Google+ Local

Google Advanced Verification & Home Services Ads – An Update

Mike Blumenthal

The top contributors met with Google and we were educated as to the changes that are taking place in both the Advanced Verification programs and the Home Service Ads program. Both are evolving from the model rolled out in San Diego which required all local listings to be advanced verified. One big change is that … Continue reading Google Advanced Verification & Home Services Ads – An Update →

Apple Maps Featured Google Voice Search

SEO and Voice Search Goes Together Like Sardines and Peanut Butter

Dan Leibson

Voice search is all the rage. To hear it from a marketing or search technologist, the voice search revolution is on us. Lowering barriers to access and radically changing the way we interact with devices. There is only one problem, take it away Dr. Malcolm I hate to break it to everyone, but voice search […]

The post SEO and Voice Search Goes Together Like Sardines and Peanut Butter appeared first on Local SEO Guide.

PPC Tips, Tricks, & Tools

Lowering Your Brand Bids Can Help Increase Conversion Volume

Lauren Miller

When businesses start an Adwords account, one of the most common questions we often hear is: “Do we really have to have a brand campaign?

The answer is yes, businesses most definitely should run a brand campaign. Here’s why.

Right now, as we speak, competitors are most likely bidding on your organization’s brand terms.…

The post Lowering Your Brand Bids Can Help Increase Conversion Volume appeared first on Seer Interactive.

All Things SEO Column Channel: SEM Google: AdWords Google: AdWords: AdWords Scripts How To: PPC Search Engine Land Search Marketing: Landing Pages Uncategorized

5 non-traditional skills to look for in a PPC account manager

Todd Saunders

You may not expect an aspiring PPC marketer to know HTML5 or JavaScript, but contributor Todd Saunders argues that these skills — among others — are key to being a stellar staffer and to hiring one.

The post 5 non-traditional skills to look for in …

Apple Channel: SEO Google: Accelerated Mobile Pages / AMP Search Engine Land Uncategorized

Will iOS 11 help solve Google’s AMP (Accelerated Mobile Pages) URL problems?

Michelle Robbins

Tests using Safari on iOS 11 beta 7 show URLs are converted back to original when saving or sharing to apps via Share Sheet.

The post Will iOS 11 help solve Google’s AMP (Accelerated Mobile Pages) URL problems? appeared first on Search Engine Land.

Please visit Search Engine Land for the full article.

AI and search: How one agency does keyword classification at scale using machine learning

Ben Davis

I asked a few questions of Owned Media Executive Josh Carty, to find out more.

Before we get stuck in, a reminder that this year’s Festival of Marketing features one stage (of 12) dedicated to AI in marketing – book your tickets now and see headliners such as Stephen Fry and Jo Malone.

On with the interview…

Econsultancy: Josh, could you give us an overview of how you use machine learning for keyword categorisation and SEO at iProspect?

Josh Carty: We work with one of the UK’s largest online retailers on their organic search and performance content strategy. We’re interested in understanding how their customers search for their products online and how we can use these insights to inform their SEO and performance content activity.

Given the size of our client’s product range (spanning hundreds of thousands of items) and the variety of ways in which customers can search for them, there were millions of relevant search terms for us to consider. To transform this data into meaningful strategic insights, our first objective was to classify keywords into categories. It would be impossible to classify millions of keywords into a complex product hierarchy manually, so we leveraged natural language processing and machine learning to complete the task.

Using a training set of 15,000 manually-labelled keywords, we developed a classifier that categorises keywords with a high level of accuracy. This performance was achieved in part through natural language pre-processing. Search queries are stemmed to their root, common words are discarded, and sequences of words and word pairs are used to discriminate keywords between categories. To ensure our insights best align with our client’s internal operations, we classify keywords into their internal product hierarchy. This is composed of three top-level categories, 11 subcategories and 52 individual product classes.

With a way to classify keywords at scale, we have been able to provide unprecedented insights into our client’s organic online visibility. For example, by combining data on search demand around keywords with click-through rate and search position data, we can model what proportion of traffic our client is capturing by product category.

In modelling the relationship between organic position and click-through rate, it’s important to consider variation in customer behaviour. Online shoppers may, for example, be much more willing to look lower down the search results when looking for a new laptop than a new pair of shoes. Using our classifier with a category-level regression analysis of position on click-through rate, we created individual click-through rate models for each product class.

Combining these two powerful machine learning techniques, we offer an unprecedented picture of our client’s opportunities in organic search. It has been the backbone of our organic and content strategy this year, helping us identify content opportunities and prioritise SEO recommendations.

E: We’re seeing supervised learning used for various functions in ecommerce – what do you think are the real success stories so far?

JC: Machine learning offers enormous opportunities in ecommerce, from product classification to customer segmentation to product recommendation. Given the variety of tasks machine learning seeks to solve, it can be useful to distinguish between types of machine learning algorithm.

One such distinction is between supervised and unsupervised machine learning methods. In the supervised setting, an algorithm is provided with examples of the desired output, such as the categorised keywords provided as training data in our classification project. In the unsupervised setting, the algorithm draws inferences without such examples and are typically involved in clustering and segmentation tasks.

While both methods have great application in ecommerce, the wealth of labelled data, made available by retailers’ analytics platforms, has seen an abundance of supervised learning applications. One particular success is in the development of recommender systems. These systems, familiar to users of Amazon and other online retailers, offer product recommendations based on the purchase histories of customers. In a supervised setting, these may be generated from users’ past views, ratings and purchases.

E: What size of retailer should be looking at machine learning, and how hard is it to achieve?

JC: Online retailers are exposed to millions of customers, making it impossible for any team of analysts or marketers to act on them all effectively. Machine learning offers retailers the opportunity to scale both their insights and operations in an efficient way. Whether it’s clustering thousands of customers into segments, or making personalised product recommendations to its customers, even the smallest retailers can benefit from machine learning.

With a wealth of open-source projects and free documentation available, machine learning is becoming increasingly accessible to all industries. Technologies once only available to large companies, with specialist research teams, are now accessible to insights and analytics teams.

More on AI and ecommerce:

AI and search: How one agency does keyword classification at scale using machine learning

Ben Davis

I asked a few questions of Owned Media Executive Josh Carty, to find out more.

Before we get stuck in, a reminder that this year’s Festival of Marketing features one stage (of 12) dedicated to AI in marketing – book your tickets now and see headliners such as Stephen Fry and Jo Malone.

On with the interview…

Econsultancy: Josh, could you give us an overview of how you use machine learning for keyword categorisation and SEO at iProspect?

Josh Carty: We work with one of the UK’s largest online retailers on their organic search and performance content strategy. We’re interested in understanding how their customers search for their products online and how we can use these insights to inform their SEO and performance content activity.

Given the size of our client’s product range (spanning hundreds of thousands of items) and the variety of ways in which customers can search for them, there were millions of relevant search terms for us to consider. To transform this data into meaningful strategic insights, our first objective was to classify keywords into categories. It would be impossible to classify millions of keywords into a complex product hierarchy manually, so we leveraged natural language processing and machine learning to complete the task.

Using a training set of 15,000 manually-labelled keywords, we developed a classifier that categorises keywords with a high level of accuracy. This performance was achieved in part through natural language pre-processing. Search queries are stemmed to their root, common words are discarded, and sequences of words and word pairs are used to discriminate keywords between categories. To ensure our insights best align with our client’s internal operations, we classify keywords into their internal product hierarchy. This is composed of three top-level categories, 11 subcategories and 52 individual product classes.

With a way to classify keywords at scale, we have been able to provide unprecedented insights into our client’s organic online visibility. For example, by combining data on search demand around keywords with click-through rate and search position data, we can model what proportion of traffic our client is capturing by product category.

In modelling the relationship between organic position and click-through rate, it’s important to consider variation in customer behaviour. Online shoppers may, for example, be much more willing to look lower down the search results when looking for a new laptop than a new pair of shoes. Using our classifier with a category-level regression analysis of position on click-through rate, we created individual click-through rate models for each product class.

Combining these two powerful machine learning techniques, we offer an unprecedented picture of our client’s opportunities in organic search. It has been the backbone of our organic and content strategy this year, helping us identify content opportunities and prioritise SEO recommendations.

E: We’re seeing supervised learning used for various functions in ecommerce – what do you think are the real success stories so far?

JC: Machine learning offers enormous opportunities in ecommerce, from product classification to customer segmentation to product recommendation. Given the variety of tasks machine learning seeks to solve, it can be useful to distinguish between types of machine learning algorithm.

One such distinction is between supervised and unsupervised machine learning methods. In the supervised setting, an algorithm is provided with examples of the desired output, such as the categorised keywords provided as training data in our classification project. In the unsupervised setting, the algorithm draws inferences without such examples and are typically involved in clustering and segmentation tasks.

While both methods have great application in ecommerce, the wealth of labelled data, made available by retailers’ analytics platforms, has seen an abundance of supervised learning applications. One particular success is in the development of recommender systems. These systems, familiar to users of Amazon and other online retailers, offer product recommendations based on the purchase histories of customers. In a supervised setting, these may be generated from users’ past views, ratings and purchases.

E: What size of retailer should be looking at machine learning, and how hard is it to achieve?

JC: Online retailers are exposed to millions of customers, making it impossible for any team of analysts or marketers to act on them all effectively. Machine learning offers retailers the opportunity to scale both their insights and operations in an efficient way. Whether it’s clustering thousands of customers into segments, or making personalised product recommendations to its customers, even the smallest retailers can benefit from machine learning.

With a wealth of open-source projects and free documentation available, machine learning is becoming increasingly accessible to all industries. Technologies once only available to large companies, with specialist research teams, are now accessible to insights and analytics teams.

More on AI and ecommerce:

AI and search: How one agency does keyword classification at scale using machine learning

Ben Davis

I asked a few questions of Owned Media Executive Josh Carty, to find out more.

Before we get stuck in, a reminder that this year’s Festival of Marketing features one stage (of 12) dedicated to AI in marketing – book your tickets now and see headliners such as Stephen Fry and Jo Malone.

On with the interview…

Econsultancy: Josh, could you give us an overview of how you use machine learning for keyword categorisation and SEO at iProspect?

Josh Carty: We work with one of the UK’s largest online retailers on their organic search and performance content strategy. We’re interested in understanding how their customers search for their products online and how we can use these insights to inform their SEO and performance content activity.

Given the size of our client’s product range (spanning hundreds of thousands of items) and the variety of ways in which customers can search for them, there were millions of relevant search terms for us to consider. To transform this data into meaningful strategic insights, our first objective was to classify keywords into categories. It would be impossible to classify millions of keywords into a complex product hierarchy manually, so we leveraged natural language processing and machine learning to complete the task.

Using a training set of 15,000 manually-labelled keywords, we developed a classifier that categorises keywords with a high level of accuracy. This performance was achieved in part through natural language pre-processing. Search queries are stemmed to their root, common words are discarded, and sequences of words and word pairs are used to discriminate keywords between categories. To ensure our insights best align with our client’s internal operations, we classify keywords into their internal product hierarchy. This is composed of three top-level categories, 11 subcategories and 52 individual product classes.

With a way to classify keywords at scale, we have been able to provide unprecedented insights into our client’s organic online visibility. For example, by combining data on search demand around keywords with click-through rate and search position data, we can model what proportion of traffic our client is capturing by product category.

In modelling the relationship between organic position and click-through rate, it’s important to consider variation in customer behaviour. Online shoppers may, for example, be much more willing to look lower down the search results when looking for a new laptop than a new pair of shoes. Using our classifier with a category-level regression analysis of position on click-through rate, we created individual click-through rate models for each product class.

Combining these two powerful machine learning techniques, we offer an unprecedented picture of our client’s opportunities in organic search. It has been the backbone of our organic and content strategy this year, helping us identify content opportunities and prioritise SEO recommendations.

E: We’re seeing supervised learning used for various functions in ecommerce – what do you think are the real success stories so far?

JC: Machine learning offers enormous opportunities in ecommerce, from product classification to customer segmentation to product recommendation. Given the variety of tasks machine learning seeks to solve, it can be useful to distinguish between types of machine learning algorithm.

One such distinction is between supervised and unsupervised machine learning methods. In the supervised setting, an algorithm is provided with examples of the desired output, such as the categorised keywords provided as training data in our classification project. In the unsupervised setting, the algorithm draws inferences without such examples and are typically involved in clustering and segmentation tasks.

While both methods have great application in ecommerce, the wealth of labelled data, made available by retailers’ analytics platforms, has seen an abundance of supervised learning applications. One particular success is in the development of recommender systems. These systems, familiar to users of Amazon and other online retailers, offer product recommendations based on the purchase histories of customers. In a supervised setting, these may be generated from users’ past views, ratings and purchases.

E: What size of retailer should be looking at machine learning, and how hard is it to achieve?

JC: Online retailers are exposed to millions of customers, making it impossible for any team of analysts or marketers to act on them all effectively. Machine learning offers retailers the opportunity to scale both their insights and operations in an efficient way. Whether it’s clustering thousands of customers into segments, or making personalised product recommendations to its customers, even the smallest retailers can benefit from machine learning.

With a wealth of open-source projects and free documentation available, machine learning is becoming increasingly accessible to all industries. Technologies once only available to large companies, with specialist research teams, are now accessible to insights and analytics teams.

More on AI and ecommerce:

audience segmentation dynamic product ads PPC Remarketing remarketing lists for search ads RLSA Search Engine Marketing Search Engine Watch SEM Uncategorized

5 remarketing strategies to prep for Q4

Sana Ansari

Remarketing is always one of the most powerful tools in an ecommerce marketer’s belt, but it takes on added importance in Q4. With the holidays fast approaching, you can do a good amount of prep now to put yourself in a great position to capitalize on the holiday rush. Here are 5 remarketing strategies that can help do just that.