2018 NYC Localogy: Using Data to Build Exceptional Local Pages

Here's my presentation on SEO basics for startups that I recently gave at #SecretSauce 2016 at Hackney as part of London Technology Week. A great time and an amazing group of entrepreneurial and inspiring people.

Google is a large-scale search engine that indexes the entire web. It makes use of the link structure between pages to determine importance and provide more relevant search results. The Google search engine consists of three main parts - the Googlebot crawler, the indexer that sorts words and builds a database, and the query processor that returns relevant results in under a second by comparing queries to the index.

A look at how Google's evolving search algorithms mean changes to SEO and search visibility across the new mobile world.

Seoindiarank is a battle hardened team of SEO experts. Get your website on the first page of Google and attract thousands of your customers.

With the rise of intelligent services and more ways to look for information than ever before, people have been retrained to interact with search engines differently. In this talk from Bristol SEO, Ric discussed why this is important, how this impacts the very fabric of search as we know it - and what to do about it.

How google search works ---------------------------------- you can visit my LinkedIn profile: https://www.linkedin.com/in/hardik-mahant/ view my portfolio: https://sites.google.com/view/hardikmahant

http://seofirstpage.ir/ هانیه غفرانی آموزش سئو سایت http://seofirstpage.ir/%D8%A2%D9%85%D9%88%D8%B2%D8%B4-%D8%B3%D8%A6%D9%88-%D9%88-%D8%A8%D9%87%DB%8C%D9%86%D9%87-%D8%B3%D8%A7%D8%B2%DB%8C-%D8%B3%D8%A7%DB%8C%D8%AA-2/افزایش رتبه سایت در موتورهای جستجو با استفاده از ابزارهای قدرتمند آنالیز، کلیه محتویات وب سایت اعم از کدها، لینک ها، تصاویر و… را بصورت روزانه و مستمر آنالیز می نماید. در این آنالیز اگر کارهای اشتباهی در وب سایت اتفاق افتاده باشد که تاثیر منفی در بهینه سازی موتور جستجو داشته باشد، مشخص می شود. بعد از آنالیز وب سایت و بررسی این معیارها آنها در قالب گزارش های متنوع به همراه راه حل جهت برطرف کردن مشکلات ارئه خواهند شد. معمولا هدف از بهینه سازی موتور جستجو رسیدن به رتبه بالا در صفحه نتایج موتورهای جستجو مختلف است. ولی نکته ای که باید به آن توجه داشته باشید این مسئله است که آنالیز بهینه سازی موتور جستجو به تنهایی نمی تواند به وب سایت شما کمک کند تا به رتبه بالا در صفحه نتایج موتور جستجو برسید. آنالیز سایت شامل چندین معیار مثل بررسی ساختار سایت، ساختار لینک ها و بررسی رسانه های اجتماعی مربوط به وب سایت است. بعد از اینکه این معیارها آنالیز شدند، با ارائه چند گزارش به همراه جزئیات کامل جهت بهینه سازی موتور جستجو را ایجاد می نماید. در این گزارش ها خلاصه ای از امتیازات کسب شده توسط وب سایت و توصیه های مهم جهت افزایش رتبه وب سایت وجود دارد. این امتیازات در گزارشهای ارائه شده توسط رنگ های مختلف نمایش داده می شود که براحتی بتوان تشخیص داد به عنوان مثال چه کارهایی به اشتباه انجام شده است و راه حل پیشنهادی جهت برطرف کردن آن چیست. با استفاده از آنالیز وب سایت و بکارگیری راه حل های پیشنهادی شما قادر خواهید بود صفحات وب سایت خود را بهینه سازی نمایید و در رتبه بالا در صفحه نتایج موتور جستجو قرار گیرید. بهینه‌سازی موتور جستجو : ‎Search engine optimization (SEO)‎ که گاهی در فارسی به آن سئو گفته می‌شود.برای وبمسترها یکی از عوامل مهم و حیاتی بدست اوردن کاربران جدید از موتورهای جستجو و بخصوص گوگل می باشد. عملیاتی است که برای بهبود دید یک وب‌گاه یا یک صفحهٔ وب در صفحه نتایج موتورهای جستجو که می‌تواند طبیعی و یا الگوریتمی باشد، می‌گویند. این یکی از روش‌های بازا��یابی موتور جستجو است. به صورت کلی سایت هایی که دارای بالاترین مکان و بیشترین تکرار در صفحهٔ نتایج موتورهای جستجو باشند، بازدیدکنندهٔ بیشتری از طریق موتورهای جستجو به دست می‌آورند. مهمترین هدف سئو، هدایت بازدید کننده هدفمند به سایت شما به صورت رایگان است. کمتر روش بازاریابی اینترنتی به صورت رایگان پیدا خواهید کرد که افرادی را به سمت سایت شما هدایت کند که قصد خرید کالا و خدمات سایت شما را دارند. گوگل نه تنها چنین کاری انجام می دهد بلکه آنها را زمانی که به سایت شما هدایت می کند که در مرحله تصمیم گیری برای خرید هستند و بنابراین بازدهی سئو بیشتر از تمام روش های تبلیغاتی دیگر است.

The document discusses how to design a local listing strategy for any business. It covers what local listings are, why they matter for businesses, how to start by confirming name, address, phone number and other critical details, and measuring progress over time by tracking rankings. The key aspects are optimizing listings on directories and search engines like Google through accurate information, photos, descriptions and structured data.

The document describes a business intelligence solution that uses a search engine to index and search web pages. It discusses using crawlers to index web pages and store them in a repository. An indexer then generates an inverted index from the repository to support keyword searches. The system architecture includes the repository, indexer, and search functionality. It also describes the database structure used to store crawled URLs, the index, and search results. The project aims to build a basic search engine to demonstrate the proposed business intelligence solution.

This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.

This presentation examines features and benefits in Microsoft Office SharePoint Server (MOSS) 2007 enteprise search. It contains configuration guidance, code snippets, tips and tricks.

For our June 2019 event, Search Social & Attribution, we had two fantastic speakers. Dale Nguyen presented: Developing a Data-Driven Link Building Strategy Using Google, Competitor, & Industry Insights Francois Goube presented: What I learned from crawling 10 billions of Pages and analyzing 5 Trillions of log lines. For full recaps of this and past events, head on over to utahdmc.org

This document discusses search engine optimization and the development of search systems. It notes that computer science has directed search system development with a focus on results relevance, while neglecting user experience. The intent is to inspire deeper engagement in designing search experiences that do more than just sell products. It also discusses challenges like the volume of online information, differences in language and perception, and the limitations of current search systems.

Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size. A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.

The document discusses map reduce and how it can be used for recommendation systems. It describes how map reduce works by mapping data into key-value pairs and then reducing them. This allows large amounts of sparse, unstructured data to be processed efficiently across many machines. It then gives an example of how map reduce could be used to build a sequential web access-based recommendation system by mapping log data into a pattern tree that is continuously updated and used to provide recommendations.

This document discusses Schema.org structured data, including its origins in the Semantic Web and Linked Open Data movements. Schema.org was created in 2011 to provide a common vocabulary for structured data markup on web pages. It allows search engines and other applications to understand the intended meaning and relationships of information on web pages. The document provides examples of using Schema.org structured data and microdata, and recommends applying it across various page types to help search engines better understand websites.

This document provides an overview of search engine optimization (SEO) strategies. It discusses what SEO is, why it is important given user behavior trends, and common barriers to implementing SEO. It then outlines potential benefits of doing SEO, such as increased traffic and brand awareness. The document also covers key on-page and off-page optimization techniques, how to research competitors, and tips for developing landing pages and backlinks. Overall, the document serves as a basic guide to SEO best practices for improving search engine rankings.

Web search engines index billions of web pages and handle hundreds of millions of searches per day. They use inverted indexes to quickly search text and return relevant results. Ranking algorithms consider factors like term frequency, popularity, and link analysis using PageRank to determine the most authoritative pages for a given query. Crawling software systematically explores the web by following links to discover and index new pages.

This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.

Slides for VU Web Technology course lecture on "Search on the Web". Explaining how search engines work, some basic information laws and inverted indices.

In this session, you’ll learn how AdTech companies use AWS services like Glue, Athena, Quicksight, and EMR to analyze your Google DoubleClick Campaign Manager data at scale without the burden of infrastructure or worries about server maintenance. We’ll live-process a click stream so you can see how Machine Learning can help maximize your revenue by finding the most optimal path of a campaign and we’ll look at a real world demo from A9’s Advertising Science Team of how they use the data to build Look-alike Model in their projects.

The document discusses using MapReduce for a sequential web access-based recommendation system. It explains how web server logs could be mapped to create a pattern tree showing frequent sequences of accessed web pages. When making recommendations for a user, their access pattern would be compared to patterns in the tree to find matching branches to suggest. MapReduce is well-suited for this because it can efficiently process and modify the large, dynamic tree structure across many machines in a fault-tolerant way.

The document provides tips and strategies for prioritizing local search engine optimization work. It recommends focusing on reviews, optimizing website landing pages and content, setting up accurate Google+ local listings, cleaning up citations across various directories, and obtaining local links and citations if more than 3 hours are available. With only 3 hours, the priorities are reviews, optimizing the website landing page, and setting up the Google+ local listing and citations on major directories.

The document discusses map reduce and how it can be used for sequential web access-based recommendation systems. It explains that map reduce separates large, unstructured data processing from computation, allowing it to run efficiently on many machines. A map reduce job could process web server logs to build a pattern tree for recommendations, with the tree continuously updated from new data. When making recommendations for a user, their access pattern would be compared to the tree generated from all user data.

This document discusses the essential elements of excellent multilingual search. It outlines three key elements: 1) Customizability, speed, scalability and cost - Search needs to be customizable to fit business needs and scale effectively without high costs. 2) Maximizing search recall and precision through language support - Improving recall and precision, especially recall, is important. Lemmatization rather than stemming can improve recall while maintaining precision. Support for different languages like Chinese, Japanese, Korean, Germanic and Arabic languages is also important to improve recall. 3) Reliability and technical support - Reliable technical support is needed to solve any issues that arise with the search solution. The document uses Basis

The document discusses how content analytics can enhance search capabilities. It provides examples of how key phrases, collocations, and statistically improbable phrases can be used to power related searches, cluster results, and enable faceted search. Beyond search, these content analytics techniques can be applied to applications like product recommendations, social media analysis, and customer experience analytics.

God søk er essentielt for et godt intranett. Likevel investeres det hverken i nødvendig teknologi eller kompetanseutvikling på søk. Resultatet er skremmende: dobbeltarbeid, dårlige beslutninger, forsinkelser og overskridelser, kaste bort ansattes tid på leting etter informasjon, treg respons på marked, konkurrenter osv. Med forholdsvis enkle grep kan du gjøre noe med dette i dag. - Hjelp - intranettet flyter over av innhold - Sammenhengen mellom søk, informasjon, arkitektur og 
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The document summarizes a webinar presented by The Local Search Association on improving customer engagement and reducing churn for SMB SaaS products. Key points include: 1) While SMB use of cloud-based services is growing, over half still don't use any SaaS and 62% underutilize the software they purchase. 2) Better training, simpler products, and setting proper expectations can improve engagement and utilization. 3) Collecting feedback from partners and customers and closely observing how SMBs complete tasks helps build better products that minimize churn. 4) Proactive communication and support also aid engagement and reduce chances of customers switching to competitors.

This document discusses how companies can focus on customer engagement and retention to drive optimal growth. It argues that improving retention is easier than maintaining rapid growth while fixing retention issues. The document provides examples from Hubspot of how focusing on customer success through strategies like sales compensation aligned with lifetime customer value can improve retention metrics like annual revenue retention above 100% and customer retention above 90%. It emphasizes defining and measuring key customer success indicators.

This document discusses strategies for turning customers into advocates by leveraging customer referrals and influencers. It provides examples of initiatives like a customer Facebook group, case studies from promoters, and targeted advertising to advocates. Data shows increasing referral incentives from $250 to $1000 drove more referrals. The presentation emphasizes empowering advocates to talk about the brand while also addressing any detractors.