Oh My MUM Strikes Back - Or how to win the battle of Generative Search

Generative Search may be scaring... and we already see AI gurus declaring the (again) soon to be death of SEO and Search Marketing.
But the reality is not such.
In this deck you will see how you must rediscover classic concepts like taxonomy and ontology to be sure you'll survive the AI revolution in Search.

Transcript

1. Gianluca Fiorelli International & Strategic SEO Consultant Website: https://www.iloveseo.net Twitter: @gfiorelli1 Chocolate and Movie lover. Painting minis expert. Italian adopted by Spain, and citizen of the world.
3. OH, MY MUM! Strikes Back
4. Universal Search
5. Search Features
6. More Search Features, E-E-A-T et al
7. More Search Features, E-E-A-T, et al, and SGE
8. The truth is that we shouldn’t despair for the future
9. As long as we think strategically and not just in a reactive way.
10. Occam's Razor: The goal of any SEO strategy is to bring as much qualified organic traffic to the right pages of our website to convert it into customers.
11. But if before these users had a clear path to reach the desired destination...
12. … now they can take very different paths to reach that same destination.
13. And with the future arrival of SGE, these paths are going to be transformed into a true labyrinth.
14. ‘’Gattopardismo’’: Everything must change so that everything continues as before.
16. In the case of Google, the integration of SGE is nothing more than another step in delving into the concept of the Messy Middle.
17. However, the evolution of generative "AI" and LLMs presents us with some challenges and some (great) opportunities.
18. The biggest challenge is to understand this evolution, to be aware that it is accelerated and that for this reason our strategy must be based on reliable constants in the medium/long term.
19. At the same time, however, we have to take advantage of this same evolution to ensure that our strategy based on reliable constants in the medium and long term is not just a one-day success.
20. BUT
21. One does not simply enter into Mordor
22. all of this has happened before.
23. all of this has happened before. and all of this will happen again
24. But in a new context.
25. With respect to the past year, some things have changed and went partly unnoticed. One of the most relevant changes is related to the Product Knowledge Graph. Here we see how it was presented in the summer of 2022 for “Star Wars: Legion minis”.
26. Google Merchant Google Manufacturer Insights based on product taxonomies Reviews. Analysis-based Q&A Expert Reviews Videos 2023
27. Google Merchant Google Manufacturer
28. Insights based on product taxonomies Expert Reviews Outreach actions are more necessary than ever, and not just to get links (which are still important), and they should even be preventive. Analyze who "speaks" about your competitors and is used by Google as a source of EXPERT information.
29. Expert Reviews Outreach actions are more necessary than ever, and not just to get links (which are still important), and they should even be preventive. Analyze who "speaks" about your competitors and is used by Google as a source of EXPERT information. Top Consideration and Critic Review are not conditioned by QDF. Insights based on product taxonomies
30. Expert Reviews Outreach actions are more necessary than ever, and not just to get links (which are still important), and they should even be preventive. Analyze who "speaks" about your competitors and is used by Google as a source of EXPERT information. Top Consideration and Critic Review are not conditioned by QDF. In some cases, Google uses comments present in the cited source. Insights based on product taxonomies
31. Expert Reviews Outreach actions are more necessary than ever, and not just to get links (which are still important), and they should even be preventive. Analyze who "speaks" about your competitors and is used by Google as a source of EXPERT information. Top Consideration and Critic Review are not conditioned by QDF. In some cases, Google uses comments present in the cited source. The taxonomy depends on the ontology that we have defined for the product. Insights based on product taxonomies
32. Reviews. Analysis-based Q&A The reviews can be both from "Google", that is, extracted from the reviews made directly on Google Merchant (paid and/or organic), but they can also be reviews indexed from websites.
33. Questions in evidence appear only in desktop searches and are generated through what seems to be NLP analysis of the reviews themselves. What implicit questions does each review answer? What are the reviews that can be grouped under the same question? What are the most relevant and common questions? Reviews. Analysis-based Q&A
34. With ChatGPT or OpenAI APIs and embeddings we can do something similar ourselves using our reviews (or others' or public sources) to be able to identify pain points and classify them by clusters and semantic neighborhood. For that we will have to prepare the dataset with Python: Reviews. Analysis-based Q&A
35. Then, we cluster with K-Means : Finally, we can take advantage of GPT-4 to get an automated description of each cluster: 1. We extract some reviews from each cluster, 2. we use them to compose a prompt that asks GPT-4 to analyze the similarity and 3. to generate a title or description accordingly. 4. Generate the questions to which they implicitly respond. Reviews. Analysis-based Q&A
36. Videos Again, outreach actions are more necessary than ever, and not just to get links (which are still important), and should even be preventive.
37. Videos Again, outreach actions are more necessary than ever, and not just to get links (which are still important), and should even be preventive. Note that the mobile SERPs also feature Shorts.
38. Videos Again, outreach actions are more necessary than ever, and not just to get links (which are still important), and should even be preventive. Note that the mobile SERPs also feature Shorts. Analyze who "speaks" about your competitors and is being used as a source of EXPERT information.
39. Videos Pay attention to the evolution of YouTube and the Creator Economy, because it too is accelerated.
40. Videos And remember that up to 45% of users watch YouTube on television (USA data). Therefore: 4K, high quality and consistent Thumbnails, Shorts, introductions with the "Best of" of what we are going to see, be careful with the "Series" function for Playlists and their titles.
41. The battle of Hoth
42. SGE is in beta and in many cases this is what it offers us. This does not mean that you should not study, quite the contrary.
43. Answer generated by Google Sources used to generate the answer Shopping Graph Los rankings orgánicos no aseguran la cita Shopping Graph Button to continue the conversation "Related Conversations". They are not the same as PAAs
44. We can expand the conversation to see which part of the answer is based on one source or another. If the query is a question, in the organic results Google shows the answer, which makes us suspect the existence of "response indexing".
45. If we click on a recommended product, the Shopping Graph tab opens in the same SERP. The description we see can be taken either from the description made by the Manufacturer or from a review, as in this example.
46. Generative AI and semantics
47. The Force is calling Just let it in
48. "For every parent, their child is the most beautiful in the world." The same goes for websites.
49. Unstructured Structured
50. T AXONOMY
51. A taxonomy is a method of organizing data. Taxonomy represents the formal structure of classes or types of objects within a domain. A taxonomy can also be a set of chosen terms used to retrieve content online (for example, a website's taxonomy).
53. Alderaan Appearances (Episode IV) Affiliation (Galactic Empire) Locations (Aldera Spaceport) Terrain (Mountains) Vehicles (Tantive IV) Videos (“Destruction of Alderaan) Gallery History Related (we will see this in a few slides) Locations
56. But as good as the taxonomy we've created is, is it consistent with potential Search Journeys within Google?
57. Contextual Search Menu Filters
58. Images Search tags Level 0 Level 1 Level 2 Level 3 Level 4
59. Images Search tags con Topically.io
60. People Also Ask Don't use PAAs just to get new content ideas. Use them to create thematic clusters and, above all, second and third level clusters, and to optimize their internal linking.
61. SGE Related Questions Collect responses and do entity analysis with embeddings to get new data to integrate with what you’ve already retrieved.
62. AI Related Questions with Perplexity.ai Collect responses and do entity analysis with embeddings to get new data to integrate with what you’ve already retrieved. Save the sources, and analyze them.
63. AI Related Questions with Perplexity.ai Dig into the ”related questions” as you do in the case of People Also Ask. Use them to explore topical clusters possibilities.
64. USE WORDLIFT ADD-ON FOR SHEET TO ANALYZE THE ENTITIES TARGETED BY THE PAGES RANKING IN TOP 10
65. S T A R W A R S L E G I O N TABLETOP GAME TERRAIN PAD MINIS ARMIES HEROES CARDS RULES REBELS STORMTROOPERS LUKE SKYWALKER DARTH VADER FIGURE ASSEMBLING AND PAINTING GUIDES EXPERTS PAINTS ATOMIC MASS GAMES (MANUFACTURER) ’’PRODUCT’’ CRITICAL REVIEWS BLOGS MAGAZINES VIDEOS SHOPPING GRAPH MERCHANTS UGC REVIEWS EXPERTS JOURNALISTS CREATORS 3D PRINTED Finally, update your architecture
66. Use Google Trends to keep the taxonomy up to date or to change it based on seasonality.
67. Speed up your Trend analysis work with a tool like Keytrends Speed up your Trend analysis work with a tool like Keytrends
68. ONTOLOGY
69. Ontology is a formal representation of knowledge about a domain, including the concepts within that domain and the relationships between those concepts.
70. An ontology provides schema information and sometimes axioms or rules for consistency. Rather, taxonomies provide information about hierarchies of concepts and things.
71. Alderaan Appearances (Episode IV) Affiliation (Galactic Empire) Locations (Aldera Spaceport) Terrain (Mountains) Vehicles (Tantive IV) Videos (“Destruction of Alderaan) Gallery History Related Locations
73. Alderaan Base Espacial de Alderaan Palacio Real de Alderaan Bail Organa Reina Breha Organa Leia Organa Alianza Rebele Crucero alderano Tantive IV Imperio Galáctico Grand Moff Tarkin Muerte Negra Obi-Wan Kenobi
74. The Alderaan Royal Palace is the seat of the Alderaan government headed by Bail Organa, a senator of the Republic and the Galactic Empire and one of the most prominent members of the Rebellion. Married to Queen Breha Organa, the two adopted the daughter of Padme Amidala and Anakin Skywalker: Leia Organa. In her childhood, Leia Organa, heir to Alderaan, was rescued by Obi- Wan Kenobi and, once they assumed diplomatic roles, was key to the theft of the Death Star's secret plans thanks to the versatility of her Alderaan cruiser. However, she was captured by Grand Moff Tarkin, who destroyed the planet Alderaan with the Death Star. Prior to her capture, Leia Organa managed to send a distress message to Obi-Wan Kenobi via an astromechanic droid.
75. schema
76. Markup for Hotels https://schema.org/docs/hotels.html basado en Accommodation Ontology Language Reference
77. Markup for automative
78. Markup for banks and financial institutions
79. SameAs era importante. Ahora todavía más
80. CONTENT
81. ENTITY SALIENCE and CONTEXT Surviving the fall of the Galactic Empire, Moff Gideon soon takes command of a massive Imperial force in disarray, reorganizing it to counter the newly established New Republic. Armed with the Mandalorian black lightsaber, Moff Gideon will find his fiercest opponents in Mando, Grogu, and the Mandalorians led by Bo-Katan Kryze. An entity represents a phrase in text that is a known entity, such as a person, organization, or location. An entity’s salience score provides information about the importance or centrality of an entity to all text in the document. Scores close to 0 are less prominent, while scores close to 1.0 are highly prominent.
82. Moff Gideon appears in all three seasons of The Mandalorian that have aired so far. Played by cult actor Giancarlo Esposito (Breaking Bad), Moff Gideon has been the subject of many theories by fans of the series, such as his role in cloning Emperor Palpatine or his own death at the end of the third season. Surviving the fall of the Galactic Empire, Moff Gideon soon takes command of a massive Imperial force in disarray, reorganizing it to counter the newly established New Republic. Armed with the Mandalorian black lightsaber, Moff Gideon will find his fiercest opponents in Mando, Grogu, and the Mandalorians led by Bo-Katan Kryze. ENTITY SALIENCE and CONTEXT
83. Moff Gideon, like all Star Wars: Legion minis, comes in hard plastic. Easy to assemble, it faithfully reproduces the face of the actor Giancarlo Esposito who played him in The Mandalorian. The mini is accompanied by 1 unit card, 1 upgrade card, 3 command cards, 1 token, and 1 instruction sheet. The Moff Gideon miniature comes not painted. Surviving the fall of the Galactic Empire, Moff Gideon soon takes command of a massive Imperial force in disarray, reorganizing it to counter the newly established New Republic. Armed with the Mandalorian black lightsaber, Moff Gideon will find his fiercest opponents in Mando, Grogu, and the Mandalorians led by Bo-Katan Kryze. ENTITY SALIENCE and CONTEXT
84. Entity analysis through embedding for Lens With Lens we know that people can search for images. Thanks to embeddings, visual components are transformed into numbers. Thanks to our previous work, we can easily see the neighborhood between products also if they are taxonomically different.
85. Lens works for objects, so if it recognizes them it highlights them so they can be selected. 1. Embeddings 2. Entity clustering 3. neighborhood analysis 4. Optimizing images for “related products” in Lens (note: this process can be used equally for related products at the website level). Entity analysis through embedding for Lens
86. In pink Entity analysis through embedding for Multisearch
87. AI Images to cover stock shortages
88. AI to improve stock image quality We can use Super-Resolution for images, which can enlarge and enhance images using generative AI and Deep Learning. A good model is ESRGAN (Enhanced Super Resolution Generative Adversarial Network) available on Tensorflow Hub (h/t Andrea Volpini aka @cyberdandy): https://www.tensorflow.org/lite/examples/super_resolution/overview https://www.tensorflow.org/hub/tutorials/image_enhancing https://arxiv.org/abs/1809.00219 https://github.com/hiram64/ESRGAN-tensorflow
89. Store window display of a colorful collection of Fender electric guitars::3 with vibrant colors and exquisite details::2 Canon EOS 5D Mark IV, 24-70mm lens, f/4, ISO 100, 1/100 sec Textures and details. Warm lighting and city reflections, high- end fashion, urban setting --ar 1:1 --v 5.1 --style raw --s 1000 --q 2 --no people, watermarks seed 901509742 And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
90. Store window display of a colorful collection of Fender electric guitars::3 (things that we want to create and relative weight of this part of the prompt) And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
91. with vibrant colors and exquisite details::2 (define the overall style and weight it) And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
92. Canon EOS 5D Mark IV, 24-70mm lens, f/4, ISO 100, 1/100 sec (indicate the tech you want the AI to simulate for you) And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
93. Textures and details (do not forget to indicate the materiality of the image) And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
94. Warm lighting and city reflections, high- end fashion, urban setting (be precise when coming to environmental or studio lighting) And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
95. --ar 1:1 --v 5.1 --style raw --s 1000 --q 2 --no people, watermarks (learn how to use the parameters, and make us of negative weighting for obtaining clean images) And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
96. seed 901509742 (discover the function of “seed” to maintain consistency between generated images). And, do not prompt things like “Imagine a unicorn riding in the clouds”. Be precise and specific!
97. Because, using prompts as a work tool, if we are not very precise, we will run the risk of working from the wrong bases and, thus, reaching disastrous decisions.
98. Happy AI… and may the Force be with you.