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Going beyond “what happened” in SERP analytics Ray Grieselhuber DemandSphere @raygrieselhuber

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Intro @raygrieselhuber ● Founded DemandSphere in 2010 ● Two main products: DemandMetrics and SERP Intelligence ● We work with brands, ecommerce and programmatic teams, both in-house and agency ● Our expertise is using very large-scale SERP analytics to drive strategy

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DemandMetrics SERP data analytics for expert SEO teams ● Millions of keywords monitored daily ● 10,000+ sites managed ● Rich customization capabilities ● Large-scale processing and turnkey data warehouses via BigQuery ● Built for complex org structures

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Shopping Ads • Position (order of items) • Product Name • Price • URL • Pixel Data Organic Results • Organic Position • Visual Position • Title • Meta Description • Product URL • Pixel Data Popular Products • Position (order of items) • Product Name • Price • URL • Pixel Data SERP Intelligence - data pipelines to BigQuery The SERP Intelligence API is a data pipeline built to your specifications, covering any aspects of the SERP your BI team requires. These pipelines typically consist of tracking hundreds of features throughout various search engines. Results are stored as JSON files on our servers and loaded into BigQuery for clients to access. Ongoing support is provided to ensure data delivery is accurate and on time. Use the power of SQL and BigQuery to accelerate your insights from SERP data.

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Slide 5 text @raygrieselhuber What? Why? Going beyond “what happened?”

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Slide 6 text @raygrieselhuber Traditional rank tracking monitors a small part of the SERP Up Down 23 12

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Slide 7 text @raygrieselhuber Search Console metrics are still “what happened?”

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Old School SERPs (“Ten blue links”) @raygrieselhuber

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GSC metrics show a basic view of the “search funnel” @raygrieselhuber Avg. Position (Max traffic potential) Impressions (depends on Avg. Pos.) Clicks CTR (clicks / impressions)

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Half of GSC metrics are derived @raygrieselhuber Avg. Position (Max traffic potential) Impressions (depends on Avg. Pos.) Clicks CTR (clicks / impressions) What happened? Derived metric What happened? Derived metric

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The missing detail is factors that impact CTR @raygrieselhuber Avg. Position (Max traffic potential) Impressions (depends on Avg. Pos.) Clicks CTR (clicks / impressions) What happened? Derived metric What happened? Derived metric WHY?!

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Traditional Rank Tracking @raygrieselhuber Metrics ● Rank ● Change ● Averages, position buckets, etc.

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Modern SERP Features @raygrieselhuber

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Share of Voice & SERP Features @raygrieselhuber Metrics ● SERP Feature Appearance ● Presence ● Visual Share of Voice ● CTR Impact

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Share of Voice & SERP Features @raygrieselhuber Big data ● Detailed SERP Feature presence ● Top 20 market and domain / URL visibility analytics ● Forecasting and CTR modeling

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Pixels = Attention @raygrieselhuber

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SGE Launch soon… @raygrieselhuber ● Google I/O rumors are unconfirmed ● SGE widget is not auto-expanded in early live tests ● Auto-expand is what will have the biggest impact on pixels and CTR

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Slide 19 text @raygrieselhuber The anxiety over SGE is (largely) anxiety about pixels 1063px

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Slide 20 text @raygrieselhuber Bad news: SEOs already have pixel problems 816px

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Slide 21 text @raygrieselhuber Getting closer to why: pixel depth affects CTR

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Pixels & Visual Rank @raygrieselhuber Metrics ● Pixels from Top ● Pixel Height ● Visual Rank ● Scroll Depth

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Pixels & CTR Impact @raygrieselhuber

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Daily layout changes on Google SERPs Paid Content ● Position (order of items) ● Product Name ● Price ● URL ● Pixel Data Organic Results ● Organic Position ● Visual Position ● Title ● Meta Description ● Product URL ● Pixel Data Popular Products ● Position (order of items) ● Product Name ● Price ● URL ● Pixel Data

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Slide 25 text @raygrieselhuber Rank tracking vs. SERP monitoring

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Holistic SERP Monitoring Visual Rank Sentiment Layout Shift Scroll Depth Pixel Depth CTR Modeling Ad Copy Suggested Keywords # of Elements Ad Location Locations Reviews Merchant IDs Business Titles Ranking URLs Title, Meta, etc. Custom Extraction SERP Screenshots Pixel Height SERP Features Search Intent Keyword Clusters Topic Modeling Search Volume Ad Presence Ad Performance Co-Occurrence Competitor Performance Competitor Discovery Share of Voice Visual Share of Voice URL Screenshots NLP Analysis Video Discussions & Forums Social Knowledge Graph SERP Feature Interiors FAQ Flight Details Hotel Details Review details PLA Text Ads People Also Ask Refine this search Organic Commerce Related Products Shops News News Details Price & Currency Google vs. Bing Buying Guide Howto Job Details @raygrieselhuber

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SQL is a decent lingua franca for turning SERP data into BI @raygrieselhuber select search_domain, url, title, desc, position, people_also_asked_snippets from results.organic_results where fetch_date = “2023-11-08” limit 100000

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Impacts: ● Fold visibility ● CTR ● Clicks / Sessions ● Conversions ● Revenue 600+ distinct elements across the SERPs Paid Content ● Pixel depth: 120px ● Pixel height: 320px ● Visual position: 1 ● Nested position: 2 Organic Result ● Pixel depth: 440px ● Pixel height: 330px ● Visual position: 3 ● Title changed

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We mapped every single element on the SERP and put it into a data warehouse @raygrieselhuber

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Accelerating integrations and data analysis @raygrieselhuber @raygrieselhuber

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Four levels of SERP analysis @raygrieselhuber Rank Tracking “10 blue links” Share of Voice SERP Features Complete Data Shape of the SERP Pixels & Visual Rank Direct Impact on CTR

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What vs. why KPIs @raygrieselhuber What? ● Impressions ● Clicks ● CTR ● Rank ● Organic Traffic Why? ● Pixel depth ● Visual rank ● Zero-click ● Layout change ● Visual SoV ● SERP Features

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Traffic planning on a statistically relevant sample size @raygrieselhuber 1%-10% of your indexed pages is a good place to start

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Analytics is getting harder @raygrieselhuber ● GA4: 󰗭 ● Third-party cookies: 󰗞 ● Ad blockers: 🔥(~40% of US users) [1] ● Very few companies have good analytics set up due to complexity [1]: 1000-a-year-cnet-survey-finds/

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Pre- vs. Post- click analytics @raygrieselhuber Pre-click (SERP data) ● Pixel depth ● Rank ● Visual rank ● Market landscape ● SERP Features ● Competitors (Easy) Post-click (website data) ● Cookie settings ● Ad blockers & privacy laws ● GA4… ● Tag issues ● ERP integration ● CRM integration (Hard)

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Slide 36 text @raygrieselhuber What? Why? Answering more questions beyond “what?” Who?

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Slide 37 text @raygrieselhuber Product and ad teams are natural adjacent teams for SERP analytics

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Slide 38 text @raygrieselhuber Good SEO is good product management, and vice-versa.

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Most SEO problems are corporate strategy problems @raygrieselhuber

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Slide 40 text @raygrieselhuber You have a product problem, not an SGE problem

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Increasing adoption of SEO data across disciplines @raygrieselhuber Complexity increases Developer ● APIs ● Data lakes SEO Analyst ● SERP data ● Crawl data ● GSC ● GA ● Exploration Content Team ● Content & topics ● Content performance ● Visibility trends Product ● Technical ● SERP rankings ● Indexation ● PAA ● Knowledge Graph Executive / Reporting ● Permissions ● Dashboards ● Speed of answers ● Forecasting ● Predictive Performance Team ● Ad data ● Hotel Ads ● Shopping ● Google for Jobs ● etc.

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Slide 42 text @raygrieselhuber How to meet the needs of all of these groups?

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Adopt existing frameworks @raygrieselhuber Data Operations Observability (log files + search index / database) Problem domains Asset / Technology Parallels to SEO ● Log files: SERPs ● Parsed log files: Parsed SERPS (JSON, etc.) ● Index: ??? Data Science BI Datasets ML Models Dashboards & Visualizations ● SERPs ● GSC ● GA4 ● Keyword sets, etc. ● Looker Studio ● Superset ● DOMO ● etc.

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Tools that can help accelerate and support many use cases @raygrieselhuber Data Lakes BI / Dashboards / Data apps Cloud Data Warehouse ● S3-compatible object storage ● BigQuery ● Snowflake ● Redshift ● Looker Studio ● Superset ● Jupyter & other notebooks ● Python data apps (Dash, Streamlit)

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Simplified access to SERP data @raygrieselhuber Developers SEO Team Content Team Product Team Executive / Reporting Performance Team APIs Data Warehouse Data Lakes Data Warehouse Data Warehouse SEO & Content Tools BI

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Understanding the workflows @raygrieselhuber

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Most “BI” is NOT intelligence @raygrieselhuber

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The winners have repeatable processes @raygrieselhuber

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Intelligence requires action @raygrieselhuber Review and plan Allow for some time after the campaign to ensure that data is vetted. Review successes and failures, establish regression baselines, and plan next phase. Execute campaign Create new content, optimize existing content, work on digital PR, etc. This execution phase should be tied to achieving strategic and operational goals Define monitoring Hone research data set into campaign and group-focused segments, which can be prioritized for action Define strategic goals Intelligence lifecycles are derived from strategic vision and initiatives Research opportunities Initial SERP and search volume focused analysis of competitive and market landscape 01 05 04 03 02

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Mapping between workflows @raygrieselhuber Organic Exploratory vs. Regression vs. Reporting Product Launch vs. Engagement Paid Baseline + ROAS

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Let’s talk! @raygrieselhuber