How to Rank Higher Than Wikipedia | Ep. #1051
Marketing School - Digital Marketing and Online Marketing Tips
In episode #1051, we discuss how you can rank higher than Wikipedia. Tune in to hear how you can be one of the top-ranking search results.
We have committed to throwing a FREE Marketing School Live Event in Los Angeles. Check out the details on this website if you would like to attend. Remember: we are capping the event at 500 people, so sign up now, if you’re interested! DM Eric if you would like to participate in the VIP dinner.
TIME-STAMPED SHOW NOTES:
- [00:27] Today’s Topic: How to Rank Higher Than Wikipedia
- [01:10] When doing keyword research, if Eric sees Wikipedia as one of the top results, he knows he can compete.
- [01:30] He follows the skyscraper technique to beat out Wikipedia and others at the top.
- [02:40] Backlinko ranks well, because his posts are high-quality.
- [03:45] Trying to compete with news sites will be the ultimate challenge. You can’t beat them.
- [04:15] Neil recommends updating your content whenever you get traction.
- [04:55] You’ll have a much better shot at beating Wikipedia if you combine a bunch of techniques (skyscraper, internal linking, etc.).
- [05:09] That’s it for today!
- [05:15] We are almost at capacity for our live event, so register while you still can: Check out Single Grain for more info.
Leave some feedback:
- What should we talk about next? Please let us know in the comments below.
- Did you enjoy this episode? If so, please leave a short review.
Connect with us:
Learn more about your ad choices. Visit megaphone.fm/adchoices
See omnystudio.com/listener for privacy information.
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
Memorable statements and important claims with attribution and source context.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.