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Tag: User

A User Programmable Automated Assistant from Google

February 4, 2022 No Comments

A History of User Programmable Automated Assistant Patents Google was granted a patent this week specifically about user-programmable automated assistants. It’s one of a number of patents about how Google is building that functionality into devices it is creating. There are a few more that have recently been granted which I haven’t written about yet. … Read more

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Measure conversions while respecting user consent choices

December 22, 2021 No Comments

With so many people around the world turning to online shopping this year, advertisers need to measure how effective their digital campaigns are at driving online sales. What’s more, data-protection authorities in Europe may now require many businesses to obtain consent from users on their digital properties for activities related to advertising and/or analytics—impacting advertisers’ understanding of how users are converting on their sites.

Last month, we shared that we’ve integrated our ads systems with the IAB Europe’s Transparency and Consent Framework (TCF) v2.0. For businesses that choose to use this method to gather user consent, Google’s ad systems will read and respect the Transparency and Consent String, so businesses can comply with applicable regulations.

For advertisers who choose not to use TCF v2.0, we’re introducing a new solution to offer more flexibility in how they use Google tags alongside their user consent tools. Consent Mode introduces two new tag settings that manage cookies for advertising and analytics purposes for advertisers using the global site tag or Google Tag Manager. These two settings can be used to customize how Google tags behave before and after users make their consent decisions – helping advertisers more effectively measure conversions, while respecting user consent choices for ads cookies and analytics cookies.

Using Consent Mode with Google’s ad platforms

Attributing conversions to the campaign that drove them is a key priority for advertisers. It helps them better optimize campaign bids and reallocate budget towards the best performers. With Consent Mode, advertisers can achieve greater insight into conversion data while also making sure that the Google tags helping them measure conversions are reflecting users’ consent choices for ads cookies.

Once Consent Mode is implemented, advertisers will have access to a new tag setting, “ad_storage,” which controls cookie behavior for advertising purposes, including conversion measurement. If a user does not provide consent for ads cookies, Google tags will not use cookies for advertising purposes.

Let’s say someone visits your website and makes their consent selection for the use of ads cookies on your cookie consent banner. With Consent Mode, your Google tags will be able to determine whether or not permission has been given for your site to use cookies for advertising purposes for that user. If a user consents, conversion measurement reporting continues normally. If a user does not consent, the relevant Google tags will adjust accordingly and not use ads cookies, instead measuring conversions at a more aggregate level.

E02573839-Google-GMP-Consent-Mode-Blog-Table-Aug20_v05_Google-Keyword-Blog-Inline.jpg

With Consent Mode, you can update Google tag behavior based on the user consent selection.

With Consent Mode, campaigns running on Google Ads, Campaign Manager, Display & Video 360, and Search Ads 360 will be able to continue reporting conversions – while respecting users’ consent choices for ads cookies. And because you’re able to retain conversion measurement in your campaign reporting, you’ll be able to continue attributing conversions to the right campaign and optimize your campaign bidding efficiently.

Using Consent Mode with Google Analytics

Consent Mode also works with Google Analytics. This means that Analytics will be able to understand and respect user consent for ads cookies. For example, when the “ad_storage” tag setting is disabled for unconsented users, Analytics will not read or write ads cookies, meaning that optional features that rely on Google signals, like remarketing, will be disabled.

In addition to the “ad_storage” tag setting, Consent Mode provides advertisers with a new tag setting, “analytics_storage,” which controls analytics cookie usage. Let’s say you would like to request consent for both analytics and ads cookies from users on your website. You can use Consent Mode to update Google tag behavior based on the user selection for each type of cookie. Analytics will adjust data collection based on user consent for each of the “ad_storage” and “analytics_storage” settings. For example, if a user does not provide consent for ads cookies (and therefore advertising purposes are disabled), but does provide consent for analytics cookies, advertisers will still be able to measure site behavior and conversions in Analytics as the “analytics_storage” setting will be enabled.

Getting started

Consent Mode is available in beta to a limited number of advertisers that operate in Europe and already use the global site tag or Tag Manager. To learn more about the feature, visit our Help Center here.

If you’re interested in getting started with Consent Mode, please reach out to your Google account team. Implementing Consent Mode requires adding a few lines of code above your global site tag or Tag Manager container. To help with this process, we have partnered closely with several Consent Management Platforms. A few are already integrated with Consent Mode and are ready to help.

cmp logo 1200.jpg

Consent Management Platforms that are already integrated with Consent Mode.

Changes designed to improve user privacy are continuing to impact the digital advertising ecosystem, and we’re committed to helping your business navigate this new environment. To learn more about steps you can take, download our privacy playbook. And stay tuned for more new capabilities to help you manage and respect user consent choices for ads and analytics cookies across platforms.

Google Analytics Blog


Malicious Google Play Apps Stole User Banking Info

December 1, 2021 No Comments

Using tricks to sidestep the app store’s restrictions, malware operators pillaged passwords, keystrokes, and other data.
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Respect user consent choices with Google Tag Manager

November 23, 2021 No Comments

More people than ever before are purchasing goods and services online, bringing new opportunities for businesses to reach a growing base of customers. At the same time, restrictions around cookies and identifiers are changing the ways businesses understand the customer journey.  We’ve heard from businesses that they need new, easy-to-use solutions to keep pace with these industry changes, especially solutions that will continue to provide critical insights on campaign performance, while maintaining user privacy.

At Google Marketing Livestream, we shared our belief that it’s possible to improve privacy while still delivering business results and highlighted a few solutions that help. For example, Consent Mode lets advertisers customize how Google tags behave before and after users make their consent decisions. Consent Mode also informs conversion modeling to help bridge any measurement gaps that may occur due to cookie consent choices.

Our customers have shared with us that they would like simpler ways to ensure that all tags on their websites respect cookie consent choices. To make this process easier, we’re unveiling a new consent experience in Tag Manager. Starting today, users of Tag Manager and Tag Manager 360 will be able to directly integrate with Consent Mode and easily incorporate user consent into the behavior of all tags on their website.

Integrate your consent management solution

If your business operates in a region that requires you to collect user consent for certain operations, like the European Economic Area or the United Kingdom, you may need a consent management solution. And if you’re using Tag Manager to manage all the tags on your site, you’ll need to integrate Tag Manager with the consent management solution you’ve selected. But integrating these two can be complicated and require changes to website code.

We’re making it possible to remove that integration step altogether. Starting today, consent management solutions can build tag templates directly into the Community Template Gallery using a new set of sandboxed JavaScript APIs that will work with Consent Mode. We’re also introducing a new trigger type, Consent Initialization, which enables tags that require user consent choice to fire before all other tags.

Let’s say you’re a clothing retailer operating in the United Kingdom. You’ve decided to work with a consent management solution to display a consent banner to your customers and want to integrate it with your Tag Manager account. If your consent management solution has a tag template available in the Community Template Gallery, you can now add it to your container. With the Consent Initialization trigger, this tag will deploy your consent banner as soon as someone lands on your website. This enables you to collect a user’s consent choice before other tags in your container load.

Benefit from consent support on all your tags

Last year we announced that tags for Google advertising and analytics products will respect consent choices for ads cookies and analytics cookies when Consent Mode is in use. But to control how other third-party tags behave for these and other types of user consent, many businesses have turned to a custom tag setup in Tag Manager, which can be difficult to implement and manage.

Now in Tag Manager, you’ll be able to see and customize each tag’s consent settings. You can see which types of consent each tag requires. For example, a specific tag may already be adjusting its behavior based on user consent for ads cookies. And you can specify whether any additional types of consent are necessary for the tag to fire, like requiring consent for analytics cookies. We’re introducing new consent types into Tag Manager as well. These consent types correspond to options you might include in your consent management solution. If a user does not give consent to the specific consent types you’ve selected for the tag, the tag will not run.

Image of “Consent Settings (beta)” section under “Advanced Settings” at the bottom of each tag configuration.

Add additional consent in order for your tag to fire

Many consent management platforms are already compatible with the ad storage and analytics storage settings. You can see a full list in our Help Center.

Gain a complete view of your tags’ consent settings

For a complete view of the consent settings across all the tags in your container, you can now enable a new Consent Overview from your container settings. Once enabled, this overview will be available from the Tags screen. From here you can also manage consent settings in bulk, like adding a personalization storage consent type to multiple tags at once.

GIF of opening the Consent Overview screen and bulk editing tags’ consent requirements.

Access the Consent Overview and manage consent settings in bulk

All of these capabilities are available in beta in all Tag Manager and Tag Manager 360 accounts today. These updates will help you preserve online measurement while respecting user consent choices. Stay tuned for more information on other privacy-safe measurement solutions that we announced today.

Google Analytics Blog


Respect user consent choices with Google Tag Manager

May 28, 2021 No Comments

More people than ever before are purchasing goods and services online, bringing new opportunities for businesses to reach a growing base of customers. At the same time, restrictions around cookies and identifiers are changing the ways businesses understand the customer journey.  We’ve heard from businesses that they need new, easy-to-use solutions to keep pace with these industry changes, especially solutions that will continue to provide critical insights on campaign performance, while maintaining user privacy.

At Google Marketing Livestream, we shared our belief that it’s possible to improve privacy while still delivering business results and highlighted a few solutions that help. For example, Consent Mode lets advertisers customize how Google tags behave before and after users make their consent decisions. Consent Mode also informs conversion modeling to help bridge any measurement gaps that may occur due to cookie consent choices.

Our customers have shared with us that they would like simpler ways to ensure that all tags on their websites respect cookie consent choices. To make this process easier, we’re unveiling a new consent experience in Tag Manager. Starting today, users of Tag Manager and Tag Manager 360 will be able to directly integrate with Consent Mode and easily incorporate user consent into the behavior of all tags on their website.

Integrate your consent management solution

If your business operates in a region that requires you to collect user consent for certain operations, like the European Economic Area or the United Kingdom, you may need a consent management solution. And if you’re using Tag Manager to manage all the tags on your site, you’ll need to integrate Tag Manager with the consent management solution you’ve selected. But integrating these two can be complicated and require changes to website code.

We’re making it possible to remove that integration step altogether. Starting today, consent management solutions can build tag templates directly into the Community Template Gallery using a new set of sandboxed JavaScript APIs that will work with Consent Mode. We’re also introducing a new trigger type, Consent Initialization, which enables tags that require user consent choice to fire before all other tags.

Let’s say you’re a clothing retailer operating in the United Kingdom. You’ve decided to work with a consent management solution to display a consent banner to your customers and want to integrate it with your Tag Manager account. If your consent management solution has a tag template available in the Community Template Gallery, you can now add it to your container. With the Consent Initialization trigger, this tag will deploy your consent banner as soon as someone lands on your website. This enables you to collect a user’s consent choice before other tags in your container load.

Benefit from consent support on all your tags

Last year we announced that tags for Google advertising and analytics products will respect consent choices for ads cookies and analytics cookies when Consent Mode is in use. But to control how other third-party tags behave for these and other types of user consent, many businesses have turned to a custom tag setup in Tag Manager, which can be difficult to implement and manage.

Now in Tag Manager, you’ll be able to see and customize each tag’s consent settings. You can see which types of consent each tag requires. For example, a specific tag may already be adjusting its behavior based on user consent for ads cookies. And you can specify whether any additional types of consent are necessary for the tag to fire, like requiring consent for analytics cookies. We’re introducing new consent types into Tag Manager as well. These consent types correspond to options you might include in your consent management solution. If a user does not give consent to the specific consent types you’ve selected for the tag, the tag will not run.

Image of “Consent Settings (beta)” section under “Advanced Settings” at the bottom of each tag configuration.

Add additional consent in order for your tag to fire

Many consent management platforms are already compatible with the ad storage and analytics storage settings. You can see a full list in our Help Center.

Gain a complete view of your tags’ consent settings

For a complete view of the consent settings across all the tags in your container, you can now enable a new Consent Overview from your container settings. Once enabled, this overview will be available from the Tags screen. From here you can also manage consent settings in bulk, like adding a personalization storage consent type to multiple tags at once.

GIF of opening the Consent Overview screen and bulk editing tags’ consent requirements.

Access the Consent Overview and manage consent settings in bulk

All of these capabilities are available in beta in all Tag Manager and Tag Manager 360 accounts today. These updates will help you preserve online measurement while respecting user consent choices. Stay tuned for more information on other privacy-safe measurement solutions that we announced today.


Google Analytics Blog


Measure conversions while respecting user consent choices

March 10, 2021 No Comments

With so many people around the world turning to online shopping this year, advertisers need to measure how effective their digital campaigns are at driving online sales. What’s more, data-protection authorities in Europe may now require many businesses to obtain consent from users on their digital properties for activities related to advertising and/or analytics—impacting advertisers’ understanding of how users are converting on their sites.

Last month, we shared that we’ve integrated our ads systems with the IAB Europe’s Transparency and Consent Framework (TCF) v2.0. For businesses that choose to use this method to gather user consent, Google’s ad systems will read and respect the Transparency and Consent String, so businesses can comply with applicable regulations.

For advertisers who choose not to use TCF v2.0, we’re introducing a new solution to offer more flexibility in how they use Google tags alongside their user consent tools. Consent Mode introduces two new tag settings that manage cookies for advertising and analytics purposes for advertisers using the global site tag or Google Tag Manager. These two settings can be used to customize how Google tags behave before and after users make their consent decisions – helping advertisers more effectively measure conversions, while respecting user consent choices for ads cookies and analytics cookies.

Using Consent Mode with Google’s ad platforms

Attributing conversions to the campaign that drove them is a key priority for advertisers. It helps them better optimize campaign bids and reallocate budget towards the best performers. With Consent Mode, advertisers can achieve greater insight into conversion data while also making sure that the Google tags helping them measure conversions are reflecting users’ consent choices for ads cookies.

Once Consent Mode is implemented, advertisers will have access to a new tag setting, “ad_storage,” which controls cookie behavior for advertising purposes, including conversion measurement. If a user does not provide consent for ads cookies, Google tags will not use cookies for advertising purposes.

Let’s say someone visits your website and makes their consent selection for the use of ads cookies on your cookie consent banner. With Consent Mode, your Google tags will be able to determine whether or not permission has been given for your site to use cookies for advertising purposes for that user. If a user consents, conversion measurement reporting continues normally. If a user does not consent, the relevant Google tags will adjust accordingly and not use ads cookies, instead measuring conversions at a more aggregate level.

E02573839-Google-GMP-Consent-Mode-Blog-Table-Aug20_v05_Google-Keyword-Blog-Inline.jpg

With Consent Mode, you can update Google tag behavior based on the user consent selection.

With Consent Mode, campaigns running on Google Ads, Campaign Manager, Display & Video 360, and Search Ads 360 will be able to continue reporting conversions – while respecting users’ consent choices for ads cookies. And because you’re able to retain conversion measurement in your campaign reporting, you’ll be able to continue attributing conversions to the right campaign and optimize your campaign bidding efficiently.

Using Consent Mode with Google Analytics

Consent Mode also works with Google Analytics. This means that Analytics will be able to understand and respect user consent for ads cookies. For example, when the “ad_storage” tag setting is disabled for unconsented users, Analytics will not read or write ads cookies, meaning that optional features that rely on Google signals, like remarketing, will be disabled.

In addition to the “ad_storage” tag setting, Consent Mode provides advertisers with a new tag setting, “analytics_storage,” which controls analytics cookie usage. Let’s say you would like to request consent for both analytics and ads cookies from users on your website. You can use Consent Mode to update Google tag behavior based on the user selection for each type of cookie. Analytics will adjust data collection based on user consent for each of the “ad_storage” and “analytics_storage” settings. For example, if a user does not provide consent for ads cookies (and therefore advertising purposes are disabled), but does provide consent for analytics cookies, advertisers will still be able to measure site behavior and conversions in Analytics as the “analytics_storage” setting will be enabled.

Getting started

Consent Mode is available in beta to a limited number of advertisers that operate in Europe and already use the global site tag or Tag Manager. To learn more about the feature, visit our Help Center here.

If you’re interested in getting started with Consent Mode, please reach out to your Google account team. Implementing Consent Mode requires adding a few lines of code above your global site tag or Tag Manager container. To help with this process, we have partnered closely with several Consent Management Platforms. A few are already integrated with Consent Mode and are ready to help.

cmp logo 1200.jpg

Consent Management Platforms that are already integrated with Consent Mode.

Changes designed to improve user privacy are continuing to impact the digital advertising ecosystem, and we’re committed to helping your business navigate this new environment. To learn more about steps you can take, download our privacy playbook. And stay tuned for more new capabilities to help you manage and respect user consent choices for ads and analytics cookies across platforms.


Google Analytics Blog


How to use content marketing to boost ecommerce conversions and user experience

December 7, 2020 No Comments

30-second summary:

  • Content marketing is a strategy focused on creating, publishing, and distributing content that’ll get people to take action.
  • It’ll help your ecommerce site to rank higher on search engines without having to spend a lot on ads.
  • Publishing content that your potential customers find helpful and packed with valuable information helps you win their trust, making it easier to convince them to buy.
  • Content like how-to videos, online courses, and infographics are easier to consume and boost your site visitors’ overall user experience.

When you think of content marketing, you might imagine this only applies to digital agencies and online service providers, not in fact for ecommerce businesses, let alone the value it creates when you boost ecommerce conversions through these efforts. But let’s take a moment to review the definition of content marketing, just so we can see exactly how it might apply to just about any business, especially ecommerce:

The Content Marketing Institute defines content marketing as:

“[the] strategic marketing approach focused on creating and distributing valuable, relevant, and consistent content to attract and retain a clearly-defined audience — and, ultimately, to drive profitable customer action.”

So while it’s a default strategy for service providers, SaaS companies, and digital agencies, we can start to see how content plays a positive role even for ecommerce businesses.

Benefits of content marketing for ecommerce businesses

For one thing, content helps you connect to your customers with real value instead of posts that only seek to close a sale. And other benefits include boosting your chances of getting discovered organically on search engines as well as establishing your company as a trusted leader in your space, thus increasing positive perceptions about your brand and business online.

This relationship can go a long way for your ecommerce store, in that users and customers are more likely to buy from you eventually because you are a brand they trust now. They’ll also know they can expect more from your ecommerce store if they’re not necessarily looking to shop, such as helpful blogs, guides, and other inspiring content.

stats on content marketing and ecommerce business

Source

We’re sure you want to get started with making content to boost ecommerce conversions. So we’ve compiled seven of the best content marketing tactics, especially for online stores.

Seven content marketing strategies to boost ecommerce conversions

1. Create products or service guides

Your ecommerce store can create product or service guides that educate your customers about important information in your industry. Take invoicing company Wave, for instance. They have a dedicated blog that publishes helpful guides within their site to improve their user experience.

Most of their content is geared towards small business owners, helping them with accounting and bookkeeping tips that go beyond simply promoting their product features and benefits. 

boost ecommerce conversions - create product guides

2. Share product reviews

You can share reviews about your product that appear on different websites and blogs both as content and social proof. Find what good things people are making about your brand, and then highlight the most compelling things from the review to share on your own platforms.

Take this blog post reviewing the top merchant service providers in Canada, for example. If a company featured in this detailed roundup, they might take a few lines from the review that was about the author’s experience with their service, then be able to share what others are saying about their brand.

You can also take screenshots of social media conversations about your products and share these on your website. This is exactly what Beauty Bakerie does on its ecommerce store and its Instagram account.

boost ecommerce conversions - share product reviews to build trust

These posts provide social proof to its would-be customers and make it easy for their existing customers to share with their networks, further expanding the makeup brand’s reach, giving them more customers that want to buy their products.

3. Post infographic “pamphlets”

Ecommerce shopping doesn’t provide consumers with the opportunity to see, hold, and feel products in person, so they may sometimes hesitate to make a purchase if there are things they’re unsure about.

Ease their doubts by answering any FAQs and questions you have about your products by compiling important information and graphics in a digital “pamphlet.” This pamphlet is a visual way to present information like product size charts, measurements, and even product care tips.

Include this in key areas of your ecommerce store, including specific product pages where applicable, but also even a dedicated page that customers can find from navigation menus and site footers.

Example of an infographic pamphlet for ecommerce stores.

boost ecommerce conversions - post infographics or pamphlets

Source

4. Provide online courses

Can you go the extra mile and give customers an entire high-value online course that will help deepen your relationship with them? Your course might even feature your own store’s products, and the information you provide complements your product use cases and benefits.

For instance, ecommerce businesses that sell computer gadgets and accessories for design professionals can provide their customers with online courses on topics such as remote user experience design that feature specific gadgets and tools they sell on their platform.

This tactic might be time-consuming but think of other ways you might offer your customers a helpful online course to complement your products. Perhaps instead of creating the course yourself, you can partner with known influencers or thought leaders in your niche. Or you can make the online course a very simple 5-day email course that features small action steps customers can take every day to get closer to a specific goal.

Get Influencers to Create User-Generated Content

Influencers are a fantastic addition to your content marketing strategy because influencers are content creators themselves. 

When you pick the right influencers, they’re likely to already have access to an audience, no matter how big or small, that trusts them and values their content. Content created by influencers are also a combination of review, product explanation, and even testimonials—and they speak from their own perspective and experiences that, to the average customer, is more authentic than branded content.

Here’s an example of this influencer tactic in action. Hikers and influencers who live and create content around the adventurous lifestyle have featured Patagonia, a well-known adventure brand selling hiking apparel, in their blog posts and YouTube videos.

boost ecommerce conversions - approach influencers for UGC

5. Submit guest posts

If your ecommerce doesn’t have your own blog, why not pitch your stories to other publications?

Guest blogging can benefit ecommerce stores because they’re able to publish their content in another site that already has its own audience. Of course, choose blogs and sites that fit into your niche and market. 

Say an ecommerce store sells all things coffee and wants to create a guest post. They might not have their own blog on their website, but they can partner with known food and even coffee bloggers like in the example below,  to create free content on their site.

The blog post can link back to their page, and get the site more backlinks. But more than that, it also appeals to the readers that already frequent this blog by providing helpful tips and tricks about things they’re interested in. The blog might also feature the ecommerce store’s products, so users not only have a high-value post to enjoy but can also find out where to shop for things that will help them implement the tips in the guest post.

boost ecommerce conversions - submit guest posts

6. Create how-to videos

How-To videos are some of the easiest things you can include in your ecommerce content marketing strategy. A survey by Social Marketing Writing found that how-to posts and case studies were the most credible types of content in their opinion, while Omnicore reports that 61% of consumers make a purchase based on a blog recommendation, with how-to content giving the best overall response rates.

boost ecommerce conversions and ux - create how to content

Source

One great thing about creating how-to videos is that you’re able to showcase your products right away. 

Here are just a few examples of how well you can use how-to videos to boost ecommerce conversions and improve the overall user experience of your products:

  • Online groceries and supermarkets can publish how-to videos that feature recipes of different dishes that then highlight the products on their store
  • Beauty brands can create how-to videos that help people achieve a specific makeup look or get better skin
  • Athleisure stores can create short workout videos to get certain results that highlight their products’ benefits
  • Furniture stores online can publish how-to videos that teach people how to set up their products step-by-step instead of referring to just a manual

There are, of course, other creative ways you can use how-to videos in your content marketing strategy, so think outside the box and aim to answer your customers’ burning desires with high-value content that complements your ecommerce products.

Key takeaways

Ecommerce stores can benefit from content marketing in that it can differentiate your brand from even the most crowded niches while deepening your relationship with your audience. Use the tips above to help you boost ecommerce conversions and improve user experience using the best content marketing tips you can implement today.

Kevin Payne is a content marketing consultant that helps software companies build marketing funnels and implement content marketing campaigns to increase their inbound leads.

The post How to use content marketing to boost ecommerce conversions and user experience appeared first on Search Engine Watch.

Search Engine Watch


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

June 23, 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 can 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 may 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 well-formed, 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 well-formed. 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, 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.

As we have seen from this patent, the analysis that is undertaken to find canonical queries also is used to surface “people also search for” queries, which may also be canonicalized and displayed in search results.

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 the intent behind the query may be.


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InVision adds new features to Freehand, a virtual whiteboard tool, as user demand surges

June 11, 2020 No Comments

No business is immune to the effects of the coronavirus pandemic. We’ve seen Airbnb — a company particularly susceptible to this Black Swan event — go through an insane design sprint. Even enterprise collaboration tools have felt it, with Box readjusting its product road map to focus on how the tool worked for remote employees.

InVision has also seen the change in its users behavior and adapted accordingly. Freehand, the company’s collaborative white boarding tool has seen a huge surge in users and the startup has added a handful of new features to the product.

The company says that Freehand is seeing 130 percent growth in weekly active users since March.

New features include sticky notes that come in multiple color, size and text options, as well as templates to give teams a jumping off point for their whiteboarding exercise. Freehand has six new templates to start — brainstorming, wireframing, retrospectives, standups, diagrams, and ice breakers &madsh; and has plans to add more soon.

InVision has also added a ‘presenting’ mode to Freehand.

Because this virtual whiteboard has no space constraints, it can literally zoom out to infinity and is restricted only by the imagination of the team working on it. In ‘presenting’ mode, a team leader can take over the view of the virtual whiteboard to guide their team through one part of the content at a time.

Freehand has an integration with Microsoft Teams and Slack, and also has a new shortcut where users can type ‘freehand.new’ into any browser to start on a fresh whiteboard.

Interestingly, the user growth around Freehand doesn’t just come from the usual suspects of design, product and engineering teams. Departments across organizations, including HR, Marketing and IT teams, are coming to Freehand to collaborate on projects and tasks. More than 60 percent of Freehand users are not coming from the design team.

InVision has also added some fine-tuning features, such as a brand new toolbar to allow for easier drawing of shapes, alignment, color and opacity features, and better controls for turning lines into precise arrows or end-points for diagrams.

One of the most interesting things about Freehand is that it allows for democratized access to the whiteboard itself. With no restraints on time or space, and with no one gatekeeping up at the front of the room holding the marker, all members of a team can go in and add their thoughts and ideas to the whiteboard before, during or after a meeting.

“One of the nice things about a whiteboard or a virtual whiteboard like this one is it removes it removes the aspects of the restrictions of time and space, so teams can have more efficient meetings where they get the benefit of democratic input without the cost of having only one person at a time being able to speak or add,” said David Fraga, InVision President. “It offers a synchronous collision of collaboration.”

InVision has raised a total of $ 350 million from investors like FirstMark, Spark, Battery, Accel and Tiger Global Management. The company now boasts more than 7 million total registered users, with 100 of the Fortune 100 companies using the product. InVision is also part of the $ 100 million ARR club.


Enterprise – TechCrunch


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

June 11, 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.


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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