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Tips and tools to combine content marketing and PPC

July 11, 2020 No Comments

30-second summary:

  • Many businesses opt for content marketing because organic traffic is free. But, this strategy makes them miss a great opportunity to grow fast because combining SEO-optimized content with PPC speeds up the lead generation process.
  • Online businesses need to know specific use cases for content marketing and PPC to assess the value of the strategy.
  • Less than half of small businesses (45%) invest in PPC.
  • PPC and SEO content marketing can bring in more leads by capturing more quality traffic with more effective keyword optimization of blog content, lead magnets, and landing pages.
  • To get the most value from content marketing and PPC, businesses need to master keyword research, searcher intent, and the consistency between the landing page and ad optimization.

As someone who primarily engaged in SEO and content writing for small businesses, I didn’t really care about PPC advertising.  

Maybe because of people like me, only 45% of small businesses invest in PPC 

I thought that the best way to bring high-quality leads was with super optimized content, so paid advertising was the realm of bigger companies. That’s the mindset of many small business owners. With teeny tiny marketing budgets, they have to choose between SEO/content and PPC. 

SEO/content often becomes their choice, especially of those with interest in content creation and a lack of real marketing experience.  

SEO was my preferred choice, too, and I saw PPC as something secondary. 

Boy, was I wrong about this!

After a couple of projects involving PPC promotion, my view of the strategy completely changed. No, they didn’t change how I thought about SEO, but they showed how amazing the results could be if you combine the power of both strategies. 

To all SEO specialists still not using PPC and the other way around, here’s what you’re missing.  

1. More effective content thanks to PPC-tested keywords

Developing a content strategy is one of the most complex and important tasks for any SEO specialist. They use keyword research tools, PPC tools, Google Search Console results, and other methods to find those precious keywords used by customers.  

When they find the keywords they think are good for targeting SEO/content marketing, they begin a slow process of creating content. I wrote oh-so-many blog articles, eBooks, checklists, reports, and other content to find out the keywords that attracted the most conversions.  

All of this takes a lot of time.  

In fact, to write a super effective blog post, you need more than six hours 

Time required to invest per blog postSource: OrbitMedia 

When you’re done with writing the draft, there’s also proofreading, editing, making visuals, and keyword optimization. To cut a long story short, you might need a few days to complete a good article that can bring quality organic traffic.  

But that’s not the end of that road.  

Google, too, needs some time to index the article and rank it. In fact, it might take between two and six months to rank in the top 10.  

That’s a bit much, agree? 

To top it all off, the keywords you’ve chosen for your content might not the best ones to target. If you make this mistake, you’ll have to learn your mistakes and start all over again (welcome to the world of SEO content writing, folks). 

Is there a way to speed this time-consuming process up? Yes. It’s PPC.  

It can get you in front of the audience and allow you to test your keyword ideas much faster. If you have content to test, use PPC ads, and equip them with the keywords.  

Get them out there and see what people respond to best. You can have some great results as early as a few days, which is pretty much impossible with SEO/content marketing.  

Another great news is that you can run A/B testing. This means running ads featuring different keywords for the same content piece. If one performs much better than the other, update the content with the more popular keywords.  

So, the takeaway here is that running PPC campaigns for content is a much faster way to test keywords. Start by finding keywords with research tools and make some ads, and you’ll be more likely to discover how your customers look for businesses like yours.  

Related:  

2. More leads from lead magnets

In content SEO, we often create lead magnets 

They are content pieces like reports, white papers, eBooks, webinars, videos, and other valuable content that people need to sign up to access.  

You’ve seen tons of them before. A common example is a banner promoting an industry report with an irresistible CTA on a blog. It says that you need to provide your email address and name to access it instantly.  

Click on that CTA, and you’ll go to a landing page with the lead capture form.  

Like this “The Ultimate Agency Guide to Video Marketing” landing page, where everyone can download a guide with helpful tips on video marketing.

Example of lead magnets landing pages

As you can see, the content is offered in exchange for some data. Not a bad deal of a guide packed with useful instructions for businesses.  

Unsurprisingly, many content producers often turn to lead magnets for quick lead generation.  

Ozan Gobert, a senior content writer at Best Writers Online said, 

“Lead magnets work well for both B2B and B2C businesses aslong as they have some value for customers. You can generate some high-quality leads with them, as they typically attract those interested in insights and tips inside.” 

If a blog has thousands of visitors every week, then there might not be a need for PPC promoting lead magnets. But is that true for your blog? 

Many people think they can manage without the ads (I was one of them). Basically, it’s because they think that great content will “sell” itself. 

Despite what they might think, not so many blogs are that successful in attracting visitors. In fact, more than 90% of web pages don’t get any organic search traffic from Google.

Ahrefs stats on PPC and content marketing

As you can see, only about 1.3 percent of web pages out there get decent traffic. Just for that tiny share, promoting a lead magnet with PPC advertising might not be necessary every time. 

Obviously, the situation is very different for the rest.  

If your website doesn’t have a lot of visitors, too, then creating lead magnets might be pointless. They’ll just sit there only to be discovered by a few people per week.

Not good because you need more leads.  

If you wish that there was a way to get more people to pay attention to, there is actually a way.

And it’s PPC, of course. To get some emails, you need a well-crafted PPC campaign that leads people to the landing page where they can sign up to receive the content.  

You can try to bring people with keyword-based ads promoting the lead magnet. If you choose the right keywords, the ads have a much greater chance to attract leads than SEO alone.  

This is how it works: PPC does the job bringing in visitors, the content converts them into leads by having them complete the capture form.  

To increase the chance of people signing up, the value of content is critical. But, the visual appeal is also a major consideration. You need tools for creating visual content like images, graphics, and infographics to add to your lead magnets.  

3. Better marketing campaign performance thanks to a smart keyword use

Many businesses out there don’t realize they can bring much more quality traffic to their websites if they focus on best-performing keywords in both SEO, content marketing and PPC.  

Much more traffic.  

When an SEO/content marketing specialist and a PPC marketer share a list of relevant keywords, they can decide how to divide them to: 

  • Target the most promising keywords together to bring the most traffic 
  • Identify the keywords that are the most difficult for SEO and target them with PPC and the other way around
  • Define which search queries to focus on with each lead acquisition strategy

Ultimately, the cooperation between the PPC and SEO teams can result in a much more effective keyword strategy. In turn, this strategy could attract more traffic to your websites. 

Important note

To make content keyword optimization work, you need to master searcher intent or purchase intentPut simply, searcher intent is the reason behind a search query.  

For example, the query “Samsung a10 review” implies that the searcher is looking to do some research but has not made the decision yet. If they search Google for “buy Samsung a10 cheap”, then they might be ready to buy.  

Each intent defines how you should create content. It matters a lot for SEO because Google’s goal is to provide its users with the most relevant results.  

Dive Deeper: Tapping into Google’s Algorithm for Searcher Intent. 

4. Create landing pages that convert more visitors

A landing page is the heart of any PPC marketing.  

But, in many cases, PPC specialists aren’t the best persons to write the copy for it. By engaging content and SEO specialists and having them work with PPC folks, you can create a keyword optimized copy that also appeals to the readers.  

For example, PPC specialists can provide keywords and ideas for optimized headings and subheadings for attracting traffic. In turn, content writers contribute by creating a copy that’s easy to read and entices the visitors to act.  

So, the collaboration of PPC and SEO/content teams can result in campaign landing pages that generate clicks and converts.  

A good way to start doing PPC campaign landing pages is to create a checklist to cover all bases. This checklist can include images, copy, sign up options, etc. 

Know more: Studying the anatomy of a successful high-conversion landing page

SEO and PPC: Two are better than one

I’m not exaggerating when I say that SEO and PPC are a marriage made in heaven. I am positive that these points described in this article prove that.

Don’t make a mistake I made by neglecting the power of PPC advertising. Combined with SEO and quality content, you can greatly increase the quality of your traffic.

If you’d like to try them together, feel free to start by doing PPC ads for your best-performing blog articles. The results you’ll see will definitely impress and inspire you to try more. Thanks to this article, you’ll know your next steps.

Ana Mayer is a project manager with 3+ years of experience. She likes to read and create expert academic materials for the Online Writers Rating writing review website.

The post Tips and tools to combine content marketing and PPC appeared first on Search Engine Watch.

Search Engine Watch


How Google Might Rank Image Search Results

July 9, 2020 No Comments

Changes to How Google Might Rank Image Search Results

We are seeing more references to machine learning in how Google is ranking pages and other documents in search results.

That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind.

It’s still worth considering some of those older ranking signals because they may play a role in how things are ranked.

As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images.

Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.

Here are signals that I would include when I rank image search results:

  • Use meaningful images that reflect what the page those images appear on is about – make them relevant to that query
  • Use a file name for your image that is relevant to what the image is about (I like to separate words in file names for images with hyphens, too)
  • Use alt text for your alt attribute that describes the image well, and uses text that is relevant to the query terms that the page is optimized for) and avoid keyword stuffing
  • Use a caption that is helpful to viewers and relevant to what the page it is about, and the query term that the page is optimized for
  • Use a title and associated text on the page the image appears upon that is relevant for what the page is about, and what the image shows
  • Use a decent sized image at a decent resolution that isn’t mistaken for a thumbnail

Those are signals that I would consider when I rank image search results and include images on a page to help that page rank as well.

A patent application that was published this week tells us about how machine learning might be used in ranking image search results. It doesn’t itemize features that might help an image in those rankings, such as alt text, captions, or file names, but it does refer to “features” that likely include those as well as other signals. It makes sense to start looking at these patents that cover machine learning approaches to ranking because they may end up becoming more common.

Machine Learning Models to Rank Image Search Results

Giving Google a chance to try out different approaches, we are told that the machine learning model can use many different types of machine learning models.

The machine learning model can be a:

  • Deep machine learning model (e.g., a neural network that includes multiple layers of non-linear operations.)
  • Different type of machine learning model (e.g., a generalized linear model, a random forest, a decision tree model, and so on.)

We are told more about this machine learning model. It is “used to accurately generate relevance scores for image-landing page pairs in the index database.”

We are told about an image search system, which includes a training engine.

The training engine trains the machine learning model on training data generated using image-landing page pairs that are already associated with ground truth or known values of the relevance score.

The patent shows an example of the machine learning model generating a relevance score for a particular image search result from an image, landing page, and query features. In this image, a searcher submits an image search query. The system generates image query features based on the user-submitted image search query.

Rank Image Search Results includes Image Query Features

That system also learns about landing page features for the landing page that has been identified by the particular image search result as well as image features for the image identified by that image search result.

The image search system would then provide the query features, the landing page features, and the image features as input to the machine learning model.

Google may rank image search results based on various factors

Those may be separate signals from:

  1. Features of the image
  2. Features of the landing page
  3. A combining the separate signals following a fixed weighting scheme that is the same for each received search query

This patent describes how it would rank image search results in this manner:

  1. Obtaining many candidate image search results for the image search query
  2. Each candidate image search result identifies a respective image and a respective landing page for the respective image
  3. For each of the candidate image search results processing
    • Features of the image search query
    • Features of the respective image identified by the candidate image search result
  4. Features of the respective landing page identified by the candidate image search result using an image search result ranking machine learning model that has been trained to generate a relevance score that measures a relevance of the candidate image search result to the image search query
  5. Ranking the candidate image search results based on the relevance scores generated by the image search result ranking machine learning model
  6. – Generating an image search results presentation that displays the candidate image search results ordered according to the ranking
    – Providing the image search results for presentation by a user device

Advantages to Using a Machine Learning Model to Rank Image Search Results

If Google can rank image search query pairs based on relevance scores using a machine learning model, it can improve the relevance of the image search results in response to the image search query.

This differs from conventional methods to rank resources because the machine learning model receives a single input that includes features of the image search query, landing page, and the image identified by a given image search result to predicts the relevance of the image search result to the received query.

This process allows the machine learning model to be more dynamic and give more weight to landing page features or image features in a query-specific manner, improving the quality of the image search results that are returned to the user.

By using a machine learning model, the image search engine does not apply the same fixed weighting scheme for landing page features and image features for each received query. Instead, it combines the landing page and image features in a query-dependent manner.

The patent also tells us that a trained machine learning model can easily and optimally adjust weights assigned to various features based on changes to the initial signal distribution or additional features.

In a conventional image search, we are told that significant engineering effort is required to adjust the weights of a traditional manually tuned model based on changes to the initial signal distribution.

But under this patented process, adjusting the weights of a trained machine learning model based on changes to the signal distribution is significantly easier, thus improving the ease of maintenance of the image search engine.

Also, if a new feature is added, the manually tuned functions adjust the function on the new feature independently on an objective (i.e., loss function, while holding existing feature functions constant.)

But, a trained machine learning model can automatically adjust feature weights if a new feature is added.

Instead, the machine learning model can include the new feature and rebalance all its existing weights appropriately to optimize for the final objective.

Thus, the accuracy, efficiency, and maintenance of the image search engine can be improved.

The Rank Image Search results patent application can be found at

Ranking Image Search Results Using Machine Learning Models
US Patent Application Number 16263398
File Date: 31.01.2019
Publication Number US20200201915
Publication Date June 25, 2020
Applicants Google LLC
Inventors Manas Ashok Pathak, Sundeep Tirumalareddy, Wenyuan Yin, Suddha Kalyan Basu, Shubhang Verma, Sushrut Karanjkar, and Thomas Richard Strohmann

Abstract

Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for ranking image search results using machine learning models. In one aspect, a method includes receiving an image search query from a user device; obtaining a plurality of candidate image search results; for each of the candidate image search results: processing (i) features of the image search query and (ii) features of the respective image identified by the candidate image search result using an image search result ranking machine learning model to generate a relevance score that measures a relevance of the candidate image search result to the image search query; ranking the candidate image search results based on the relevance scores; generating an image search results presentation; and providing the image search results for presentation by a user device.

The Indexing Engine

The search engine may include an indexing engine and a ranking engine.

The indexing engine indexes image-landing page pairs, and adds the indexed image-landing page pairs to an index database.

That is, the index database includes data identifying images and, for each image, a corresponding landing page.

The index database also associates the image-landing page pairs with:

  • Features of the image search query
  • Features of the images, i.e., features that characterize the images
  • Features of the landing pages, i.e., features that characterize the landing page

Optionally, the index database also associates the indexed image-landing page pairs in the collections of image-landing pairs with values of image search engine ranking signals for the indexed image-landing page pairs.

Each image search engine ranking signal is used by the ranking engine in ranking the image-landing page pair in response to a received search query.

The ranking engine generates respective ranking scores for image-landing page pairs indexed in the index database based on the values of image search engine ranking signals for the image-landing page pair, e.g., signals accessed from the index database or computed at query time, and ranks the image-landing page pair based on the respective ranking scores. The ranking score for a given image-landing page pair reflects the relevance of the image-landing page pair to the received search query, the quality of the given image-landing page pair, or both.

The image search engine can use a machine learning model to rank image-landing page pairs in response to received search queries.

The machine learning model is a machine learning model that is configured to receive an input that includes

(i) features of the image search query
(ii) features of an image and
(iii) features of the landing page of the image and generate a relevance score that measures the relevance of the candidate image search result to the image search query.

Once the machine learning model generates the relevance score for the image-landing page pair, the ranking engine can then use the relevance score to generate ranking scores for the image-landing page pair in response to the received search query.

The Ranking Engine behind the Process to Rank Image Search Results

In some implementations, the ranking engine generates an initial ranking score for each of multiple image—landing page pairs using the signals in the index database.

The ranking engine can then select a certain number of the highest-scoring image—landing pair pairs for processing by the machine learning model.

The ranking engine can then rank candidate image—landing page pairs based on relevance scores from the machine learning model or use those relevance scores as additional signals to adjust the initial ranking scores for the candidate image—landing page pairs.

The machine learning model would receive a single input that includes features of the image search query, the landing page, and the image to predict the relevance (i.e., relevance score, of the particular image search result to the user image query.)

We are told that this allows the machine learning model to give more weight to landing page features, image features, or image search query features in a query-specific manner, which can improve the quality of the image search results returned to the user.

Features That May Be Used from Images and Landing Pages to Rank Image Search Results

The first step is to receive the image search query.

Once that happens, the image search system may identify initial image-landing page pairs that satisfy the image search query.

It would do that from pairs that are indexed in a search engine index database from signals measuring the quality of the pairs, and the relevance of the pairs to the search query, or both.

For those pairs, the search system identifies:

  • Features of the image search query
  • Features of the image
  • Features of the landing page

Features Extracted From the Image

These features can include vectors that represent the content of the image.

Vectors to represent the image may be derived by processing the image through an embedding neural network.

Or those vectors may be generated through other image processing techniques for feature extraction. Examples of feature extraction techniques can include edge, corner, ridge, and blob detection. Feature vectors can include vectors generated using shape extraction techniques (e.g., thresholding, template matching, and so on.) Instead of or in addition to the feature vectors, when the machine learning model is a neural network the features can include the pixel data of the image.

Features Extracted From the Landing Page

These aren’t the kinds of features that I usually think about when optimizing images historically. These features can include:

  • The date the page was first crawled or updated
  • Data characterizing the author of the landing page
  • The language of the landing page
  • Features of the domain that the landing page belong to
  • Keywords representing the content of the landing page
  • Features of the links to the image and landing page such as the anchor text or source page for the links
  • Features that describe the context of the image in the landing page
  • So on

Features Extracted From The Landing Page That Describes The Context of the Image in the Landing Page

The patent interestingly separated these features out:

  • Data characterizing the location of the image within the landing page
  • Prominence of the image on the landing page
  • Textual descriptions of the image on the landing page
  • Etc.

More Details on the Context of the Image on the Landing Page

The patent points out some alternative ways that the location of the image within the Landing Page might be found:

  • Using pixel-based geometric location in horizontal and vertical dimensions
  • User-device based length (e.g., in inches) in horizontal and vertical dimensions
  • An HTML/XML DOM-based XPATH-like identifier
  • A CSS-based selector
  • Etc.

The prominence of the image on the landing page can be measured using the relative size of the image as displayed on a generic device and a specific user device.

The textual descriptions of the image on the landing page can include alt-text labels for the image, text surrounding the image, and so on.

Features Extracted from the Image Search Query

The features from the image search query can include::

  • Language of the search query
  • Some or all of the terms in the search query
  • Time that the search query was submitted
  • Location from which the search query was submitted
  • Data characterizing the user device from which the query was received
  • So on

How the Features from the Query, the Image, and the Landing Page Work Together

  • The features may be represented categorically or discretely
  • Additional relevant features can be created through pre-existing features (Relationships may be created between one or more features through a combination of addition, multiplication, or other mathematical operations.)
  • For each image-landing page pair, the system processes the features using an image search result ranking machine learning model to generate a relevance score output
  • The relevance score measures a relevance of the candidate image search result to the image search query (i.e., the relevance score of the candidate image search result measures a likelihood of a user submitting the search query would click on or otherwise interact with the search result. A higher relevance score indicates the user submitting the search query would find the candidate image search more relevant and click on it)
  • The relevance score of the candidate image search result can be a prediction of a score generated by a human rater to measure the quality of the result for the image search query

Adjusting Initial Ranking Scores

The system may adjust initial ranking scores for the image search results based on the relevance scores to:

  • Promote search results having higher relevance scores
  • Demote search results having lower relevance scores
  • Or both

Training a Ranking Machine Learning Model to Rank Image Search Results

The system receives a set of training image search queries
For each training image search query, training image search results for the query that are each associated with a ground truth relevance score.

A ground truth relevance score is the relevance score that should be generated for the image search result by the machine learning model (i.e., when the relevance scores measure a likelihood that a user would select a search result in response to a given search query, each ground truth relevance score can identify whether a user submitting the given search query selected the image search result or a proportion of times that users submitting the given search query select the image search result.)

The patent provides another example of how ground-truth relevance scores might be generated:

When the relevance scores generated by the model are a prediction of a score assigned to an image search result by a human, the ground truth relevance scores are actual scores assigned to the search results by human raters.

For each of the training image search queries, the system may generate features for each associated image-landing page pair.

For each of those pairs, the system may identify:

(i) features of the image search query
(ii) features of the image and
(iii) features of the landing page.

We are told that extracting, generating, and selecting features may take place before training or using the machine learning model. Examples of features are the ones I listed above related to the images, landing pages, and queries.

The ranking engine trains the machine learning model by processing for each image search query

  • Features of the image search query
  • Features of the respective image identified by the candidate image search result
  • Features of the respective landing page identified by the candidate image search result and the respective ground truth relevance that measures a relevance of the candidate image search result to the image search query

The patent provides some specific implementation processes that might differ based upon the machine learning system used.

Take Aways to Rank Image Search Results

I’ve provided some information about what kinds of features Google May have used in the past in ranking Image search results.

Under a machine learning approach, Google may be paying more attention to features from an image query, features from Images, and features from the landing page those images are found upon. The patent lists many of those features, and if you spend time comparing the older features with the ones under the machine learning model approach, you can see there is overlap, but the machine learning approach covers considerably more options.


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How to increase organic traffic: 14 Practical tips

July 7, 2020 No Comments

30-second summary:

  • Organic traffic is the best shareware way to attract visitors who already want to make a deal. You should conduct a good SEO analysis and take care of the quality of your site to increase it.
  • You can get more organic visits if you develop a strategy, eliminate technical errors of your site, use its good mobile version, make correct external and internal optimization, optimize URLs, update the site content regularly, develop a blog with unique content, analyze competitors, and promote your site through social networks, press releases, newsjacking, emails, and messengers.
  • The correct implementation of the above-mentioned tasks will provide a long-lasting result for you.

Ordinary users trust SERP more than advertising and links marked as “ads”. Correctly performed optimization, troubleshooting and the use of promising channels will quickly bring a good result.

14 Practical tips to increase organic traffic

You can get organic visits using a set of working methods, tools, and recommendations. The best 14 ways are summarized in the review below. 

1. Developing a strategy to increase organic traffic

The solution to any problem begins with the development of a strategy to leave room for financial and time planning. Strategy development is carried out in stages:

  • You should set goals and objectives at first
  • Then, identify weaknesses using a comprehensive site audit and analyze the competitive environment
  • As the next step, you can eliminate identified errors and problems
  • Also, you need to select priority methods to attract organic traffic and increase the position of the site in SERP
  • Then, map the work and budget, prepare a content plan (golden rule for a content plan – 60/30/10 – third-party content 60%, unique content 30%, advertising 10%)
  • If you need, you should select specialists and form technical tasks
  • As the last step, perform tasks, analyze results using Google Analytics

Attracting organic traffic is a rather lengthy process that distinguishes it from contextual advertising. Ads start working immediately after launch. However, organic traffic will work for a long time without any additional investments. 

2. Elimination of technical errors of the site

You can identify and eliminate technical errors of the site using the following methods:

  • Surface self-check
  • Comprehensive site audit with the help of professionals
  • Usage of paid and free services. It’s an optimal solution for those who want to get a quick result with minimal financial investments. Services allow you to identify SEO errors and ones in other key positions. The best of them are Semrush, Ahrefs, and SEOptimizer

You should eliminate identified errors by yourself or with the help of professionals. It’ll make your website more attractive to users and search engines. After the site audit, you can get rid of duplicates, speed up the download of the site, identify affiliates, and solve other problems. 

3. Mobile version of the site

More than 65% of internet users prefer to select and order products from mobile devices. You can’t lose such a huge audience and should take care of your site mobile version. It allows you to increase target audience coverage several times, increase sales and subscriptions. You can create a separate mobile version or use an adaptive design of your main site. In the last case, there will be an automatic adjustment to the screens of different devices. 

4. Correct external and internal website optimization

External optimization

It’s aimed to obtain links from third-party sites. External links that aren’t protected from indexing transfer a part of a donor weight to the acceptor site. When working on building an external link mass, you should consider:

  • Donor site trust, spam level of backlinks. The first index should be high, the second one – low
  • Rules of posting links. It’s recommended to surround them with content
  • Donor site topics (should be related)
  • Frequency of placement. You should increase the link juice gradually. It’s especially important for young sites that have a low level of trust in search engines. A sharp increase can lead to the pessimization of the acceptor site

Internal optimization

It helps to make the site relevant to those queries you carry out the promotion. It consists of:

  • Keyword list collection
  • Keywords grouping
  • Preparing and publishing content optimized with LSI and SEO
  • Formation and optimization of meta tags: title and description, headings and subheadings, image tags
  • Creation of robots.txt files and sitemap.xml (if it’s not generated automatically)
  • Interlinking and other related work

It’s important to ensure that meta tags and content are supplemented with relevant keywords but are not spammed. Otherwise, you can fall under search engine filters.

5. URLs optimization

You can complement URLs with keywords. It makes them more understandable for website visitors. When optimizing URLs, it’s recommended:

  • Use from three to five relevant words, longer links will be cut off in the SERP
  • Use hyphens rather than underscores
  • Take into account spam indicators. Keywords from URLs are added to the overall frequency on the page

Optimized URLs look more attractive so visitors click on them more likely. 

6. Regular content updates

Content updates are a rather important factor which influences on ranking. We speak about updating previously posted materials as well as publishing new ones. It helps to keep pace, increase credibility, have a positive effect on indexing. 

You should carry out updates regularly following the content plan. It allows you to work with new keywords and attract organic traffic from search engines. 

7. Blogging

A blog is a valuable resource necessary for attracting organic traffic not only for commercial but also for information requests. We used to carefully choose the goods before the deal. A blog with interesting and relevant content increases chances that after reading the review, the visitor will perform the target action.

On the blog, you can publish news, information materials, as well as infographics, video reviews – everything that can attract attention and encourage visitors to make a deal. When writing articles for a blog, you can use the links to the catalog. So that the client can immediately buy the product they like without spending time searching the site.

8. Expertise and uniqueness of the content

Usage of non-unique content is a deliberately losing thing. As a result of it, you can get a claim from the copyright holder. Therefore, it’s necessary to create and optimize your content that will provide organic visits. This rule applies not only to texts but also to photos, pictures, videos. In the case of publishing someone else’s content, you must obtain the permission of the copyright holder and give a link to the source.

There is one more caveat – expertise, which plays an important role in ranking issues. Search engines don’t focus on quality optimization but on the semantic uniqueness and benefit that the content of the site can bring to the visitor. The content should answer the question that the user enters in the search bar. If the materials contain outdated, uninteresting, or knowingly untruthful data, the visitor will leave the site. An increasing number of failures will hurt ranking.

9. Promotion in social networks

Social networks are an effective tool with which you can manage opinions and drive traffic to your website. You can create a group for communication with potential customers and publish their announcements, information about promotions, discounts, updates of the assortment, and other content that encourages them to click on the link. Before starting the campaign on social networks, you need to analyze groups of your competitors, look at the situation with ordinary user’s eyes. If the posts are interesting, the subscribers will start to like and share them. This will provide additional free advertising and reach.

10. Competitive analysis

To be the first, you should know what is happening in the competition. To solve this problem, you need to use an audit which will help:

  • Define a keywords cluster
  • Keep abreast of all events, updates and new products introduced by competitors
  • Form advertising budgets and solve other strategic tasks

For audit, you can use online services, questionnaires, secret shoppers, newsletter subscription, analysis of social networks groups, and other tools. You can use the information you’ve got to improve and optimize your website.

11. Press releases on third party resources

Regular publication of press releases on popular sites will help to solve several problems. The first one is traffic attraction, the second – external optimization. News sites visitors click the links willingly. The only negative aspect is that it’s difficult to place such publications. You should make the most of your efforts to get a positive result in outreach and lead generating.

12. Using newsjacking

Newsjacking is one of the varieties of guerrilla marketing that provides unobtrusive advertising. The latter is served against the background of an important event not being a priority. The plus is that users will often visit the site using both search queries and aggregators or news portals. The main rule is to link the offer with a really interesting and important event. Otherwise, the tool will not work. 

13. Email marketing setup

From year to year, newsletters demonstrate their effectiveness. They allow you not only to communicate with customers but also to receive visits to the site. To configure the newsletter, you must have your contact base. To collect the latter, you need to place a simple registration or subscription form on the site consisting of a minimum number of lines. After that, you can establish communication with customers, notifying them of promotions, catalog updates, and other important events.

14. Mailing in messengers

Mailing in messengers is similar to emails. However, messages in Facebook Messenger, Snapchat, or WhatsApp have a higher percentage of opening. A smartphone is always near the person, such messages are more familiar and convenient. Therefore, you should not ignore the potential of this channel. Before starting such mailing, it’s necessary to ask the client whether he/she doesn’t mind receiving advertising materials. Otherwise, the sender (you) may be blocked.

To round up

Correct external and internal optimization, work in social networks and messengers, competitive analysis, technical errors eliminating, and usability improving is priority tasks to increase organic traffic. You can perform some tasks on your own. Other ones will have to be entrusted to professionals. The correct implementation of these tasks will provide a long-lasting result, an increase in organic traffic, sales, and an influx of hot customers.

The post How to increase organic traffic: 14 Practical tips appeared first on Search Engine Watch.

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The three most critical marketing metrics to measure right now

July 3, 2020 No Comments

30-second summary:

  • Regardless of your industry, the marketing strategy you are currently executing is completely different from the marketing strategy you had in place just six months ago.  
  • The situation that has unfolded over the last few months has thrown “business as usual” out of the window.
  • Budgets are tight, events are canceled, and your buyers’ needs have dramatically changed in the last few months.
  • Credly’s VP of Marketing, Adam Masur shares three of the most critical marketing metrics to measure in these unique circumstances.

When it comes to your marketing efforts, there are specific numbers you should be constantly tracking and working to improve. Yet, regardless of your industry, the marketing strategy you are currently executing is completely different from the marketing strategy you had in place just six months ago.  

The situation that has unfolded over the last few months has left marketing teams in every industry at a loss for the best way to move forward. It is no longer “business as usual”. What works today may not work tomorrow, so marketers must be prepared to pivot quickly during this time of uncertainty. And we don’t expect that to change any time soon. Even when the pandemic is over and things start returning to “normal,” everyone is going to have to adapt to what the new “new world of work” looks like.   

As you start to navigate a new way of marketing your product or services following the COVID-19 outbreak, you must reevaluate your strategies and develop a new plan of action. Budgets are tight, events are canceled and your buyers’ needs have dramatically changed in the last few months. Given the unique circumstances, here are three of the most critical metrics to measure right now.  

Metric one: Cost per acquisition

Familiarizing an audience with your product or service and converting them to a paying customer comes at a price. Even in the best of times, I may argue that cost-per-acquisition (CPA), which measures the aggregate cost to acquire just one paying customer, is the most important metric. When it comes to how you’re spending your precious marketing dollars during this time, your CPA has to be top of mind.  

These days, it’s possible that you’re encountering prospects with different risk tolerances, at different stages of product knowledge and purchase intent. It’s a great time to rethink ad copy and realign landing pages with more focused, more compelling, and more relevant content. It’s also a great time to look for the emergence of new keywords that have suddenly become more important in your customers’ minds. The best way to optimize your CPA is by addressing your audiences’ immediate concerns directly, and continuing the dialog until they’re ready to take the next step. Your quality scores will thank you for it. 

Marketers have chased vanity marketing metrics like ad clicks from the beginning of time. But, most marketing teams can’t rely on metrics with empty promises. If you haven’t seen any of your numbers moving lately, maybe you aren’t looking hard enough. Maybe it’s bounce rates, session length, pages viewed, or the number or site visits before filling out a form–there’s something to be learned. Now is the time to test your hypotheses to figure out what’s changing in your customers’ worlds, and address these topics directly. You’ll get a better picture of the true health of your business rather than a false sense of success.  

Metric two: Social media engagement

It’s always been hard for marketing teams to truly measure social media interactions, but social media is a critical avenue for establishing and developing organic relationships with your audience in today’s digital world. With billions of active users, social media provides modern marketers with more exposure, improved traffic, and increased brand loyalty. 

Engagement on social media platforms can present itself in various ways: shares, likes, comments, and reposts are all the digital marketing metrics used to gauge your audience’s level of engagement. By tracking social media engagement, you have a better idea of your content’s reach and if it’s landing in front of the right people.  

You can’t just rely on hard numbers. The sentiment, intent, objections, and accolades are all there for you to learn from, but you have to invest the time to dive in beyond a high-level engagement graph.  Understanding how your audience is interacting with that content allows you to readjust your message as needed and create valuable interactions that continue to push your brand forward.  

Focusing on your social media strategy right now helps your brand maximize limited resources. With tight budgets, authentically engaging in social media can help your team meet your audience where they are, provide valuable information, and generate meaningful relationships.  

Metric three: Website traffic 

Regardless of what the business landscape looks like, one goal every marketer has is to drive traffic back to their company’s website. While every marketing channel–inbound, outbound, events, social, content–brings in new leads and new prospects, it’s unlikely that anyone becomes a customer without visiting your website.  

That’s why it’s not enough to drive traffic to your home page. You want to see that those people are visiting multiple pages, engaging with your content, and finding what they need to make the decision that’s right for them. Only then will they take the step to try, buy, or fill out the form that connects them with your sales team. 

While marketers are working with limited resources and under unprecedented circumstances right now, we have to remember that so are our buyers. Marketers have to lean into actionable metrics from their website traffic, including bounce rate, average session duration, and pages per session. Are pages that used to get 100 visits a month, now getting 100 visits a day or vise versa? It could be a sign of your buyers’ shifting needs or priorities.  

Spend the effort to get a clear understanding of your buyers’ current situation. Rely on data and analytics, and check your work by engaging and actively listening. Evaluating how these important marketing metrics are faring provides insight into how your overall strategy is doing and helps you allocate resources while still connecting with your audience in a meaningful way.  

When it comes to marketing your product or service in the current climate, you have to be proactive. Marketers who are able to pivot, use data and analytics to guide their efforts, and tap into the new needs of their buyers will continue to be successful as we enter the new world of work.  

Adam Masur, Vice President of Marketing at Credly, is driven by a passion for optimizing the way marketing teams and technology work together to grow businesses.

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

July 1, 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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Ecommerce marketing this Independence Day will be tricky: Four must dos

June 29, 2020 No Comments

30-second summary:

  • This 4th of July is coming at a time when the US is seeing waves of social justice protests.  
  • People who are extremely anxious and frustrated in the current climate will pay little attention to your cookie-cutter July 4th themed advertisement.
  • What does this mean for ecommerce marketing?
  • Instead of tone-deaf messages and empty platitudes, how can businesses walk the walk?
  • Evelyn Johnson discusses how businesses need to keep peoples’ sentiments in mind as they try to drive their campaign home.

Independence Day this year will be the first big holiday after the relaxation of lockdowns across the US. While thanksgiving took place when most states had enforced strict social distancing protocols, Americans will celebrate the 4th of July with much more freedom. 

Not to forget, this 4th of July is also coming at a time when the country is seeing waves of social justice protests.  

But what does this mean for ecommerce marketers? For starters, the usual Independence Day campaign might not cut it anymore. People who are extremely anxious and frustrated in the current climate will pay little attention to your cookie-cutter July 4th themed advertisement.

So instead of generic Independence Day deals, you will have to take a new approach to Independence Day marketing. 

Here’s how you can go about it. 

Understand how people will celebrate this year

Independence Day celebrations have remained pretty much the same for decades. There are fireworks, barbecues, carnivals, parades, and a whole slew of activities. Some 4th of July stats from last year show around 48.9 million travel on this day.  

But due to the current outbreak, many people will avoid public gatherings this year. While there will still be parades and festivals, the scope of these events will be extremely limited. Instead, many people will be staying in for Independence Day this time around.  

This is where you have a window to cater to millions who will be staying at home. Products like board games and DIY items for crafting patriotic decorations might prove to be a big hit in the coming holiday.  

After singling out items you believe might sell well on 4th July, check out their popularity on Google trends leading up to the day. Understanding which new products are in demand will allow you to produce a holistic marketing campaign and target consumers with personalized advertisements. 

1. Convey to people that you care about their safety

Americans are hoping that the worst of the pandemic is behind them. But states and CDC are still recommending precautions such as washing hands, wearing masks, and maintaining some level of social distance.  

Here, brands can help customers in their quest to celebrate the 4th of July safely. Little things such as adding complimentary masks, face shields, gloves, and hand sanitizers to Independence Day deals will show consumers that you care about them.  

Of course, fireworks and soaps would be an odd pairing. But considering that 2020 has been the year of pandemics, a potential World War and UFOs — it would be the least bizarre offering of this year. 

2. Participate in a virtual parade

The coming Independence Day has marketing opportunities that previously did not exist, especially in the digital realm. Many communities in the United States have decided to take parades online, creating an unprecedented marketing avenue for brands.  

Sponsoring virtual parades might be uncharted territory for you but it’s a territory worth exploring. The pandemic has caused life to move online and marketers should get with the times.  

Since Independence Day parades are usually organized by communities, there’s also an opportunity in them for location-based marketing. Lookout for any virtual parades your customers might be interested in and reach out for sponsorship.  

Using this new medium, you can put out their message of solidarity, unity, and cooperation. This message will resonate with Americans who are feeling pessimistic due to everything that has unfolded in 2020. 

3. Add a flavor of patriotism to your products

Fourth of July is all about celebrating America and everything it has to offer. So don’t shy away from incorporating patriotic imagery and colors in your marketing efforts. To take things a step further, you can even combine patriotic imagery with products that are currently in demand and create new items.  

A recent study has shown widespread mask-wearing could prevent a second outbreak of COVID-19. Currently, the Centers for Disease Control (CDC) is also recommending that Americans should cover their faces to stop the spread of novel coronavirus.  

Retail businesses can help Americans curtail outbreaks by promoting special flag-themed face masks. These masks will promote a sense of responsibility in Americans to not only stay uninfected themselves but to stop others from getting the virus as well.  

4. Keep the focus on diversity

America is fortunate enough to have people from diverse backgrounds. People belonging to different ethnicities and colors have shaped this country. Independence Day is an opportunity to celebrate American diversity.  

Now more than ever, it’s a time to shine a light on the minority communities. And in light of recent events, it is the time for brands to uplift voices of those that are feeling suppressed and discriminated against.

Instead of tone-deaf messages and empty platitudes, enterprises should walk the walk. This means consciously joining forces with African-America, Latino, and Native-American influencers while promoting products designed by individuals from disadvantaged communities.

Independence Day this year will allow millions of Americans to blow off steam after months of distress. It will also be a day where they look towards their national identity as a source of strength and unity in a polarizing time. Businesses need to keep this sentiment in mind as they try to drive their campaign home.

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How Google May Annotate Images to Improve Search Results

June 25, 2020 No Comments

How might Google improve on information from sources such as knowledge bases to help them answer search queries?

That information may be learned from or inferred from sources outside of those knowledge bases when Google may:

  • Analyze and annotate images
  • Consider other data sources

A recent Google patent on this topic defines knowledge bases for us, why those are important, and it points out examples of how Google looks at entities while it may annotate images:

A knowledge base is an important repository of structured and unstructured data. The data stored in a knowledge base may include information such as entities, facts about entities, and relationships between entities. This information can be used to assist with or satisfy user search queries processed by a search engine.

Examples of knowledge bases include Google Knowledge Graph and Knowledge Vault, Microsoft Satori Knowledge Base, DBpedia, Yahoo! Knowledge Base, and Wolfram Knowledgebase.

The focus of this patent is upon improving upon information that can be found in knowledge bases:

The data stored in a knowledge base may be enriched or expanded by harvesting information from a wide variety of sources. For example, entities and facts may be obtained by crawling text included in Internet web pages. As another example, entities and facts may be collected using machine learning algorithms, while it may annotate images.

All gathered information may be stored in a knowledge base to enrich the information that is available for processing search queries.

Analyzing Images to Enrich Knowledge Base Information

This approach may annotate images and select object entities contained in those images. It reminded me of a post I recently wrote about Google annotating images, How Google May Map Image Queries

This is an effort to better understand and annotate images, and explore related entities in images, so Google can focus on “relationships between the object entities and attribute entities, and store the relationships in a knowledge base.”

Google can learn from images of real-world objects (a phrase they used for entities when they started the Knowledge Graph in 2012.)

I wrote another post about image search becoming more semantic, in the labels they added to categories in Google image search results. I wrote about those in Google Image Search Labels Becoming More Semantic?

When writing about mapping image queries, I couldn’t help but think about labels helping to organize information in a useful way. I’ve suggested using those labels to better learn about entities when creating content or doing keyword research. Doing image searches and looking at those semantic labels can be worth the effort.

This new patent tells us how Google may annotate images to identify entities contained in those images. While labeling, they may select an object entity from the entities pictured and then choose at least one attribute entity from the annotated images that contain the object entity. They could also infer a relationship between the object entity and the attribute entity or entities and include that relationship in a knowledge base.

In accordance with one exemplary embodiment, a computer-implemented method is provided for enriching a knowledge base for search queries. The method includes assigning annotations to images stored in a database. The annotations may identify entities contained in the images. An object entity among the entities may be selected based on the annotations. At least one attribute entity may be determined using the annotated images containing the object entity. A relationship between the object entity and the at least one attribute entity may be inferred and stored in a knowledge base.

For example, when I search for my hometown, Carlsbad in Google image search, one of the category labels is for Legoland, which is an amusement park located in Carlsbad, California. Showing that as a label tells us that Legoland is located in Carlsbad (the captions for the pictures of Legoland tell us that it is located in Carlsbad.)

Carlsbad-Legoland-Attribute Entity

This patent can be found at:

Computerized systems and methods for enriching a knowledge base for search queries
Inventors: Ran El Manor and Yaniv Leviathan
Assignee: Google LLC
US Patent: 10,534,810
Granted: January 14, 2020
Filed: February 29, 2016

Abstract

Systems and methods are disclosed for enriching a knowledge base for search queries. According to certain embodiments, images are assigned annotations that identify entities contained in the images. An object entity is selected among the entities based on the annotations and at least one attribute entity is determined using annotated images containing the object entity. A relationship between the object entity and the at least one attribute entity is inferred and stored in the knowledge base. In some embodiments, confidence may be calculated for the entities. The confidence scores may be aggregated across a plurality of images to identify an object entity.

Confidence Scores While Labeling of Entities in Images

One of the first phrases to jump out at me when I scanned this patent to decide that I wanted to write about it was the phrase, “confidence scores,” which reminded me of association scores which I wrote about discussing Google trying to extract information about entities and relationships with other entities and confidence scores about the relationships between those entities, and about attributes involving the entities. I mentioned association scores in the post Entity Extractions for Knowledge Graphs at Google, because those scores were described in the patent Computerized systems and methods for extracting and storing information regarding entities.

I also referred to these confidence scores when I wrote about Answering Questions Using Knowledge Graphs because association scores or confidence scores can lead to better answers to questions about entities in search results, which is an aim of this patent, and how it attempts to analyze and label images and understand the relationships between entities shown in those images.

The patent lays out the purpose it serves when it may analyze and annotate images like this:

Embodiments of the present disclosure provide improved systems and methods for enriching a knowledge base for search queries. The information used to enrich a knowledge base may be learned or inferred from analyzing images and other data sources.

Per some embodiments, object recognition technology is used to annotate images stored in databases or harvested from Internet web pages. The annotations may identify who and/or what is contained in the images.

The disclosed embodiments can learn which annotations are good indicators for facts by aggregating annotations over object entities and facts that are already known to be true. Grouping annotated images by the object entity help identify the top annotations for the object entity.

Top annotations can be selected as attributes for the object entities and relationships can be inferred between the object entities and the attributes.

As used herein, the term “inferring” refers to operations where an entity relationship is inferred from or determined using indirect factors such as image context, known entity relationships, and data stored in a knowledge base to draw an entity relationship conclusion instead of learning the entity-relationship from an explicit statement of the relationship such as in text on an Internet web page.

The inferred relationships may be stored in a knowledge base and subsequently used to assist with or respond to user search queries processed by a search engine.

The patent then tells us about how confidence scores are used, that they calculate confidence scores for annotations assigned to images. Those “confidence scores may reflect the likelihood that an entity identified by an annotation is contained in an image.”

If you look back up at the pictures for Legoland above, it may be considered an attribute entity of the Object Entity Carlsbad, because Legoland is located in Carlsbad. The label annotations indicate what the images portray, and infer a relationship between the entities.

Just like an image search for Milan Italy shows a category label for Duomo, a Cathedral located in the City. The Duomo is an attribute entity of the Object Entity of Milan because it is located in Milan Italy.

In those examples, we are inferring from Legoland being included under pictures of Carlsbad that it is an attribute entity of Carlsbad and that the Duomo is an attribute entity of Milan because it is included in the results of a search for Milan.

Milan Duomo Attribute Entity

A search engine may learn from label annotations and because of confidence scores about images because the search engine (or indexing engine thereof) may index:

  • Image annotations
  • Object entities
  • Attribute entities
  • Relationships between object entities and attribute entities
  • Facts learned about object entities

The Illustrations from the patent show us images of a Bear, eating a Fish, to tell us that the Bear is an Object Entity, and the Fish is an Attribute Entity and that Bears eat Fish.

Anotate images with Bear (Object Entity) and Fish (Attribute-Entity) entities

We are also shown that Bears, as object Entities have other Attribute Entities associated with them, since they will go into the water to hunt fish, and roam around on the grass.

Bears and attribute Entities

Annotations may be detailed and cover objects within photos or images, like the bear eating the fish above. The patent points out a range of entities that might appear in a single image by telling us about a photo from a baseball game:

An annotation may identify an entity contained in an image. An entity may be a person, place, thing, or concept. For example, an image taken at a baseball game may contain entities such as “baseball fan”, “grass”, “baseball player”, “baseball stadium”, etc.

An entity may also be a specific person, place, thing, or concept. For example, the image taken at the baseball game may contain entities such as “Nationals Park” and “Ryan Zimmerman”.

Defining an Object Entity When Google May Annotate Images

The patent provides more insights into what object entities are and how they might be selected:

An object entity may be an entity selected among the entities contained in a plurality of annotated images. Object entities may be used to group images to learn facts about those object entities. In some embodiments, a server may select a plurality of images and assign annotations to those images.

A server may select an object entity based on the entity contained in the greatest number of annotated images as identified by the annotations.

For example, a group of 50 images may be assigned annotations that identify George Washington in 30 of those images. Accordingly, a server may select George Washington as the object entity if 30 out of 50 annotated images is the greatest number for any identified entity.

Confidence scores may also be determined for annotations. Confidence scores are an indication that an entity identified by an annotation is contained in an image. It “quantifies a level of confidence in an annotation being accurate.” That confidence score could be calculated by using a template matching algorithm. The annotated image may be compared with a template image.

Defining Attribute Entities When Google May Annotate Images

An attribute entity may be an entity that is among the entities contained in images that contain the object entity. They are entities other than the object entity.

Annotated images that contain the object entity may be grouped and an attribute entity may be selected based on what entity might be contained in the greatest number of grouped images as identified by the annotations.

So, a group of 30 annotated images containing object entity “George Washington” may also include 20 images that contain “Martha Washington.”

In that case, “Martha Washington,” may be considered an attribute entity

(Of Course, “Martha Washington Could be an object Entity, and “George Washington, appearing in a number of the “Martha Washington” labeled images could be considered the attribute entity.)

Infering Relationships between entities by Analyzing Images

If more than a threshold of images of “Michael Jordon” contains a basketball in his hand, a relationship between “Michael Jordan” and basketball might be made (That Michael Jordan is a basketball player.)

From analyzing images of bears hunting for fish in water, and roaming around on grassy fields, some relationships between bears and fish and water and grass can be made also:

inferences between entities

By analyzing images of Michael Jordan with a basketball in his hand wearing a Chicago Bulls jersey, a search query asking a question such as “What basketball team does Michael Jordan play for?” may be satisfied with the answer “Chicago Bulls”.

To answer a query such as “What team did Michael Jordan play basketball for, Google could perform an image search for “Michael Jordan playing basketball”. Having those images that contain the object entity of interest can allow the images to be analyzed and an answer provided. See the picture at the top of this post, showing Michael Jordan in a Bulls jersey.

Take Aways

This process to collect and annotate images can be done using any images found on the Web, and isn’t limited to images that might be found in places like Wikipedia.

Google can analyze images online in a way that scales on a web-wide basis, and by analyzing images, it may provide insights that a knowledge graph might not, such as to answer the question, “where do Grizzly Bears hunt?” an analysis of photos reveals that they like to hunt near water so that they can eat fish.

The confidence scores in this patent aren’t like the association scores in the other patents about entities that I wrote about, because they are trying to gauge how likely it is that what is in a photo or image is indeed the entity that it might then be labeled with.

The association scores that I wrote about were trying to gauge how likely relationships between entities and attributes might be more likely to be true based upon things such as the reliability and popularity of the sources of that information.

So, Google is trying to learn about real-world objects (entities) by analyzing pictures of those entities when it may annotate images (ones that it has confidence in), as an alternative way of learning about the world and the things within it.


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How to manage a high-performing content marketing team

June 23, 2020 No Comments

30-second summary:

  • What’s the difference between a regular content marketing team and a high-performing content marketing team?
  • A high-performing content marketing team creates, promotes, and distributes content that helps not only their team but their business to scale. 
  • Kevin Payne shows you the exact steps to build and manage your own high-performing marketing team.

There’s a difference between a regular content marketing team and a high-performing content marketing team. 

The former creates, promotes, and distributes content. The latter creates, promotes, and distributes content that helps not only their team but their business to scale. 

In this post, we’ll show you some of the exact steps you should take to manage your own high-performing marketing team. 

Eight great tips to manage a high-performing content marketing team 

1. Align your content marketing team’s goals to your business goals 

Take time to highlight the goals that your entire company has and emphasizing these with your team members. When you’re first onboarding new members when you’re meeting for new campaigns and strategies, constantly reiterate your business goals so that everyone has these at the top of their minds all the time. 

So you don’t forget to always align new campaign goals to your company’s goals, consider putting these goals somewhere you’ll always see them, like as a section in a new campaign brief or written down on a whiteboard during a strategy meeting. 

2. Equip your content marketing team with excellent collaboration tools

It’s no secret that you can’t run an effective content marketing team if you don’t equip them with the tools they need to succeed. Here’s a rundown of some of the tools your team will need: 

  • Robust project management software to track campaigns, tasks, and deadlines 
  • A suite of content creation tools for writing, graphic design, video editing, and the like 
  • Social media scheduling and analytics software 
  • Website performance and analytics tools 
  • Team-centric communication tools for sending messages and doing video conferencing 

Investing in the right software services may seem like you end up shelling out a lot of money from the get-go. But statistics show that investing in software can help enhance collaboration between teams scattered across multiple locations, streamline work processes, and even offload tasks like maintaining and protecting data from your own team. 

3. Review your buyer journey

High-performing content marketing teams are able to create highly relevant content that meets their customers where they’re at so that customers are moved through the marketing funnel or flywheel effectively. 

Make it a habit to review your buyer journey as you create new content and promotions, and always ask the question, “How does this [content piece] serve my customer in this particular stage?” 

If your team doesn’t have a buyer journey yet, you can start by creating buyer personas that help you understand your customers’ goals and pain points. 

Then, you can start to create customer journey maps that highlight what your customers might be thinking or looking for when they’re in certain stages, such as

  • Awareness
  • Interest
  • Consideration 

Content marketing team productivity

Source: Content Marketing Institute 

4. Clarify everyone’s roles 

Your content marketing team members need to have clear roles with set boundaries. While it’s not absurd to expect that everyone knows a little about each role, it’s important to make sure every person has a role to play. 

This is important for two reasons: the first reason being that by clarifying roles in your team, you can identify if there are roles with too much overlap or roles that haven’t been filled; and the second reason is you’re giving your team the space to focus on one particular goal or outcome and doing that well, instead of spreading themselves out too thinly. 

5. Invest in diverse creators with unique skill sets 

If you can afford it, you can outsource some specialized tasks to talented contractors who have a specific skill set that you’re looking for. After all, it’s more costly to work with cheap amateurs than it is to hire experienced professionals. 

For example, if you need a parody video that’s humorous, look for video teams that specialize in just that. If you need graphics delivered in a particular art style, search for illustrators with an impressive portfolio with the style you’re looking for. 

Let your team focus on tasks they work on best as well. You may have writers who are excellent in long-form content, but other writers might be more adept at writing email campaigns or social media captions. 

6. Encourage experimenting with new creative strategies

As a content marketing team, it’s important to keep on top of new creative strategies and test new ideas regularly.  

For example, can your business benefit from creating microsites – or hyper-focused sites and landing pages designed to help customers in specific stages of your buyer journey?  

This strategy in particular means buying multiple domain names and then creating dedicated sites, blogs, and content just for this purpose. As a practical example, imagine an athleisure brand launching microsites for targeted content in mountain climbing, in snowboarding, and in city cycling.  

OfficeMax launched an entertainment microsite that lets customers create fun images from their photos.

Content marketing example

Source: ElfYourself  

7. Develop and keep a style guide

style guide will help organize and streamline your processes from the beginning, letting your teamwork more productively and spend less time creating micro-changes to content pieces. 

In your style guide, you’ll want to include guides, templates, and styles for the following things: 

  • Tone: What is the tone you use in your blog posts, social media posts, and email newsletters? What words and phrases do you avoid?  
  • Visual branding: What colors and fonts do you frequently use? How should logos and colors be used together? What is the hierarchy of your brand assets? What is the general style of your graphics and images? 
  • Content styling: What headings and formats do you use when publishing new content? How do you cite sources within articles? How do you present images and visual data? How do you use certain words or phrases?  

Your style guide may evolve as time goes on, and that’s normal. But by creating one now, you’re able to help your team structure and create content that’s as close to publishing quality from the get-go. 

8. Regularly review campaign performance and analytics

The best content marketing teams aren’t those who can churn out new content every single day – the best teams, instead, are the ones who can churn out the right kind of content regularly. 

And there is no better way to accomplish just that when you make it a habit to review your content’s performance 

Check how your campaigns are performing, evaluate top-performing, and low-performing content pieces. What do you think made these pieces get the results that they did? 

Encourage everyone on the team to constantly review the performance of their own work without judgment. You want to give your content marketing team the space to see where they can always do better, so treat everything – even posts and campaigns that performed poorly – as feedback. 

Key takeaways

Are you ready to take your content marketing team further? With a little time and effort, you can scale your team to help scale your content strategies and campaigns – just be sure to follow these eight essential tips to help you get there. 

Kevin Payne is a Growth & Content Marketer, Kevintpayne.com.

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Search Engine Watch


Google Podcasts App and Making Podcasts Easier to Find

June 21, 2020 No Comments

Podcasts Can Be Hard to Find

I’ve been listening to a lot of podcasts lately. They can be fun to listen to while doing chores around the house, like watering plants, washing dishes, cooking meals, and cleaning up. There are podcasts on many different subjects that I am interested in. A good number about Search Engine Optimization.

Someone asked me If I had seen any patents about podcasts on Twitter recently. I hadn’t at the time and I told them that. A patent application later appeared on January 9, 2020. I returned to the tweet where I replied that I hadn’t seen any, and tweeted that I had found a new one, and would be writing about it. This is that post.

I am not the only one listening more to podcasts. Techcrunch from last year had an article about the growth of audiences for podcasts: After a Breakout Year: looking ahead to the future of Podcasting.

It seems Google noticed this trend and has worked on making podcasts easier to find in search results and by releasing a Google Podcasts app.

Google Tries to Make Podcasts Easier to Find

At the Google Blog, the Keyword, a post last August from Sack Reneay-Wedeen, Product Manager at Google Podcasts, called: Press play: Find and listen to podcast episodes on Search

If you produce a podcast or are looking for one to listen to, you may find this article from last autumn helpful: Google will start surfacing individual podcast episodes in search results.

It tells us that:

Google is taking the next step in making podcasts easier to find. The company will now surface individual podcast episodes in search results, so if someone searches for a show about a niche topic or an interview with a specific person, Google will show them potential podcast episodes that fit their query.

In Google Search Help is a page about finding Podcasts titled Listen to podcasts with Google Podcasts

There are also Google Developer pages about how to submit your Podcasts for them to be found using Google on this page: Google Podcasts, which offer guidelines, management of podcasts information, and troubleshooting for Google Podcasts.

The Google Play Music Help pages offer information about using that service to subscribe and listen to podcasts.

There are also Google Podcast Publisher Tools, which allows you to submit your podcast to be found on the Google Podcasts App, and preview your podcast as it would appear there.

The Google Podcasts App is at: Google Podcasts: Discover free & trending podcasts

How the New Podcast Patent Application Ranks Shows and Episodes

The new Google patent application covers “identifying, curating, and presenting audio content.” That includes audio such as radio stations and podcasts.

The application starts with this statement:

Many people enjoy listening to audio content, such as by tuning to a radio show or subscribing to a podcast and playing a podcast episode. For example, people may enjoy listening to such audio content during a commute between home and work, while exercising, etc. In some cases, people may have difficulty identifying specific content that they would enjoy listening to, such as specific shows or episodes that align with their interests. Additionally, in some cases, people may have difficulty finding shows or episodes that are of a duration that is convenient for them to listen to, such as a duration that aligns with a duration of a commute.

It focuses on solving a specific problem – people being unable to identify and listen to audio content.

The method this patent uncovers for presenting audio content includes:

  • Seeing categories of audio content
  • Being able to select one of those categories
  • Seeing shows based upon that selected category
  • Being able to select from the shows in that category
  • Seeing episodes from those shows
  • Being able to select from an episode, and seeing the duration of playing time for each show
  • Ranking the episodes
  • Seeing the episodes in order of ranking.

Rankings are based on a likelihood that a searcher might enjoy the episodes being ranked.

The episodes can also be shown based upon a measure of popularity.

The episodes may also be shown based upon how relevant they might be to a searcher.

The identification of a group of candidate episodes is based on an RSS feed associated with shows in the subset of shows.

The patent application about podcasts at Google is:

Methods, Systems, and Media for Identifying, Curating, and Presenting Audio Content
Inventors Jeannette Gatlin, Manish Gaudi
Applicants Google LLC
Publication Number 20200012476
Filed: July 3, 2019
Publication Date January 9, 2020

The methods described in the patent cover podcasts and can apply to other types of audio content, such as:

  • Music
  • Radio shows
  • Any other suitable type of audio content
  • Television shows
  • Videos
  • Movies
  • Any other suitable type of video content

The patent describes several techniques that podcasts are found with.

A group of candidate shows are selected, such as podcast episodes using factors like:

  • Popularity
  • Inclusion of evergreen content relevant to a listener
  • Related to categories or topics that are of interest to a particular user

Recommendations of shows look at whether a show:

  1. Is associated with episodic content or serial content.
  2. Typically includes evergreen content (e.g., content that is generally relevant at a future time) or whether the show will become irrelevant at a predetermined future time
  3. Is likely to include news-related content based on whether a tag or keyword associated with the show includes “news.”
  4. Has tags indicating categories or topics associated with the show.
  5. Has tags indicating controversial content, such as mature language, related to particular topics, and/or any other suitable type of controversial content
  6. Has previously assigned categories or topics associated with a show that are accurate.
  7. Has episodes likely to include advertisements (e.g., pre-roll advertisements, interstitial advertisements, and/or any other suitable types of advertisements).
  8. Has episodes that are likely to include standalone segments that can be viewed or listened to individually without viewing the rest of an episode of the show.
  9. Has episodes often with an opening monologue.
  10. Has episodes featuring an interview in the middle part of an episode.
  11. Features episodic content instead of serial content, so it does not require viewing or listening to one episode before another.
  12. is limited in relevance based on a date (after the fact).

Human evaluators can identify episode based upon features such as:

  • General popularity
  • Good audio quality
  • Associated with particularly accurate keywords or categories
  • Any other suitable manner

Some podcasts may have a standalone segment within an episode that may feature:

  • A monologue
  • An interview
  • Any other suitable standalone segment
  • That standalone segment could be trimmed as a new episode and included to be selected with the other episodes.

    Blacklisted Content

    Episodes that are deemed too long in duration could be blacklisted or deemed not suitable for selection as a candidate episode.

    An episode that contains adult-oriented content may be blacklisted from being presented to a user during daytime hours based on parental controls.

    An episode containing a particular type of content may be blacklisted from being presented to a user during weekdays based on user preferences (e.g., particular topics for presentation on the weekdays as opposed to particular topics for presentation on the weekends).

    Ranking of Candidate Episodes

    Ranking can be based upon:

  • Popularity
  • Likelihood of enjoyment
  • Previous listening history
  • Relevance to previously listen to content
  • Audio quality
  • Reviewed by human evaluators

The patent tells us that this process can rank the subset of the candidate episodes in any suitable manner and based on any suitable information.

It can be based on a popularity metric associated with a show corresponding to each episode and/or based on a popularity metric associated with the episode.

That popularity metric may also be based on any suitable information or combination of information, such as:

  • A number of subscriptions to the show
  • A number of times a show and/or an episode has been downloaded to a user device
  • A number of times links to a show have been shared (e.g., on a social networking service, and/or in any other suitable manner)
  • Any other suitable information indicating popularity.

This process can also rank the subset of the candidate episodes based on a likelihood that a particular user of a user device will enjoy the episode.

That likelihood can be based on previous listening history, such as:

  • How relevant a category or topic of the episode is to categories/topics of previously listened to episodes (Is it associated with a show the user has previously listened to?)
  • Many times the user has previously listened to other episodes associated with the show
  • Any other suitable information related to listening history

This process can also rank candidate episodes based on the audio quality of each episode.

Alternatively, this process may also rank candidate episodes based on whether each episode has been identified by a human evaluator, and episodes that have been identified by human evaluators are ranked higher than other episodes.

A combined episode score might be based upon a score from:

  • A trusted listener
  • The audio quality
  • The content quality
  • The popularity of the show from which the episode originates

Takeaways

This patent appears to focus primarily upon how podcasts might be ranked on the Google Podcasts App, rather than in Google search results.

The podcasts app isn’t as well known as some of the other places to get podcasts such as iTunes.

I am curious about how many podcasts are being found in search results. I’ve been linking to ones that I’ve been a guest in from the about page on this site, and that helps many of them show up in Google SERPs on a search for my name.

I guess making podcasts easier to find in search results can be similar to making images easier to find, by the text on the page that they are hosted upon, and the links to that page as well.

SEO Industry Podcasts

making podcasts easier to find with Google SEO Podcases

I thought it might be appropriate if I ended this post with several SEO Podcasts.

I’ve been a guest on many podcasts, and have been involved in a couple over the past few years. I’ve also been listening to some, with some frequency, and have been listening to more, both about SEO and other topics as well. I decided to list some of the ones that I have either been a guest on or have listened to a few times. They are in no particular order

Experts On The Wire Podcast

Hosted by Dan Shure. Dan interviews different guests every week about different aspects of SEO and Digital Marketing. I’ve been on a couple of podcasts with Dan and enjoyed answering questions that he has asked, and have listened to him interview others on the show as well. There are some great takeaways in some of the interviews that I have listened to learn from.

Search News You Can Use – SEO Podcast with Marie Haynes

A Weekly podcast about Google Algorithm updates, and news and articles from the digital marketing industry. This is a good way to keep informed about what is happening in SEO. She provides some insights into how to deal with updates and changes at Google.

Webcology

Jim Hedger and Dave Davies have been running this podcast for a few years, and I’ve been a guest on it about 4-5 times. They discuss a lot of current industry news and invite guests to the show to talk about those. My last guest appearance was with David Harry, where we talked about what we thought were the most interesting search-related patents of the last year.

The Search Engine Journal Show!

Danny Goodwin, Brent Csutoras, Greg Finn, and Loren Baker take turns hosting and talking with guests from the world of SEO. No two SEOs do things the same way, and learning about the differences in what they do can be interesting.

Edge of the Web Podcast

Erin Sparks hosts a weekly show about Internet Marketing, and he takes an investigative approach to this show, asking some in-depth questions. He asks some interesting questions.

Search Talk Live Digital Marketing Podcast

Hosted by Robert O’Haver, Matt Weber, and Michelle Stinson Ross. They offer “Expert Advice on SEO and SEM. I had fun talking with these guys – I just listened half of my last appearance on the show.

The Recipe For SEO Success Show

Kate Toon is the host of this show, and she focuses on actionable tips and suggestions from guests on doing digital marketing.

Last Week in Local

Hosted by Mike Blumenthal, Carrie Hill, and Mary Bowling. They often discuss news and articles that focus on local search, but also discuss topics that have a broader impact on sites such as image optimization.

#AEO is SEO Podcast

This is hosted by Jason Barnard. The “AEO” in the title is “Answer Engine Optimization” and Jason has been attending conferences to give him a chance to interview people for his podcast. The last time we did a show it was in a bakery across the street from my hotel in a suburb of Paris, talking about Entities at Google.

Connecting the Digital Dots

Martha van Berkel is the host of this show and is one of the people behind Schemaapp. She and I talked about featured snippets.

Search Engine Roundtable Vlog

Barry Schwartz runs Search Engine Roundtable, which is originally based upon the roundtable in tales of King Author that knights would sit at. In this VLOG, he visits people where they work, and asks them questions about what they do. It’s fun seeing where people are from and learning more about them.

Bill and Ammon’s Bogus Hangout

This is a weekly conversation between several SEOs having discussions, often about marketing and SEO, but sometimes veering off into different topics. It takes inspiration from early days of SEO where conferences such as Pubcon were often meetups in bars, with people sharing stories about what they had been doing. I am one of the hosts, and recently I’ve been joined by Doc Sheldon, Terry van Horne, Zara Altair, and Steve Gerencser.

Page 2 Podcast

Hosted by Jacob Stoops and Jeff Louella. They have guests join them from the world of SEO, and they ask them about their origin stories as SEOs. They have added a news section to the show as well,

Deep Crawl’s Open Dialogue

These shows feature interviews with some sharp and interesting SEOs and provide details on tips and techniques involving digital marketing and technical SEO.

SEO Training Dojo

With David Harry, and Terry van Horne. The Dojo is a center for training and learning SEO. It often includes guests who have been sharing ideas and approaches about SEO for years.


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Top five B2B digital marketing tips during COVID-19 times

June 19, 2020 No Comments

30-second summary:

  • Review your channel mix to find new sources of traffic. 
  • Update your website messaging to show and understanding of the evolving situation. 
  • Monitor user intent to understand how your market is shifting and how you need to react.
  • Focus on CRO, traffic is more valuable than ever, so make sure it converts.
  • Keep on top of content, evolve your strategy with the changing needs of your clients, be useful, creative, and engaging.  
  • Five ways to get through B2B digital marketing and improve your website performance during COVID-19.

COVID-19 has undoubtedly hit global business hard and that is bound to create a knock-on effect on your digital marketing activity.  

In this piece I have provided five key areas to look at that can help you minimize the current impact on your business and build up back to normality and hopefully grow in the future.  

1. Re-assess your channel mix

Throughout the course of a normal year, your traffic channel mix will remain largely consistent, notwithstanding any huge changes in strategy or budget. However, COVID-19 has caused a shift in the way traffic is hitting websites.  

You should start off analyzing your channel report and then drill down into source/medium to start to find any changes in where your traffic is coming from.  

B2B digital marketing tips - Check your analytics

What you see here will greatly shape what you are doing, for example:

  • Specific channels increasing – Delve into this further to understand why. If search volume for your product/service is higher than usual look for ways to capitalize on this, like increasing paid search activity or double down on improving organic keyword rankings.  
  • Specific channels decreasing – Again, it is key to understand what is driving the decrease, it could be due to lack of search interest, drops in rankings, and reductions of activity. You need to understand why if you are to remedy it. 
  • Everything decreasing – It is quite possible that this may happen to your business, it’s likely that it will be something impacting your whole industry, but you need to look to minimize this as much as you can. The key thing is not to panic, be objective, analyze, and create a strategy to bounce back.  
  • Everything increasing – You’re winning, sit back, and enjoy. Seriously, if this is happening you may be entering the territory of having to work out if your business can cope with increased demand and you may need to scale back if you cannot.  

It is worth digging into your Google Search Console data and your Search Query Reports in any paid advertising you are doing to see if there are any changes in the amount of brand and non-brand traffic your site is seeing. It might be that generic traffic is declining due to less search interest, but if you have been building a strong brand you see consistency here. It is important to understand the difference here.  

It would also be worth looking at new channels you could utilize to bridge the gap. For instance, if you find your Google Ads are not getting as much visibility and you have the budget available maybe it is a good time to try some paid social activity. Or it could even be worthwhile spending your time creating compelling email campaigns and hitting your audience this way.  

2. Update website messaging

It is important that you show your website visitors, be they current customers or future prospects, that you understand the current environment and the challenge it presents to their business.  

Likewise, you need to show them that your business is prepared, functioning correctly, and can still provide your products or services throughout this pandemic.  

Further still, if your business is in a position that means your services are now more important, or you have new services that could offer more value to prospects then it is important that people know about them.  

Salesforce for example has a banner across their UK site promoting how their tools can help businesses right now.

Upgrade messaging on website  

Many businesses have followed suit with similar banners, it is just important that you track interaction with these and make sure they aide your website performance and don’t reduce conversion.  

As the situation changes, lockdowns are relaxed and people go back to work in some sense of normality your messaging should be updated to reflect the situation and the role your business can provide.  

3. Monitor intent and respond accordingly

Search intent is such a key consideration for B2B digital marketing. We know that our buyers will typically be in the consideration phase much longer than a B2C consumer and they are more likely to explore the whole market before committing to a purchase, or even an inquiry.  

Couple that with the economic uncertainty the COVID-19 is causing globally it is likely that we will see businesses taking more time before making an inquiry, especially when it comes to the higher value or long term contracts.  

It, therefore, becomes important to look at what users are searching for and to understand what they expect to see when they are searching.  

How can you do that?

  • Google Search Console – Mine your search performance report to see what users are searching for and what is driving clicks to your site.  
  • Google Ads Search Query Reports – Mirror the actions above to see if your ads are now matching to different or new queries. 
  • Trends or Exploding Topics – These tools will let you see user interest in specific topics or keywords, highlighting opportunities.

B2B digital marketing tips - Exploding topics example

The data you get from these actions will prove invaluable in shaping your content strategy over the coming months. If you see a spike in organic impressions for a particular search query that your rank on page 3 for, you know you should be creating a new piece of content to improve this. Likewise, if you see a topic starting to peak in Google Trends that is relevant to your business, that should become your next blog.  

Done properly this will help you improve your SEO performance as well as engaging with your audience.  

4. Focus on website conversions

CRO should be a priority every day of every month of every year. In an environment where the intent is changing and search interest for many services is down it becomes much more important for your website to work hard to convert traffic into leads and sales. Moreover, something that worked in early March may not be as effective in this new world.  

First, you should review your Google Analytics data to see how users are interacting with your site. Look at whether your top landing pages are changing, whether your conversion rates are shifting or the lead sources are different. Couple this with heatmap analysis from tools like HotJar to see how users are interacting with your site.  

Your goal from this is to find areas that could be improved, either because they have dropped off or because they have started to improve and have presented an opportunity to capitalize on. If they haven’t changed then you should be looking for continuous improvement.  

Once you have the data in front of you it’s time to start coming up with test hypotheses, these can be as simple or as complex as you like, but you should always be testing something.  

Some CRO tests you could look at in these times are

  • Change the USPs you promote – If your product/service has a particular use case in the new environment you should get this across in a prominent position on your website. Maybe by changing the copy in the header of your product page.  
  • Test new CTAs – If demand is increasing for your services it could be worth changing your main CTA from a “Get a quote” or “Free trial” message to “Call us now” to drive the urgency.  
  • Try new response methods – Look for new lead generation channels. Things like live chat, instant call-back widgets, and online setup forms can all drive a new stream of leads.  
  • Pricing offers – During and post-COVID-19 times B2B buyers are becoming more careful with their budget, maybe due to reductions, so think about whether you can offer a promotion to get them to convert.  
  • Free products – If your business has the ability to give something away for free try it! Showing value and the desire to help businesses now could reap huge rewards in the long term.  

5. Keep on top of content strategy

We know that during lockdown user intent has shifted for many search queries, but also people have started to search for new things and lockdown specific things too. And that is the same for B2B too.  

In a B2B context, we are seeing people searching for lockdown tips on how best to work from home, business owners are searching for ways to prepare to get back to normality and people want to know what the office of the future looks like. As B2B digital marketing professionals, it is our duty to provide them with answers to those questions.  

To that end, it means that we should move away from our typical product or business-related content and shift more into blogs with advice, tips on how people can prepare their offices for the return to work, and even articles on how your business can help them.B2B digital marketing tips - Creative content example

There’s also something to be said around having some fun with your content and producing something more light-hearted. For instance, my team surveyed Brits to find out how lockdown was impacting them, where they were working in their homes, and what they were doing to relax. This was a great piece as it drove new traffic to our site and supported our SEO campaigns by generating coverage in national publications.  

 

It is important that we pivot our content strategies, not only because of COVID-19 but more importantly to meet the changing needs of our customers moving forward. 

In conclusion

Whilst we will see many changes to B2B website performance due to COVID-19, in both the short and long term, it is our role as B2B digital marketing professionals to analyze the changes in performance and to create strategies that help our businesses to recover and grow again in the future.

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