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A Well-Formed Query Helps a Search Engine Understand User Intent in the Query

June 9, 2020 No Comments

A Well-Formed Query Helps a Search Engine understand User Intent Behind the Query

To start this post, I wanted to include a couple of whitepapers that include authors from Google. The authors of the first paper are the inventors of a patent application that was just published on April 28, 2020, and it is very good seeing a white paper from the inventors of a recent patent published by Google. Both papers are worth reading to get a sense of how Google is trying to rewrite queries into “Well-Formed Natural Language Questions.

August 28, 2018 – Identifying Well-formed Natural Language Questions

The abstract for that paper:

Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well-formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.

The paper provides examples of well-formed queries and ill-formed queries:

Examples of Well forned and non wll formed queries

November 21, 2019 – How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions

The abstract for that paper:

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting (MQR) dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2%in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.

examples of ill-formed and well-formed questions

The patent application I am writing about was filed on January 18, 2019, which puts it around halfway between those two whitepapers, and both of them are recommended to get a good sense of the topic if you are interested in featured snippets, people also ask questions, and queries that Google tries to respond to. The Second Whitepaper refers to the first one, and tells us how it is trying to improve upon it:

Faruqui and Das (2018) introduced the task of identifying well-formed natural language questions. In this paper,we take a step further to investigate methods to rewrite ill-formed questions into well-formed ones without changing their semantics. We create a multi-domain question rewriting dataset (MQR) from human contributed StackExchange question edit histories.

Rewriting Ill-Formed Search Queries into Well-Formed Queries

Interestingly, the patent is also about rewriting search Queries.

It starts by telling us that “Rules-based rewrites of search queries have been utilized in query processing components of search systems.”

Sometimes this happens by removing certain stop-words from queries, such as “the”, “a”, etc.

After Rewriting a Query

Once a query is rewritten, it many be “submitted to the search system and search results returned that are responsive to the rewritten query.”

The patent also tells us about “people also search for X” queries (first patent I have seen them mentioned in.)

We are told that these similar queries are used to recommend additional queries that are related to a submitted query (e.g., “people also search for X”).

These “similar queries to a given query are often determined by navigational clustering.”

As an example, we are told that for the query “funny cat pictures”, a similar query of “funny cat pictures with captions” may be determined because that similar query is frequently submitted by searchers following submission of the query “funny cat pictures”.

Determining if a Query is a Well Formed Query

The patent tells us about a process that can be used to determine if a natural language search query is well-formed, and if it is not, to use a trained canonicalization model to create a well-formed variant of that natural language search query.

First, we are given a definition of “Well-formedness” We are told that it is “an indication of how well a word, a phrase, and/or another additional linguistic element (s) conform to the grammar rules of a particular language.”

These are three steps to tell whether something is a well-formed query. It is:

  • Grammatically correct
  • Does not contain spelling errors
  • Asks an explicit question

The first paper from the authors of this patent tells us the following about queries:

The lack of regularity in the structure of queries makes it difficult to train models that can optimally process the query to extract information that can help understand the user intent behind the query.

That translates to the most important takeaway for this post:

A Well-Formed Query is structured in a way that allows a search engine to understand the user intent behind the query

The patent gives us an example:

“What are directions to Hypothetical Café?” is an example of a well-formed version of the natural language query “Hypothetical Café directions”.

How the Classification Model Works

It also tells us that the purpose behind the process in the patent is to determine whether a query is well-formed using a trained classification model and/or a well-formed variant of a query and if that well-formed version can be generated using a trained canonicalization model.

It can create that model by using features of the search query as input to the classification model and deciding whether the search query is well-formed.

Those features of the search query can include, for example:

  • Character(s)
  • Word(s)
  • Part(s) of speech
  • Entities included in the search query
  • And/or other linguistic representation(s) of the search query (such as word n-grams, character bag of words, etc.)

And the patent tells us more about the nature of the classification model:

The classification model is a machine learning model, such as a neural network model that contains one or more layers such as one or more feed-forward layers, softmax layer(s), and/or additional neural network layers. For example, the classification model can include several feed-forward layers utilized to generate feed-forward output. The resulting feed-forward output can be applied to softmax layer(s) to generate a measure (e.g., a probability) that indicates whether the search query is well-formed.

A Canonicalization Model May Be Used

If the Classification model determines that the search query is not a well-formed query, the query is turned over to a trained canonicalization model to generate a well-formed version of the search query.

The search query may have some of its features extracted from the search query, and/or additional input processed using the canonicalization model to generate a well-formed version that correlates with the search query.

The canonicalization model may be a neural network model. The patent provides more details on the nature of the neural network used.

The neural network can indicate a well-formed query version of the original query.

We are also told that in addition to identifying a well-formed query, it may also determine “one or more related queries for a given search query.”

A related query can be determined based on the related query being frequently submitted by users following the submission of the given search query.

The query canonicalization system can also determine if the related query is a well-formed query. If it isn’t, then it can determine a well-formed variant of the related query.

For example, in response to the submission of the given search query, a selectable version of the well-formed variant can be presented along with search results for the given query and, if selected, the well-formed variant (or the related query itself in some implementations) can be submitted as a search query and results for the well-formed variant (or the related query) then presented.

Again, the idea of “intent” surfaces in the patent regarding related queries (people also search for queries)

The value of showing a well-formed variant of a related query, instead of the related query itself, is to let a searcher more easily and/or more quickly understand the intent of the related query.

The patent tells us that this has a lot of value by stating:

Such efficient understanding enables the user to quickly submit the well-formed variant to quickly discover additional information (i.e., result(s) for the related query or well-formed variant) in performing a task and/or enables the user to only submit such query when the intent indicates likely relevant additional information in performing the task.

We are given an example of a related well-formed query in the patent:

As one example, the system can determine the phrase “hypothetical router configuration” is related to the query “reset hypothetical router” based on historical data indicating the two queries are submitted proximate (in time and/or order) to one another by a large number of users of a search system.

In some such implementations, the query canonicalization system can determine the related query “reset hypothetical router” is not a well-formed query, and can determine a well-formed variant of the related query, such as: “how to reset hypothetical router”.

The well-formed variant “how to reset hypothetical router” can then be associated, in a database, as a related query for “hypothetical router configuration”—and can optionally supplant any related query association between “reset hypothetical router” and “hypothetical router configuration”.

The patent tells us that sometimes a well-formed related query might be presented as a link to search results.

Again, one of the features of a well-formed query is that it is grammatical, is an explicit question, and contains no spelling errors.

The patent application can be found at:

Canonicalizing Search Queries to Natural language Questions
Inventors Manaal Faruqui and Dipanjan Das
Applicants Google LLC
Publication Number 20200167379
Filed: January 18, 2019
Publication Date May 28, 2020

Abstract

Techniques are described herein for training and/or utilizing a query canonicalization system. In various implementations, a query canonicalization system can include a classification model and a canonicalization model. A classification model can be used to determine if a search query is well-formed. Additionally or alternatively, a canonicalization model can be used to determine a well-formed variant of a search query in response to determining a search query is not well-formed. In various implementations, a canonicalization model portion of a query canonicalization system can be a sequence to sequence model.

Well-Formed Query Takeaways

I have summarized the summary of the patent, and if you want to learn more details, click through and read the detailed description. The two white papers I started the post off with describing databases of well-formed questions that people as Google (including the inventors of this patent) have built and show the effort that Google has put into the idea of rewriting queries so that they are well-formed queries, where the intent behind them can be better understood by the search engine.

A well-formed query is grammatically correct, contains no spelling mistakes, and asks an explicit question. It also makes it clear to the search engine what they intent behind the query may be.


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Job Search Engine Using Occupation Vectors

June 1, 2020 No Comments

I worked for the Courts of Delaware at Superior Court.

I started working there as the Assistant Criminal Deputy Prothonotary.

I changed positions after 7 years there, and I became a Mini/Micro Computer Network Administrator.

The Court used an old English title for that first position which meant that I supervised Court Clerks in the Criminal Department of the Court. In the second position, I never saw a mini/micro-computer but it was a much more technical position. I was reminded of those titles when writing this post.

What unusual job titles might you have held in the past?

A Job Search Engine Based on Occupation Vectors and a Job Identification Model

An Example of Job Search at Google:

job search example

For two weeks, Google was granted patents with the same name each of those 2 weeks. This is the first of the two patents during that period granted under the name “Search Engine.”

It is about a specific type of search engine. One that focuses upon a specific search vertical – A Job Search Engine.

The second patent granted under the name “Search Engine,” was one that focused upon indexing data related to applications on mobile devices. I wrote about it in the post A Native Application Vertical Search Engine at Google

The reason why I find it important to learn about and understand how these new “Search Engine” patents work is that they adopt some newer approaches to answering searches than some of the previous vertical search engines developed by Google. Understanding how they work may provide some ideas about how older searches at Google may have changed.

This Job Search Engine patent works with a job identification model to enhance job search by improving the quality of search results in response to a job search query.

We are told that the job identification model can identify relevant job postings that could otherwise go unnoticed by conventional algorithms due to inherent limitations of keyword-based searching. What implications does this have for organic search at Google that has focused upon keyword searches?

This job search may use methods in addition to conventional keyword-based searching. It uses an identification model that can identify relevant job postings which include job titles that do not match the keywords of a received job search query.

So, the patent tells us that in a query using the words “Patent Guru,” the job identification model may identify postings related to a:

  • “Patent Attorney”
  • “Intellectual Property Attorney”
  • “Attorney”
  • the like

The method behind job searching may include (remember the word “vector.” It is one I am seeing from Google a lot lately):

  • Defining a vector vocabulary
  • Defining an occupation taxonomy includings multiple different occupations
  • Obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least:
    • (i) a job title
    • (ii) an occupation
  • Generating an occupation vector which includes a feature weight for each respective term in the vector vocabulary
  • Associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector
  • Receiving a search query that includes a string related to a characteristic of one or more potential job opportunities, generating a first vector based on the received query
  • Determining, for each respective occupation of the multiple occupations in the occupation taxonomy, a confidence score that is indicative of whether the query vector is correctly classified in the respective occupation
  • Selecting the particular occupation that is associated with the highest confidence score
  • Obtaining one or more job postings using the selected occupation
  • Providing the obtained job postings in a set of search results in response to the search query

These operations may include:

  • Receiving a search query that includes a string related to a characteristic of one or more job opportunities
  • Generating, based on the query, a query vector that includes a feature weight for each respective term in a predetermined vector vocabulary
  • Determining, for each respective occupation of the multiple occupations in the occupation taxonomy, a confidence score that is indicative of whether the query vector is correctly classified in the respective occupation
  • Selecting the particular occupation that is associated with the highest confidence score
  • Obtaining one or more job postings using the selected occupation, and providing the obtained job postings in a set of search results in response to the search query
  • Feature Weights for Terms in Vector Vocabularies

    It sounds like Google is trying to understand job position titles and how they may be connected, and developing a vector vocabulary, and build ontologies of related positions

    A feature weight may be based on:

    • A term frequency determined on several occurrences of each term in the job title of the training data item
    • An inverse occupation frequency that is determined based on many occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present.
    • An occupation derivative based on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy
    • Both (i) a second value representing the inverse occupation frequency that is determined based, at least in part, on several occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present and (ii) a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy
    • A sum of (i) the second value representing the inverse occupation frequency, and (ii) one-third of the third value representing the occupation derivative

    The predetermined vector vocabulary may include terms that are present in training data items stored in a text corpus and terms that are not present in at least one training data item stored in the text corpus.

    This Job Search Engine Patent can be found at:

    Search engine
    Inventors: Ye Tian, Seyed Reza Mir Ghaderi, Xuejun Tao), Matthew Courtney, Pei-Chun Chen, and Christian Posse
    Assignee: Google LLC
    US Patent: 10,643,183
    Granted: May 5, 2020
    Filed: October 18, 2016

    Abstract

    Methods, systems, and apparatus, including computer programs encoded on storage devices, for performing a job opportunity search. In one aspect, a system includes a data processing apparatus, and a computer-readable storage device having stored thereon instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations.

    The operations include defining a vector vocabulary, defining an occupation taxonomy that includes multiple different occupations, obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation, generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary and associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector.

    The Job Identification Model

    Job identification model

    Job postings from many different sources may be related to one or more occupations.

    An occupation may include a particular category that encompasses one or more job titles that describe the same profession.

    Two or more of the obtained job postings may be related to the same, or substantially similar, occupation while using different terminology to describe a job title for each of the two or more particular job postings.

    Such differences in the terminology used to describe a particular job title of a job posting may arise for a variety of different reasons:

    • Different people from different employers draft each respective job posting
    • Unique job titles may be based on the culture of the employer’s company, the employer’s marketing strategy, or the like

    occupation taxonomy

    How an Job Identification Model May Work

    An example:

    1. At a first hair salon marketed as a rugged barbershop, advertises a job posting for a “barber”
    2. At a second hair salon marketed as a trendy beauty salon, advertises a job posting for a “stylist”
    3. At both, the job posting seeks a person for the occupation of a “hairdresser” who cuts and styles hair
    4. In a search system limited to keyword-based searching, a searcher seeking job opportunities for a “hairdresser” searchings for job opportunities using the term “barber” may not receive available job postings for a “stylist,” “hairdresser,” or the like if those job postings do not include the term “barber”
    5. The process in this patent uses a job identification model seeking to address this problem

    The job occupation model includes:

    • A classification unit
    • An occupation taxonomy

    The occupation taxonomy associates known job titles from existing job posts with one or more particular occupations.

    During training, the job identification model associates each occupation vector that was generated for an obtained job posting with an occupation in the occupation taxonomy.

    The classification unit may receive the search query and generate a query vector.

    The classification unit may access the occupation taxonomy and calculate, for each particular occupation in the occupation taxonomy, a confidence score that is indicative of the likelihood that the query vector is properly classified into each particular occupation of the multiple occupations in the occupation taxonomy.

    Then, the classification unit may select the occupation associated with the highest confidence score as the occupation that is related to the query vector and provide the selected occupation to the job identification model.

    An Example of a Search Under this Job Opportunities Search Engine:

    1. A searcher queries “Software Guru” into a search box
    2. The search query may be received by the job identification model
    3. The job identification model provides an input to the classification unit including the query
    4. The classification unit generates a query vector
    5. The classification unit analyzes the query vector given the one or more occupation vectors that were generated and associated with each particular occupation in the occupation taxonomy such as occupation vectors
    6. The classification unit may then determine that the query vector is associated with a particular occupation based on a calculated confidence score, and select the particular occupation
    7. The job identification model may receive the particular occupation from the classification unit
    8. Alternatively, or besides, the output from the classification unit may include a confidence score that indicates the likelihood that the query vector is related to the occupation output by the occupation taxonomy
    9. The occupation output from the occupation taxonomy can be used to retrieve relevant job postings
    10. Specifically, given the output of a particular occupation, the job identification model can retrieve one or more job postings using a job posting index that stores references to job postings based on occupation type

    11. The references to job postings that were identified using the job posting index are returned to the user device
    12. The obtained references to job postings may be displayed on the graphical user interface
    13. The obtained references to job postings may be presented as search results and include references to job postings for a “Senior Programmer,” a “Software Engineer,” a “Software Ninja,” or the like
    14. The job postings included in the search results were determined to be responsive to the search query “Software Guru” based at least in part on the vector analysis of the query vector and one or more occupation vectors used to train the occupation taxonomy and not merely based on keyword searching alone

    Takeaways About this Job Search Engine

    In addition to the details about, the patent tells us how an occupation taxonomy may be trained, using training data. It also provides more details about the Job identification model. And then tells us about how a job search is performed using that job identification model.

    I mentioned above that this job search engine patent and the application search engine patent are using methods that we may see in other search verticals at Google. I have written about one approach that could be used in Organic search in the post Google Using Website Representation Vectors to Classify with Expertise and Authority

    Another one of those may involve image searching at Google. I’ve written about Google Image Search Labels Becoming More Semantic?

    I will be posting more soon about how Google Image search is using neural networks to categorize and cluster Images to return in search results.


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    COVID-19 has altered paid search: How marketers can adjust strategies

    May 30, 2020 No Comments

    30-second summary:

    • Since shelter-in-place rules were enacted, the way people use the internet has changed. They’re consuming more media and increasing web research and browsing. 
    • Paid search strategy is not one-size-fits-all. Each vertical must be treated differently, as some industries like ecommerce have seen improved performance while others have seen a declined performance. 
    • A pandemic is not the time to cut ad budget. Instead, investing in advertising now should pay dividends when the market normalizes. 
    • Ensure your ad copy is appropriate for the landscape. That means even going back to a campaign that started before the pandemic to update any language that isn’t applicable to the current landscape. 
    • Marketers must stay flexible and agile during this time and monitor what’s working or not working and creating a quick plan to adjust. 

    When COVID-19 began spreading across the U.S., marketers scrambled to figure out how to respond. Sudden work-from-home mandates, cancelled business trips, postponed conferences and frozen budgets threw a wrench into usual expectations and plans. Users’ needs and online behaviours have changed in tandem, forcing marketers to meet them on their new terms.  

    Search is more important than ever now because people are spending almost all of their time at home and online, consuming media, researching, browsing and shopping. According to Forbes, total internet hits have surged by 50% to 70% with people under lockdown, while 32% of people say they are spending longer on social media. Hours spent in non-gaming apps are up as people turn to TikTok, WhatsApp, Instagram and Twitter to keep entertained, connected and informed. To stay relevant in these turbulent times, it’s imperative that marketers maintain their paid search presence while adjusting to the needs of the moment.  

    Vary strategy by vertical 

    While no industry is immune from the impact of coronavirus, businesses are affected differently and should adapt their paid search strategies accordingly. Industries like B2B and ecommerce have seen improved performance, while industries like travel and healthcare have struggled with poor results.  

    The fact that healthcare is struggling may seem paradoxical, given the overwhelming need for healthcare services right now. While hospitals are busy with COVID-19 patients, people who don’t have the virus are avoiding medical centres, hospitals, and non-essential medical services like bariatric surgery and physical therapy.

    Users are shifting their searches for their healthcare needs. Notably, people under shelter-in-place orders are seeking to receive care while staying in their homes. eMarketer published data from CivicScience which found that between February and March 2020, the number of U.S. adults who reported intent to use telemedicine rose from 18% to 30%. As a result, healthcare providers have to switch their offerings – along with their messaging – to emphasize virtual and telehealth services. The same is true for many restaurants as they pivot to pick up or delivery only.  

    The situation is different for B2B companies

    The situation is different for B2B companies, which have longer sales cycles. While businesses like restaurants are worried about running out of money now, B2B companies are concerned about how they’ll fare months and, in some cases, years from now. The instinct may be to cut down on marketing budgets to save money, but extreme changes in paid search strategies can have long-lasting effects on performance. During this time, it’s important B2B companies continue filling the funnel and building brand awareness to alleviate large sales gaps that can occur later in the year.  

    Financial service-related searches are surging

    Financial service-related searches are surging right now as people explore their options for economic relief like loans. Many companies in this space are smartly increasing their ad spending and shifting the bulk of it toward campaigns that push their best performing service lines. The same is true for ecommerce companies, especially those that sell household products and cleaning supplies, loungewear, cooking equipment, workout gear and entertainment items like board games and puzzles. Shares of Hasbro, for instance, have soared. For these companies, the adjustment is less about the offerings and more about the messaging.  

    Don’t stop advertising when times are tough 

    There are universal principles for how to optimize paid search strategies that apply to marketers in every industry. The first is not to neglect paid search, even during difficult times. The World Federation of Advertisers (WFA) recently ran a survey which found 81% of large advertisers deferred planned ad campaigns and cutting budgets due to the coronavirus pandemic. Of those surveyed, 57% said they had decreased budgets greatly or somewhat due to the virus outbreak; however, cutting out advertising or marketing completely can make the road to recovery more challenging.  

    Experts advise not to stop advertising during a downturn. Evidence from recent economic downturns like the 2008 housing crash show that companies come out stronger in the end if they continue investing in brand awareness. According to Google, “Even in categories where consumers have pulled back spending right now, creating a branding impact now will have a halo and pay dividends when the market normalizes. Research and historical examples of economic downturn have shown this to work.” It’s important to keep investing in your brand and branded keywords, regardless of industry. The last thing an organization wants is competitors monetizing on branded search results.  

    Every cent counts these days. Not only is paid search cost-effective with a low barrier to entry, but it also enables companies to be extremely agile. A company can get a campaign up and running pretty quickly, run tests, collect data and easily alter the messaging as things change day-to-day. Marketers can also see the results of engagement, click-through rates and conversions in real time, so they know whether their investment is paying off. COVID-19 is an unprecedented situation, so testing and learning are critical during this volatile time in the market.  

    Best practices for paid search 

    For any marketer thinking about how to adjust during COVID-19, here are a few best practices for how to optimize paid search.

    1. Pivot messaging

    Messaging needs to be both accurate and appropriate for the current landscape. Confirm that messaging is updated with current business hours and offerings, and revise CTAs away from messages like “Visit in-store.”  

    2. Keep an eye on the tone of messaging

    Is your copy appropriate or empathetic? An ad for booking a vacation package could feel out-of-touch. Customers will be turned off by companies that seem like they are trying to profit or gain from the pandemic, so craft communication to focus more on brand identity and values. Businesses can also use marketing to let customers know how they are responding to the pandemic. A construction firm or ecommerce company could talk about safety practices for workers, for example.   

    3. Adapt offerings to what your customers need

    As mentioned above, healthcare companies are moving to telehealth, restaurants are moving to pick up, delivery and B2B companies are repurposing content planned for conferences into virtual webinars. Marketers should be connecting with customers virtually to let them know how you are supporting them.  

    4. Adapt your strategy to your customers’ changing digital behaviour

    During the quarantine, desktop usage has increased. Conversely, the rise of remote work conditions and people being less on-the-go has caused mobile search traffic to decline by nearly 25%. We’ve all become accustomed to a mobile-first world, but given the predominance of desktop, it’s especially important to ensure all search ads and landing pages are optimized for both mobile and desktop.

    Move fast 

    This pandemic has caused so much of what used to be normal out of the window. Whereas before, marketers might have used a multiphase process for developing campaigns that involved planning and back-and-forth and feedback, now they have to act fast to keep up with the rapidly changing world. Marketers need to craft campaigns that are affordable, cost-effective and agile – and that means paid search.  

    As marketing and advertising professionals, we’re all trying to figure this out together as we go. There is no roadmap or rules, but there’s no doubt that staying flexible and using this time to connect with customers is a smart strategy.

     Brianna Desmet is Media specialist at digital and demand gen agency, R2i.

    The post COVID-19 has altered paid search: How marketers can adjust strategies appeared first on Search Engine Watch.

    Search Engine Watch


    Job Search Engine Using Occupation Vectors

    May 23, 2020 No Comments

    I worked for the Courts of Delaware at Superior Court.

    I started working there as the Assistant Criminal Deputy Prothonotary.

    I changed positions after 7 years there, and I became a Mini/Micro Computer Network Administrator.

    The Court used an old English title for that first position which meant that I supervised Court Clerks in the Criminal Department of the Court. In the second position, I never ever saw a mini/micro-computer but it was a much more technical position. I was reminded of those titles when writing this post.

    What unusual job titles might you have held in the past?

    A Job Search Engine Based on Occupation Vectors and a Job Identification Model

    An Example of Job Search at Google:

    job search example

    For a two week period, Google was granted patents with the same name each of those 2 weeks. This is the first of the two patents during that period granted under the name “Search Engine.”

    It is about a specific type of search engine. One that focuses upon a specific search vertical – A Job Search Engine.

    The second patent granted under the name “Search Engine,” was one that focused upon indexing data related to applications on mobile devices. I wrote about it in the post A Native Application Vertical Search Engine at Google

    The reason why I find it important to learn about and understand how these new “Search Engine” patents work is that they adopt some newer approaches to answering searches than some of the previous vertical search engines developed by Google. Understanding how they work may provide some ideas about how older searches at Google may have changed.

    This Job Search Engine patent works with a job identification model to enhance job search by improving the quality of search results in response to a job search query.

    We are told that the job identification model can identify relevant job postings that could otherwise go unnoticed by conventional algorithms due to inherent limitations of keyword-based searching. What implications does this have for organic search at Google that has focused upon keyword search?

    This job search may use methods in addition to conventional keyword-based searching. It uses an identification model that can identify relevant job postings which include job titles that do not match the keywords of a received job search query.

    So, the patent tells us that in a query using the words “Patent Guru,” the job identification model may identify postings related to a:

    • “Patent Attorney”
    • “Intellectual Property Attorney”
    • “Attorney”
    • the like

    The method behind job searching may include (remember the word “vector.” It is one I am seeing from Google a lot lately):

    • Defining a vector vocabulary
    • Defining an occupation taxonomy includings multiple different occupations
    • Obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least:
      • (i) a job title
      • (ii) an occupation
    • Generating an occupation vector which includes a feature weight for each respective term in the vector vocabulary
    • Associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector
    • Receiving a search query that includes a string related to a characteristic of one or more potential job opportunities, generating a first vector based on the received query
    • Determining, for each respective occupation of the multiple occupations in the occupation taxonomy, a confidence score that is indicative of whether the query vector is correctly classified in the respective occupation
    • Selecting the particular occupation that is associated with the highest confidence score
    • Obtaining one or more job postings using the selected occupation
    • Providing the obtained job postings in a set of search results in response to the search query

    These operations may include:

  • Receiving a search query that includes a string related to a characteristic of one or more job opportunities
  • Generating, based on the query, a query vector that includes a feature weight for each respective term in a predetermined vector vocabulary
  • Determining, for each respective occupation of the multiple occupations in the occupation taxonomy, a confidence score that is indicative of whether the query vector is correctly classified in the respective occupation
  • Selecting the particular occupation that is associated with the highest confidence score
  • Obtaining one or more job postings using the selected occupation, and providing the obtained job postings in a set of search results in response to the search query
  • Feature Weights for Terms in Vector Vocabularies

    It sounds like Google is trying to understand job position titles and how they may be connected with each other, and developing a vector vocabulary, and build ontologies of related positions

    A feature weight may be based on:

    • A term frequency determined on a number of occurrences of each term in the job title of the training data item
    • An inverse occupation frequency that is determined based on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present.
    • An occupation derivative based on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy
    • Both (i) a second value representing the inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present and (ii) a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy
    • A sum of (i) the second value representing the inverse occupation frequency, and (ii) one-third of the third value representing the occupation derivative

    The predetermined vector vocabulary may include terms that are present in training data items stored in a text corpus and terms that are not present in at least one training data item stored in the text corpus.

    This Job Search Engine Patent can be found at:

    Search engine
    Inventors: Ye Tian, Seyed Reza Mir Ghaderi, Xuejun Tao), Matthew Courtney, Pei-Chun Chen, and Christian Posse
    Assignee: Google LLC
    US Patent: 10,643,183
    Granted: May 5, 2020
    Filed: October 18, 2016

    Abstract

    Methods, systems, and apparatus, including computer programs encoded on storage devices, for performing a job opportunity search. In one aspect, a system includes a data processing apparatus, and a computer-readable storage device having stored thereon instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations.

    The operations include defining a vector vocabulary, defining an occupation taxonomy that includes multiple different occupations, obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation, generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary and associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector.

    The Job Identification Model

    Job identification model

    Job postings from many different sources may be related to one or more occupations.

    An occupation may include a particular category that encompasses one or more job titles that describe the same profession.

    Two or more of the obtained job postings may be related to the same, or substantially similar, occupation while using different terminology to describe a job title for each of the two or more particular job postings.

    Such differences in the terminology used to describe a particular job title of a job posting may arise for a variety of different reasons:

    • Different people from different employers draft each respective job posting
    • Unique job titles may be based on the culture of the employer’s company, the employer’s marketing strategy, or the like

    occupation taxonomy

    How an Job Identification Model May Work

    An example:

    1. At a first hair salon marketed as a rugged barbershop, advertises a job posting for a “barber”
    2. At a second hair salon marketed as a trendy beauty salon, advertises a job posting for a “stylist”
    3. At both, the job posting seeks a person for the occupation of a “hairdresser” who cuts and styles hair
    4. In a search system limited to keyword-based searching, a searcher seeking job opportunities for a “hairdresser” searchings for job opportunities using the term “barber” may not receive available job postings for a “stylist,” “hairdresser,” or the like if those job postings do not include the term “barber”
    5. The process in this patent uses a job identification model seeking to address this problem

    The job occupation model includes:

    • A classification unit
    • An occupation taxonomy

    The occupation taxonomy associates known job titles from existing job posts with one or more particular occupations.

    During training, the job identification model associates each occupation vector that was generated for an obtained job posting with an occupation in the occupation taxonomy.

    The classification unit may receive the search query and generate a query vector.

    The classification unit may access the occupation taxonomy and calculate, for each particular occupation in the occupation taxonomy, a confidence score that is indicative of the likelihood that the query vector is properly classified into each particular occupation of the multiple occupations in the occupation taxonomy.

    Then, the classification unit may select the occupation associated with the highest confidence score as the occupation that is related to the query vector and provide the selected occupation to the job identification model.

    An Example of a Search Under this Job Opportunities Search Engine:

    1. A searcher queries “Software Guru” into a search box
    2. The search query may be received by the job identification model
    3. The job identification model provides an input to the classification unit including the query
    4. The classification unit generates a query vector
    5. The classification unit analyzes the query vector in view of the one or more occupation vectors that were generated and associated with each particular occupation in the occupation taxonomy such as occupation vectors
    6. The classification unit may then determine that the query vector is associated with a particular occupation based on a calculated confidence score, and select the particular occupation
    7. The job identification model may receive the particular occupation from the classification unit
    8. Alternatively, or in addition, the output from the classification unit may include a confidence score that indicates the likelihood that the query vector is related to the occupation output by the occupation taxonomy
    9. The occupation output from the occupation taxonomy can be used to retrieve relevant job postings
    10. Specifically, given the output of a particular occupation, the job identification model can retrieve one or more job postings using a job posting index that stores references to job postings based on occupation type

    11. The references to job postings that were identified using the job posting index are returned to the user device
    12. The obtained references to job postings may be displayed on the graphical user interface
    13. The obtained references to job postings may be presented as search results and include references to job postings for a “Senior Programmer,” a “Software Engineer,” a “Software Ninja,” or the like
    14. The job postings included in the search results were determined to be responsive to the search query “Software Guru” based at least in part on the vector analysis of the query vector and one or more occupation vectors used to train the occupation taxonomy and not merely based on keyword searching alone

    Takeaways About this Job Search Engine

    In addition to the details about, the patent tells us how an occupation taxonomy may be trained, using training data. It also provides more details about the Job identification model. And then tells us about how a job search is performed using that job identification model.

    I mentioned above that this job search engine patent and the application search engine patent are using methods that we may see in other search verticals at Google. I have written about one approach that could be used in Organic search in the post Google Using Website Representation Vectors to Classify with Expertise and Authority

    Another one of those may involve image searching at Google. I’ve written about Google Image Search Labels Becoming More Semantic?

    I will be posting more soon about how Google Image search is using neural networks to categorize and cluster Images to return in search results.


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    How search giants are helping spread COVID-19 awareness

    May 14, 2020 No Comments

    30-second summary:

    • Misinformation can be even more harmful that the danger itself, Michael McManus highlights the initiatives search and social giants have and are promoting COVID-19 awareness.
    • Google has made some of the biggest changes, modifying the SERPS for COVID-19 related search queries to provide all the needful information people could need.
    • Google also made changes to Google My Business to help companies navigate through this difficult time.
    • Facebook created a coronavirus information centre that has been added to the top of everyone’s Facebook feed.
    • Bing has added a quick link below the search bar on the homepage which on clicking opens the COVID-19 tracker page that has all the information you could ask for about the Coronavirus.  
    • Pinterest is making sure they display pins with the right information from internationally-recognized health organizations.
    • More details on the initiatives Snapchat, Twitter, and Instagram have taken.

    There’s no doubt that the Coronavirus (COVID-19) has affected everyone in one way or another and that we all know that this virus that has shut down most of the world at one point or another isn’t going to go away anytime soon. 

    As you can imagine, with such a huge worldwide pandemic happening, there’s a large number of people that are searching for different kinds of information related to the coronavirus. 

    coronavirus search trends

    For all the searches that are being done on a daily basis and all the news and people talking about the virus, there’s the potential for lots of misinformation about COVID-19 to appear in SERPs and across social media.  

    To help combat the sharing of misinformation, some of the major players in both search and social have been able to provide us with the right information at the right time, so that we are not being led to believe the wrong information, that can cause us to panic and worry even more than we already are. 

    Search giants and COVID-19 awareness

    1. Google

    Google has made lots of changes to both their search results and their tools to help with getting people the right information about the coronavirus as well as to help make things easy for people working from home and for businesses to be able to update their clients on a company’s status during this unprecedented time. 

    One of the biggest changes that Google has made to help spread awareness about the COVID-19 situation is how they have changed the SERPS when you do a search related to the coronavirus, Google will display all the information you need about the virus, from the number of cases for your given country and the world as well maps, headlines and a very well labelled “ COVID-19 alert” in red on the left-hand side that has links that open up a “zero-click” search box with the relevant information from the CDC. This also changes the SERP to correspond with the link that was clicked. 

    Google COVID-19 awareness

    Google also made changes to Google My Business to help companies navigate through this difficult time. You are now able to set your business to “temporarily closed” without it having an effect on your site’s local rankings. Google is also letting businesses know that they should update their business hours as well as to post your COVID-19 updates.

    GMB COVID-19 updates GMB COVID-19 updates

    Just keep in mind that your Google My Business account may not be functioning as expected under the COVID-19 strain and that many of your updates might take considerably longer than normal. 

    Google has been working really hard at making sure that the right COVID-19 information is being found and to help with this, the Google Search team is helping official health organizations get more visibility in search with a new best practices guide as well as through a private support group. 

    As if that wasn’t enough, Google is also publishing coronavirus mobility reports that allow you to see how your community is moving around differently due to COVID-19 and how the pandemic has affected your area. These reports get their data from Google’s different products, such as Youtube, as well as from users’ location history. 

    Google mentions the following – “These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.”

    So head on over to the site and download the coronavirus mobility reports to see insights on your city. 

    2. Facebook

    Facebook is doing its part to help with providing COVID-19 information and awareness by launching its coronavirus information centre. The new information centre has been added to the top of everyone’s Facebook feed.

    Facebook COVID-19 awareness hub

    Along with the Coronavirus (COVID-19) information centre, Facebook has also created a Coronavirus (COVID-19) information hub for media that has a wealth of information.

    Other initiatives that Facebook have done and are doing to help spread COVID-19 awareness: 

    3. Bing

    With almost seven percent of the search engine market share, Bing has a big platform to help spread COVID-19 awareness and they have done exactly that. They have added a quick link below the search bar on the homepage. 

    Bing home page

    When clicked, you are taken to their newly created COVID-19 tracker page that has all the information you could ask for about the Coronavirus.  

    Bing COVID-19 awareness

    This new page allows you to see the total confirmed cases globally and a breakdown of the active cases, recovered cases, and sadly fatal cases. You can then choose a country and you will be able to see how the virus is spreading in all the cities and or states of that respective country. You will also get up to date news related to that country as well.

    The page is continuously being updated with data that is collected from CDC, WHO, ECDC, Wikipedia, 24/7 Wall St., and BNO News.

    Other initiatives that Bing have done and are doing to help spread COVID-19 awareness include:

    • hub dedicated to explaining the Bing Places features and attributes businesses can use during COVID-19. 
    • Partnered with GoFundMe to integrate it into Bing Places. 
    • Added a CDC coronavirus self-checker chatbot to search results pages.

    4. Snapchat

    Snapchat has worked with the World Health Organization to create filters that display facts on how to stay safe and social distance during the pandemic. Snapchat has also created new lenses in a bid to encourage social distancing during the coronavirus pandemic. 

    They have also created a COVID-19 business resource centre to share resources to help their partners navigate this uncertain time in light of COVID-19. 

    5. Twitter

    Twitter has also created a COVID-19 hub with the goal of helping people find reliable information, connect with others, and follow what’s happening in real-time. You can head over to to the hub to get comprehensive information on how Twitter is helping spread COVID-19 awareness and how they are providing guidance to help businesses.  

    Along with their COVID-19 hub, they’ve also created a great resource on crisis communication for brands on Twitter.  

    6. Instagram

    Instagram is doing its part to help spread COVID-19 awareness and have taken steps to help people access accurate information, stay safe, and stay connected. These new features were designed to help encourage users to stay home, stay connected, and for people to be able to access accurate information during the Coronavirus pandemic.  These new features are the “Stay Home” sticker to help promote social distancing and the new video feature “Co-Watching” that allows you to view Instagram posts together with your friends over video chat. 

    Instagram's "Stay home" for COVID-19 awareness

    To help people get relevant and up-to-date information and resources, Instagram intends to show more information from @WHO and local health ministries at the top of Instagram’s Feed. You may have already noticed the message that says, “Help Prevent the Spread of Coronavirus: See the latest information from the World Health Organization so you can help prevent the spread of COVID-19. — Go to who.int

    Other initiatives that Instagram have done and are doing to help spread COVID-19 awareness:

    • More educational resources in Instagram search results. 
    • Adding stickers to promote accurate information.
    • Removing COVID-19 accounts from recommendations, unless posted by a credible health organization. 
    • Rolling out the donation sticker in more countries and helping people find relevant nonprofits to support. 

    7. Pinterest

    Pinterest is also doing their part to help spread COIVD-19 awareness and making sure that they are displaying pins with the right information. In order to make sure that they are only showing pins with the right information, they are only displaying search results to Pins from internationally-recognized health organizations.

    Pinterest COVID-19 awareness

    They have also created a one-pager guide for brands with suggestions for creating content that helps Pinners cope with this extraordinary time.

    Other initiatives that Pinterest have done and are doing to help spread COVID-19 awareness: 

    • custom search experience featuring results from experts 
    • Prohibiting ads that claim to offer cures or treatments or that are looking to exploit the crisis 
    • A banner across the site that directs to WHO facts 
    • An easy way to report health misinformation through the “health misinformation” option on Pins. We’re removing any misinformation we find about COVID-19 because it violates our health misinformation policy (which has been in place since 2017) 
    • Stay safe, stay inspired board for even more ideas. 
    • Collection of emotional wellbeing activities to help relax and feel better with content from emotional health experts 

    Free tools 

    It’s great to see companies big and small come through in a big way to help businesses out during this difficult time. There are lots of companies that are offering a wide range of services, products, consulting services, charitable donations, etc.. We have put together a small list of companies in the search and marketing industry who are  

    Unbounce

    When it comes to designing beautiful landing pages that convert more, Unbounce is the company to go to. Not only are they the giants in landing page conversions, but they have also really come through during this global pandemic, by offering their essential plan for free to anyone in healthcare, education, nonprofit, or government as well as offering free coaching and strategy sessions with their success managers and so much more. 

    Google

    Along with all that they provide via GMB, Google is helping remote workers as well as students by providing their video conferencing service for free. Google Meet, is Google’s premium video conferencing product that you had access to if you had a paid G Suite account.

    Google is opening up access to Meet to free users gradually, so keep on checking their site to see when it will be available in your area. 

    In addition to all the efforts, products, and services that Google’s doing, they are also providing $ 340 million in Google Ads credits to all SMBs. You do not have to do anything to get the credits, you just simply have to have an active account over the past year. The credits will then appear in your Ads account. 

    Facebook

    Facebook has created a Small Business Grants program that offers $ 100 million in cash grants and advertising credits for up to 30,000 small businesses in over 30 countries. 

    Hubspot

    HubSpot is a great company that provides a wide range of marketing, sales, and customer service tools and software as well as completely free CRM. HubSpot is doing its part in responding to COVID-19 and its economic impact by reducing the cost of its Starter Growth Suite from $ 112 USD to $ 50 USD per month.  

    They are also making their paid Meetings functionality, Quotes, E-Sign, and 1:1 Video tools available for free for 90 days from activation. 

    Moz

    Moz is a great site that offers lots of insights from industry experts as well as providing great SEO tools. Moz is providing its Academy Courses for Free till May 31st. If you haven’t done so already, you should head over to Moz and sign up for their academy courses. There’s something there for everyone. 

    Mailchimp

    Mailchimp is another great example of a company stepping up to help businesses in a time of need, by offering up to three months free service for businesses with 25 employees or less in the restaurant, travel, brick-and-mortar retail, healthcare, and more industries.

    Mailchimp is also generously giving away custom domains free for five years along with their free website builder. This will help small businesses by giving them two fewer things to worry about during the COVID-19 crisis and help them get up and running online quickly.

    You can find out all about theses offers and more that Mailchimp is doing to help out by heading over to Mailchimp’s Statement on COVID‑19.

    As if that wasn’t enough, Mailchimp is also providing free standard Mailchimp accounts for eligible public service organizations.

    Hootsuite

    Hootsuite is another great example of a company stepping up to help businesses during the COVID-19 pandemic. They are giving away their Professional plan to small businesses and nonprofit organizations until July 1, 2020, to help them stay connected with their customers and communities.

    Hootsuite is also launching a series of free online workshops designed to show how brands can build better processes for crisis management. They are also working on a brand new virtual conference. 

    Similarweb

    Similarweb is well known for all the data that they have across all markets and industries, that allow you to gather market intelligence to help you understand different trends, track and grow your digital market share. They have used their data and insights and have created a ‘Coronavirus Data and Insights Hub’ that offers great insights on how COVID-19 is impacting business as well as to help you understand how customers’ needs are changing due to the pandemic. 

    Conclusion 

    It’s great to see all these companies both big and small all come together during these unprecedented times, by creating COVID-19 awareness so that we can get the information that we are looking for right away and not have to search over and over only to give up and not find what we are looking for or to end up getting the wrong information, offering their services and their time for free to help those in need. 

    We are all in this together, stay healthy, and stay safe.  

    Michael McManus is Earned Media (SEO) Practice Lead at iProspect.

    The post How search giants are helping spread COVID-19 awareness appeared first on Search Engine Watch.

    Search Engine Watch



    How writers can optimize content for a variety of search engines

    May 9, 2020 No Comments

    30-second summary:

    • If you think optimizing your content for Google is tough, then youre going to be amazed by how many factors youll have to consider when optimizing your writing for multiple search engines. 
    • Two benefits of doing so can be seen in local SEO and voice search.
    • UK Linkology’s Content Marketing Manager, Hannah Stevenson walks you through the complex process to understand and implement how you can optimize content for search engines beyond Google – Bing, DuckDuckGo, Ask.com, and more.

    Optimizing your content for just one search engine can be a challenge, as weve still got no idea what Google expects. 

    There is a range of different tools out there designed to help, but theyre all merely making educated guesses. To use them effectively, you need to be assessing what they tell you and, where possible, using more than one metric to evaluate your sites success and boost its rankings. 

    If you think optimizing your content for Google is tough, then youre going to be amazed by how many factors youll have to consider when optimizing your writing for multiple search engines.  

    Read on to find out why its important that you dont overlook alternative search engines and how you can include them in your optimization process.  

    Why you need to optimize content for a range of search engines 

    Google has the largest market share of any search engine in the world, so, understandably, most writers and SEOs focus on optimizing for it. 

    However, there are a wide variety of alternative search engines out there. Bing, Microsoftsearch engine offering, has 5.53% of this market. This might seem like a small percentage, but when you consider that the digital population around the world is in the billions, it is still a significant number of users that youre overlooking by only optimizing your content for Google. 

    A percentage of users of Bing will have it set as their prefered search tool due to the browser or device they are using. Microsoft favours its own tools, which is why Bing is the default search engine on Windows phones, tablets and computers.  

    Some developers have deals to make certain search engines their default. Many of these deals involve Google, but in some cases, the titan of the search engine market is usurped.  

    For example, AOL chose Bing over Google in 2015, meaning that Bing is the default engine on AOL browsers. While this might not seem significant, many users will not bother to change their settings, and simply use the default search engine, meaning if this option is not Google, then other search engines will rise in popularity.  

    Additionally, some smaller search engines target specific demographics, such as Ecosia, which is marketed at environmentally-conscious users and donates money towards planting trees with every search that users make.  

    For users who are concerned about privacy and data storage, DuckDuckGo is a search engine that promises not to store information and block out hidden tracking software.  

    As such, if you are targeting these specific demographics, then you need to make sure that you optimize your content for these tools.  

    Research the search engines on the market 

    Before you start optimizing your content, you need to check out the search engines on offer and work out which ones are the most relevant to your website.  

    Some of the key search engines on the market, not including Google, are: 

    • Bing: As mentioned earlier, Bing is Microsoft’s search engine, which has a strong market share.  
    • Yahoo!: Powered by Bing, Yahoo! Uses the same technology, but is a different platform, meaning that you can optimize for this solution using the same techniques you use for Bing.  
    • Ecosia: An eco-friendly search engine that promotes itself by offering to donate money towards tree planting efforts for every search users make on its platform.  
    • DuckDuckGo: A privacy-focused search engine that does not track user data, making it harder to optimize for and less-informative than other tools.  
    • Qwant: Another search engine that’s dedicated to privacy, Qwant has it’s own indexing engine and doesn’t track user activity.  
    • Ask.com: Using a question and answer format, Ask.com providers users with answers to any queries they may have by showing them relevant pages and content.  

    Look beyond Google Analytics 

    The first step towards to optimize content for alternative search engines is to find new sources of traffic information.

    Most webmasters use Google Analytics to review their traffic and site information, but this platform only shows clicks from Google searches. 

    If you want to find out where youre getting all of your page visits from, then youll need to find alternative ways to review your traffic.  

    Analytics tools such as SEMrush, SimilarWeb and Ahrefs all show you where your traffic is coming from, as well as offering a wide range of additional tools such as keyword searches and top page analysis. As such, theyre definitely worth investing in if you want to boost your site, both on Google and a range of other search engines.   

    Follow them on social media to stay updated 

    One of the easiest ways you can learn about the latest developments in the way these alternative search engines operate, and how you can optimize content around them is to stay updated.  

    As such, you should follow them on social media and sign up to their newsletters to read the latest developments and advice that theyre offering to users and content creators.  

    Keeping tabs on so many different search engines can be a challenge, particularly if youre trying to optimize your content around several different tools.  

    Youll be able to get all of the updates as and when theyre released. Youll also receive expert commentary on what these developments mean for you and your content.  

    Local SEO benefits some alternative search engines

    Some search engines offer tailored local insight, meaning that you can use local SEO practices to target these platforms. 

    For example, Bing offers Bing Places, a directory of local companies, and is committed to offering users search results tailored around their location.  

    As Yahoo! is powered by Bing, boosting your reach on one platform will translate to growth on the other.  

    Bings dedication to sharing local search results means that, if you use local search terms in your content, you will be more likely to rank on this platform.  

    Flash is Bings favourite

    Bing also has technical preferences, with a focus on Flash and Silverlight based applications: 

    Rich Internet Applications (RIAs), such as Microsoft Silverlight and Adobe Flash Player, can improve the aesthetic appearance or the functional ability of a site for end-users. However, the way these technologies are typically implemented often causes problems with the ability of search engine bots to crawl and fetch any meaningful data from the site. 

    Sites extensively employing JavaScript and AJAX technologies can also cause the same problems for search. This is because search bots are primarily readers of the text. It is much more difficult to parse and derive indexable, relevant content from graphic and multimedia content. As a result, sites who implement these technologies without regard to search bot accessibility often unexpectedly see their search rankings drop off (from the search bot perspective, the site simply has little-to-no indexable content available, which adversely affects its relevance to the site’s main theme).

    As such, you need to try to move your site onto RIAs where possible and optimize the meta tags and description tags to help you achieve strong results on Bing.  

    Ask.com is optimized in a similar way to voice search

    Voice search is one of the fastest-growing trends in the SEO market currently, with so many consumers now turning to their smart devices and virtual assistants to give them the information they need. 

    When optimizing content for voice search, the key is to answer questions, as the majority of verbal searches are questions. 

    This is because voice search queries use natural voice commands, as users are speaking rather than typing. Google has identified that almost 70% of searches on Google Assistant are performed in natural language, rather than the keywords that you often find in written searches.  

    Its more natural to ask a question than it is to yell keywords towards your device. As such, optimizing your content for voice search involves including questions and providing the answers.  

    Creating content with question and answers in it not only helps you to boost your voice search results, but also helps you to optimize your content for Ask.com. 

    As Ask.com focuses on providing users with the answers to questions, by also focusing on this format, you can kill two birds with one stone and optimize your writing for both Ask.com and voice search.  

    Never sacrifice quality and relevance

    The key to search engine visibility and increased traffic will always be quality and relevance. No matter what tools you use and what search engines you choose to target, you should always focus on creating readable content that grabs your readers attention.  

    Always make sure that your content is thoroughly proofread and that you havent stuffed too many keywords into obscure positions. If you start every sentence with your target keywords, then search engines will pick up on this and may penalize your site.  

    A Google penalty is a serious issue, but a penalty from any other search engine can also cause you major problems.  

    Tools such as Grammarly or Hemingway Editor can help with readability, while SEO Surfer can help you understand keyword density and content layout. It should be mentioned that SEO Surfer takes much of its data from Google, but the tool can be useful for spreading out your keywords to help boost your rankings in a variety of search engines.  

    Conclusion  

    At the end of the day, content remains king in the SEO market. Creating quality content needs to remain your key focus, with optimizing it and getting it in front of your target audience your second priority.  

    As with any business decision, when youre optimizing your content, you should try to spread your risk. Aim to create content that is valuable not only for Google but a range of other search engines too.   

    Hannah Stevenson is the Content Marketing Manager at UK Linkology. 

    The post How writers can optimize content for a variety of search engines appeared first on Search Engine Watch.

    Search Engine Watch


    COVID-19 and Pharma paid search: How should SEM marketers optimize amid the changing landscape?

    April 28, 2020 No Comments

    30-second summary:

    • Johnson & Johnson CEO Alex Gorsky has said that the COVID-19 pandemic is one of the most significant events ever experienced.
    • Pharma search engine marketers want answers to these key questions – “How are these shifts impacting the search messaging landscape? How to adapt SEM campaigns to remain relevant to shifting patient/provider needs and stay competitive in the search auction?”
    • Ian Orekondy shares insights from AdComplyRx analysis of 50k+ SEM text ads from over 500 prescription treatment brands serving on thousands of keywords.
    • The analysis showed that copy messaging with search terms like “savings” and “coverage” has rapidly risen.
    • He highlights some quotes from industry veterans to help paid search marketers gain clarity on these conundrums.

    Johnson & Johnson CEO Alex Gorsky said on Tuesday’s earnings call that the worldwide COVID-19 pandemic is “one of the most significant events that any of us have ever experienced”.

    It has caused more than 120,000 deaths and is impacting every industry, especially the pharmaceutical sector.    

    According to IQVIA’s ‘COVID-19 Market Report’, published April 10th with data through March 27th, “increasing unemployment pushes more patients onto Medicaid and into economic fragility. [Likewise], providers are financially strained due to decreased visits, reduced elective surgeries, and lower reimbursement rate [and] many pharma companies are expanding patient support in response to the COVID-19 crisiswith free branded drugs through Patient Assistance Programs…and copay reduction & patient savings programs. 

    These companies include Pfizer, Johnson & Johnson, Allergan, Lilly, Bristol-Myers Squibb, Abbvie, and more. 

    So the question for pharma search engine marketers is? 

    How are these shifts impacting the search messaging landscape?

    And how do we adapt our campaigns to remain relevant to shifting patient/provider needs and stay competitive in the search auction?

    To help answer this question, AdComplyRx analyzed over 50k SEM text ads from over 500 prescription treatment brands serving on thousands of keywords (condition, treatment and brand terms) comparing the frequency of mentions of various pandemic-relevant messages over two time periods: 4/1-4/13 (post-stay-at-home orders) vs 3/1-3/13 (pre-stay-at-home orders).

    Pharma search ads shift to “savings” and “coverage” amid COVID-19 pandemic 

    Since Google’s ad policy restricts ad messages containing terms like COVID-19 or Coronavirus, AdComplyRx also measured terms like “cost”, “savings”, “co-pay”, “supply chain”, “availability”, “fill”, and “delivery” in terms of frequency of mention within paid search (SEM) text ad copy from prescription drug brands in the U.S. 

    Based on this analysis, AdComplyRx is observing the following shifts in Rx Pharma SEM advertising during the COVID-19 pandemic. 

    Mentions of “savings” grew by 11%

    These included “savings card”, “co-pay card savings”, “patient savings, register for savings.

    Mentions of “coverage” grew by 40%

    These included “insurance coverage” and “formulary coverage” saw the most significant increases in the frequency of mentions within Rx pharma brand SEM text ads. 

    Category-level insights  

    The top categories that showed the greatest increase in the frequency of the words “savings” or “coverage” were Hematology, Diabetes and Respiratory.

    The Oncology category showed little change in the frequency of any of these messages during these time periods. 

    Mentions of “savings” in paid search (SEM) ad copy messaging 

    • Haematology (+96%) 
    • Diabetes (+48%)

    Mentions of “coverage” in paid search (SEM) ad copy messaging

    • Respiratory (+184%) 
    • Haematology (+156%) 

    COVID-19 and Pharma paid search (SEM) insights infographic by AdComplyRx

    Since it is unlikely most pharma brands have already created new search ad messages and received approval from their med-legal team, this data suggests that Google’s advertising platform may be dynamically adjusting existing pharma brand search campaigns to better serve patients and providers based on their search behaviour (for example, search interest and click behaviour).  

    How should pharma brands shift their search engine marketing (SEM) campaigns? 

    • Search Engine Machine Learning algorithms are working on behalf of advertisers by prioritizing the most engaging messaging themes thanks to functionality like:
    • Responsive search ads
    • Optimized ad rotation
    • Extension prioritization

    In light of this, GVP of Search at Publicis Health Media, Peter Levin suggests,

    “Think beyond refreshing creative through new asset submissions.”

    “Brands should strive to maximize the impact of already approved assets and prioritize patient assistance and treatment accessibility messages.

    Pharma brands can ensure a nimble response in the short term by enabling these features and making sure priority messages are available across campaigns while trimming away lower priority message themes. Don’t neglect planning out long-term responses with new messaging strategies and landing page content, as well as the potential for new paid keywords as the editorial landscape evolves.” 

    Sri Nagubandi, Director of Search at Syneos Health suggests that,

    Pharma brands should take both a shortterm and a long-term approach as this will be an ongoing concern globally until there is a viable vaccine. In the short term, brands should begin tracking ‘Brand name + Covid and ‘Category Name + Covid’-related search volume to understand how patients and providers are searching around their specific brand situationFor example, we are seeing some categories like oncology where search activity is increasing as patients and caregivers seek to understand the impact on ongoing treatment, especially for the immunocompromised.”

    Additionally, from an organic SEO perspective, Nagubandi cautions,

    “Do not use a takeover modal window on your brand.com website to communicate Covid-19 related messaging. Google has not suspended best practices because of the pandemic. Instead, use an updated header above the top navigation.” 

    How can these insights impact pharma brands SEM campaigns to improve patients’ lives? 

    With marketing budgets in flux and paid search (SEM) being one of the few marketing channels that many marketers will continue to invest inbrands will be relying more than ever on search traffic to support their business and serve patients and providers during this challenging time. Since search advertising is a competitive, auction-based marketplace where advertisers with the most relevant and engaging ad copy typically see improved ad click-through rate (CTR) and reduced cost per click (CPC), ensuring your brands’ search campaigns contain frequent mentions of the above messages can help brands reach more patients and providers with potentially life-saving treatment messages within limited marketing budgets.

    Ian Orekondy is the founder and CEO of AdComplyRx, a pharma ad tech stack based in New York.

    The post COVID-19 and Pharma paid search: How should SEM marketers optimize amid the changing landscape? appeared first on Search Engine Watch.

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