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    Business

    Fastlane Founders and Legacy with Jason Barnard: Personal Branding, AI Strategies, and SEO Insights for Visionary CEOs

    Entrepreneurial insights from CEOs on fast success, enduring impact, and building long-lasting companies. “Fastlane Founders and Legacy” hosted by Jason Barnard, CEO of Kalicube, explores the delicate balance between rapid growth and enduring success in the business world. This podcast features insightful conversations with entrepreneurs, CEOs and executives who have mastered the art of building lasting legacies while navigating the fast-paced demands of modern business. Each episode uncovers a blend of agile tactics for immediate impact and strategic thinking for long-term sustainability. Listeners will gain valuable insights into how these leaders accelerate progress without sacrificing future stability, and how they transform innovative ideas into companies built to stand the test of time. Join us to learn how today’s most influential business figures harmonize quick wins with lasting achievements, creating both rapid success and enduring impact.

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    Latest Episodes:
    How the Q&A / Featured Snippet Algorithm Works (Ali Alvi with Jason Barnard) Apr 14, 2020
    Show notes

    Ali Alvi with Jason Barnard at The Bing Series Ali Alvi talks to Jason Barnard about the search algorithm for featured snippets. First thing we learn is that this feels a lot like a soccer interview. https://www.youtube.com/watch?v=l0UhsQb5iAU Then Ali confirms what Gary Illyes said in 2019 - the different candidate sets use the core algo in a modular fashion. Ali is team lead for the Q&A candidate (Q&A is Bing's name for featured snippet) But also that all of the algos are end-to-end neural networks. We know what goes in, we see what comes out… but nobody knows what goes on in between :) And a nice clarification - Q&A are pulled from the blue links below it. Other rich elements such as video and images don't rely on the pages the 10 blue links provide - they have a separate selection process. Now that is interesting. Even more interesting - Ali answers the intriguing question "where do the descriptions for the blue link / core results come from?" (spoiler alert - it isn't from the core algo!) We talk a great deal about trust - Bing must trust the website providing the answer. So building trust over time has to be key. And then onto the main factors / features that affect ranking for Q&A are: accuracy, trust, authoritativeness, freshness… and not being offensive (aka safeguarding Microsoft's reputation). We also discuss Google's decision to remove the result from the main results when content is used as a featured snippet (Ali doesn't agree with Google here). And finally, dependence on annotations by the crawling and indexing team, as discussed with Fabrice Canel in the previous episode. It all fits together so nicely ! Catch the rest of the Bing Series: How Ranking Works at Bing - Frédéric Dubut, Senior Program Manager Lead, BingDiscovering, Crawling, Extracting and Indexing at Bing - Fabrice Canel Principal Program Manager, BingHow the Q&A / Featured Snippet Algorithm Works - (this episode) Ali Alvi, Principal Lead Program Manager AI Products, BingHow the Image and Video Algorithm Works - Meenaz Merchant, Principal Program Manager Lead, AI and Research, BingHow the Whole Page Algorithm Works - Nathan Chalmers, Program Manager, Search Relevance Team, Bing Full Corrected Transcript for How the Q&A / Featured Snippet Algorithm Works (Ali Alvi with Jason Barnard) Jason Barnard: The camera is kind of far away. They usually have cameras right in people's faces. Anyway, welcome to the show, Ali Alvi. Ali Alvi: Thank you. Great to be here. Jason Barnard: That's the best name I've heard all day. I love your name. Ali Alvi: Thank you. Ali Alvi, rolls off the tongue, doesn't it? Jason Barnard: Yes, brilliant. Ali Alvi: Like the boxer. People debate whether it's "Ali" or "Ali." I say it like the boxer. Jason Barnard: All right. So here we are, looking out over Seattle from the Bing offices. You're the team lead for Q&A? Ali Alvi: Yes, I'm the lead PM for the team that handles Q&A in Bing, including the captions and the snippets you see under the URLs in the search results. Jason Barnard: The blue link descriptions. Better descriptions that get pulled dynamically. That's part of Q&A, so they can be generated as well? Ali Alvi: Yes. The algorithms we use to generate the snippets are essentially the same algorithms we use for Q&A. Google calls it "featured snippets" because a snippet is just a feature. We use a slightly different framing at Bing: we're saying this is an answer to a question, which is more explicit. And when you look at the architecture, we're not just taking a snippet and featuring it. We actually do a lot more than that in many cases. So broadly, it falls into this category: when a user comes and asks a question, or a query that looks like a question we can answer directly, that's the domain my team handles. In addition to that, I'm also the lead PM for a high-ambition AI initiative called Project Turing. Jason Barnard: Project Turing. That's a Microsoft initiative, particularly within Bing? Ali Alvi: There's a team of scientists and applied researchers working on high-ambition natural language processing algorithms. We're kind of the hub for those algorithms across all of Microsoft. That same team provides some of the models we use in Q&A. Think of it as a horizontal team that provides the brains for a lot of these scenarios, and Q&A is one of them. Jason Barnard: So you're the brains behind Bing? Ali Alvi: I wouldn't go that far. I represent the brilliant minds who are the minds behind Bing. Jason Barnard: So for Q&A, my journey to this conversation started when I asked Gary Illyes from Google whether there's a separate algorithm for featured snippets. He said, very dryly, "No," and then explained how it works. The idea is that you've got the basic algorithm for the blue links, and then there's a module alongside it that uses either different features, or the same features with different weightings? Ali Alvi: Maybe I should take a step back. Jason Barnard: Absolutely. I went too fast. Ali Alvi: You jumped straight to what makes results rank at the top. Let me back up. Search engines historically have been just ten blue links, and that's how it was for around fifteen years. Q&A, or featured snippets, started coming around about three or four years ago. The idea is: we have a query, we narrow it down to the ten, fifteen, or twenty most relevant documents, and then Q&A asks, "Can we have the machines read through those documents, do some comprehension on top, and extract the specific part that actually answers this question right on the spot?" Jason Barnard: So Q&A is actually based on the results underneath it. And video, images, and knowledge panels are based on completely separate processes? I'm beginning to understand. You're working vertically from the blue links and asking: what can we pull out that gives a definitive answer? Ali Alvi: Well, it depends on the question. If you ask "How tall is the Eiffel Tower?" that's very definitive. But sometimes you ask things like "What is the average salary for a computer scientist?" and there's no single definitive answer, so sometimes we give a range. You can also ask subjective questions like "What vegetables take the shortest amount of time to grow?" and there's no one right answer there either. Jason Barnard: I've been saying this for a while: if you want Bing to put you at number one, you're asking Bing to recommend your content as the solution or the answer. But the featured snippet is different. It's not recommending, it's saying, "This is the answer we've found to be the best." Ali Alvi: Yes. When you have a featured snippet or a Q&A, it becomes tricky from a user's perspective. They think Bing is telling them "This is definitively it." But the reality is, we're saying: given the context, this is the best answer we found. We're not declaring it the absolute truth. Jason Barnard: But isn't that a sign that people trust you and Google? We've got to the point where we just accept it as the answer, and when it's wrong, we get really upset. Ali Alvi: Absolutely. Part of my job is to channel that sentiment from users and drive that empathy through the whole product. Even when we picked the best answer we could find, sometimes it's not correct, or it's off-topic, or it's hurting people's sensibilities. When that happens, users perceive it as something Bing did. So we have to own the message. Jason Barnard: You have a feedback button on the SERP, so you get a lot of direct feedback from people? Ali Alvi: We get it right a lot of the time, but we do get it wrong. We make sure we're as close to the customer as possible. We call it "zero distance to our customers." That means doing user surveys, bringing people in-house, asking questions. And any feedback we get, we respond to internally, at least to direct it to the right people. Jason Barnard: Do you read everything? Ali Alvi: Yes. Everything. Jason Barnard: Back to the algorithm. It's based on the blue links. What are the most important features you feed into the machine learning algorithm? I'd immediately think expertise, authority, and trust. Ali Alvi: I was actually going to flip the question: as a user, when you come and ask Bing a question, what would you say makes a good answer? Jason Barnard: I'd imagine you're looking at expertise, authority, and trust. And it also needs to be accurate. Ali Alvi: Accuracy is a hard thing to judge. Jason Barnard: Good point. But I keep hearing that accuracy is based on accepted opinion. Ali Alvi: You need to figure out what accepted opinion is, and that's the biggest part of my job: defining the right metric. As a product manager for an AI team, we don't write code. We define what the algorithm needs to do and how to measure whether it's doing it correctly. That responsibility is 100% on the product manager. I'm the one who defines that metric and holds the entire team accountable for meeting it. Jason Barnard: So the metric is the secret sauce you'd never tell me. Ali Alvi: You already said what it was. It has to be relevant, authoritative, trustworthy, not offensive, and fresh. What I would add is that we're using almost entirely neural networks and deep learning-based solutions. And by definition, with deep learning, we don't know exactly what the features are. The machine takes text, gets the query, gets the passages we give it, and comes back and tells us which one to show. Jason Barnard: That reminds me of conversations where people say there's no point asking what the ranking factors are. So what I understand is: your team labels this as a question and this as a correct answer, you build a dataset, and you feed it into the machine with the metrics you're looking for? Ali Alvi: Exactly. And you can see that if the metric is wrong, the machine will latch on to whatever the metric says....


    Bingbot: Discovering, Crawling, Extracting and Indexing (Fabrice Canel with Jason Barnard) Apr 07, 2020
    Show notes

    Fabrice Canel with Jason Barnard at The Bing Series Fabrice canel talks to Jason Barnard about Bingbot. Fabrice was on the podcast last year talking about Javascript and the new indexing API. That was very interesting and he shared quite a few insights... If you would rather read than watch or listen, here is an article I wrote based on this conversation >> This Episode Takes the Conversation a BIG step Further https://youtu.be/dSSZeTYOtMk This conversation is on a whole different planet. Fabrice is head of the entire discovery-crawling-extracting-indexing process. Think about how much that involves. And how important he and his team are to the process of getting your content to the top of the results. You cannot hope to get your content into search results if it isn't found, crawled, extracted and indexed... and since he manages every single one of those steps, he is a person we really need to listen to. Bingbot and Googlebot Function in Much the Same Way Obviously they don't function exactly the same way down to the tiniest detail. But close enough ... the process is exactly the same (discover, crawl, extract, index) the content they are indexing is exactly the same the problems they face are exactly the same the underlying technology they use is the same So the details of exactly how they achieve each step will differ. But they are faced with the same environment and aim to do the same thing - index the web effectively. So, we can safely assume Google deals with the discovery-crawling-extracting-indexing process in a manner very, very close to Bing. Just think about whatever industry you are in - details differ, but every competitor uses the same foundation. Easy to forget, but this is just another industry. So same here. Google functions much the same way as Bing. And vice versa. Close enough for us not to need to worry too much about the differences. Stunning Insights. I Learned sooooo Much. The conversation with Frédéric Dubut that kicked off this series (this episode recorded at UnGagged) suddenly looks tame and unrevealing. A simple 'mise en bouche', as we say in French. Listen and Learn Google collaborate with Bing on Chromium They discover 70 billion new webpages every day Bingbot pre-filters to stores only the 'best' content New technology is coming out for rendering (Machine Learning + Javascript) Standardised HTML is powerful Bing (and we can safely assume Google) is getting exponentially better at extracting information The process of storing the content is MUCH more important than you probably imagine Every candidate set team at Bing relies on Bingbot Nofollow has always been just a hint Sitemaps and RSS are incredibly important Indexing includes annotation, and annotations are fundamentally important to all the other teams and their algos Indexing includes classification, and classification is fundamentally important to all the other teams and their algos In short, as SEOs, we all depend on Fabrice and his team to an extent most of of us have probably will only start to grasp after watching the episode. This is the foundation of ranking in search. Everything else depends on this. Fabrice is a truly lovely guy who wants to help you as a website manager... if only you'd help him help you. Here he tells you what he (and, presumably, his equivalent at Google) wants from you so that he can help you get your content to rank. Help them overcome their problems, and you WILL be rewarded. Groovy ! Catch the rest of the Bing Series: How Ranking Works at Bing - Frédéric Dubut, Senior Program Manager Lead, Bing Discovering, Crawling, Extracting and Indexing at Bing - Fabrice Canel Principal Program Manager, Bing How the Q&A / Featured Snippet Algorithm Works - (this episode) Ali Alvi, Principal Lead Program Manager AI Products, Bing How the Image and Video Algorithm Works - Meenaz Merchant, Principal Program Manager Lead, AI and Research, Bing How the Whole Page Algorithm Works - Nathan Chalmers, Program Manager, Search Relevance Team, Bing Full transcript of "Bingbot: Discovering, Crawling, Extracting and Indexing (Fabrice Canel with Jason Barnard)" Jason Barnard: A quick hello, an we're good to go. Welcome to the show, Fabrice Canel! Welcome, lovely — you know, we're in the Bing offices. Yes, again, I had you on the show last year, it was just audio, now we've got video so everyone can see what Fabrice looks like. Fabrice, incredibly important person at Bing who crawls, extracts, and stores. Fabrice Canel: Yes, I do all of it. Every day I am in charge of discovering internet content — all the internet content. I am in charge of selecting the best content on the internet, as you said. I am fetching and crawling the best content from the internet, then processing it and understanding it. Jason Barnard: So one question is: when you crawl, you're actually looking for what's best, so there's a pre-filter even before the ranking engine? Fabrice Canel: Every day we discover more than seventy billion URLs that we have never seen before. Jason Barnard: Every day? Seventy billion? Fabrice Canel: Seventy billion — it's a lot of content. Obviously we will remove useless URLs. Just to give you a sense of the size of the internet: the size of the internet is really infinite, there is an infinite number of URLs out there. People create content, but then there are systems that are auto-generating content. You have pages with calendars where we can follow links — all kinds of useless links — but often you have to follow those links to discover whether they're good or not. You have to fetch them. Jason Barnard: So when you say you select… Fabrice Canel: Yes, we select the best content for indexing, but often we have to fetch first to discover whether a link is good or not. Jason Barnard: So my initial idea was that you do a pre-sorting, but in fact you're just getting rid of the junk. Fabrice Canel: We first get rid of the junk, then we still fetch to discover if it is useful or not, because we don't know. Sometimes it's just a link to a page we've never seen, and we take a decision based on what we find — whether this page is useful for satisfying user queries or not. Jason Barnard: So with every page, you're going to crawl it, extract information, figure out if it's useful or not, and if it's not useful, do you still store it? Fabrice Canel: Obviously, if we continuously see that these pages are dead links, we will stop indexing them at some point. Jason Barnard: But in processing a page, how do you tag it to not be crawled again — or do you just keep crawling it? Fabrice Canel: Dead links are a very good challenge, because often you have pages that are dead links but come back. You may buy a domain and not populate it — we call that a parked domain, where there is no useful content yet. Then you publish some content, and maybe you forget to renew the domain, so it becomes a parked domain again, and somebody else buys it. There is a lifecycle of URLs. At the end, yes, we take decisions based on URLs — especially what we call tail URLs, which are very long URLs that are essentially useless — and we will stop visiting them, especially if nobody links to them anymore. Jason Barnard: So you've got a lifecycle of URLs — already an interesting concept. You keep tracking them just in case they come back. Fabrice Canel: Yes. Jason Barnard: And another thing you just said: very long URLs are a signal that the page is rubbish? Fabrice Canel: It can be a signal, especially if we continue to see dead links, the URL is very long, and nobody is linking to it anymore. We may decide, okay, this page is a dead link and nobody is visiting it — until we see somebody linking to it again, at which point we say, well, there is a new link to this page, so maybe we should visit it again. Jason Barnard: Right, so you crawl the URLs, look at what's in there, and decide if it's junk or if it's actually useful. What are the problems with extracting the information? I love HTML5, and John Mueller from Google said it's probably not worth using because people use it so badly that they can't rely on it and don't really pay attention to it. Fabrice Canel: I disagree a little bit. The web is built not only from pages created by hand in Notepad, but also from content management systems using templates that are well structured with very good information. It's important to tag content properly to help search engines understand it — h1, h2, and h3 tags are useful for telling the story of headings, for marking the head of a section. Tables that are well structured also help search engines understand the concept of a table, the concept of a list. Jason Barnard: Incredible. Sorry — I heard that 85% of tables are used for design, which creates an enormous problem for you, because a lot of tables are just there for layout and you'd expect data in them, but in fact they're just… Fabrice Canel: Yes, we do not recommend that. We prefer divs, spans, and CSS positioning, and reserve tables for data — for saying, okay, this is the list of planets in the solar system, this is a real table with real data. Using tables for design confuses the understanding of a page. Jason Barnard: Okay, none of that. So — WordPress tends to be structured more or less the same way. That must really help you. Whereas when I code myself, it never works out the way I intend. Fabrice Canel: What you need to understand is that search engines these days are machine-learning based. Machine learning is about judgment — we look at a lot of content, tag it, and define what perfect tagging should look like. There is a variety of pages on the internet, some well structured, some not. If you are concretely outside the norm — doing something really random — machine learning wil


    Practical Advice on Patents and Trademarks in Marketing (Rich Goldstein with Jason Barnard) Mar 24, 2020
    Show notes

    Rich Goldstein, Principal Patent Attorney at Goldstein Patent Law explains patents and trademarks in simple terms so you can protect your business.


    How to do Competitive Research Using Search (Purna Virji with Jason Barnard) Mar 17, 2020
    Show notes

    Purna Virji, the Senior Manager of Global Engagement at Microsoft spills her top secrets for researching competitors using search.


    The Convergence of SEO and Content (Eric Enge with Jason Barnard) Mar 10, 2020
    Show notes

    Digital Marketing Excellence Practitioner, Eric Enge shares the latest tips and trends to help you drive great SEO gains through high quality content.


    How to Develop Your Personal Brand (Kate Toon with Jason Barnard) Mar 03, 2020
    Show notes

    Jason Barnard interviews SEO copywriting specialist, Kate Toon where she shared some ace tips to help you develop your personal brand


    6 Steps to Bulletproof Your Video Shoot (Joyce Ong with Jason Barnard) Feb 25, 2020
    Show notes

    Jason Barnard interviews Event Photographer & Video Producer, Joyce Ong as she shares great tips to help you produce a bulletproof video shoot


    The traffic light sales process (Daniel Hunjas with Jason Barnard) Feb 15, 2020
    Show notes

    Daniel Hunjas explains his analogy between traffic lights and the sales process. When selling, we often have a tendency to speed up at a yellow light, and that is the wrong thing to do - a yellow light is an objection, so you should slow down and take the time to explain to the potential customer… Daniel advises taking people to their pain points right from the start, which reduces churn and avoids wasting everyone's time. Some lovely quotes "solutions hold no value, they only derive value from the problems they solve", "tension is the chemistry of sales"…. this is the day I switched from being an SEO to being a marketer. Thanks Daniel. Daniel on LinkedIn


    The secrets of building great ecommerce sites (Jason Mun with Jason Barnard) Feb 15, 2020
    Show notes

    Jason Mun with Jason Barnard at Chiang Mai SEO Standing by the pool in a posh hotel in Chiang Mai, I start by mis-singing his name very terribly. I then insult him very rudely. And despite that, Jason remains really delightful. Building an ecommerce site from scratch is easy. Getting the foundations right is the only way to make it work long term. And that is very very difficult. It isn't just me who thinks that Prestashop is particularly tough… Shopify is the platform Jason recommends. No doubt in his mind. Then onto reviews, how they help convert - pre and post purchase reassurance - but also with features, functionality and attributes. He likes Trustpilot quite a lot. And at the end, I sing to myself. Jason Barnard (The Brand SERP Guy) on Linkedin and Twitter Poolside in Chiang Mai


    The secrets of outsourcing as a one-person outfit (Lee Louis Gung with Jason Barnard) Feb 15, 2020
    Show notes

    People think "what do I have to do" rather than "what do I want to do"… outsourcing allows you to get to the place where you can choose what you want to do, and not be stuck in the rut of "need to do". BUT, that can lead to a midlife crisis, even at 25 years old. It's easy to outsource, but apparently, attaching your name to a project tends to make the process much more difficult, especially if you are perfectionist like Lee Louis Gung. We didn't get to the practical stuff - how do you outsource successfully… so we recorded a follow up episode that is a must-listen if you listened all the way to the end of this one :).


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