Identifying Desired Content Formats & Platforms from SERP Data (Lab Notebook)

Manually determining which content formats and platforms Google prefers for thousands of keywords is impractical and subjective—this practical Google Colab notebook automates the process of analyzing SERP (Search Engine Results Page) features from keyword research data to systematically identify user-desired content formats and platform preferences by mapping SERP snippets to content recommendations through customizable dictionary-based classification. Created by Lazarina Stoy for MLforSEO Academy as a hands-on lab exercise, this workflow enables SEO professionals and content strategists to transform raw SERP feature data (exported from tools like SEMrush, Ahrefs, or DataForSEO) into actionable content strategy insights by first analyzing which SERP features appear most frequently across a keyword universe, then mapping each feature to its corresponding content format recommendation (blogs, videos, FAQs, reviews, product pages) and platform preferences (YouTube, Pinterest, Reddit, TikTok, Amazon)—revealing at scale whether keywords require video content, visual formats, comparison tables, or other specific content types based on what Google actually serves in search results.
The notebook implements a multi-stage dictionary-based mapping workflow with visualization capabilities. The initial SERP feature analysis function uploads a master keyword CSV file containing a SERP features column (typically comma-separated values like Related searches, Reviews, Video, People also ask, Images), processes the data by splitting and flattening the SERP feature lists, counts occurrences of each unique feature, and generates frequency tables with bar chart visualizations. The example dataset demonstrates analysis of 86,881 Related searches occurrences, 74,621 Reviews, 71,726 Video features, 59,384 People also ask boxes, and 29,476 Images across the keyword universe—providing data-driven insights into dominant SERP patterns for the niche or industry being analyzed.
The content format mapping section introduces customizable SERP-to-format dictionaries: users upload a two-column CSV (SERP snippet, Description) that defines the relationship between SERP features and desired content formats—for example, mapping Related searches to content expanding on or closely related to the query topic, Reviews to product comparison content, Video to multimedia tutorials or demonstrations, People also ask to FAQ-structured content, Featured snippets to concise answer-focused pages. The script converts this CSV into a Python dictionary, then processes the master keyword file by matching each keyword’s SERP features against the dictionary and adding a Desired Content Formats column that specifies which content types should be created—enabling content teams to prioritize video production for keywords with Video SERP features, FAQ content for People also ask queries, or comparison tables for Review-heavy keywords.
The platform identification section follows the same pattern but focuses on where content should be distributed: users upload a SERP snippet to Content platform mapping CSV that associates features with likely serving platforms (Video features map to YouTube, Image features to Pinterest, Shopping results to Amazon/e-commerce platforms, Local pack to Google My Business). The notebook applies this platform dictionary to add a Served Content Platforms column showing which channels Google favors for each keyword—informing cross-platform content distribution strategies by identifying opportunities for YouTube optimization, Pinterest visual content, Reddit discussions, or platform-specific targeting based on SERP evidence rather than assumptions.
The final visualization section processes the enriched keyword data to generate three comprehensive analyses: SERP features frequency distribution (showing top 20 most common features), desired content formats distribution (revealing which content types are most needed across the keyword universe), and served content platforms distribution (identifying which platforms appear most frequently). All visualizations export as bar charts with customizable styling, and summary tables save as downloadable CSV files for reporting or further analysis—providing executive-friendly dashboards that translate technical SERP data into strategic content planning insights.
Use this for:
‧ Content format prioritization by analyzing SERP feature frequency across keyword universes to determine whether video, visual, FAQ, review, or other content types should dominate content production roadmaps
‧ Platform-specific content strategy by identifying which platforms (YouTube, Pinterest, TikTok, Reddit) Google serves most frequently for target keywords, informing channel investment decisions
‧ Scalable keyword categorization enrichment by adding Desired Content Formats and Served Content Platforms columns to existing keyword databases for content brief automation
‧ Data-driven content brief generation using SERP feature insights to specify required content formats, recommended platforms, and user intent signals for writers and designers
‧ Competitive content gap analysis by comparing your current content formats against SERP feature recommendations to identify where video, visual, or FAQ content is missing
‧ Resource allocation justification using frequency visualizations to demonstrate why video production, visual design, or platform-specific optimization deserves budget priority
‧ Custom SERP dictionary development for industry-specific mapping between SERP features and content requirements that reflect niche-specific user preferences and competitive patterns
‧ Quality control validation ensuring keyword research exports are properly enriched with content format and platform recommendations before distribution to content teams
‧ Multi-niche comparison by running the analysis separately on different keyword segments to identify how content format needs vary across topics, user intents, or market verticals
‧ Historical trend analysis by repeatedly analyzing SERP features over time to detect shifts in Google’s content format preferences (like increasing video prominence or declining featured snippet opportunities)
This is perfect for content strategists, SEO managers, editorial directors, and digital marketing teams managing large-scale content operations (100+ keyword targets) who need systematic, data-driven approaches to content format decisions—particularly valuable when building content calendars that require format specifications (blog vs video vs infographic), when allocating production budgets across content types and platforms, when creating content briefs that specify required formats based on SERP evidence rather than intuition, when justifying platform expansion (like investing in YouTube or Pinterest) with concrete SERP data showing Google’s preference for those channels, or when enriching keyword research databases with actionable content recommendations that connect search intent to execution specifications—all implemented through CSV workflows, customizable dictionaries that adapt to industry-specific needs, frequency visualizations for stakeholder communication, and bulk processing capabilities that handle thousands of keywords with consistent mapping logic.