Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

YouTube SEO: How a data-driven approach can boost visibility

In this session, Billy will explain why marketers should approach YouTube with the same amount of rigour and data-mindedness that they do with Google. He'll also explore the different data types that everyone should explore when it comes to YouTube, going far beyond simply keywords.

Join this session to:
- Understand the data and insights available from YouTube
- Get to grips with the key considerations for optimising YouTube content - from titles and descriptions to product links and playlists
- Learn how to use data to build a strategy for new content, and prioritise video content ideas
- Uncover the key metrics to determine success on YouTube

  • Be the first to comment

  • Be the first to like this

YouTube SEO: How a data-driven approach can boost visibility

  1. 1. YouTube: How a data-driven approach can boost visibility
  2. 2. Speaking today / Présenté par Billy Leonard Content Marketing Account Director
  3. 3. Croud was built to deliver more for our clients Traditional agencies Timedeliveredforfee Strategy Operational Work Manual Tasks
  4. 4. "Without data, you're just another person with an opinion" - W. Edwards Deming
  5. 5. YouTube is the 2nd biggest search engine in the world, and should be treated with the same robust methodology as Google.
  6. 6. YouTube prioritises engagement over everything else.
  7. 7. Source: Backlinko Low Impact Medium Impact High Impact ● Keyword-Rich Tags ● Keyword-Optimized Titles ● Keyword-Optimized Descriptions ● Subscriber Count ● Number of Likes ● Subscribers Generated ● Video Comments ● Length, i.e. longer ranks over shorter ● Video Shares ● View Count ● Filmed in HD This is how what we know about how YouTube weights all of the different ranking signals
  8. 8. But remember, you can’t trust Google to tell you anything.
  9. 9. However, you can infer things from Google properties and products Google Video AI has machine learning models that automatically recognize objects, places, and actions in video. Google Video AI The Google Cloud Speech tool is a machine-learning tool that specialises in recognising speech in all languages. Google Cloud Speech
  10. 10. All of this is data that we should be collecting and optimising.
  11. 11. As we see it, there are 3 types of data needed to increase YouTube visibility Data Type What it means How we get it Deliverables 1 Top-level keyword data Topic + thematically relevant data YouTube keyword research Idea, title + summary 2 Granular keyword data LSI + long-tail keywords Topic research + LSI generators Script + video description 3 Visual data Objects, backdrops, actions, animation style, visuals Video research Storyboard
  12. 12. 1. Top-level keyword data For video ideas, titles + summaries
  13. 13. We use traditional KWR to get this data Keyword Research Categorise our queries Ideate! Overlay other metrics We do YouTube specific keyword research We then group queries together into larger categories We then add other performance metrics for analysis Lastly, we ideate based on the data we have collected
  14. 14. But we use other metrics to better quantify the opportunity This is the available search volume for all queries that fall into these categories 1. Search volume Engagement is based on the total number of video views that videos in this category achieve 2. Engagement Difficulty tells us how hard it will be for us to rank for videos that fall into this category 3. Difficulty
  15. 15. To scale this easily, we use our machine learning ideation tool, Solomon Search Volume Ahrefs Engagement YouTube Difficulty Custom Script
  16. 16. And we visualise where the opportunities are
  17. 17. Create overall structure for video, including script + storyboard Create video idea, title and summary All identical and thematically-related queries are grouped together It then becomes quite easy to drill down into categories and ideate from the data
  18. 18. Hook + Explainer – Show Information – Brand Name And integrate that data into video titles in a structured format
  19. 19. 2. Granular keyword data For the script and video description
  20. 20. We should treat the script like it’s on-page copy Regular keywords identified in the first step that need to be spoken about 1. Keywords Latent semantic indexing keywords that are related to the topic chosen for the video 2. LSI Keywords
  21. 21. We don’t always need to reinvent the wheel, there are existing tools that can be used
  22. 22. It’s important to create structure for descriptions 1. Intro sentences - this should be 2-3 attention-grabbing sentences 2. Detailed video description - 200 words to explain the video further 3. CTA - any relevant call-to-actions, including further reading, resources etc 4. Links - links to social media profiles etc
  23. 23. 3. Visual data For the storyboard
  24. 24. Research videos for that topic and see what common themes you can aggregate together Text explainer of product benefits The product is in shot Hands indicate the product is being used Buildings show an urban location
  25. 25. Google Video AI has machine learning models that automatically recognize objects, places, and actions in video. The Google Cloud Speech tool is a machine-learning tool that specialises in recognising speech in all languages. But using Google products, we can start to do our own, more in-depth analysis at scale Google Video AIGoogle Cloud Speech
  26. 26. Video Rich Annotations Features Logo Scene Changes Camera Phone Buildings Hands Text Number of Scene Changes: 6 Logo Screentime: 3 seconds | 7% Logo first shown at: 00:38 Camera Screentime: 34 seconds | 83% Phone Screentime: 3 seconds | 7% Buildings Screentime: 4 seconds | 10% Hands Screentime: 21 seconds | 51% Text Screentime: 32 seconds | 79% The video has the following attributes: ● Explainer/Product information video (due to high screentime of both the ‘hands’ object and text) ● Product shown prominently (due to the high screentime of the ‘camera’ object) ● Closing with Brand Logo (video ends with the shining logo sequence) ● Product detail view throughout the video Data Aggregation
  27. 27. Video AI pulls features from top performing videos Detail goes into the brief for creative direction Features analysed at scale and recommendations created We then feed this data into our creative briefs and storyboards
  28. 28. In Summary
  29. 29. Treat YouTube with the same data-focused methodology that you would Google Data Type What it means How we get it Deliverables Top-level keyword data Topic + thematically relevant data YouTube keyword research Idea, title + summary Granular keyword data LSI + long-tail keywords Topic research + LSI generators Script + video description Visual data Objects, backdrops, actions, animation style, visuals Video research Storyboard
  30. 30. Thanks so much for listening! billy.leonard@croud.com if you have questions :)

×