Big Data for Publishers

Transcription

1 Big Data for Publishers Using BigQuery to Measure Content Engagement and Performance by Article Topic By Hazem Mahsoub Cloud & Data Architect Measuring Performance by Article Topic using BigQuery Whitepaper 1

2 In a recent blog post, Getting Your Feet Wet in the Data Lake: Analytics 360 in BigQuery, we reviewed the benefits that BigQuery offers to data engineers, analysts, and marketers. BigQuery is a distributed big data platform that Google first developed internally but has since made available publicly as an efficient solution for its low-cost storage and fast querying of large data sets. BigQuery s Benefits to Data Engineers, Marketers, and Analysts As a no-ops cloud-based solution, BigQuery offers data engineers tremendous operational advantages over platforms such as Hadoop and Spark by eliminating the need for on-premises resources to support hardware infrastructure and software updates. Marketers and analysts, for their part, can take advantage of BigQuery to output results from ad hoc queries within minutes or seconds and begin to gain insight about online/offline attribution, user funnels, and long-term customer value. Measuring Content Performance Gauging the performance of content presents more of a challenge than retail transactions or lead generation: for content, we re measuring engagement levels rather than specific outcomes (such as purchases, lead generation, or signups), or we re attempting to correlate pages and subjects with any specific outcomes that we are measuring. In his blog post Google Analytics on Accelerated Mobile Pages (AMP): Measuring User Behavior and Segmenting Content for Publishers, E-Nor s Digital Analytics Manager Allaedin Ezzedin posed the following questions about content performance: Which content types users interact with the most (examples: Textual, Video, Audio, Infographic, Illustration)? What kinds of topics, tags and categories interest your audience the most and hence you should write more about? Which authors have the most popular content? Which content language gives your site higher engagement and conversion? Which posts are performing the best. This will help you for example in selecting content to feature on the homepage s hero banner or on your monthly newsletter? What users do with content. (Read, , Print, Save, Share, Comment)? Walled/premium content performance vs. public content? How much revenue each content category is generating? Which traffic sources and user segments (based on geography or device, as examples) are driving the most paid/premium memberships? While we can extend the data set within Google Analytics to answer some of these questions, the BigQuery approach - that is, combining your Google Analytics data set with your CMS data set within BigQuery - will offer much greater flexibility, as we ll discuss below. Measuring Performance by Article Topic using BigQuery Whitepaper 2

3 Content Performance Insights Google Analytics, on its own, supports several ways to combine with other data sources and generate great reports. With native integration of Analytics 360 (formerly known as Google Analytics Premium) into BigQuery, Google has made it much easier to join Google Analytics with other data sources, perform sophisticated querying, and take advantage of Big Data s vast opportunities for deep analysis and insight. In this post, we ll continue to explore BigQuery as a more straightforward solution than any workaround in GA 360 for the following scenario. business news (or publishers, media, government information portals): a range of articles published daily multiple tags per article: each article can have one or more tags (e.g., stock market, inflation, mortgages, interest rates, European Union) metrics by tag, not just by landing page: we ll use BigQuery to analyze metrics such as session count, bounces, session duration, and pages per session not just for each landing page, but for the tags used in the landing pages impact on monetization: by measuring bounce and pages per session for each tag, we can begin correlating tags with monetization that the business news site generates from selling ad space newsletter signups: in addition to monetizing pageviews, an objective for the business site is to generate newsletter signups: the s themselves are monetized through ads, and they drive clickthroughs back to the website - BigQuery will allow us to evaluate newsletter signup goal completion by content tags tag views by traffic/source medium: with BigQuery, we ll also be able to see how different tags correspond with different traffic sources Measuring Performance by Article Topic using BigQuery Whitepaper 3

4 BigQuery Advantages: Many-to-Many, No Code, Retroactive Many-to-Many BigQuery offers the flexibility of associating multiple tags to multiple articles. What do we mean by many-to-many relationship? Let s say you have a content site and each article is associated with several tags, such as the business news website in our example. Many-to-many relationship here means that a tag is associated with many articles and an article is associated with many tags. One of the fundamental benefits of BigQuery is the many-to-many advantage. Within Google Analytics, we d be able to easily report on performance of a single tag captured as a custom dimension per page or landing page, but the difficulty comes when multiple tags are assigned to multiple pages, as is often the case. After bringing the URL and tag dataset into BigQuery, we can freely join any number of tags with the Google Analytics landing page metrics and thereby begin to correlate the tag occurrence with bounce, session duration, goal completion, and source/medium. As a note, our example focuses on subject tags, but the concept applies to the many-to-many relationships for all metadata with multiple values. No Additional Code on Website As mentioned above in the alternative approach, we d normally add tag data to Google Analytics through a custom dimension. If all tag values appeared on the page, you d be able to read them into Google Analytics though a DOM Element variable (assuming that you were using Google Tag Manager). If, however, the tag values did not appear anywhere in the page text or metadata, you d need to work with your developers to write the tags from the CMS into the data layer so that you could then read it in through a Google Tag Manager data layer variable. With the BigQuery approach, the join between CMS and Google Analytics occurs only within Measuring Performance by Article Topic using BigQuery Whitepaper 4

5 BigQuery, so there s no need to add JavaScript code to write additional CMS fields to the page s data layer. Retroactive As another advantage of BigQuery, we can perform our tag analysis retroactively. Since we re integrating the tag data and performance data outside of Google Analytics, there s no need to populate custom dimensions or metrics at the time that the Google Analytics data is captured. If you discover you missed some data relevant to the articles, e.g. language, number of comments, has-images, or has-videos, or even that the metadata has changed, and you would like to combine it with old articles and generate reports, BigQuery provides the needed flexibility. Retroactivity is also available with query-time data imports within Google Analytics 360, and with no need for additional code to populate custom dimensions. BigQuery, however, offers these advantages, plus much better support for complex joins and large data sets. Combining Data Sources in BigQuery To gain the insights listed above, we ll take the following steps (with the assumption that automated export from Analytics 360 to BigQuery is already configured). 1. Extract the URLs and tags from the CMS. 2. Import the extracted CMS data into BigQuery. 3. Run queries in BigQuery to produce: a. Intermediate table with landing page dimension. b. Basic performance metrics (e.g., bounce) by landing page tag. c. Goal completions by landing page tag. d. Sessions by source/medium by landing page tag. Step 1: Extract the metadata from the CMS Steps for extracting the data from the CMS vary a lot depending on the used CMS and the version. In this example, we are using WordPress for its popularity. The concepts apply to all CMSs, but feel free to read through step 1 quickly if you re using a CMS other than WordPress. 1. In our example, the article URL start with /blog/, followed by the category, followed by the article title. The following SQL script should construct the friendly URL and retrieve the article information, along with the author, and the number of comments: 2. To extract the tags, the SQL statement looks like this: Measuring Performance by Article Topic using BigQuery Whitepaper 5

6 wp_posts.id as POST_ID, wp_posts.post_title as POST_TITLE, CONCAT('/blog/',wp_terms.slug,'/',wp_posts.post_name) AS USED_URL, wp_posts.post_date as POST_DATE, wp_posts.post_author as AUTHOR_ID, wp_users.display_name as AUTHOR, wp_posts.comment_count as comment_count FROM wp_terms INNER JOIN wp_term_taxonomy ON wp_terms.term_id = wp_term_taxonomy.term_id AND wp_ term_taxonomy.taxonomy='category' INNER JOIN wp_term_relationships ON wp_term_taxonomy.term_taxonomy_id = wp_term_ relationships.term_taxonomy_id INNER JOIN wp_posts ON wp_posts.id = wp_term_relationships.object_id INNER JOIN wp_users on wp_posts.post_author = wp_users.id WHERE post_type LIKE 'post' AND post_status LIKE 'publish' ORDER BY USED_URL; </> 3. Now, we need to combine both tables, using the wp_term_relationships table. Since the wp_terms.term_id AS ID, wp_terms.name AS TAG, wp_terms.slug AS TAG_SLUG FROM wp_terms INNER JOIN wp_term_taxonomy ON wp_terms.term_id = wp_term_taxonomy.term_id WHERE wp_term_taxonomy.taxonomy='post_tag'; </> data is small let s sacrifice some performance for clarity of the SQL statements. Measuring Performance by Article Topic using BigQuery Whitepaper 6

7 wp_terms.slug AS TAG, p.post_title, p.post_url, p.author, p.comment_count FROM wp_terms INNER JOIN wp_term_taxonomy ON wp_terms.term_id = wp_term_taxonomy.term_id AND wp_ term_taxonomy.taxonomy='post_tag' INNER JOIN wp_term_relationships ON wp_term_taxonomy.term_taxonomy_id = wp_term_ relationships.term_taxonomy_id INNER JOIN ( wp_posts.id as POST_ID, wp_posts.post_title as POST_TITLE, CONCAT('/blog/',wp_terms.slug,'/',wp_posts.post_name) AS POST_URL, wp_users.display_name as AUTHOR, wp_posts.comment_count as Comment_Count FROM wp_terms INNER JOIN wp_term_taxonomy ON wp_terms.term_id = wp_term_taxonomy.term_id AND wp_ term_taxonomy.taxonomy='category' INNER JOIN wp_term_relationships ON wp_term_taxonomy.term_taxonomy_id = wp_term_ relationships.term_taxonomy_id INNER JOIN wp_posts ON wp_posts.id = wp_term_relationships.object_id INNER JOIN wp_users on wp_posts.post_author = wp_users.id WHERE post_type LIKE 'post' AND post_status LIKE 'publish') as p ON p.post_id = wp_term_relationships.object_id order by wp_terms.slug; </> 4. Export the result into a CSV file. query result Measuring Performance by Article Topic using BigQuery Whitepaper 7

8 Step 2: Import CMS data into BigQuery There are several ways to load data into BigQuery, depending on the nature and size of the data. For small data sets, like our article-tags table, we can upload the CSV file directly into BigQuery as follows: 1. Create a dataset: Create a dataset by selecting the arrow next to your project and click on Create new dataset. For this example, we will call our dataset CMS_Data. Create new dataset 2. Create tables within the dataset: To do this, click the dropdown arrow next to the dataset and select Create new table. We ll call the table article_tags. 3. Create the table and Upload the data: We ll select the article_tags.csv file that we created in step #1. Source Data: Location: File Upload Choose file: article_tags.csv File Format: CSV Destination Table: Table name: CMS_Data.article_tags Table type: Native table Create new table Create table settings Measuring Performance by Article Topic using BigQuery Whitepaper 8

9 4. Specify schema: We also need to describe the schema of the table to BigQuery. That is: name, type and mode for each of the columns. Create table schema 5. Specify advanced options: optionally configure field delimiters, header rows to skip, and error handling. Advanced options 6. Click on Create Table, wait a moment, and your CSV data should now be fully uploaded into your new BigQuery table. After doing all the steps above, clicking the Create Table button will load the data into the table(s) we created. Based on the size of the data, the upload can take seconds or minutes. Watch out for error messages indicated by a red exclamation mark. Step 3: Intermediate table to generate the Landing Pages Not all Analytics dimensions and metrics available in BigQuery. For example, the landing page, which is a session-level dimension, is not available in BigQuery but it can be calculated by a query. The query below will identify the landing pages and the result can be saved as an intermediate BQ table. We prefer to save the result into a intermediate BQ table to keep the upcoming queries simple and to overcome a BigQuery limitation, which is: one cannot join two tables if the join key is a repeated field, as the pagepath field is here. Once we have this intermediate table, we can generate the landing page report. Remember to replace XX_Viewid_XX with your dataset name. Measuring Performance by Article Topic using BigQuery Whitepaper 9

10 /* generating the intermediate table for landing page report */ </> concat(main.fullvisitorid,string(main.visitid)) as sessionid, main.totals.bounces as bounces, main.totals.pageviews as pageviews, main.totals.timeonsite as timeonsite, main.trafficsource.source as source, main.trafficsource.medium as medium, l.hits.page.pagepath AS landingpage, * fullvisitorid, visitid, totals.bounces, totals.pageviews, totals.timeonsite, trafficsource.source, trafficsource.medium, TABLE_DATE_RANGE ([XX_ViewID_XX.ga_sessions_],TIMESTAMP(' '), TIMESTAMP (' ')))) AS main LEFT OUTER JOIN ( * fullvisitorid, visitid, hits.page.pagepath, MIN(hits.hitNumber) WITHIN RECORD AS firsthit, hits.hitnumber AS HN TABLE_DATE_RANGE ([XX_ViewID_XX.ga_sessions_],TIMESTAMP(' '), TIMESTAMP (' '))) WHERE hits.type = 'PAGE' AND hits.page.pagepath<>'') WHERE HN = firsthit) AS l ON main.fullvisitorid = l.fullvisitorid AND main.visitid = l.visitid) Measuring Performance by Article Topic using BigQuery Whitepaper 10

11 To save the result as a BQ table click on the options and choose the table name as Query result destination settings Step 4: Generate a Landing Page Report The query below generates a report similar to the Landing Pages report accessible within the Google Analytics interface. /* landing Page report: sessions, bounce_rate, pageviews and timeonsite */ landingpage, COUNT(sessionId) AS sessions, 100 * SUM(bounces)/COUNT(sessionId) AS BounceRate, AVG(pageviews) AS AvgPageviews, SUM(timeOnSite)/COUNT(sessionId) AS AvgTimeOnSite from ( * FROM [XX_ViewID_XX.SessionsLandingPage201605] ) GROUP BY landingpage </> Measuring Performance by Article Topic using BigQuery Whitepaper 11

12 Landing page query result Step 5: Join Tags to Landing Page Metrics The query below combines the landing page table with the tags table imported into BigQuery in step 2 above and retrieves the number of sessions, bounce rate, average pageviews and average time-on-site per tag. For this query and the other queries below, we reiterate that one page can contribute to the numbers under several tags, due to the many-to-many relationship between pages and tags. Measuring Performance by Article Topic using BigQuery Whitepaper 12

13 /* join our CMS table with landing page temp table */ /* and retrieve sessions, bounce rate, average pagewviews, average time on site by tags */ tag, COUNT(sessionId) AS sessions, 100 * SUM(bounces)/COUNT(sessionId) AS BounceRate, AVG(pageviews) AS AvgPageviews, SUM(timeOnSite)/COUNT(sessionId) AS AvgTimeOnSite t.tag AS tag, l.landingpage AS landingpage, l.sessionid AS sessionid, l.bounces AS bounces, l.pageviews AS pageviews, l.timeonsite AS timeonsite * FROM [XX_ViewID_XX.SessionsLandingPage201605] ) AS l JOIN ( tag, url FROM CMS_Data.article_tags) AS t ON l.landingpage=t.url) GROUP BY tag ORDER BY 2 DESC </> Landing page metrics by tags Measuring Performance by Article Topic using BigQuery Whitepaper 13

14 Additional Query: Report Goal Conversion Rate per Tag As mentioned previously, bounce rate, pages per session, and average session duration are relevant to the business news site s objectives for page monetization, but we also want to gauge how the page tags are supporting newsletter signups. </> /* conversion rate by tag query */ t.tag AS tag, COUNT(l.sessionId) AS sessions, COUNT(g.eventCategory) AS goals, 100 * COUNT(g.eventCategory) / COUNT(l.sessionId) as conversion_rate * tag, url FROM CMS_Data.article_tags) AS t LEFT OUTER JOIN ( sessionid, landingpage, source +' / '+ medium AS source_medium, FROM [XX_ViewID_XX.SessionsLandingPage201605] ) AS l ON t.url = l.landingpage LEFT OUTER JOIN ( CONCAT(fullVisitorId,STRING(visitId)) AS sessionid, hits.eventinfo.eventcategory AS eventcategory, hits.eventinfo.eventaction AS eventaction, hits.eventinfo.eventlabel AS eventlabel, hits.eventinfo.eventvalue AS eventvalue, hits.page.pagepath AS pagepath, TABLE_DATE_RANGE ([XX_ViewID_XX.ga_ sessions_],timestamp(' '), TIMESTAMP (' '))) WHERE hits.type='event' AND hits.eventinfo.eventcategory='xx_eventcat_xx') AS g ON l.sessionid = g.sessionid ) GROUP BY tag ORDER BY 4 DESC Measuring Performance by Article Topic using BigQuery Whitepaper 14

15 Remember to replace XX_ViewId_XX with your dataset name and XX_eventct_XX with your event category of your goal. You may need to make it more specific by adding event action and event label. Goal completion by tag Additional Query: Report Sessions by Tag by Source/Medium The query will combine the landing page table with the tags table and retrieve the number of sessions per tag and source/medium. One can see the top source/medium for each tag, and thereby gauge which topics gain the most traction on various channels. Is European Union accounting for the greatest proportion of your Twitter traffic? You probably want to make sure that your social team keeps tweeting about European business news. Is mortgages the top tag from your channel? Continue to feature mortgage-related stories in your newsletters. Measuring Performance by Article Topic using BigQuery Whitepaper 15

16 /* sessions by tag and source/medium */ tag, source_medium, COUNT(sessionId) AS sessions, t.tag AS tag, l.landingpage AS landingpage, l.source_medium as source_medium, l.sessionid AS sessionid, landingpage, source +' / '+ medium as source_medium, sessionid, FROM [XX_ViewID_XX.SessionsLandingPage201605] ) AS l JOIN ( tag, url FROM CMS_Data.article_tags) AS t ON l.landingpage=t.url) GROUP BY tag, source_medium ORDER BY tag, sessions DESC </> Sessions by source/medium for tags Measuring Performance by Article Topic using BigQuery Whitepaper 16

17 Limitless Possibilities There are limitless possibilities to the queries and reports we can generate by combining GA data with data from other sources. Here, we combined large data from GA with just one data source, the CMS for one piece of metadata (the tag) and capitalized on one feature of BigQuery. Imagine the possibilities if we make BigQuery our main repository, our data lake, for all our backend systems. Measuring Performance by Article Topic using BigQuery Whitepaper 17