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Stop Blogging and Start Building Evergreen Content

Stop Blogging and Start Building Evergreen Content

Search Marketing Summit, Sydney, 2020.

Amanda King

March 18, 2020

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  1. 3 [Google’s] mission is to organise the world’s information and

    make it universally accessible and useful. Source: https://about.google/
  2. 8 So ask yourself - is your blog article from

    2013 the most useful information on the web for your customers search?
  3. 11 A rough timeline of Google’s algorithmic relationship with content

    & intent Source: https://moz.com/google-algorithm-change 2005 Bourbon “Bourbon changed how duplicate content and non-canonical URLs were treated” 2009 Real Time Search 2010 Mayday 2011 Attribution update Schema announced Panda #1-9 Freshness update 2012 Panda #10-23 Penguin Knowledge graph x2 2013 Panda #24-25 Penguin Knowledge Graph In-depth articles Hummingbird 2014 Panda #26, 27 Pigeon (local intent) 2015 Quality Raters Guidelines released Panda #28 - Rank Brain 2017 Fred 2018 Medic Updated Quality Raters guidelines released 2019 3x core updates BERT 2020 Core update Featured snippets
  4. 12 That’s more than 50 major algorithm changes over the

    span of 15 years to try and understand the quality and intent of content.
  5. 13 Natural language processing and latent semantic indexing are not

    the same thing. LSI is a very specific, patented technology from the 80’s. Notes: 1. <use a full stop after the numeral. Note number superscript in slide body> 2. <use a full stop after the numeral. Note number superscript in slide body> First, a word about natural language processing Source:http://www.seobythesea.com/2018/01/google-use-latent-semantic-indexing/
  6. 14 • Entity databases • Co-occurrence • Knowledge graphs and

    entity relationships • Disambiguations and ontology Source: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&f=G&l=50&d=PALL&S1=08538984&OS =PN/08538984&RS=PN/08538984 http://www.seobythesea.com/2014/01/entity-associations-websites-related-entities/ http://www.seobythesea.com/2012/05/should-you-be-doing-concept-research-instead-of-keyword-research/ http://www.seobythesea.com/2017/06/fact-answers/ http://www.seobythesea.com/2012/11/ranking-webpages-relationships-co-occurrence-patent/ http://www.seobythesea.com/2013/08/relationships-search-entities/ http://static.twoday.net/blackcat/files/applying%20meaning%20to%20information%20management.pdf http://www.seobythesea.com/2013/08/relationships-search-entities/ http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&f=G&l=50&d=PALL&S1=08504562&OS =PN/08504562&RS=PN/08504562 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&p=1&f=G&l=50&d=PTXT&S1=7,877,371.PN. &OS=pn/7,877,371&RS=PN/7,877,371 Natural language processing - so what?
  7. 16 User context based search engine “For example, a horse

    to a rancher is a type of animal. A horse to a carpenter is an implement of work. A horse to a gymnast is an implement on which to perform certain exercises.” https://patentscope.wipo.int/search/en/detail.jsf?docId=US17761872 http://www.seobythesea.com/2017/11/semantic-keyword-research-topic-models/ 4
  8. 17 Ranking scores for Related entities can be based at

    least in part on: - How often you search related terms - Search volume - How often related queries come up in searches together - If searches tend to happen in a particular hierarchical order - If we know the search is related to a broader search Some soundbytes on entities http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%22201302385 94%22.PGNR.&OS=DN/20130238594&RS=DN/20130238594
  9. 18 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=% 2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10,496,691.PN.&OS=PN/10,496, 691&RS=PN/10,496,691 Implementations provide an improved system for

    presenting search results based on entity associations of the search items. An example method includes generating first-level clusters of items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, merging the first-level clusters based on entity ontology relationships, applying hierarchical clustering to the merged clusters, producing final clusters, and initiating display of the items according to the final clusters.
  10. 19 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=% 2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10,496,691.PN.&OS=PN/10,496, 691&RS=PN/10,496,691 Basically, what you see in a

    search result is taking into consideration much more than what words a visitor types into Google, but also things like: already-known correlations with the search already-known related topics already-known disambiguations your own personal search history the context of both the page and your query and what broader topic your search is a part of.
  11. 20 The natural language API is a way to help

    identify co-occurrence and related entities
  12. 21 Let’s start from the start - Panda. What was

    it for? What was Google trying to do?
  13. 23 Quality Rater Guidelines • Raters evaluate the success of

    what Google returns for a search result, in terms of what they feel is most relevant. • YMYL pages confirmed • EAT confirmed • The importance of supplementary content https://searchengineland.com/google-releases-the-full-version-of-their-search-quality-rating-guidelines-236572 https://moz.com/blog/google-search-quality-raters-guidelines https://support.google.com/websearch/answer/9281931?hl=en https://webmasters.googleblog.com/2019/08/core-updates.html
  14. 24 The evolution of major algo changes for content Panda

    Diminishing low quality content Quality Rater Guidelines Confirming high quality content Hummingbird Understanding user intent through entity mapping BERT Understanding user intent through natural language processing
  15. 25 Time-bound Catch-all Try to answer too many questions at

    once (or too few) Blogs are buried in your footer – we’re ashamed of them The overall architecture doesn’t make sense or isn’t related So why don’t blogs work?
  16. 27 Simple persona creation •Layer data from quantitative sources including:

    •Google Analytics •Facebook •Performance media •Gather data from qualitative sources including: • Surveys or feedback polls • Focus groups or interviews •If time, validate in internal journey mapping workshop •Build persona including name, age, gender, interests, goals and values Notes: 1. <use a full stop after the numeral. Note number superscript in slide body> 2. <use a full stop after the numeral. Note number superscript in slide body>
  17. 29 Sources for evergreen content Customer verbatims via NPS surveys

    Customer service team Google’s Knowledge graph panels (people also ask, etc) Answerthepublic https://ai.google.com/resear ch/NaturalQuestions Case studies Competitor comparisons Source: https://unsplash.com/photos/r-enAOPw8Rs
  18. 30 Validate topics are evergreen • Avoid timely references •

    Topics that will always be relevant • Topics that actually have search volume • Topics that people are still interested in and the trend looks like they still will be Source: https://knowyourmeme.com/memes/you-know-nothing-jon-snow
  19. 31 How many people’s sites actually look like this? They

    probably should. Source: https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcTZbOkwJlAdcPgh1XlYcuIcg5zdtTXzq7Eff1K70mOsGdDcnP70
  20. 33 Know what you have, write what you don’t, put

    it where customers will see it, and make sure Google understands it. Easy, right?
  21. 34 Who, me? •10 years in marketing • 8 years

    in Analytics • 5 years in CRO / UX • Lived in 3 countries, visited 40+