Month: July 2020

Get Your Free Simplified MECLABS Institute Data Pattern Analysis Tool to Discover Opportunities to Increase Conversion

To increase conversion for your company, you must pinpoint your marketing and sales funnel leaks so you know what to repair.

Do you know precisely where customers are dropping out of your funnel? Where you should focus marketing efforts most efficiently to attract customers to conversion? The best next steps to make the biggest gains fast?

The MECLABS Institute Data Pattern Analysis tool provides a structured way to uncover the answers through intense examination of your customer’s data — their digital body language.

The goal is to thoroughly understand customer thought processes at each point in the marketing funnel where they have to make a decision to continue moving through it. By understanding what prospects are thinking at each stage of the buying process, you will be able to better match their motivation and move them through the sales funnel faster.

The Data Pattern Analysis tool includes the following:

  • Summary profit analysis
  • Demographics
  • New versus returning visitors
  • Device type
  • Page depth
  • The origin of your traffic (Source medium)
  • Top pages
  • Top landing pages
  • Previous and next page
  • Funnel analysis
  • Supporting resources

It also includes a summary so you can have all critical data points, questions that need answering, and research questions in an easy-to-view and easy-to-present dashboard. The tool can work with any metrics platform but has been set up to be easily used with (and includes basic directions for) Google Analytics, due to that platform’s popularity.

Episode 2 of The Marketer as Philosopher: Become a Force for the Good provides an in-depth example, showing how to use the Data Pattern Analysis tool.


Are you having trouble increasing leads or sales? This Data Pattern Analysis is one of the tools we use in Quick Win Intensives. You can get expert help to boost your conversion rate in these intensives.

The journalism produced by the MarketingExperiments editorial team is powered by the research developed by its parent organization, MECLABS Institute.

For over 20 years, MECLABS has been the world’s largest independent research program focused on understanding why customers say “yes.” The Institute’s scientific approach, discoveries and patented heuristics have enabled marketers to achieve their goals.

There are three ways you can leverage MECLABS research:

  1. Access the 20+ years of research and discoveries published by our institute, available for free (and ungated) at meclabs.com/research
  2. Advance your career with online education and in-person training. On-demand courses allow busy professionals to study and master the material as their schedule allows. MECLABS instructors have conducted team-based training on corporate campuses and company headquarters across the nation. Learn more at meclabs.com/education
  3. Boost your Conversion Rate with a MECLABS Quick Win Intensive. Get MECLABS scientists to help you find the fastest way to drive a major revenue increase. Learn more at MECLABS.com/QuickWinIntensive

  MECLABS Institute Data Pattern Analysis by MECLABS Institute is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. Based on a work at https://www.marketingsherpa.com/freestuff/meclabs-data-pattern-analysis-tool. Permissions beyond the scope of this license may be available at https://meclabs.com/about/contact.

Get Your Free Simplified MECLABS Institute Data Pattern Analysis Tool to Discover Opportunities to Increase Conversion was originally posted by Video And Blog Marketing

How to Create Customer Service Emails That Drive Repeat Business

Conversion Optimization

Top-notch customer service is vital to the success of any business. It is one of the best ways to improve customer retention and create loyal brand ambassadors who will buy from you for years to come.

Reacting quickly and attentively will impress your customers and keep them coming back for more.

Online customer service is just as, if not more important, as in-store service. Customers expect instantaneous communication and to have their problems fixed on the first exchange.

Failure to exceed a customer’s expectations could cause them to abandon your brand in droves. That’s why a complete customer service strategy is essential to your success – and without a plan, you may be doomed to fail.

Email is one of the many tools at your disposal, it’s also one of the most effective. It’s simple, effective and gives your customers the freedom to reach you at any time.

What’s more, is that you can talk to your customers as well. By reaching out to your customers at specific points in their customer journey, you can encourage an open line of communication. This shows that you value their business and opinions which can create a lasting consumer relationship. If this isn’t a regular part of your marketing strategy, it should be.

Want to craft the perfect customer service email? This article will highlight the six essential steps that go into writing a perfect email response to keep your customers satisfied. After, we’ll tell you when to communicate to ensure they’ll have the ultimate email experience.

The 6 Steps To Writing a Flawless Customer Service Email

There are six essential steps you’ll need to follow if you want to impress your customers via email. When you solve a customer’s problem, the right way, you’ll have a customer for life. Do it the wrong way, and they may shop elsewhere.

There is one thing though that must stay top of mind every step of the way.

Empathy.

(Image Source)

You have to empathize with the customer’s issue and show a legitimate desire to help them. Demonstrating that you, as a company, see customers as more than a dollar sign is vital.

Without empathy, customer service emails can fall flat. Remember, it’s hard to convey tone through text, so you have to pay careful attention to your wording.

As we go through these steps, be sure to keep empathy in mind.

1. Greet the Customer

Your greeting sets the tone for the entire email. It should be warm and inviting. Never just jump into the heart of the problem without first saying hello. If you don’t, the email may seem cold and insincere.

You’d never just approach someone and start talking without a proper greeting, would you? Think of customer service emails like a conversation you’re having with a friend.

2. Use the Customer’s Name

After saying hello, use their name. It gives a personalized touch and the impression that this is not just a generic email you sent out to everyone.

Personalized customer service helps to alleviate customer churn. Every customer wants to feel valued, so show them that they matter.

Plus, it’s simple. All you need to do is this:

“Hi, (Customer’s Name)”

3. Thank Them For Reaching Out

Always be appreciative when a customer reaches out, even if they weren’t particularly friendly. Show them you value their feedback and are taking the issue at hand seriously.

“We hear you, and thanks so much for letting us know. We value your feedback, as it helps us to improve every day. Please leave this with me and I will get back to you with a solution shortly.”

4. Summarize The Issue

When you receive an email with a problem, you may want to offer a solution immediately. Sometimes, however, it is better to take a step back and restate, in your own words, what the issue seems to be. This will get you and your customers on the same page.

Something like:

“I’d be happy to help. Just wanted to make sure I understand the problem first. It sounds like (issue) is causing problems.”

5. Provide an Answer

Once you’ve determined the problem, it’s time to give a solution. Be sure to offer as complete of a solution as you can.

If your company has an online knowledge base, be sure to link there. Be proactive whenever possible. Show that you are not only handling the issue, but giving them a way to help themselves in the future. It makes for a complete customer service experience.

6. Thank Them Again/Keep The Line of Communication Open

After you’ve resolved their issue, thank them again for reaching out. You can never be too gracious when dealing with a customer and making them feel valued.

If your solution hasn’t resolved the issue, make sure that the customer has an open line of communication back to you.

“Thanks again for reaching out. If you have any other questions or need further clarification, please do not hesitate to contact us.”

Customer Service Email Tip 1: Communicate Post Sale

You don’t have to wait for customers to reach out to you with an issue to send them an email. There are several instances where you should be reaching out to ensure that they will keep coming back for more.

(Image Source)

Follow up emails are a perfect touchpoint for new customers who have just made their first purchase with your business.

Remember, closing a sale is not the end of the customer lifecycle. You also have to retain the customer.

When reaching out with a welcome email, make sure that you express gratitude to the customer for their patronage.

“Hi, (Customer’s Name)! Thank you so much for becoming a valued member of the (Company Name) family! We’re thrilled to have you here, and we hope that you’re enjoying (Product or Service).

Next, ask them if there’s anything else you can do to help. The customer may be experiencing issues with your product or service, and sometimes, instead of reaching out, they just find another company. Let them know you’re here to help.

“I wanted to ensure that you’re enjoying (Product or Service) and offer any assistance if you’re experiencing issues or have any lingering questions.”

It’s also a chance to give them an exclusive offer as a customer to drive future purchases.

“As a new valued member of the (Company Name) family, we wanted to welcome you with an exclusive offer to help you get the most out of your next purchase.”

Why should you do all of this?

For starters, it establishes a sense of brand loyalty in the customer by showing that you are attentive and that you care about their experience.

It’s an initiative like this that drives your retention rate, which is the backbone of profitability. You want to build long-term relationships with the people who are already buying from you.

Consider for a moment that it costs five times more to acquire a new customer than it does to retain an existing one. That makes customer retention efforts extremely worthwhile.

Gratitude helps to generate a repeat purchase. According to TD Bank, 77% of consumers appreciate a show of gratitude.

Customer Service Email Tip 2: Ask For Customer Feedback

You should routinely reach out to your existing customers to ask them for feedback. Asking for reviews and feedback works on more than one level.

Reviews can be posted on your website or product pages and go a long way towards establishing trust with prospective customers. They also help you gather information about what is and isn’t working within your company.

Polling your customers for feedback through email is a great way to solve problems you might not have known existed.

“Hi (Customer’s Name), As a valued (Company Name) member, we value your opinion. We wanted to take this opportunity to reach out and see if you’re having any issues, or would like to provide feedback that we can use to improve our service going forward.”

You want reviews on your site because it enhances social proof. By seeing the success stories of their peers, prospective customers will trust in the value of your service.

Reviews are almost always better than company messaging for customer acquisition, as prospects trust them more.

Reviews can also be turned into user-generated content to feature on product pages that will drive conversions.

(Image Source)

The addition of 50 or more reviews of a product has been shown to improve ecommerce conversion rates by 4.6%.

On top of that, 52% of worldwide shoppers believe that companies should take action on the feedback provided by their customers. Reaching out to your customers via email for feedback shows a willingness to do just that.

Put yourself in the customer’s shoes for a second. You’re having an issue with a product or service but instead of bringing it to the attention of the company, you just decide to look for a new one that fits better.

But then, the company reaches out to you instead. They ask for feedback in your court and you can easily tell them what’s going on.

The company acknowledges the problem and fixes it! Now, you can buy from them again, knowing that if there’s a problem, they’ll find a solution.

It’s an incredible way to build customer trust and create customer loyalty. You could even save sales that you didn’t even know were in jeopardy.

Customer Service Email Tip 3: Acknowledge Special Milestones

You should make sure that you’re reaching out to customers and letting them know that their lives outside of transactions matter to you.

Customers appreciate an acknowledgment of their special milestones. This marketing strategy can include things like birthdays, anniversaries, or even the anniversary of their first purchase with the company. Little moments like this can mean a lot to a customer.

Make sure that when you’re onboarding new customers, you gather as much information about them as possible.

Say you wanted to send them an email for their birthday. You can tag on a special promotion as a birthday gift.

“Happy Birthday, (Customer Name)! All of your friends here at (Company Name) want to wish you the happiest of birthdays and hope that it is an exceptional one! To celebrate, we wanted to offer you a special birthday gift. We’re giving you one free (Product Name), redeemable through the next month.”

Sephora, a makeup retailer, gives all of its customers a free product on their birthday as an exclusive marketing gift.

(Image Source)

The restaurant chain Denny’s once ran a marketing promotion where customers could get a free Grand Slam breakfast on their birthdays.

AMC movie theaters give free popcorn and soda to Stubs members for their birthday month as well.

You can also let your customers know about company milestones. Some companies, like New York Sports Clubs, have taken to special marketing promotions to commemorate the anniversary of their company.

“Hi, (Customer’s Name) We wanted to reach out to let you know that (Company Name) is celebrating a very special anniversary! It’s (Company Name’s) birthday this month, and we’re reaching out to all of our valued customers to offer them a personalized gift in commemoration of the occasion.”

Why should you do this?

Everyone likes to be acknowledged, and customers will be appreciative of the gift. It also is a sure-fire way to bring them back onto your site.

Initiatives like this can promote repeat sales, which is invaluable for your revenue.

In Conclusion

Customer service, both reactive and proactive, is the cornerstone of customer retention. By reaching out with a follow-up email, acknowledging special occasions, gathering feedback, or responding to customer issues, you can ensure that your company is serving its audience to the best of its ability, creating happy repeat buyers.

Those loyal customers will continue to stick around for years to come, confident that your company values their presence and appreciates their business.

How to Create Customer Service Emails That Drive Repeat Business was originally posted by Video And Blog Marketing

Things You Need to Know About Optimizing Core Web Vitals for SEO

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The Importance of Core Web Vitals to SEO_Cover Photo

User Experience is officially a ranking factor in 2021 and SEOs who are failing to optimize for it will be in a world of trouble come the next year. If you are well-versed in white hat SEO, you are also probably in the know about Google’s updates on their ranking signals.

Having the knowledge on how you can make your business rank in the most ethical way will be your best weapon towards growth after all. I wrote about the update on Core Web Vitals last month but as businesses continue their transition to digital, it is very important that we revisit these elements now and then.

Core Web Vitals are nothing new to webmasters but Google’s announcement of their plan to update the algorithm in accordance with these elements spurred on a heightened urgency to optimize for it. This is true for digital marketing experts, but for newbies who are scratching their heads wondering what Core Web Vitals are about, this brief guide is for you.

Measuring Performance with Core Web Vitals

Search_Page_Experience_Graphic

Page experience is important for SEO because it will help you stay valuable to your audience. How your site functions will affect a person’s perspective of your business. It’s pretty self-explanatory why users would prefer a site with great user experience over one that is a pain to navigate. Having a negative user experience will also set you up for disaster in the Search Engine Results Pages (SERPs) since it will harbor a bad reputation for your website.

You may not feel it as explicitly as a negative feedback published online, but word-of-mouth can hurt your reputation since friends or family usually recommends businesses to each other. With this in mind, how can you effectively measure your performance for the Core Web Vitals? Thankfully, there are a handful of tools that you can use to do just that.

PageSpeed Insights

good page speed insights

If you are looking for a quick way to diagnose your site’s performance for Core Web Vitals then PageSpeed Insights can help you. If you are not familiar with this tool, you can visit it here. User-experience metrics such as Lab Data that covers First Contentful Paint, Speed Index, Total Blocking Time, Cumulative Layout Shift, and Time to Interactive. All of these should be well-optimized to have an improved user experience. Given that the values are just estimates, this is especially useful if you don’t know where to start optimizing for Core Web Vital elements.

pagespeed insights score

PageSpeed Insights performance scores are calculated based on Google’s Lighthouse Scoring Calculator. You can choose Mobile or Desktop as your device type and see what areas you are thriving on or needs improvement.

Public Google BigQuery Project

The BigQuery Project is a heaven sent for those who would need to analyze big data sets in SQL Queries. This is what the dashboard looks like:

big query

How can you use it for Core Web Vital signals? According to Google, it works by consolidating user experience metrics by origins that are known by Google web crawlers. This tool hosts the user experience metrics from the Chrome User Experience Report.

Core Web Vital Scores

There are only three ways that your optimization for core web vitals can arrive at: Poor, Needs Improvement and Good. There are different factors affecting the scores and it varies for each web vital:

Largest Contentful Paint (LCP)

largest-contentful-paint

This is for the Largest Contentful Paint (LCP), which is what you need to focus on to know how well your page loads for the users who visit your site.The metric is there to help you see how long your large content renders on the page. The best score you can have for this is to have a rendering time below 2.5 seconds. Given that fact, you should not beat yourself up too much on achieving that figure, just make it fast enough to satisfy a user. Do not sacrifice usability in your pursuit to get a great score for these metrics.

First Input Delay (FID)

first-input-delay

Focusing on the First Input Delay (FID) means that you value how a user interacts with your page. User experience deals heavily with user engagement since this measures how your user can receive a response from your site. With FID, the areas you should pay attention to would be the clickability of the buttons, selecting sections from a drop-downmenu, or filling out forms on the site. There should also be great consideration for the actions that your users can perform on the site. Google looks at a score that is less than 100ms for this web vital and the best way to go about this is to carefully look at the code and measure the performance based on the areas you can optimize.

Cumulative Layout Shift (CLS)

cumulative-layout-shift

Google treats the Cumulative Layout Shift as a web vital that primarily deals with the visual aspects of a site, particularly on mobile view. This is the continuous position of the page no matter what fraction of the page moves during loading time. Any unstable element on your site will set you up for a lower Core Web Vital score. However, this does not include interactive features such as a layout change when a user clicks on a button.

Key Takeaway

Although the new ranking factor would not roll out until 2021, it would still be best if you optimize as early as now. What’s great is that you have an array of tools at your disposal for this purpose. All it takes is a visit to Google Search Console and you can see what area would need your attention. Scoring low for Core Web Vitals will be detrimental for your SEO since you need users to stay on your page. Not only are you guaranteed users into your site, you would know that it is valuable traffic that can help your online presence thrive as well.

What are the steps you’re taking to optimize for Core Web Vitals? Comment down below!

Things You Need to Know About Optimizing Core Web Vitals for SEO was originally posted by Video And Blog Marketing

Should you be Worried about Content Scrapers?

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Have you seen a website that posted the exact same article, from top to bottom, of a recent article you published? It can be infuriating knowing that other people are trying to benefit from the content that you worked hard on without your permission at all.

If you own a high-quality website that is getting a good amount of traffic, then you are most likely a victim of content scrapers. Content scraping is the illegal process of copying content from high-quality sources and publishes them as their own.

Content scraping is not something new and this is not uncommon either. In fact, I discover websites scraping from SEO Hacker from time to time. Check out this website that completely copied the guide I published a few weeks ago about Google News.

And if you scroll to the bottom of the webpage, you’ll see a line indicating the article was “originally” by Video and Marketing blog.

In this article, I will discuss if content scrapers have an effect on your SEO and what should you do about them.

How does Content Scraping Work?

The way people scrape content out of other websites varies. They can do it manually or they use software that automatically crawls websites for new content and creates a copy of it. If you come across a software that does this, I highly recommend staying away from it.

The goal of content scrapers varies as well. Some who scrape content may just want to increase the number of pages of their website. They would usually link back to your website and give you credit for publishing it and in some cases, they would use a canonical tag pointing to the same page on your website.

Other content scrapers would blatantly rip off your content. Similar to the example I showed earlier, they would take the credit for writing the article and confuse users. They would usually do it to get more ad revenue for their website.

Can Content Scrapers Hurt your Website?

Scraped content is black hat SEO and it is strictly against Google’s guidelines. Not only does it constitute copyright infringement in some cases but it is also duplicate content. If you are using scraped content on your website, then you are most likely going to be penalized or if not, is penalized already. 

Google does a great job of weeding out these websites. Most websites that rely on scraped content does not rank at all and does not receive any traffic. 

The good news is if people are scraping content from your website, you shouldn’t be worried at all. Google always rewards websites that publish original high-quality content. So in cases where people copy your content, you can be sure that Google is going to ignore those and give the rewards to you.

Google’s algorithm is able to identify who is the original publisher of an article even though it is unlinked or there is no mention of the original publisher in the copied article. That is why you shouldn’t worry that Google will penalize your website if many websites are scraping your content.

Should you Disavow Links from Content Scrapers?

As I’ve mentioned, some content scrapers would link to your website. Usually, they link to the website of origin just to avoid being penalized by Google. I’ve seen people ask around if they should disavow links from these websites since most, if not all, of them, are low-quality websites. 

In my opinion, disavowing links from these websites is not necessary because sometimes, it can help your website, even just for a little. You may consider disavowing links from content scrapers if there are red flags like bad anchor texts, extremely poor website quality, adult content, etc. I highly recommend reading this disavow guide I wrote to properly judge if you should disavow a link or not.

Key Takeaway

As long as you publish original content on your website, you could sleep better at night knowing that your website is not going to be affected by those that copy you. Think of Google as your personal guardian. They will protect you from these content scrapers.

Always avoid copying content from other websites, whether it’s one article, a paragraph, or a sentence illegally through content scraping. You can always cite other websites as part of an original article you are writing. And remember to properly cite them by linking to them and giving them credits.

Should you be Worried about Content Scrapers? was originally posted by Video And Blog Marketing

Entity Seeking Queries and Semantic Dependency Trees

Entity Seeking Queries and Semantic Dependency Trees

Queries for some searches may be entity seeking queries.

Someone may ask something like, “What is the hotel that looks like a sail.” That query may be looking for an entity that is the building, the Burj Al Arab Jumeirah.

Those entities may be identified in Semantic Dependency Trees, that answer the question in the query (example below)

Other queries are not entity seeking queries, and don’t look for answers about specific entities, such as “What is the weather today?” The answer to that query might respond to “The weather will be between 60-70 degrees Fahrenheit, and sunny today.”

Actions May Accompany Queries that Seek Entities

Google was granted a patent about answering entity seeking queries.

The process under the patent may perform particular actions for queries that seek one or more entities.

One action the system may perform involves:

  • Identifying one or more types of entities that a query is seeking
  • Determining whether the query is seeking one specific entity or potentially multiple entities

For example, the process may determine that a query of “What is the hotel that looks like a sail” is looking for a single entity that is a hotel.

In another example, the system may determine that a query “What restaurants nearby serve omelets” seeks potentially multiple entities that are restaurants.

An additional or alternative action the system may perform may include finding a most relevant entity or entities of the identified one or more types, and presenting what is identified to the user if sufficiently relevant to the query. For example, the system may identify that the Burj Al Arab Jumeirah is an entity that is a hotel and is sufficiently relevant to the terms “looks like a sail,” and, in response, audibly output synthesized speech of “Burj Al Arab Jumeirah.”

Additional Dialog about a Query to Concatenate an Entity Seeking Query

Yet another addition or alternative action may include initiating a dialog with the user for more details about the entities that are sought.

For example, the system may determine that a query is seeking a restaurant and there may be two entities that are restaurants are very relevant to the terms in the query and, in response, ask the searcher “Can you give me more details” and concatenate additional input from the user to the original query and re-execute the concatenated query.

Identifying SubQueries of Entity Seeking Queries

Another additional or alternative action may include identifying subqueries of a query which are entity-seeking, and using the above actions to answer the subquery, and then replacing the subqueries by their answers in the original query to obtain a partially resolved query which can be executed.

For example, the system may receive a query of “Call the hotel that looks like a sail,” determine that “the hotel that looks like a sail” is a subquery that seeks an entity, determine an answer to the subquery is “Burj Al Arab Jumeirah,” in response replace “the hotel that looks like a sail” in the query with “The Burj Al Arab Jumeirah” to obtain a partially resolved query of “Call the Burj Al Arab Jumeirah,” and then executes the partially resolved query.

Looking at Previous Queries

Another additional or alternative action may include identifying that a user is seeking entities and adapting how the system resolve queries accordingly.

For example, the system may determine that sixty percent of the previous five queries that a user searched for in the past two minutes sought entities and, in response, determine that a next query that a user provides is more likely an entity seeking query, and process the query accordingly.

An Advantage From Following this Process

An advantage may be more quickly resolving queries in a manner that satisfies a searcher.

For example, the system may be able to immediately provide an actual answer of “The Burj Al Arab Jumeirah” for the query “What hotel looks like a sail” where another system may instead provide a response of “no results found” or provide a response that is a search result listing for the query.

Entity Seeking Queries and Semantic Dependency Trees

Entity Seeking Queries
Another advantage may be that the process may be able to more efficiently identify an entity sought by a query. For example, it may determine an entity seeking query is looking for an entity of the type “hotel” and, in response, limit a search to only entities that are hotels instead of searching across multiple entities including entities that are not hotels.

Entities in Semantic Dependency Trees

Semantic Dependency Tree

This is an interesting approach to an entity seeking queries. Determining an entity type that may correspond to an entity sought by a query based on a term represented by a root of a dependency tree includes:

Determining the term represented by the root of the dependency tree represents a type of entity.

Determining an entity type that corresponds to an entity sought by the query based on a term represented by a root of the dependency tree includes:

Identifying a node in the tree that represents a term that represents a type of entity
Includes a direct child that represents a term that indicates an action to perform.
In response to determining that the root represents a term that represents and type of entity and includes a direct child that represents a term that indicates an action, identifying the root.

In some implementations, identifying a particular entity based on both the entity type and relevance of the entity to the terms in the query includes:

  • Determining a relevance threshold based on the entity type
  • Determining a relevance score of the particular entity based on the query satisfies the relevance threshold
  • In response to determining the relevance score of the particular entity based on the query satisfies the relevance threshold, identifying the particular entity

This patent on Entity Seeking Queries can be found at:

Answering Entity-Seeking Queries
Inventors: Mugurel Ionut Andreica, Tatsiana Sakhar, Behshad Behzadi, Marcin M. Nowak-Przygodzki, and Adrian-Marius Dumitran
US Patent Application: 20190370326
Published: December 5, 2019
Filed: May 29, 2018

Abstract

In some implementations, a query that includes a sequence of terms is obtained, the query is mapped, based on the sequence of the terms, to a dependency tree that represents dependencies among the terms in the query, an entity type that corresponds to an entity sought by the query is determined based on a term represented by a root of the dependency tree, a particular entity is identified based on both the entity type and relevance of the entity to the terms in the query, and a response to the query is provided based on the particular entity that is identified.

Mapping a Query to a Semantic Dependency Tree

A process that handles entity seeking queries

This process includes:

  • A query mapper that maps a query including a sequence of terms to a semantic dependency tree
  • An entity type identifier that may determine an entity type based on the semantic dependency tree
  • An entity identifier that may receive the query
  • The entity type that is determined
  • Data from various data stores and identify an entity
  • Subquery resolver that may partially resolve the query based on the entity that is identified
  • Query responder that may provide a response to the query

An Example Semantic Dependency Tree

This is how a Semantic Dependency Tree may be constructed:

  1. A semantic dependency tree for a query may be a graph that includes nodes
  2. Each node represents one or more terms in a query
  3. Directed edges originating from a first node and ending at a second node may indicate that the one or more terms represented by the first node are modified by the one or more terms represented by the second node
  4. A node at which an edge ends may be considered a child of a node from which the edge originates
  5. A root of a semantic dependency tree may be a node representing one or more terms that do not modify other terms in a query and are modified by other terms in the query
  6. A semantic dependency tree may only include a single root

An Entity Type Identifier

An entity type identifier may determine an entity type that corresponds to an entity sought by the query based on a term represented by a root of the semantic dependency tree.

For example, the entity type identifier may determine an entity type of “Chinese restaurant” that corresponds to an sought by the query “Call the Chinese restaurant on Piccadilly Street 15” based on the term “Chinese restaurant” represented by the root of the semantic dependency tree.

In another example, the entity type identifier may determine an entity type of “song” for the query “play the theme song from the Titanic” based on the term “play” represented by the root of the semantic dependency tree for the query not representing an entity type and determining that the root has a child that represents the terms “the theme song” which does represent an entity type of “song.”

Entities from a Location History of a Searcher

The entity identifier may extract all the entities from a mobile location history of a searcher which have a type identified by the entity type identifier, such as hotels, restaurants, universities, etc. along with extracting features associated to each such entity such as the time intervals when the user visited the entity or was near the entity, or how often each entity was visited or the user was near the entity.

Entities from a Past Interaction History of a Searcher

In addition to that location history, the entity identifier may extract all the entities that the user was interested in their past interactions that have a type identified by the entity type identifier, such as:

  • Movies that the user watched
  • Songs that the user listened to
  • Restaurants that the user looked up and showed interest in or booked
  • Hotels that the user booked
  • Etc.

Confidence in Relevance for Entity Seeing Queries

The patent also tells us that the entity identify may obtain a relevance score for each entity that reflects a confidence that the entity is sought to be the query.

The relevance score may be determined based on one or more of the features extracted from the data stores that led to the set of entities being identified, the additional features extracted for each entity in the set of entities, and the features extracted from the query.


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Entity Seeking Queries and Semantic Dependency Trees was originally posted by Video And Blog Marketing

Entity Seeking Queries and Semantic Dependency Trees

Entity Seeking Queries and Semantic Dependency Trees

Queries for some searches may be entity seeking queries.

Someone may ask something like, “What is the hotel that looks like a sail.” That query may be looking for an entity that is the building, the Burj Al Arab Jumeirah.

Those entities may be identified in Semantic Dependency Trees, that answer the question in the query (example below)

Other queries are not entity seeking queries, and don’t look for answers about specific entities, such as “What is the weather today?” The answer to that query might respond to “The weather will be between 60-70 degrees Fahrenheit, and sunny today.”

Actions May Accompany Queries that Seek Entities

Google was granted a patent about answering entity seeking queries.

The process under the patent may perform particular actions for queries that seek one or more entities.

One action the system may perform involves:

  • Identifying one or more types of entities that a query is seeking
  • Determining whether the query is seeking one specific entity or potentially multiple entities

For example, the process may determine that a query of “What is the hotel that looks like a sail” is looking for a single entity that is a hotel.

In another example, the system may determine that a query “What restaurants nearby serve omelets” seeks potentially multiple entities that are restaurants.

An additional or alternative action the system may perform may include finding a most relevant entity or entities of the identified one or more types, and presenting what is identified to the user if sufficiently relevant to the query. For example, the system may identify that the Burj Al Arab Jumeirah is an entity that is a hotel and is sufficiently relevant to the terms “looks like a sail,” and, in response, audibly output synthesized speech of “Burj Al Arab Jumeirah.”

Additional Dialog about a Query to Concatenate an Entity Seeking Query

Yet another addition or alternative action may include initiating a dialog with the user for more details about the entities that are sought.

For example, the system may determine that a query is seeking a restaurant and there may be two entities that are restaurants are very relevant to the terms in the query and, in response, ask the searcher “Can you give me more details” and concatenate additional input from the user to the original query and re-execute the concatenated query.

Identifying SubQueries of Entity Seeking Queries

Another additional or alternative action may include identifying subqueries of a query which are entity-seeking, and using the above actions to answer the subquery, and then replacing the subqueries by their answers in the original query to obtain a partially resolved query which can be executed.

For example, the system may receive a query of “Call the hotel that looks like a sail,” determine that “the hotel that looks like a sail” is a subquery that seeks an entity, determine an answer to the subquery is “Burj Al Arab Jumeirah,” in response replace “the hotel that looks like a sail” in the query with “The Burj Al Arab Jumeirah” to obtain a partially resolved query of “Call the Burj Al Arab Jumeirah,” and then executes the partially resolved query.

Looking at Previous Queries

Another additional or alternative action may include identifying that a user is seeking entities and adapting how the system resolve queries accordingly.

For example, the system may determine that sixty percent of the previous five queries that a user searched for in the past two minutes sought entities and, in response, determine that a next query that a user provides is more likely an entity seeking query, and process the query accordingly.

An Advantage From Following this Process

An advantage may be more quickly resolving queries in a manner that satisfies a searcher.

For example, the system may be able to immediately provide an actual answer of “The Burj Al Arab Jumeirah” for the query “What hotel looks like a sail” where another system may instead provide a response of “no results found” or provide a response that is a search result listing for the query.

Entity Seeking Queries and Semantic Dependency Trees

Entity Seeking Queries
Another advantage may be that the process may be able to more efficiently identify an entity sought by a query. For example, it may determine an entity seeking query is looking for an entity of the type “hotel” and, in response, limit a search to only entities that are hotels instead of searching across multiple entities including entities that are not hotels.

Entities in Semantic Dependency Trees

Semantic Dependency Tree

This is an interesting approach to an entity seeking queries. Determining an entity type that may correspond to an entity sought by a query based on a term represented by a root of a dependency tree includes:

Determining the term represented by the root of the dependency tree represents a type of entity.

Determining an entity type that corresponds to an entity sought by the query based on a term represented by a root of the dependency tree includes:

Identifying a node in the tree that represents a term that represents a type of entity
Includes a direct child that represents a term that indicates an action to perform.
In response to determining that the root represents a term that represents and type of entity and includes a direct child that represents a term that indicates an action, identifying the root.

In some implementations, identifying a particular entity based on both the entity type and relevance of the entity to the terms in the query includes:

  • Determining a relevance threshold based on the entity type
  • Determining a relevance score of the particular entity based on the query satisfies the relevance threshold
  • In response to determining the relevance score of the particular entity based on the query satisfies the relevance threshold, identifying the particular entity

This patent on Entity Seeking Queries can be found at:

Answering Entity-Seeking Queries
Inventors: Mugurel Ionut Andreica, Tatsiana Sakhar, Behshad Behzadi, Marcin M. Nowak-Przygodzki, and Adrian-Marius Dumitran
US Patent Application: 20190370326
Published: December 5, 2019
Filed: May 29, 2018

Abstract

In some implementations, a query that includes a sequence of terms is obtained, the query is mapped, based on the sequence of the terms, to a dependency tree that represents dependencies among the terms in the query, an entity type that corresponds to an entity sought by the query is determined based on a term represented by a root of the dependency tree, a particular entity is identified based on both the entity type and relevance of the entity to the terms in the query, and a response to the query is provided based on the particular entity that is identified.

Mapping a Query to a Semantic Dependency Tree

A process that handles entity seeking queries

This process includes:

  • A query mapper that maps a query including a sequence of terms to a semantic dependency tree
  • An entity type identifier that may determine an entity type based on the semantic dependency tree
  • An entity identifier that may receive the query
  • The entity type that is determined
  • Data from various data stores and identify an entity
  • Subquery resolver that may partially resolve the query based on the entity that is identified
  • Query responder that may provide a response to the query

An Example Semantic Dependency Tree

This is how a Semantic Dependency Tree may be constructed:

  1. A semantic dependency tree for a query may be a graph that includes nodes
  2. Each node represents one or more terms in a query
  3. Directed edges originating from a first node and ending at a second node may indicate that the one or more terms represented by the first node are modified by the one or more terms represented by the second node
  4. A node at which an edge ends may be considered a child of a node from which the edge originates
  5. A root of a semantic dependency tree may be a node representing one or more terms that do not modify other terms in a query and are modified by other terms in the query
  6. A semantic dependency tree may only include a single root

An Entity Type Identifier

An entity type identifier may determine an entity type that corresponds to an entity sought by the query based on a term represented by a root of the semantic dependency tree.

For example, the entity type identifier may determine an entity type of “Chinese restaurant” that corresponds to an sought by the query “Call the Chinese restaurant on Piccadilly Street 15” based on the term “Chinese restaurant” represented by the root of the semantic dependency tree.

In another example, the entity type identifier may determine an entity type of “song” for the query “play the theme song from the Titanic” based on the term “play” represented by the root of the semantic dependency tree for the query not representing an entity type and determining that the root has a child that represents the terms “the theme song” which does represent an entity type of “song.”

Entities from a Location History of a Searcher

The entity identifier may extract all the entities from a mobile location history of a searcher which have a type identified by the entity type identifier, such as hotels, restaurants, universities, etc. along with extracting features associated to each such entity such as the time intervals when the user visited the entity or was near the entity, or how often each entity was visited or the user was near the entity.

Entities from a Past Interaction History of a Searcher

In addition to that location history, the entity identifier may extract all the entities that the user was interested in their past interactions that have a type identified by the entity type identifier, such as:

  • Movies that the user watched
  • Songs that the user listened to
  • Restaurants that the user looked up and showed interest in or booked
  • Hotels that the user booked
  • Etc.

Confidence in Relevance for Entity Seeing Queries

The patent also tells us that the entity identify may obtain a relevance score for each entity that reflects a confidence that the entity is sought to be the query.

The relevance score may be determined based on one or more of the features extracted from the data stores that led to the set of entities being identified, the additional features extracted for each entity in the set of entities, and the features extracted from the query.


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Entity Seeking Queries and Semantic Dependency Trees was originally posted by Video And Blog Marketing

SERanking Introduces a Big Update for SEOs

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<!–a>SERanking Introduces a Big Update to Their Software_Cover Photo

<p< span=””>>One of our partners included in our toolbox, </p<>SE Ranking has recently made a big change to their interface. I would like to write about it since the new features will be beneficial to new and seasoned SEOs alike. Webmasters who are looking for an all-in-one SEO tool, here’s your chance to try out the improved tool for your optimization efforts.

 

When I do my audits or random checks on websites, I tend to do it manually because software can only do so much as compared to the trained eye. With over a decade of SEO experience on my belt, I am wary of the tools I use and that is why I only have a handful of partners under our toolbox. Since SE Ranking has proven its powerful performance in helping my team reach their KPIs, I will take this opportunity to share it with you fellow digital marketers.

Comparison of Old and New Dashboard<!–h2>

The SE Ranking team introduced this as a huge update which is why I took the liberty to check it out myself. I am surprised to see a cleaner interface and easier navigation in comparison to its old version.

before seranking
Thi
s is the old version of the tool – as you can see, the dashboard is pretty straightforward. You will see the rankings report immediately which is a simple way to track the performance of the sites you optimize. However, the new interface makes the old one look cluttered as it has new features that can make for easy navigation of the tool.

after seranking
The new and improved SE Ranking has a more crisp design as they have also overhauled their buttons and navigation bars. Although the new interface is simpler, this is more useful especially if you are a beginner and you want to explore opportunities to optimize your site.

It is also cleaner with a sidebar on the main dashboard which you can help you easily navigate the tools that you need to optimize your site. The sidebar organizes features like:

      • Rankings report

    <li< span=””>>Competitor Analys

      • is

      • Analytics and Traffic
      • Website Auditsi>
      • Marketing Plan
      • Backlink Monitoring</li>
      • Social Media Metrics

    </li<>

    features new

    <p>The adaptive data display makes it possible to see all of the SEO metrics that you would need to optimize as well as your performance on the SERP. With the new interface, the old platform that we have come to love is now faster and more friendly in terms of User Experience. Having a modern design is one of the more noticeable changes to the SE Ranking platform since the old version kind of looks outdated with its plain and straightforward design. The main dashboard retained its simple summary of all your projects which will make it easier for you to go back to the rankings data once in a while.

     

    Update on the Rankings Report

    new keywords.png”>

    <p>No matter how many projects you have on your platform, the new interface can make it load faster. What I find great about the new update is the new dynamics tab in the keyword platform. It is an all-encompassing feature that will help you understand how your rankings dropped or improved over time. The new look of the rankings module with the data on the top half of the page makes it easier to see performance metrics without looking at each one of the keywords. This is what the graph looks like:

     

    data top half

    Competitor Research Integration on the Main Platform

    The new update highlights the competitor analysis tool, which was initially part of the navigation bar but not the main platform. The old design requires you to take a few steps before you access the competitor analysis tool but now you can switch between the tools with one click. Since the SE Ranking team has declared that they have improved every single line of code in the platform, the tool loads easier and quicker now.

    new competitor research

    The SEO/PPC Competitor Research proves to be beneficial for your SEO efforts because you can see who you are up against. You will be well-equipped with this knowledge so you would know what areas you would need to improve and which are the areas that you can already bank on.

    Keyword Research Module Improvements

    <p>As a new separate tool in the platform, it is now equipped with fresh elements. Keyword research is essential to foundational strategies for SEO so having the right tool to help you will help you drive traffic and leads to your site. With a concise keyword research tool, you can see keywords that have the most potential to empower your online visibility. Getting your pages ranked among the top 10 in Google’s search engine pages can be possible with a reliable tool such as SE Ranking. With their update, the Keyword Efficiency index was replaced with the Keyword Difficulty score which can help you assess how the value of a keyword can help your growth.

     

    Quick notes on the SE Ranking Update>

    This is not the last update that the SE Ranking platform will have but considering that it is a huge revamp that is very different from their initial design, this should be news to SEOs. I expect that the tool will give us more in the future. The tool’s big update exhibits what an all-around SEO tool should be, clean and with no-frills features. This eliminates distractions and unnecessary attention to areas of your website which cannot help you rank at all in the long run. Check out SE Ranking’s new update and comment down below if you like it just as much as I do.

    </p

SERanking Introduces a Big Update for SEOs was originally posted by Video And Blog Marketing

Entity Seeking Queries and Semantic Dependency Trees

Entity Seeking Queries and Semantic Dependency Trees

Queries for some searches may be looking for one or more entities.

Someone may ask something like, “What is the hotel that looks like a sail.” That query may be looking for an entity that is the building, the Burj Al Arab Jumeirah.

Those entities may be identified in Semantic Dependency Trees, that answer the question in the query (example below)

Other queries may not look for answers about specific entities, such as “What is the weather today?” The answer to that query might respond to “The weather will be between 60-70.degrees Fahrenheit, and sunny today.”

Actions May Accompany Queries that Seek Entities

Google was granted a patent about answering entity seeking queries.

The process under the patent may perform particular actions for queries that seek one or more entities.

One action the system may perform involves:

  • Identifying one or more types of entities that a query is seeking
  • Determining whether the query is seeking one specific entity or potentially multiple entities

For example, the process may determine that a query of “What is the hotel that looks like a sail” is looking for a single entity that is a hotel.

In another example, the system may determine that a query “What restaurants nearby serve omelets” seeks potentially multiple entities that are restaurants.

An additional or alternative action the system may perform may include finding a most relevant entity or entities of the identified one or more types, and presenting what is identified to the user if sufficiently relevant to the query. For example, the system may identify that the Burj Al Arab Jumeirah is an entity that is a hotel and is sufficiently relevant to the terms “looks like a sail,” and, in response, audibly output synthesized speech of “Burj Al Arab Jumeirah.”

Additional Dialog about a Query to Concatenate an Entity Seeking Query

Yet another addition or alternative action may include initiating a dialog with the user for more details about the entities that are sought.

For example, the system may determine that a query is seeking a restaurant and there may be two entities that are restaurants are very relevant to the terms in the query and, in response, ask the searcher “Can you give me more details” and concatenate additional input from the user to the original query and re-execute the concatenated query.

Identifying SubQueries of Entity Seeking Queries

Another additional or alternative action may include identifying subqueries of a query which are entity-seeking, and using the above actions to answer the subquery, and then replacing the subqueries by their answers in the original query to obtain a partially resolved query which can be executed.

For example, the system may receive a query of “Call the hotel that looks like a sail,” determine that “the hotel that looks like a sail” is a subquery that seeks an entity, determine an answer to the subquery is “Burj Al Arab Jumeirah,” in response replace “the hotel that looks like a sail” in the query with “The Burj Al Arab Jumeirah” to obtain a partially resolved query of “Call the Burj Al Arab Jumeirah,” and then executes the partially resolved query.

Looking at Previous Queries

Another additional or alternative action may include identifying that a user is seeking entities and adapting how the system resolve queries accordingly.

For example, the system may determine that sixty percent of the previous five queries that a user searched for in the past two minutes sought entities and, in response, determine that a next query that a user provides is more likely an entity seeking query, and process the query accordingly.

An Advantage From Following this Process

An advantage may be more quickly resolving queries in a manner that satisfies a searcher.

For example, the system may be able to immediately provide an actual answer of “The Burj Al Arab Jumeirah” for the query “What hotel looks like a sail” where another system may instead provide a response of “no results found” or provide a response that is a search result listing for the query.

Entity Seeking Queries and Semantic Dependency Trees

Entity Seeking Queries
Another advantage may be that the process may be able to more efficiently identify an entity sought by a query. For example, it may determine an entity seeking query is looking for an entity of the type “hotel” and, in response, limit a search to only entities that are hotels instead of searching across multiple entities including entities that are not hotels.

Entities in Semantic Dependency Trees

Semantic Dependency Tree

This is an interesting approach to an entity seeking queries. Determining an entity type that may correspond to an entity sought by a query based on a term represented by a root of a dependency tree includes:

Determining the term represented by the root of the dependency tree represents a type of entity.

Determining an entity type that corresponds to an entity sought by the query based on a term represented by a root of the dependency tree includes:

Identifying a node in the tree that represents a term that represents a type of entity
Includes a direct child that represents a term that indicates an action to perform.
In response to determining that the root represents a term that represents and type of entity and includes a direct child that represents a term that indicates an action, identifying the root.

In some implementations, identifying a particular entity based on both the entity type and relevance of the entity to the terms in the query includes:

  • Determining a relevance threshold based on the entity type
  • Determining a relevance score of the particular entity based on the query satisfies the relevance threshold
  • In response to determining the relevance score of the particular entity based on the query satisfies the relevance threshold, identifying the particular entity

This patent on Entity Seeking Queries can be found at:

Answering Entity-Seeking Queries
Inventors: Mugurel Ionut Andreica, Tatsiana Sakhar, Behshad Behzadi, Marcin M. Nowak-Przygodzki, and Adrian-Marius Dumitran
US Patent Application: 20190370326
Published: December 5, 2019
Filed: May 29, 2018

Abstract

In some implementations, a query that includes a sequence of terms is obtained, the query is mapped, based on the sequence of the terms, to a dependency tree that represents dependencies among the terms in the query, an entity type that corresponds to an entity sought by the query is determined based on a term represented by a root of the dependency tree, a particular entity is identified based on both the entity type and relevance of the entity to the terms in the query, and a response to the query is provided based on the particular entity that is identified.

Mapping a Query to a Semantic Dependency Tree

A process that handles entity seeking queries

This process includes:

  • A query mapper that maps a query including a sequence of terms to a semantic dependency tree
  • An entity type identifier that may determine an entity type based on the semantic dependency tree
  • An entity identifier that may receive the query
  • The entity type that is determined
  • Data from various data stores and identify an entity
  • Subquery resolver that may partially resolve the query based on the entity that is identified
  • Query responder that may provide a response to the query

An Example Semantic Dependency Tree

This is how a Semantic Dependency Tree may be constructed:

  1. A semantic dependency tree for a query may be a graph that includes nodes
  2. Each node represents one or more terms in a query
  3. Directed edges originating from a first node and ending at a second node may indicate that the one or more terms represented by the first node are modified by the one or more terms represented by the second node
  4. A node at which an edge ends may be considered a child of a node from which the edge originates
  5. A root of a semantic dependency tree may be a node representing one or more terms that do not modify other terms in a query and are modified by other terms in the query
  6. A semantic dependency tree may only include a single root

An Entity Type Identifier

An entity type identifier may determine an entity type that corresponds to an entity sought by the query based on a term represented by a root of the semantic dependency tree.

For example, the entity type identifier may determine an entity type of “Chinese restaurant” that corresponds to an sought by the query “Call the Chinese restaurant on Piccadilly Street 15” based on the term “Chinese restaurant” represented by the root of the semantic dependency tree.

In another example, the entity type identifier may determine an entity type of “song” for the query “play the theme song from the Titanic” based on the term “play” represented by the root of the semantic dependency tree for the query not representing an entity type and determining that the root has a child that represents the terms “the theme song” which does represent an entity type of “song.”

Entities from a Location History of a Searcher

The entity identifier may extract all the entities from a mobile location history of a searcher which have a type identified by the entity type identifier, such as hotels, restaurants, universities, etc. along with extracting features associated to each such entity such as the time intervals when the user visited the entity or was near the entity, or how often each entity was visited or the user was near the entity.

Entities from a Past Interaction History of a Searcher

In addition to that location history, the entity identifier may extract all the entities that the user was interested in their past interactions that have a type identified by the entity type identifier, such as:

  • Movies that the user watched
  • Songs that the user listened to
  • Restaurants that the user looked up and showed interest in or booked
  • Hotels that the user booked
  • Etc.

Confidence in Relevance for Entity Seeing Queries

The patent also tells us that the entity identify may obtain a relevance score for each entity that reflects a confidence that the entity is sought to be the query.

The relevance score may be determined based on one or more of the features extracted from the data stores that led to the set of entities being identified, the additional features extracted for each entity in the set of entities, and the features extracted from the query.


Copyright © 2020 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately.
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Entity Seeking Queries and Semantic Dependency Trees was originally posted by Video And Blog Marketing

Google’s Rich Results Test Tool is Now Out of Beta

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In the Official Google Webmaster Central blog, Google announced that the Rich Results Test tool is now officially out of beta and is now supporting all of Google’s rich results feature.

Google also announced that the Structured Data Testing tool is going to be deprecated. It is still available for a short time but there is no specific date yet on when Google will officially remove it.

Moving forward, Google urges webmasters to use the Rich Results Test tool to validate all structured data markups. Here’s what you need to know about the Rich Results Test tool.

How does the Rich Results Test Work?

Rich results (formerly known as rich card and rich snippets) are special types of search results that are different from the regular blue links in Google. It uses structured data to identify which type of rich results a page is eligible for.

In the Rich Results Test tool, there are two options to validate; either via the specific URL of the page you want to check or a code snippet. There’s also an option to select a user-agent the tool will use but right now, only Google Smartphone bot is available.

Once the tool is done analyzing the page, it will show the types of rich results a page or code is valid for. It will also identify any errors that may hinder your webpage from appearing in the rich results. You could also click on “Preview Results” and it will show how your page will look as a Rich Result.

Here are the types of rich results that are available in the Google search results right now:

  • Article
  • Book
  • Breadcrumb
  • Carousel
  • Course
  • Critic Review
  • Dataset
  • Employer Aggregate Rating
  • Event
  • Fact Check
  • FAQ
  • How-to
  • Image License Metadata
  • Job Posting
  • Job Training
  • Local Business
  • Logo
  • Movie
  • Estimated Salary
  • Podcast
  • Product
  • Q&A
  • Recipe
  • Review Snippet
  • Sitelink Searchbox
  • Software App
  • Speakable
  • Subscription and Paywalled Content
  • Video

If you want to learn more about how each type of rich result looks and the proper structured data markup to be eligible for them, you could check out Google’s Search Gallery.

Reactions from SEOs

In the official Google Webmasters Twitter account announcement, many SEOs were disappointed about the tool and the planned depreciation of the structured data testing tool.

As pointed out by Barry Adams and Digitaleer, the Structured Data Testing tool can validate any type of structured data markup, not only those that are valid for rich results. In my opinion, the structured data testing tool was a great tool on its own because I use markups that are not included yet in the rich results list.

The Structured Data Testing tool was also great for debugging. SEOs have noticed that compared to it, the Rich Results Test tool would usually vague error messages.

It seems like the release did not go smoothly as Google planned but I do believe that they will add more rich results types and make it available in the tool. And with the deprecation of the Structured Data Testing Tool, I do hope that they move the feature of being able to validate all types of structured data markup in the Rich Results Test.

Key Takeaway

Since rich results are visually appealing to users, they tend to get a large chunk of traffic from the search results similar to featured snippets. That is why it is important that you check the structured data markups of your website and make sure they are eligible for rich results.

Prior to the release from beta, the rich results test tool can only validate a handful of structured data markup but it is great news that it can now validate all rich results types. I highly recommend that you make use of this tool and grab the opportunity to rank for rich results.

 

Google’s Rich Results Test Tool is Now Out of Beta was originally posted by Video And Blog Marketing

Website Rankings Drop: Identifying the Reasons Why

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Website Rankings Drop Identifying the Reason Why_Cover Photo

It’s a common occurrence for SEOs and webmasters that track their rankings to experience sudden website ranking drops. Since Google’s algorithm is continuously updating, SERP volatility is common and affects hundreds or even thousands of websites in different industries. The problem with undiagnosed ranking drops is the decrease in your website’s search visibility. This could lead to less traffic, and for business websites, this leads to fewer conversions and profit. Identifying the cause of the rankings drop is tantamount to fixing it and, hopefully, recovering your rankings. So, how do you identify the cause of your website’s rankings drop?

Knowing Where to Look

The first step to identifying the cause of your rankings drop is to identify the variables that changed. This is because a drop in rankings won’t happen for no reason. There will always be a reason for changes in rankings (positive and negative) and this usually happens due to a change in a factor/s that affects your page’s rankings. 

This may sound easy but it’s not. There are numerous factors that affect a page’s rankings, some we can identify and some are still a mystery. That is why determining the exact factor why your rankings dropped is easier said than done. 

But here’s what you should do: Limit the scope of your search. Since you’ll just tire yourself out investigating factors that no one knows about, limit your scope to factors that YOU have control over. So, once you have limited your scope of search, this is when you’ll investigate deeper. 

The way we do it in SEO Hacker is to identify changes in 3 major places that contain a variety of factors that can affect our rankings. Then, we’ll dive deeper and deeper until we find the changes that COULD contribute to the website rankings drop and we either change them back or improve them further than they initially were. So, where exactly are these 3 major places?

INSIDE Your Website

Since we need to limit the scope of our search to things we have control over, it makes sense that we start with the page/website immediately. You have to first identify if the website is experiencing ranking drops in multiple keywords or if you only experience a drop for a specific keyword that a single page is ranking for. Take a look at this example:

Page ranking drop screenshot

The rankings drop happened to a single page that has been consistently ranking positively for numerous months. We automatically limited the scope of our search to the page itself. After determining that technical factors such as title tag, h1, etc. have been changed, we took it a step further and changed it for the better (not revert it back to the original). After a few days waiting for it to be recrawled, It ranked higher than it has ranked for the past few months. 

Since we limited our scope, easily found out the changes made and fixed them immediately, we didn’t waste our energy and time checking other factors that would have turned out to be unrelated to the drop. So, we highly suggest that you start INSIDE your website.

But what happens if nothing changed inside the page or website? Then, you check out other factors outside your website. 

OUTSIDE Your Website

The scope for this is limited to your search competitors overtaking you on the results page. This usually happens when they update their page to serve better intent, contain more information and become more relevant and useful for the users, or an overall upgrade of their website’s technical factors. Here’s an example:

page ranking drop outside the website screenshot

This happened recently and we’re confident that we did not change anything on the page that’s ranking for this specific keyword. So, we started our diagnosis in the search results of the page and that’s where we saw it. Competitors updated their pages with new content, a new design, or a fully revamped one. All of these changes enabled them to overtake our page in the search results which resulted in us having a lower ranking than before. 

So, it’s important for you as an SEO or webmaster to take note of the competitors you have for a specific keyword. Taking note of their content, their page design, and every other factor that they could change will enable you to know which of them they changed. This allows you to quickly adapt and regain your rankings as soon as possible.

Google Algorithm Update

We continuously report confirmed Google algorithm updates and its details to help our readers be able to understand why they experienced a change in rankings immediately. It is well-known in the industry that an algorithm update causes ranking volatility in the search results. A massive drop in rankings usually means that you’re doing something wrong and Google is penalizing you for that. 

Sticking to proven white-hat strategies and having an in-depth understanding of Google’s search algorithm will enable you to avoid penalties every time Google updates their algorithms since what you’re doing is in-line with what they want you to do. 

Key Takeaway

Identifying the reason behind your website or page’s rankings drop is important to achieving search success. Knowing where to pour your efforts in and properly invest the time and energy to investigate them enables you to grow as an SEO and webmaster.

Problem-solving is a necessity in the SEO industry since it continuously changes and gives us no choice but to adapt our strategies to reach success, that’s hopefully, for the long-term. How do you investigate whenever your website’s ranking drops? Let me know in the comments below!

Website Rankings Drop: Identifying the Reasons Why was originally posted by Video And Blog Marketing