRESSIONS\r\nWHERE campaign_id='174631'\r\nGROUP BY 1\r\n)\r\nselect\r\n  day,\r\n  sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\r\nfrom DAILY_IMPRESSIONS\r\norder by 1;\r\n```\r\n\r\nThe first step (in the with clause), is to get the impressions on each day.  Then, we query over this and sum over a window function to get the cumulative number of impressions.\r\n\r\nRunning this, you see each day of the campaign in order.  We serve 666,622 impressions on the first day of the campaign, and on 2022-10-01 we reach the total of 20m impressions for the campaign.\r\n\r\n### Calculating cumulative uniques per day\r\nThe next step is to calculative uniques per day.  What is tricky here is that uniques, unlike impressions, do not sum.  If I have 10 impressions on the first day, and 10 impressions on the second day, then I had 20 impressions across those two days combined.  However, if I had 10 *uniques* on the first day, and 10 *uniques* on the second day, then I **did not necessarily** have 20 uniques across the two days combined.\r\n\r\nOn day one, we would expect nearly all of our impressions to go to unique people.  On the second day, we reach some new people, but some of them were people who saw the ad on day one.  And so forth, and so on.  So let's see what this looks like.\r\n\r\n```sql\r\n-- calculate cumulative uniques by day\r\nWITH firstseen AS (\r\n  SELECT user_id, MIN(TO_DATE(event_time)) firstday\r\n  FROM IMPRESSIONS\r\n  WHERE campaign_id='174631'\r\n  GROUP BY 1\r\n)\r\nSELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) daily_cumulative_uniques\r\nFROM firstseen\r\nORDER BY 1;\r\n```\r\n\r\nFirst, inside the WITH clause, we select the first date on which each user gets an impression.  This is the only day on which they would be considered to be a unique.  Then, we count the number of new uniques per day, using the window function.\r\n\r\nWhen we run this, we see that on day one, we reach 606,839 unique users.  On the last day of the campaign, this is up to the full 3,488,543 unique users reached by the campaign.\r\n\r\n### Combining to one query and visualizing the result\r\n\r\nTo make it easy to visualize these together, we can query the two WITH clauses used in previous sections and join them together.\r\n\r\n```sql\r\n-- show cumulative impressions and uniques side-by-side to understand diminishing returns\r\nWITH DAILY_CUMULATIVE_IMPRESSIONS AS (\r\n    WITH DAILY_IMPRESSIONS AS\r\n    (\r\n    SELECT TO_DATE(event_time) as day,\r\n       COUNT(1) as day_impressions\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    )\r\n    select\r\n      day,\r\n      sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\r\n    from DAILY_IMPRESSIONS\r\n),\r\nDAILY_CUMULATIVE_UNIQUES AS\r\n(\r\n    WITH firstseen AS\r\n    (\r\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    )\r\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\r\n    FROM firstseen\r\n    ORDER BY 1\r\n)\r\nSELECT dci.DAY, dci.CUMULATIVE_IMPRESSIONS, dcu.CUMULATIVE_UNIQUES\r\nFROM DAILY_CUMULATIVE_IMPRESSIONS dci\r\n JOIN DAILY_CUMULATIVE_UNIQUES dcu on dcu.day=dci.day\r\nORDER BY 1;\r\n```\r\n\r\nWhere this gets really interesting is when we can visualize the results as a classic \"reach curve.\"  Click the \"Chart\" option in Snowsight.\r\n\r\n![Click Chart to visualize data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/results-chart-click.png)\r\n\r\nNext, for the X-Axis, select \"CUMULATIVE_IMPRESSIONS\" and for the Y-Axis (above) select \"CUMULATIVE_UNIQUES\" and select \"None\" for aggregation.  Now you have the classic reach curve you want to see.\r\n\r\n![View reach curve](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/reach-curve.png)\r\n\r\n### Reach curve given minimum frequency\r\n\r\nYou might not think that a single impression is impactful enough to drive the outcomes you are looking for with your advertising.  So your reach curve is misleading - your uniques are flattening out, but many of those consumers may have only seen the ad once.  You are willing to spend more money to reach consumers up to what you consider to be the minimum effective frequency.  To implement this, we can change our uniques query to only consider unique consumers who have had 3 or more impressions.\r\n\r\n```sql\r\n-- show cumulative impressions and uniques side-by-side to understand diminishing returns, with minimum of 3 impressions\r\nWITH DAILY_CUMULATIVE_IMPRESSIONS AS (\r\n    WITH DAILY_IMPRESSIONS AS\r\n    (\r\n    SELECT TO_DATE(event_time) as day,\r\n       COUNT(1) as day_impressions\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    )\r\n    select\r\n      day,\r\n      sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\r\n    from DAILY_IMPRESSIONS\r\n),\r\nDAILY_CUMULATIVE_UNIQUES_MIN_3 AS\r\n(\r\n    WITH firstseen AS\r\n    (\r\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    HAVING count(1) \u003E 2\r\n    )\r\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\r\n    FROM firstseen\r\n    ORDER BY 1\r\n)\r\nSELECT dci.DAY, dci.CUMULATIVE_IMPRESSIONS, dcu.CUMULATIVE_UNIQUES\r\nFROM DAILY_CUMULATIVE_IMPRESSIONS dci\r\n JOIN DAILY_CUMULATIVE_UNIQUES_MIN_3 dcu on dcu.day=dci.day\r\nORDER BY 1;\r\n```\r\n\r\nIf you want to compare the slopes of these curves, you can view it all together.\r\n\r\n```sql\r\n-- show cumulative impressions and uniques, comparing to minimum of 3 impressions\r\nWITH DAILY_CUMULATIVE_IMPRESSIONS AS (\r\n    WITH DAILY_IMPRESSIONS AS\r\n    (\r\n    SELECT TO_DATE(event_time) as day,\r\n       COUNT(1) as day_impressions\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    )\r\n    select\r\n      day,\r\n      sum(day_impressions) over (order by day asc rows between unbounded preceding and current row) as CUMULATIVE_IMPRESSIONS\r\n    from DAILY_IMPRESSIONS\r\n),\r\nDAILY_CUMULATIVE_UNIQUES_MIN_3 AS\r\n(\r\n    WITH firstseen AS\r\n    (\r\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    HAVING count(1) \u003E 2\r\n    )\r\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\r\n    FROM firstseen\r\n    ORDER BY 1\r\n),\r\nDAILY_CUMULATIVE_UNIQUES AS\r\n(\r\n    WITH firstseen AS\r\n    (\r\n    SELECT user_id, MIN(TO_DATE(event_time)) firstday\r\n    FROM IMPRESSIONS\r\n    WHERE campaign_id='174631'\r\n    GROUP BY 1\r\n    )\r\n    SELECT DISTINCT firstday as day, COUNT(user_id) OVER (ORDER BY firstday) CUMULATIVE_UNIQUES\r\n    FROM firstseen\r\n    ORDER BY 1\r\n)\r\nSELECT dci.DAY, dci.CUMULATIVE_IMPRESSIONS, dcu.CUMULATIVE_UNIQUES, dcu3.CUMULATIVE_UNIQUES as \"UNIQUES MIN 3 IMPRESSIONS\"\r\nFROM DAILY_CUMULATIVE_IMPRESSIONS dci\r\n JOIN DAILY_CUMULATIVE_UNIQUES dcu on dcu.day=dci.day\r\n JOIN DAILY_CUMULATIVE_UNIQUES_MIN_3 dcu3 on dcu3.day=dci.day\r\nORDER BY 1;\r\n```\r\n\r\nViewing the chart, and adding a second line, you can see the following.\r\n\r\n![Two curves at different frequency](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/two-curves-uniques.png)\r\n\r\n## Calculating frequency\r\n\r\nFrequency management is very important in advertising.  On the one hand, a single impression may not be enough to drive effectiveness.  On the other hand, deliver too many impressions to the same people, and they can get \"creative wear-out\" and KPIs will likely decline.  In addition, although it is common to look at average frequency, frequency is often not normally distributed, so looking at the distribution of frequency is also important.  For more detail on why distribution of ad frequency is important, see [this AdExchanger article](https://www.adexchanger.com/data-driven-thinking/the-seventh-deadly-sin-of-digital-reach-and-frequency/).\r\n\r\n### Basic frequency\r\n\r\nTo get the average frequency of the campaign, run the following statement.\r\n\r\n```sql\r\n-- basic frequency query\r\nWITH impressions_by_user AS\r\n (SELECT user_id, count(1) as num_impressions\r\n  FROM IMPRESSIONS\r\n  WHERE campaign_id='174631'\r\n  GROUP BY 1)\r\nselect avg(num_impressions)\r\nfrom impressions_by_user;\r\n```\r\n\r\nNote that the inner query in the WITH clause selects the count of impressions for each user, and the outer averages those to get average frequency.\r\n\r\n### Frequency distribution\r\n\r\nTo get the distribtuion of frequency, we start with the same inner query, but then we group on the frequency per user and count the number of consumers with that frequency to build a histogram.  Run the following SQL.\r\n\r\n```sql\r\n-- see distributions of frequency\r\nWITH impressions_by_user AS\r\n (SELECT user_id, count(1) AS num_impressions\r\n  FROM IMPRESSIONS\r\n  WHERE campaign_id='174631'\r\n  GROUP BY 1)\r\nSELECT num_impressions, count(1) AS num_users\r\nFROM impressions_by_user\r\nGROUP BY 1\r\nORDER BY 1;\r\n```\r\n\r\nTo visualize this data as a histogram, click \"Chart\" as before.  Choose \"NUM_IMPRESSIONS\" for the X-Axis, and \"NUM_USERS\" with no aggregate for the Y-Axis.  The \"Appearance\" should be \"Fill area.\" This generates the following.\r\n\r\n![Frequency histogram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/reach-and-frequency-queries/frequency-histogram.png)\r\n\r\nNotice that this histogram looks ideal - this is because the data is generated randomly.  With real data, it may not be so clean.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Incremental reach\r\n\r\nUnderstanding how many people your campaign reached is important, but when planning the next campaign, it is common to think about *incremental reach*. Incremental reach tells you how many unique people a site, publisher, platform or placement reached that could not be reached elsewhere.\r\n\r\n### Queries for overlaps\r\nImagine I am analyzing two placements, placement ID 201571 and placement ID 200420.  I want to understand how many people each placement reaches, but I want to understand how many people are reached by *both* placements.\r\n\r\n```sql\r\n-- only only both\r\nselect count(1), count(distinct user_id) from impressions where campaign_id='174631' and placement_id='201571';\r\nselect count(1), count(distinct user_id) from impressions where campaign_id='174631' and placement_id='200420';\r\nselect count(1), count(distinct user_id) from impressions where campaign_id='174631' and placement_id in ('200420','201571');\r\n```\r\n\r\nRunning the first query, we see that placement 201571 reached 9,939 unique users, across 9,953 impressions.  Plaement 200420 reached 10,143 unique users across 10,167 impressions.  Running the third, we see that 20,056 users were reached across 20,120 impressions.  The impressions is just the sum of the two placements.  However, the uniques is not - summing the uniques of each placement gives 20,082, but the actual number of uniques is 20,056.\r\n\r\n### Simple incremental reach\r\nContinuing with the two placements of interest above, we can run the following query to understand the number of users reached by placement 201571 which are not reached by placement 200420.\r\n\r\n```sql\r\n-- how many people does this placement have that the other doesnt\r\nselect count(distinct i.user_id)\r\nfrom impressions i\r\nwhere i.campaign_id='174631' and i.placement_id='201571'\r\n AND i.user_id not in (select user_id from impressions i2 where i2.campaign_id='174631' and i2.placement_id='200420');\r\n ```\r\n\r\n So we see that there are 9,913 such users.  However, if we are trying to judge the overlap importance of this placement to our media plan, we may want to understand how many users this placement reaches that *no other* placement in the campaign reaches.\r\n\r\n ```sql\r\n -- how many users does this placement have that no other placement does?\r\nselect count(distinct i.user_id)\r\nfrom impressions i\r\nwhere i.campaign_id='174631' and i.placement_id='201571'\r\n AND i.user_id not in (select user_id from impressions i2 where i2.campaign_id='174631' and i2.placement_id\u003C\u003E'201571');\r\n ```\r\n\r\nSo we see that this placement reaches 35 unique users that no other placement reaches. So, if we had removed this placement from the media plan, the campaign would have reached 35 fewer users.\r\n\r\n### Incremental reach for all placements\r\nWhen planning my next campaign, I may want to reduce the number of placements in order to trim my overall campaign size and spend.  To do this, I may want to cut the placement that reaches the fewest unique users, so I can reduce impressions while reducing uniques as little as possible.  We can run this query to get an ordered list.\r\n\r\n```sql\r\n-- ordered placements with least unique people\r\nselect i.placement_id, count(distinct i.user_id)\r\nfrom impressions i\r\nwhere i.campaign_id='174631'\r\n AND i.user_id not in (select user_id from impressions i2 where i2.campaign_id='174631' and i2.placement_id\u003C\u003Ei.placement_id)\r\ngroup by 1\r\norder by 2 asc;\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion\r\n\r\nCongratulations, you have completed this Quickstart for a quick overview of writing queries for Reach and Frequency on Snowflake and visualizing the results in Snowsight.\r\n\r\n### What We've Covered\r\n- How to create test impression data.\r\n- How to write reach queries and visualize reach curves.\r\n- How to query for both average frequency and frequency distribution.\r\n- How to query for incremental reach.\r\n\r\n### Related Resources\r\n- [Quickstart: Build an Ad Tag in Python on AWS](/en/developers/guides/build-an-ad-tag-in-python-on-aws/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-a332d3d20e","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-a946d54201",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c9f69df06f","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2023-03-16",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-df2b0036b9","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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