Friday, April 19, 2019

Using Python to recover SEO site traffic (Part three)

When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.

This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.

As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.

Let’s get started.

URL matching vs content matching

When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.

Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.

How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.

Example of using DOM elements to organize pages by their content

For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.

We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.

A scientist’s bottom-up approach

In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.

BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.

When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”

One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.

Hamlet’s observation and a simpler solution

For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.

Hamlet's observation for a simpler approach based on domain-level observationsHamlet's observation for a simpler approach by testing the quantity and size of images

I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.

I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.

Now let’s get to the fun part and get to code some machine learning code in Python!

Collecting training data

We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.

In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.

What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).

Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.

Here we load the raw data already collected.

Feature engineering

Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.

Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.

It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.

We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.

Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.

Here is what our processed data set looks like.

Example view of processed data for "binning"

Adding ground truth labels

As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.

We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:

Model training and grid search

Finally, the good stuff!

All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.

We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!

Evaluating performance

Graph for evaluating image performances through a linear pattern

After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.

In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.

Graph of the confusion matrix to evaluate image performance

When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.

Here is the code to put together the confusion matrix:

Finally, here is the code to plot the model evaluation:

Resources to learn more

You might be thinking that this is a lot of work to just tell page groups, and you are right!

Screenshot of a query on custom PageTypes and DataLayer

Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!

I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.

If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:

Got any tips or queries? Share it in the comments.

Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter @hamletbatista.

The post Using Python to recover SEO site traffic (Part three) appeared first on Search Engine Watch.

Thursday, April 18, 2019

Study: How ready are businesses for voice search?

“So… most businesses know about voice search. But has this knowledge helped them optimize for it?”

An interesting report recently released by Uberall sought to address that exact question. For as much as we talk about the importance of voice search, and even how to optimize for it — are people actually doing it?

In this report, researchers analyzed 73,000 business locations (using the Boston Metro area as their sample set), across 37 different voice search directories, as well as across SMBs, mid-market, and enterprise.

They looked at a number of factors including accuracy of address, business hours, phone number, name, website, and zip code, as well as accuracy across various voice search directories.

In order, this was how they weighted the importance of a listing’s information:

the most important business information to optimize for voice search

And pictured below are “the 37 most important voice search directories” that they accounted for.

Uberall analysts did note, however, that Google (search + maps), Yelp, and Bing together represent about 90% of the score’s weight.

the 37 most important voice search directories

How ready are businesses for voice search?

The ultimate question. Here, we’ll dive into a few key findings from this report.

1. Over 96% of all business locations fail to list their business information correctly

When looking just at the three primary listings locations (Google, Yelp, Bing), Uberall found that only 3.82% of business locations had no critical errors.

In other words, more than 96% of all business locations failed to list their business information correctly.

Breaking down those 3.82% of perfect business location listings, they were somewhat evenly split across enterprise, mid-market, and SMB, with enterprise having the largest share as one might expect.

only 3.82% of business locations had no critical errors, breakdown according to size

2. The four most common types of listing errors

In their analysis, here’s the breakdown of most common types of missing or incorrect information:

  • Opening hours: 978,305 errors (almost half of all listings)
  • Website: 710,113 errors (almost one-third of all listings)
  • Location name: 510,010 errors (almost one-quarter of all listings)
  • Street: 421,048 errors (almost one-fifth of all listings)

the most glaring business listing errors and missing data

3. Which types of businesses are most likely to be optimized for voice search?

industries that are most voice search ready

Industries that were found to be most voice search ready included:

  • Dentists
  • Health food
  • Home improvement
  • Criminal attorneys
  • Dollar stores

Industries that were found to be least voice search ready included:

  • Consumer protection organizations
  • Congressional representatives
  • Business attorneys
  • Art galleries
  • Wedding services

Not much surprise on the most-prepared industries relying heavily on people being able to find their physical locations. Perhaps a bit impressed that criminal attorneys landed so high on the list. Surprising that art galleries ranked second to last, but perhaps this helps explain decline in traffic of late.

And as ever, we can be expectedly disappointed by the technological savvy of congressional representatives.

What’s the cost of businesses not being optimized for voice search?

The next question, of course, is: how much should we care? Uberall spent a nice bit of their report discussing statistics about the history of voice search, how much it’s used, and its predicted growth.

Interestingly, they also take a moment to fact check the popular “voice will be 50% of all search by 2020” statistic. Apparently, this was taken from an interview with Andrew Ng (co-founder of Coursera, formerly lead at both Google Brain and Baidu) and was originally referring to the growth of a combined voice and image search, specifically via Baidu in China.

1. On average, adults spend 10x more hours on their phones than they did in 2018

This data was compiled from a number of charts from eMarketer, showing overall increase in digital media use from 2008 to 2017 (and we can imagine is even higher now). Specifically, we see how most all of the growth is driven just from mobile.

The connection here, of course, is that mobile devices are one of the most popular devices for voice search, second only perhaps to smart home devices.

graph daily hours spent with digital media per adult user 2008-2017

2. About 21% of respondents were using voice search every week

According to this study, 21% of respondents were using voice search every week. 57% of respondents said they never used voice search. And about 14% seem to have tried it once or twice and not looked back.

In general, it seems people are a bit polarized — either it’s a habit or it’s not.

over the last year, how often have you used voice search?

Regardless, 21% is a sizable number of consumers (though we don’t have information about how many of those searches convert to purchases).

And it seems the number is on the rise: the recent report from voicebot.ai showed that smart speaker ownership grew by nearly 40% from 2018 to 2019, among US adults.

Overall, the cost of not being optimized for voice search may not be sky high yet. But at the same time, it’s probably never too soon to get your location listings in order and provide accurate information to consumers.

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The post Study: How ready are businesses for voice search? appeared first on Search Engine Watch.

Wednesday, April 17, 2019

Three fundamental factors in the production of link-building content

One of the most overused phrases in content marketing is how it is an ever-changing landscape, forcing agencies and marketers to adapt and improve their existing processes.

In a short space of time, a topic can go from being newsworthy to negligible, all while certain types of content become tedious to the press and its readers.

A vast amount of the work we do — at Kaizen and many other similar agencies — is create content with the sole purpose of building high authority links, making it all the more imperative that we are conscious of the changes and trends outlined above.

If we were to split the creative process into three sections — content, design, and outreach strategy — how are we able to engineer our own successes and failures to provide us with a framework for future campaigns?

Three important factors for producing link-worthy content

Over the past month, I’ve analyzed over 120 pieces of content across 16 industries to locate and define the common threads between campaigns that exceed or fall short of their expectations. From the amount of data used and visualized to the importance of effective headline storytelling, the insight is a way of both rationalizing and reshaping our approach to content production.

1. Not too much data — our study showed an average of just over five metrics

Behind every great piece of content is (usually) a unique or noteworthy set of data. Both static and interactive content enables us to display limitless amounts of research which provide the origins of the stories we try to communicate. However many figures or metrics you choose to visualize, there is always a point where a journalist or reader switches off.

This glass ceiling is difficult to pinpoint and depends on the type of content, and the industry or readership you’re looking to appeal to, but a more granular study of good and poor performing campaigns that I performed suggested some benefits of refining data sets.

Observations

A starting point for any piece of research is the individual metrics, whether it is cost, type, or essentially anything worth measuring and comparing. In my research, in the content campaigns that exceed our typical KPI, there was an average of just over 5 metrics used on each piece compared to almost double in campaigns with either a normal or below satisfactory performance. The graph below shows the correlation between a lower number of metrics and a higher link performance.

Graph of various metrics and high link performance

An example of these findings in practice can be found in an infographic study completed for online travel retailer Lastminute.com that sought to find the world’s most chilled out countries. Following a comprehensive study of 36 countries across 10 metrics, the task was to refine these figures in a way that can be translated well through its design. The number of countries was whittled down to just the top 15, and the metrics were condensed to have four indexes which the rankings were based on. The decision to not showcase the data in its entirety proved fruitful, securing over 50 links, covered by the Mail Online and Lonely Planet.

As an individual who very much enjoys partaking in the research process, it can be extremely difficult to sacrifice any element of your work, but it is that level of tact in the production of content that distinguishes one piece from another.

2. Simple, powerful data visualizations — our analysis showed highest achievers had just one visualization

Regardless of how saturated the content marketing industry becomes, we are graced every year with new and innovative ways of visualizing data. The balancing act between originality in your design and an unnecessarily complex data-visualization is often the point on which success and failure can pivot. As is the case with data, overloading a piece of content with an amass of multi-faceted graphs and charts is a surefire way of alienating your users, leaving them either bored or confused.

Observations

For my study, I decided to look at the content that contained data visualizations that failed to hit the mark and see whether the quality is as much of a problem as quantity in terms of design. As I carried out the analysis, I denoted the two examples where one visual would incorporate most or all of the study, or the same illustration was replicated several times for a country, region or sector. For instance, this study, from medical travel insurance provider Get Going, on reliable airlines condenses all the key information into one single data-visualization. Conversely, this piece from The Guardian on the gender pay gap shows how it can be effective to use one visual several times to present your data.     

Unsurprisingly, many of the low scorers in my research averaged around eight different forms of data visualizations while high achievers contained just one. The graph below showcases how many data-visualizations are used on average by high and low performing pieces, both static and interactive. Low performing static examples contained an average of just over six, with less than one for their higher-scoring counterparts. For interactive content, the optimum is just over one with poor performing content containing almost nine per piece.

Graph on analyzing the performance of static and interactive content types

In examples where the same type of graph or chart was used repeatedly, poor performers had approximately 33 per piece, with their more favorable counterparts using just three.

It is important to note that ranking-based pieces often require the repetition of a visual in order to tell a story, but once again this is part of the balancing act for creatives in terms of what type and how many data-visualizations one utilizes.

A fine example of an effective illustration of the data study contained in one visual comes from a 2017 piece by Federica Fragapane for Italian publication La Lettura, showcasing the most violent cities in the world. The chart depicts each city as a shape sized by its homicide rate, with other small indicators defined in the legend to the right of the graphic. The aesthetic qualities of the graph give a campaign, fairly morbid in the topic, an extended appeal beyond the subject of just global crime. While the term “design-led” is so-often thrown around, this example proves how effective it can be to integrate visuals effectively through your data. The piece, produced originally for print, proved hugely successful in the design space, with 18 referring domains from sites such as Visme.co.

An example graph of integrating visuals effectively through data

3. Pandering to the press — over a third of our published links used the same headline as our pitch email subject line

Kaizen produces hundreds of campaigns on a yearly basis across a range of industries, meaning the task of looking inward is as necessary today as it ever has been. Competition means that press contacts are looking for something extra special to warrant your content’s publication. While ingenuity is required in every area of content marketing, it’s equally important to recognize the importance of getting the basics right.

The task of outreach can be won and lost in several ways, but your subject line is, and will always be, the most significant component of your pitch. Whether you encapsulate your content in a single sentence or highlight your most attention-worthy finding, an email headline is a laborious but crucial task. My task through my research was to find how vital it is in terms of the end result of achieving coverage.

Observations

As part of my analysis, I recorded the backlinks of a sample of our high and average content and recorded the headlines used in the coverage for each campaign. I found in better-performing examples, over a third of links used the same headlines used in our pitch emails, emphasizing the importance of effective storytelling in every area of your PR process. Below is an illustration in the SERPs of how far an effective headline can take you, with example coverage from one of our most successful pieces for TotallyMoney on work/life balance in Europe.

Example of effective headlines for high-link performance

Another area I was keen to investigate, given the time and effort that goes into it, is how press releases are used across the coverage we get. Using scraping software, I was able to pull out the copy from each article where a follow link was achieved and compare it to the press releases we have produced. It was pleasing to see that one in five links contained at least a paragraph of copy used in our press materials. In contrast, just seven percent of the coverage within the lower performing campaigns contained a reference to our press releases, and an even lower four percent using headlines from our email subject lines.

Final thoughts

These correlations, similar to the ones discussed previously, suggest not only how vital the execution of basic processes are, but serve as a reminder that a campaign can do well or fall down at so many different points of production. For marketers, analysis of this nature indicates that a refinement of creative operations is a more secure route for your content and its coverage. Don’t think of it as “less is more” but a case of picking the right tools for the job at hand.

Nathan Abbott is Content Manager at Kaizen.

The post Three fundamental factors in the production of link-building content appeared first on Search Engine Watch.

Tuesday, April 16, 2019

Top 19 Instagram marketing tools to use for success

Instagram is a phenomenon of our time. The photo-sharing app has 7.7 billion users by now (and counting).

One billion people use Instagram every month and 500 million use the platform every day. Its engagement is also 10 times higher than that of Facebook, 54 times higher than Pinterest’s, and 84 times higher than Twitter’s.

All kinds of businesses ranging from your teen neighbor making earrings to huge corporations and media are on Instagram. And for a good reason — 80% of Instagram accounts follow at least one business.

Instagram business statistics

[Screenshot taken from the Instagram Business homepage]

At times when Facebook is becoming more and more Messenger-based and Twitter revolves around politics and social issues, Instagram stands to be the platform for friends, strangers, and brands alike.

It’s no surprise we’re so serious about Instagram marketing and the tools that help us with it.

Below is the list of such tools which covers everything from filters to analytics.

19 top Instagram marketing tools

1. Grum

Grum is a scheduling tool that lets you publish content (both photos and videos) on Instagram. You can publish from multiple accounts at the same time and tag the users. You can do that right from your desktop.

Price: Starts at $9.9/month. Offers a free trial for 3 days.

2. Awario

Awario is a social media monitoring tool that finds mentions of your brand (or any other keyword) across the web, news/blogs, and social media platforms, including Instagram. By analyzing mentions of your brand on the platform, it tells you who your brand advocates and who the industry influencers are, what the sentiment behind your brand (positive, negative, or neutral) is, as well as the languages and locations of your audience. It also analyzes the growth and reach of your mentions, and tells you how you compare to your competitors.

Price: Starts at $29/month. Offers a free trial for 14 days.

3. Buffer

Buffer is another scheduling tool. However, it includes Instagram among other social networks rather than focusing on Instagram alone. With Buffer, you can schedule content to be published across Instagram, Facebook, Twitter, Pinterest, and LinkedIn. You can publish the same or different messages across different platforms. You can also review how your posts are performing in terms of engagement, impressions, and clicks.

The tool can be used by up to 25 team members, and you can assign them the appropriate access levels.

Price: Starts at $15/month. Offers free 7-day or 14-day trials depending on the plan.

4. Hashtags for likes

Hashtags for likes is a simple tool that suggests you the most trending relevant hashtags. Knowing the most popular hashtags in real time helps brands keep up with trends, bandwagon on the news, and ultimately grow followers.

Price: $9.99/month.

5. Iconosquare

Iconosquare is a social media analytics tool that works for Instagram and Facebook. It shows you the metrics on content performance and engagement as well as on your followers. You’ll discover the best times to post and understand your followers better. The tool also analyzes Instagram Stories.

Besides analytics, you can schedule posts, monitor tags and comments about your brand.

Price: Starts at $39/month. A free 14-day trial is available.

6. Canva

Canva is a design tool that is a great fit for marketers and companies that don’t have an in-house designer. Among other things, Canva helps create perfect Instagram stories. Stylish templates and easy design tools ensure that your Story stands out, which, again, isn’t easy in the world of Instagram.

Price: Free

7. Shortstack

Shortstack is a tool to run Instagram contests. Contests are huge on this platform, they cause loads of buzz, increase brand awareness, and attract new followers. They are a practice loved by marketers.

ShortStack gathers all user-generated content, such as images that have been posted on your content hashtag, and displays them. It also keeps track of your campaign’s performance, showing your traffic, engagement, and other valuable data.

Price: Free up to 100 entries. Paid plans start at $29/month.

8. Soldsie

Soldsie is a handy tool that helps you to sell on Instagram and Facebook using comments. All you have to do is upload a product picture with relevant product information. Users who are registered with Soldsie can simply comment on the photo, and Soldsie will turn that into a transaction.

More expensive Soldsie plans are also integrated with Shopify.

Price: Starts at $49/monthly and 5.9% transaction fee.

9. Social Rank

Social Rank is a tool that identifies and analyzes your audience. You can identify influencers among your followers, see who engages with your brand and with what frequency. You can sort your followers in lists that are easy to work with (for example: most valuable, most engaged, and others).

You can also filter your audience by bio keyword, word/hashtag, and geographic location.

Price: Available on request.

10. Plann

Plann is an Instagram social media management tool. It allows you to design, edit, schedule, and analyze your posts. For example, you can edit the Instagram grid to look just as you wish. You can rearrange, organize, crop, and schedule your Instagram Stories. All exciting stats, from best times to post and best-performing hashtags to your best-performing color schemes are available. And you can also collaborate with other marketers to run your Instagram account together.

Price: Free, paid plans start from $6/month.

11. Social Insights

Social Insights is another platform that offers many important Instagram marketing features, such as scheduling and posting from your computer, identifying and organizing your followers, and analyzing followers’ growth, interactions, and engagement. You can add other team members without sharing your Instagram login.

Price: Starts at $29/month. A free 14-day trial is available.

12. Instagram Ads by Mailchimp

If you’re already using MailChimp, its Instagram Ads feature might come in handy. The tool lets you use MailChimp contact lists to create Instagram campaigns. The whole process (creating, buying, and tracking results of your ads) is, therefore, in the familiar place and powered by data.

Price: No extra fees if you’re using MailChimp.

13. Unfold – Story Creator

Unfold – Story Creator is an iOS app that makes lifestyle, fashion, and travel content more professional-looking. The app offers stylish templates, advanced fonts and text tools, and exports your stories in high resolution so that you can share them to other platforms besides Instagram.

Price: Free

14. Picodash

Picodash is an Instagram tool that finds target audiences and influencers on the platform. It lets you export your and your competitors’ Instagram followers and following lists, users that have used a specific hashtag, posted at a specific location or venue, commented or liked a specific post, as well as tagged users. You can also download any account stories or highlighted stories.

Price: Starts from $10 for a Followers/Hashtag Posts export. You can also request a sample of 100 for free before you order a full export report.

15. Wyng

Wyng is an enterprise-level platform that finds user-generated content with a specific mention or hashtag, exports it, and gets the rights to this content. This is very helpful for running contests. Instagram is, however, a tiny fraction of what the tool covers.

Price: Available on request. A free 14-day trial is available.

16. Afterlight

Afterlight is the iOS/Android image editing app that makes your content look more professional and refined. It offers plenty of unique filters, natural effects, and frames.

Price: $2.99

17. Sendible

Sendible is a popular social media management platform that lets you run accounts on different social media platforms, including Instagram. It’s integrated with some other tools that are useful for Instagram, such as Canva. The tool does scheduling, monitors mentions, and tracks the performance of your Instagram posts. You can also team up with other marketers and work together on your Instagram marketing (and other) goals.

Price: Starts at $29/month. A free 14-day trial is available.

18. Olapic

Olapic is an advanced visual commerce platform. It collects user-generated video content in real time, publishes it to your social media channels (including Instagram) makes it shoppable, measures and predicts which content will perform best. It goes far beyond Instagram and even social media. What is more, it obtains rights for the content for you so that you’re able to use it across your advertising, email, and offline channels.

Price: Available on request.

19. Pablo

Pablo (made by Buffer) is a platform that lets you easily create beautiful images for your Instagram marketing purposes. You can choose photos from Pablo’s own library which includes more than 500,000 images, add text (25+ stylish fonts are available) and format. The resizing option for various social platforms, including Instagram, will ensure your image fits perfectly.

Price: Free

Conclusion

As you can see, there’re plenty of tools to choose from. Check them out, spot the ones that you need, and take your Instagram marketing to a whole new level.

Aleh is the Founder and CMO at SEO PowerSuite and Awario. He can be found on Twitter at @ab80.

Read next:

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How to optimize paid search ads for phone calls

There have been an abundance of hand-wringing articles published that wonder if the era of the phone call is over, not to mention speculation that millennials would give up the option to make a phone call altogether if it meant unlimited data.

But actually, the rise of direct dialing through voice assistants and click to call buttons for mobile search means that calls are now totally intertwined with online activity.

Calling versus buying online is no longer an either/or proposition. When it comes to complicated purchases like insurance, healthcare, and mortgages, the need for human help is even more pronounced. Over half of consumers prefer to talk to an agent on the phone in these high-stakes situations.

In fact, 70% of consumers have used a click to call button. And three times as many people prefer speaking with a live human over a tedious web form. And calls aren’t just great for consumers either. A recent study by Invoca found that calls actually convert at ten times the rate of clicks.

However, if you’re finding that your business line isn’t ringing quite as often as you’d like it to, here are some surefire ways to optimize your search ads to drive more high-value phone calls.  

Content produced in collaboration with Invoca.

Four ways to optimize your paid search ads for more phone calls

  1. Let your audience know you’re ready to take their call — and that a real person will answer

If you’re waiting for the phone to ring, make sure your audiences know that you’re ready to take their call. In the days of landlines, if customers wanted a service, they simply took out the yellow pages and thumbed through the business listings until they found the service they were looking for. These days, your audience is much more likely to find you online, either through search engines or social media. But that doesn’t mean they aren’t looking for a human to answer their questions.

If you’re hoping to drive more calls, make sure your ads are getting that idea across clearly and directly. For example, if your business offers free estimates, make sure that message is prominent in the ad with impossible-to-miss text reading, “For a free estimate, call now,” with easy access to your number.

And to make sure customers stay on the line, let them know their call will be answered by a human rather than a robot reciting an endless list of options.

  1. Cater to the more than half of users that will likely be on mobile

If your customer found your landing page via search, there’s a majority percent chance they’re on a mobile device.

While mobile accounted for just 27% of organic search engine visits in Q3 of 2013, its share increased to 57% as of Q4 2018.

Statistic: Mobile share of organic search engine visits in the United States from 3rd quarter 2013 to 4th quarter 2018 | Statista

That’s great news for businesses looking to boost calls, since mobile users obviously already have their phone in hand. However, forcing users to dig up a pen in order to write down your business number only to put it back into their phone adds an unnecessary extra step that could make some users think twice about calling.  

Instead, make sure mobile landing pages offer a click to call button that lists your number in big, bold text. Usually, the best place for a click to call button is in the header of the page, near your form, but it’s best practice to A/B test button location and page layouts a few different ways in order to make sure your click to call button can’t be overlooked.

  1. Use location-specific targeting

Since 2014, local search queries from mobile have skyrocketed in volume as compared to desktop.

Statistic: Local search query volume in the United States from 2014 to 2019, by platform (in billions) | Statista

In 2014, there were 66.5 billion search queries from mobile and 65.6 billion search queries from desktop.

Now in 2019, desktop has decreased slightly to 62.3 billion — while mobile has shot up to 141.9 billion — nearly a 250% increase in five years.

Mobile search is by nature local, and vice versa. If your customer is searching for businesses hoping to make a call and speak to a representative, chances are, they need some sort of local services. For example, if your car breaks down, you’ll probably search for local auto shops, click a few ads, and make a couple of calls. It would be incredibly frustrating if each of those calls ended up being to a business in another state.

Targeting your audience by region can ensure that you offer customers the most relevant information possible.

If your business only serves customers in Kansas, you definitely don’t want to waste perfectly good ad spend drumming up calls from California.

If you’re using Google Ads, make sure you set the location you want to target. That way, you can then modify your bids to make sure your call-focused ads appear in those regions.  

  1. Track calls made from ads and landing pages

Keeping up with where your calls are coming from in the physical world is important, but tracking where they’re coming from on the web is just as critical. Understanding which of your calls are coming from ads as well as which are coming from landing pages is an important part of optimizing paid search. Using a call tracking and analytics solution alongside Google Ads can help give a more complete picture of your call data.

And the more information you can track, the better. At a minimum, you should make sure your analytics solution captures data around the keyword, campaign/ad group, and the landing page that led to the call. But solutions like Invoca also allow you to capture demographic details, previous engagement history, and the call outcome to offer a total picture of not just your audience, but your ad performance.

For more information on how to use paid search to drive calls, check out Invoca’s white paper, “11 Paid Search Tactics That Drive Quality Inbound Calls.”

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