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Ready to Start Blogging? Tips & Tools to Help You Get Started

You know there’s a content marketing epidemic when everyone from your plumber to your dry cleaner has a blog. With statistics such as:

“Marketers who prioritize blogging are 13x more likely to achieve a positive ROI on their efforts” (source)

“Companies that blog have 97% more inbound links” (source)

Blogging is a huge factor from an SEO and lead generation standpoint. As blogging becomes an increasingly large part of content marketing strategy, it’s important that the blogs you put out can provide value to your readers.

While blogs written by practitioners, rather than spectators, can offer actionable insight into the industry, not every technical person has a desire to participate in writing content. As you may have noticed from the diverse subjects covered in the iPullRank blog, all of us get a chance to write – even our primary communicators of data and code. How else could we offer the full spectrum of digital marketing viewpoints from creating effective content strategy, to improving designer and developer collaboration, to scraping every single page on the internet?

Blogging is challenging enough for content teams, but for those of you that don’t regularly write, it can be hard to get started. Or even worse, you have a billion ideas, only to face incurable writer’s block.

As someone who toggles between writing little to churning out 25 pages of content in a week, even I am often at a loss for what to write. So, what helps? Here, I’ll show you some useful tools to add to your writing process, that I hope will help you break through tough areas like ideation, writing headlines, and effectively communicating your expertise.


First things, first…

We need a topic. Sometimes, if you’re completely void of ideas, the best way to choose your topic is to see what others are writing about. Sure, you can just use Google, but adding a tool like Ahrefs or Buzzsumo to your research will make it easier to view valuable metrics, in addition to seeing which topics are trending. For example, if I’m interested in writing about “wearable tech” the content searches below show me what’s been written, when it was written, who’s linking to it, and how many shares it got.



Although you’ll probably get slightly different results whether you choose Ahrefs or Buzzsumo, the important metrics are all there. Based on this information, you can also decide if a topic has been getting too much attention lately. A quick sort based on your preferred social platform, can also help you decide if your budding content idea might perform well on your most social active channel.

As you continue with your research, don’t forget to read down to the comments section. Sometimes this section can be even more interesting than the content itself, and very revealing as to which questions are left unanswered by the existing content. The goal of your blog could be as simple as addressing these types of questions using your own expertise.

Or, perhaps you think that there is just too much content about wearable tech accessories recently. Think about what you would have liked to have read, and then write it yourself. The goal is to put a fresh spin on existing content about a particular topic.


Too many ideas, too little writing

If you have an idea that quickly becomes 30 ideas, it’s easy to lose focus and get stuck. I like to control these tangents by first writing the conclusion, and then writing the headline. Think of a jigsaw puzzle – when you build the frame first, you know the boundaries for the rest of the pieces and how they fit into the broader picture. Of course you can edit your conclusion as you go, but having this gives you a roadmap for writing the rest– not only will you have all of your points summarized, but psychologically, the blog already feels finished (you just go back and fill in some spots.)



There are many tools to help you get the right words when you are contemplating your headline. Whereas some tools are better for content ideation, CoSchedule’s headline analyzer is one of my favorites overall. It’s a great starting point for optimizing your headline for word choice and length.


Coschedule headlines


I took a look at the most popular content on wearable tech and ran them through this tool. Even without Coschedule’s scoring, it’s obvious why some headlines might be more appealing the others.

CoSchedule’s tool will help you break down a headline word for word, analyzing the common, uncommon, emotional, and powerful words, giving you a data-driven approach to writing your headline.



Although headline score is not the sole indicator of whether or not a piece of content will perform, the headline is what will initially capture a user’s attention. Therefore, your audience should also be a huge factor in crafting your headline. Case in point: that lowest scoring headline above – Wearable Tech Meets Real-Life Avengers Toys? My geeky side would click on that even with its failing score.


Put it in your own “words”Coding

Once you’ve got your headline and conclusion squared away, it’s time to get started on the body copy. If you’re feeling challenged for words, write in the language that feels most natural to you. If you’re a developer and want to explain responsive design, start writing the code that conveys your concept. If you speak in graphics or design, show images of how you would’ve created better UX/UI. Data analysts, start by creating all the graphs for your data-set before you begin that whitepaper.

By putting your thoughts into a medium you are more comfortable with, you can freely get your ideas on paper. Blogs, technical or otherwise, are far different from a thesis. Code and images will help you get your message across better than words alone. If you’re strapped for time, there are great tools for creating quick diagrams, infographics, other data visualizations here.


Let’s talk about it

After you have most of your thoughts outlined and are ready to flesh it out, you can start your editing process by reading it out loud. Hearing your writing will not only help you catch small grammatical errors, but also helps to keep your words flowing by naturally adding or deleting unnecessary words or phrases. Additionally, you prevent yourself from overthinking word choice, and can better elaborate on concepts that you’re having trouble putting on paper (even though you know you’ve explained it to your colleagues at least a billion times before with ease.)

Although not perfect, there are two tools that I like to use as a second (and third) pair of eyes.

Hemingway App is a free tool that will scan your writing and find all of the sentences that are overly complicated or confusing. You can also compose directly into the editor and format your text as you go.



However, it’s important to note that Hemingway’s readability scale is based on the Automated Readability Index, which factors in the characters per word.  The Flesch–Kincaid Reading Ease model, which we prefer to use for our content audits, factors in syllables per word in its model. If you’re looking for that, or have another favorite readability scale, those are offered here.

When considering the grade level, higher isn’t always better and lower isn’t always worse. Again, think about your audience and their education level. A technical blog will probably skew on the higher side, but still be easily understood by its readers.

Although I have more Chrome extensions than I actually use, I do think Grammarly is a good one to have in your back pocket. It picks up spelling, grammar, and punctuation mistakes in pretty much any text box, and integrates with Word. Upgrading to the premium version provides additional features like plagiarism checking and vocabulary enhancement.


Summing it up…

I hope this blog gives you a good start for getting your blogging game going. Blogging is a great way to share your expertise with the community, while helping with your company’s content marketing efforts. If anyone has a favorite content tool, that I missed, let me know! I’d love to try it too.

Good luck and happy blogging!

How to Empower Digital Marketing with Data Science

When I first heard about data science in 2012, I immediately fell in love with this nerdy field and started my 6-year journey in Statistics. I was inspired by a New York Times cover story about how Target broke through to a new level of customer tracking with the help of statistics genius Andrew Pole.

Pole identified 25 products that when purchased together indicate a woman is likely pregnant. This pregnancy-prediction model once exposed a teen girl’s pregnancy through sending coupons to the girl’s family even before the father was aware of this situation. I was really amazed at how smart an algorithm can be, and my next thought was, am I also likely to receive promotions for things I want, before I even know I want them? The answer is YES! That’s why we need data science in digital marketing.



What Can Data Science Do?

The ongoing information explosion has created a situation where it’s possible to utilize rich data and different tools to improve digital marketing performance. There are countless examples of what you can achieve with data science. In this blog, I will discuss using data science to segment current customers, score new customer profile data, launch data-driven email marketing campaigns, and in the end, drive customer loyalty.

Customer Segmentation

Successful business starts with knowing your customer. It’s important to first divide your customer base into groups of individuals with shared characteristics that are relevant to marketing, such as age, gender, interests, profession and spending habits. Then target different segments with separate approaches tailored to specific customers.

A major challenge with customer segmentation is the lack of good, clean data. When people sign up for a mailing list, they often don’t answer additional questions in the questionnaire, or they purposefully lie about their personal information such as age, marital status, and etc. In many cases, all they care about is getting the 10% off their next purchase; they couldn’t care less about whether the company has accurate data.

That’s where big data comes in. Instead of asking people to describe themselves, the modern marketers can use external sources of data and advanced analytics to infer things about their customers. Marketers can accurately ascertain facts just by observing their customers’ purchase behavior.

After gathering data, we can conduct clustering or classification based on the data type and ideal output. Clustering produces the output of grouped customers based on a distance matrix. However, where a cluster should be split is based on a criterion which specifies the dissimilarity of elements in the cluster in the sets. In other words, clustering provides a little knowledge on which variables differentiate one customer from another.

Decision trees, on the other hand, are interpretable and can also be easily visualized in an organized format. The decision tree can be used to represent the shared characteristics of high business value customers. Essentially, decision trees automatically derive a set of if-then-else rules to classify data (in this case the data describing the customers) into classes (in this case high business value customers vs. low business value).

decision tree

Source: Ask Analytics


Or you can build regression models to quantitatively score the customers.  Once you have the segmentation model, it’s very easy to apply the model to score/classify new customer profile data. In the next blog, I will develop a step-to-step guide for customer segmentation.

Launch a Data-Driven Email Marketing Campaign

2015 was predicted as the year for “smart use of data” by EMarketer. This trend will continue and be fueled by personalizing email communications with customers. To achieve personalization, the first step is to collect valuable demographic and behavioral data. Here are some important metrics or information that help you better understand your customers:

  • Demographic information: gender, age, profession, interest, and etc.
  • Click-through rates, conversion rates and click-to-open rates
  • Purchase activities on specific products
  • Downloaded white papers or participated in a webinar
  • Signed up for your newsletter or joined the email list
  • Interacted with your social media sites as Facebook and Twitter
  • Watched your promotional video

Gathering this data will allow you to segment your customers based on demographic and behavioral information and then send personalized messages with relevant content to each group. For example, send suggestions or promotions of other products people might be interested in purchasing based on products they’ve already purchased, or remind them that the products they’ve purchased will expire soon.

Timing is important to open rates. Research by MailChimp found that readers are more likely to open emails after 12 p.m. And that 23% of all email opens occur during the first hour after delivery. After 24 hours, an email’s chance of being opened drops below 1%!

Further research by Get Response shows that the best day to send emails in order to get the highest open rate is Tuesday.


Source: SuperOffice

Now that you know on which day to send your emails, what about the time of day?

An interesting fact is that college aged recipients have an optimal time of 1 p.m. versus 10 a.m. for the people over 40s. College students during the summer likely drag themselves out of bed later and check their email later than older professionals.

If you collected the data for the time when your customer open emails, you can send an email customized to their behavior, or A/B test common-sense hypotheses, such as younger people open email at night, while older people open it at morning. A/B tests on send time give on average a 22% lift in engagement.

Drive customer loyalty

According to Forrester Research, it costs five times more to acquire new customers than it does to satisfy and retain current ones. As a result, smart companies are increasing their focus on retaining customer loyalty. The process to drive customer loyalty can be split into two parts: understanding of each customer’s behavioral patterns and churn prediction.

Understand customer’s behavioral patterns

If companies want to make their offers as targeted and personal as possible, they have to integrate and analyze large unstructured and streaming data from various data sources, such as text messages, e-mails, call center notes, voice recordings, surveys, GPS units and social media. Data scientists and engineers can help you incorporate and integrate your data into analytics and predictive models so that you can generate actionable information in minutes.

A good example in this case is how a global bank can improve their customer support. In the short time before the customer is connected to the representative, the representative can access customer data that may help predict why the customer is calling. Certain data can even indicate a major life event for this customer. As a result, agents are able to make the best response for that situation. For example, a customer may have a child ready to graduate from high school – this is a great time to discuss college loans.

Churn Prediction

To maximize your customer retention, companies should be focused on those customers that will likely leave. This is exactly what we do in Churn Prediction: building statistical models which estimate the likelihood that these customers will churn. Knowing which customers will likely churn is however not enough; you also need to know why these customers would leave you.

As we have discussed before in the customer segmentation, the decision tree algorithm can be selected as the predictive analytics algorithm for this application. From the decision tree, we can determine why customers will be churning, in order to properly address them, and to try to retain them through a personalized approach.


Source: Flickr


While the decision tree is not necessarily the model with the highest predictive accuracy; it is, however, a good model which can be easily interpreted and acted upon. For example, This would be useful in applications in which it is important to very accurately predict whether a customer will churn, but not so much why he or she would churn. Based on the predictions and insights obtained from this tree, well-targeted customer retention actions can be set up.


This blog is the first in a series and I hope it expands awareness of digital marketers on what can be achieved with data science. In next blog, I will develop a step-to-step guide for customer segmentation. If anyone has commentsfeedback, or questions, please feel free to post Comments on the blog.

Here are some useful resources where you can learn more about the concepts I mentioned in the blog:

Decision Tree

Decision Tree in R

Cluster Analysis

Clustering in R

Churn Prediction


How To Uncover Your Competitors’ Strategies With A Brand Mention Audit

This week, we’re thrilled to welcome Razvan Gavrilas, Founder and Chief Architect of cognitiveSEO to the iPullRank blog.

Uncover Your Competitor strategies

By Razvan Gavrilas | @razvan_gavrilas | cognitiveSEO


Having a lot of mentions of your brand on the web and basically earning media attention is a great asset. Yet, it is just half of the battle. Knowing where brand mentions come from and understanding which pieces of content drive a brand is the other half of the battle, and it is just as important as the first one.


Running an in-depth brand mentions audit could help you make sure that you are not overlooking any efficient strategies that might be working wonders for your competitors. There might be many ways in which you can boost your business, but the truth is that the more you can find out about your niche and your competitors, the more you can learn, adapt and ultimately flourish.


Therefore, by doing a bit of reverse engineering, let’s see how doing an effective brand audit can help you uncover your competitor’s strategies. In line with the old saying, “I hear and I forget, I see and I remember, I do and I understand,” let’s take a very specific niche and apply our research on it: the top Michelin Starred Restaurants in the UK.


1. Research Your Brand & Competitors

To best explain how to research your brands’ and competitors’ mentions, I will do a case study on the Michelin Starred Restaurants in the UK. There is probably no higher recognition in the restaurant business than a Michelin star. Getting one has got to be one of the most coveted awards. Getting the maximum of three is, for most chefs, a dream and the pinnacle of their life’s work.


In 2015 there were only 4 restaurants in the UK awarded with three Michelin stars:

  • Restaurant Gordon Ramsay
  • Alain Ducasse at The Dorchester (both in London)
  • The Fat Duck
  • The Waterside Inn (both in Berkshire)


The Fat Duck has ostensibly been dropped from the list in 2016 after having re-opened in the fall of 2015 (not in time to be considered). Given its previous prestige and influence, we’ll give it a pass.


Uncover Your Competitor strategies2


These are all world-class restaurants with no shortage of customers. But is there still something to learn from their online presence? They are, after all, in a particular strand of competition – the attention of wealthy gourmands in the UK (or just wealthy status-seeking people).


In order to find out how each restaurant is performing (and along with them, their niche), we’ve started doing some media monitoring. We did this in order to get insights on the web mentions linked to the brands (the four restaurants mentioned above) and the topics related the them that interested us most.


Some tools you could use for web mentions monitoring are:

Each of these tools provided different results. You should test them all and decide which one fits you best. We found BrandMentions as being the one who provided the largest list of mentions as well as the freshest. Therefore, we decided to continue our research using this tool for web mentions monitoring.


So, using BrandMentions, we ran three searches on the three-star culinary wonders. Taken in consideration:

  1. All mentions from the previous month
  2. All mentions in English from the previous month
  3. All mentions in the UK from the previous month


Using this tool, we exported all of the mentions into Excel. By analyzing the data, we can figure out how our UK starred restaurants are really performing in a side by side comparison.


2. Find the Most Popular Restaurant in its Niche

Finding the most “mentioned” player or the one who performs best on the web is quite a difficult task without having the proper tools. You can try figuring out which is the most trending, yet Google Trends will only give you relative and not absolute data. When it comes to searching big topics (2016 elections, Oscars, Valentine’s Day, etc), Google Trends can be of real help. Google Trends aggregates data from Google Search, YouTube and Google News, and ranks the most searched for stories. It could be one of the most comprehensive trends aggregate you’ll find on the web as watching spikes in search terms during major events, you can quickly determine what topics are grabbing people’s interest. Yet, when you are looking for some particular brands in a niche, it doesn’t bring much added value to your research as the data is not very helpful.


As you can see in the screenshot below where the restaurants that we took for analysis are compared within Google Trends, we are only offered a relative interest over time and not a complete picture of what’s going on. Interpreting analytics effectively to gain useful insights about your customers is no easy task. Customer behavior can easily become lost in the math behind results or overlooked and simply taken at face value.


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Since we are interested in the best player out of the 4 brands between Restaurant Gordon Ramsay, Alain Ducasse at The Dorchester, The Fat Duck and The Waterside Inn, we used the BrandMentions tool to identify the exact number of mentions each brand has in order to measure their online popularity.


Judging just by the number of mentions from the last month, as we see in the screenshot below, the winner is The Fat Duck, followed by Restaurant Gordon Ramsay, Waterside Inn and Alain Ducasse at the Dorchester.


Uncover Your Competitor strategies 4


Numbers are indeed a great indicator, yet, we can go a bit further with our research and see how the 4 brands performed throughout the month. Spotting their highs and lows might tell us a lot about their strategy and their online presence.


What we did after the initial search, was to download a CSV file of each brand’s mentions, and play around with the data in order to best understand strategies and directions.


It seems like all four brands are very active, if we look at the date of their latest mentions. Even if some of them (for instance Alain Ducasse at The Dorchester) have considerably less mentions than its competitors, they manage to have a lot of fresh mentions reported to its total number of shares. Further, when we looked at the quality and shareability, all four restaurants have a good ratio of high performance, shared mentions. We are going to investigate this data in terms of performance and shares later on the article.


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By comparing number of mentions over a month, we can clearly see how each brand performed by day. The chart is based on all the mentions gathered from the period 15 January – 15 February.


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As we’ve mentioned above, The Fat Duck is the big winner however, as we look at the chart below we can see that it’s victory is due to the fact that it has quite a constant number of mentions (except some spikes) and not the biggest spikes. Also, it seems that it’s doing far better at the end of the month unlike its competitors who are reporting just a few mentions in the same period of time. The Fat Duck also starts and finishes the month strong.


Waterside Inn who is on the 3rd place out of 4 seems to be having the highest spikes. Yet, its lack of “being mentioned” consistently pulled this brand down.


Gordon Ramsay’s Restaurant finishes the month in a descendent trend yet, the number of mentions from the very beginning of the month and also from 18th until 21st of the month manage to place him 2nd on the overall top. For Restaurant Gordon Ramsay, but also its competitors, it should be interesting to see what kind of mentions appeared during that period of time, or what kind of strategy was applied and to try to replicate it.


Alain Ducasse at the Dorchester has a poor and (unfortunately for the brand) constant trend across the entire month. On the last days of the month, we can spot a throb, yet nothing that can place it on another place than the last in the top of 4.


3. Unveiling Each Competitor’s Strategy

Numbers are indeed important, yet there are a lot of things we can learn from the type of mentions themselves. Each mention has a main performance score, a subdomain authority score and a number of shares. When trying to find the best practices from the competitor’s strategy, these criteria are of great importance.


Uncover Your Competitor strategies 7


By filtering the mentions by country, language or date, we can also uncover: Does a brand have a strong presence in a certain country? Are the mentions written in English only? How is the brand standing in terms of the number of mentions in the last 24 hours?


Let’s take each of the brands that we chose for this analysis and try to uncover their strategies.


The Fat Duck

The Fat Duck doesn’t impress only with the sheer number of mentions, but also in terms of “volumes”. Set aside the Live Stream golden-egg-laying-goose and there’s still plenty to look at: close to 28000 shares in total. Roughly a third of those come from Metropoli, a supplement to El Mundo (one of Spain’s leading newspapers). Argentine (with a mention from leading conservative paper La Nacion) and Norway (with a mention from well-circulated tabloid VG) also make rounds, each raking in over 1000 shares.


Uncover Your Competitor strategies 8


Still, most of the attention comes from English-speaking websites, in particular from specialty websites such as True Cooks (over 3,500 shares) or The World’s 50 Best (over 2,500 shares).


Uncover Your Competitor strategies 9


Even when narrowing down to the UK sites, The Fat Duck is still the winner. There’s simply no substitute for 3 mentions from The Guardian, averaging 1500 shares per mention.


Restaurant Gordon Ramsay

Restaurant Gordon Ramsay enjoys quite a bit more attention both within and outside UK. In fact, the bulk of its shares (close to 70% out of the total of over 12000) comes from Straits Times, Singapore’s highest-selling paper. There’s also quite a bit of attention (even if in the hundreds, rather than thousands) from Spanish-speaking Infobae and French-speaking Opaz.


Uncover Your Competitor strategies 10


When narrowing searches to English-speaking sites only, it becomes evident that foreign appeal is not Ramsay’s only weapon. Over 1000 shares come from Anglotopia, a site bound to attract both tourists and passionate Anglophiles.


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Perhaps somewhat surprisingly, there’s little media presence in the UK (compared to the total number of English-speaking mentions) when it comes to Restaurant Gordon Ramsay. Not to fear though, as the attention of specialty websites such as The Staff Canteen (which markets itself as “The UKs leading networking website for chefs”) more than compensates, to the tune of a few hundred shares.


The Waterside Inn

Waterside Inn proves that more mentions don’t necessarily bring in proportionately more shares. That’s especially true for the international entries. Mentions as diverse as Israel or Vietnam bring in exactly zero shares. While that’s in no way harmful to the restaurant, it also doesn’t help as much as it could. Still, it ultimately manages to bring in over 5700 shares from its 77 mentions.


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Faring better with mentions from English-speaking websites, Waterside Inn gets close to 1,500 shares from Canada’s In Sauga publication, 230 shares from the US, with LA Times and quite a few hundred from It also gets mentions from specialty sites such as Top 50 Gastropubs, but it doesn’t seem to have as big an impact as you might expect (only a dozen of shares).


Uncover Your Competitor strategies 13


Its greatest success however, seems to lie with UK-specific mentions. It gets attention not only from The Guardian (raking in some 1,300 social shares), but also from The Telegraph (with 450 shares), while also capturing the interest of more local publications such as Henley on Thames or South Wales Evening Post (each a few hundred).


Alain Ducasse at the Dorchester

Alain Ducasse at the Dorchester has the smallest online presence of the four. It can only be found in a couple of mentions with a performance score of over 70 and has an overall number of shares of just over 1,500 (about a quarter of the number boasted by the Waterside Inn). It only holds 3 mentions with more than 100 shares (and none with more than 1,000). Two of these mentions come from respectable general audience news sources – Bloomberg and Finance Yahoo -, while the third comes from a Russian travel site, Conde Nast Traveler.


Uncover Your Competitor strategies 14


When narrowing results to English-speaking sites, you can notice that it is where almost all of the results were coming from anyway (just 2 sites were dropped from this new search). That’s not necessarily surprising, but it does suggest that Alain Ducasse at The Dorchester might not have a lot of visibility in non-English speaking countries. Which, if you’re a 3-star Michelin restaurant, might be an avenue worth pursuing.


Uncover Your Competitor strategies 15


When restricting the search to UK websites only, quite a few results are discarded from the previous search (more than half), which means that at the very least, Alain Ducasse at The Dorchester has some worldwide notoriety in English speaking countries.


If we were to sum up their basic strategy, in terms of performance, shares, and UK vs worldwide mentions, things look something like this:

When it comes written in English and relevant for UK:

  • For Alain Ducasse at the Dorchester, almost all of the results are written in English and relevant for UK.
  • Waterside Inn’s greatest success seems to lie with UK-specific mentions.
  • There’s little media presence in the UK when it comes to Restaurant Gordon Ramsay.
  • Even when narrowing down to the UK sites, The Fat Duck is still the winner.


When it comes to “shareability”:

  • The Fat Duck had the highest number of mentions and highest number of shares (though the discrepancy between it and the others was much higher for shares than for mentions).
  • Alain Ducasse at The Dorchester had the lowest number of mentions and lowest number of shares.
  • Although Waterside Inn has considerably more mentions than Restaurant Gordon Ramsay, it turns out that the latter has more high-shares mentions than the former. (We considered “high-share mentions” as mentions which are responsible for more than 1,000 overall shares.)


When it comes to performance:

  • There is a direct correlation between the mentions publication and the performance score
  • While three of the restaurants managed to have mentions in high quoted publications, which implicitly gave them mentions with high shares, Alain Ducasse at the Dorchester can only be found in a couple of mentions with a performance score of over 70


4. Putting it all together

So what are, the main takeaways from this audit, precious info that any brand from a competitive niche should look at when trying to outsmart its competitors?


The Mentions Source

We cannot pretend to understand our restaurants’ mentions trend without taking into consideration the mentions’ source. Anytime people talk about us or our professional achievements is great. However, although it is nice to capture peoples’ attention, it often occurs that some opinions are valued more than others. If I were a physicist, I am sure that my family’s appreciation on my work would have mattered, yet, not as much as an opinion coming from Stephen Hawking. Same things happen in the case of our restaurants; there are some sources that weight more than others, usually bringing with it high authority and credibility, and also a high number of shares.

  • The largest number of mentions comes from Tripadvisor (or some specific country-version of it: .com,,,,,
  • Out of the 325 total mentions that all 4 restaurants got together, 35 came from Tripadvisor.
  • Quite a few of the entries are from blogs and general discussion boards (such as Reddit or 4chan), but they are generally low performance and attract few shares. At its best, a blog that holds some external credit (Forbes Travel Guide blog) holds a 44 performance score and attracts only 56 shares.
  • The one consistent source of high performance scores and high number of shares appears to be The Guardian. Also Bloomberg (2 entries) and The Independent (1 entry) hold similar, but smaller appeal, with lower performance scores.
  • The one anomaly in all of this is a Live Stream mention for The Fat Duck, which amounts for over 80,000 shares, a ridiculous amount by any means (although the performance score is at a solid 82). While this goes to show that pure luck can still play a role, it is worth noting that even without this mention, The Fat Duck would still be in the lead, with more than double the number of total mentions compared to the next competitor, Restaurant Gordon Ramsay.


There is no such thing as a “mention source formula”, meaning that if you get a mention from source X, your brand will automatically be boosted. However, in the Michelin starred Restaurant niche, it seems that there are some important pin-points in terms of sources from which we mention: The Guardian, The Independent and Tripadvisor.


The Mentions Performance

The performance score of each mention is calculated based on the number of shares and the domains authority. Therefore, the number of mentions and their source is of high importance. And it’s true that all mentions matter, but some of them matter most than others. When looking into the competitor’s yard, we tend to look at its best practices and try to replicate what worked best for them.


The Fat Duck seems to remain the indisputable leader when it comes to high-performance mentions. What is very interesting is that Waterside Inn, which does not perform very well in terms of mentions’ number, but is the second best when it comes to mentions with very high performance score.


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The Mentions Shares

We’ve realized that the relationship between the number of mentions and volumes of shares is hardly linear. It looks like this:

  • The Fat Duck had the highest number of mentions but also the highest number of shares
  • Alain Ducasse at The Dorchester had the lowest number of mentions and lowest number of shares.
  • Waterside Inn had more mentions than Restaurant Gordon Ramsay, but the latter had many more shares than the former.


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There were some interesting findings when we researched this. It made us realize that shareable content can be difficult to come by. From the total number of mentions for all 4 restaurants:

  • There are 79 mentions with 0 shares, some of them including pretty heavyweight names like TripAdvisor, Daily Telegraph or MSN.
  • More than 60% of mentions have 10 shares or less.
  • There are only 13 mentions with between 1001 and 10k shares. This is the “share heaven” and the realm of quality media with national coverage (leading publications from Spain, Argentine, UK, etc.)
  • 1 mention with over 10k shares.


Mentions vs. Link Profile

It’s always interesting to correlate the online mentions with the site’s link profile. As we can see in the screenshot below, The Fat Duck remains the leader. Just as in the case of mentions, studying the competitor’s link profile can bring many great link opportunities.


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Perhaps the easiest way to take advantage of your competitor’s link profile to crawl your competitor’s broken backlinks and using the Link Reclamation Technique, try to “borrow” its link juice to get higher traffic and rankings.



There is no secret that niche and brand audits are a must for every business that wants to succeed in the ever-changing online environment, and that they are the first step in the development of any digital marketing plan. We all want to know as much as possible from the niche we are competing in, whether we are a well-established business or a small company that wants to expand into new markets.  And the more you can find out about your competitors, the more you can learn, adapt and flourish.


Although it may take a lot of time and energy, such a brand mention audit will eventually pay off. Think of the perspective that any of the analyzed restaurants could have if they were aware of what their competitors are doing. They can see what type of content is working for their competitor and where they can replicate that success to their advantage. What publications are the most authoritative in the niche, and which attention you should grab? What social networks are most interested in this niche, and what type of content is working on each of them? Which country should we concentrate our efforts? Is there a market for non-English speakers as well? All of this are ideas that put together in a digital marketing strategy will probably not make overnight miracles, but will constantly and consistently boost any brand.

6 Marketing Tools We Use That No One Else Really Talks About

As a marketing technologist, I can tell you that, frankly, there are many too many marketing technology solutions out there. I’m sure you’ve seen that aesthetically challenged image that keeps getting busier and busier each year with all of the marketing and ad tech solutions. We’re in the middle of the MarTech cash grab.

I spend a lot of time playing around with these tools and separating the good from the bad. Specifically with regard to marketing analysis, there are number of great all-in-ones and point solutions that everyone is using like Searchmetrics, Ahrefs, Screaming Frog, et al. However, there are a number of solutions that we get a lot of value out of that I don’t hear talked about much in the online marketing echo chamber. I feel that some of these tools give us an unfair advantage to scale our approaches and do a lot of great work with our small team. So today, I just want to introduce you to some of these tools in hopes that you may find value in adding them to your workflow and toolbox.


When I started doing link building in 2006, everything we did was tracked in Excel sheets. Then many of the non-tinfoil hat people moved to Google Docs due to its ImportXML features. At some point a lot of people with dev chops realized there’s a lot of things that can be automated and tracked to improve and scale outreach. As a practitioner that really spent the time to get to know my prospects, I’ve been a big fan of BuzzStream because of how conducive it is to living within workflows outside of its system, and I very much appreciate some of their latest features (hello BuzzStream Discovery).

As a manager, I’ve found a lot of value in using Pitchbox due to its process-driven approach and its UI that keeps you within the system and focused on your specific tasks.

Pitchbox is the process-driven outreach tool. It has features that let you identify prospects in a variety of ways based on queries. You can import a list of prospects and it pulls many features and information about these sites and their authors. The tool also allows you to maintain a number of email templates with complex mail merge fields. Best of all, the system allows you to review prospects without even leaving it and provides analytics to show the activity of your team.

I love how Pitchbox helps inform strategy based on its prospecting. I also love that it makes it easy to provide complete transparency to our clients regarding our outreach efforts and, more importantly, it allows for a separation of concerns. The reality is that someone who is good at finding contacts, and knowing what a good link is, is not always someone who is good at outreach. Think about it, there are definitely SEO people that you wouldn’t want speaking to people without prior approval in real life or otherwise.

We can specify a user that just reviews link opportunities, and we can specify a user that just looks for features and data about the writer and their site and article to fill in the details for the mail merge fields in the emails. While I have historically been against form letters, I recognize that in order to develop predictable results across a larger team, we need to use very tailored form letters and Pitchbox allows for the best of both universes. Also, you have the ability to approve what’s going out a number of times before the emails are sent.

Judge for yourself though, Jon Cooper wrote an epic post comparing all of the available solutions for managing outreach. He brings up great points about all of the solutions, but I still consider Pitchbox the go to for my current use case.


I appreciate innovation, and I respect tool companies that are able to maintain a high quality of output that continues to shift what we can do as marketers. CognitiveSEO is one of those companies that continually releases features that are very relevant to the changing landscape of Organic Search. They were one of the first tools to really capitalize on the changes in lieu of Penguin and prepare actionable tools that allow you to get to the bottom of issues in your link profile.
CognitiveSEO is positioned as all-in-one providing rank reporting, penalty analysis, link reporting and quantitative content analysis. For our purposes, we primarily leverage it for its link analysis features.

A long time ago, I’d built a tool that crawled all of my links from all the available link indices to collect data and segment. What I love about Cognitive, is that they’ve taken the same ideas and built them to a point well beyond my imagination. One of my favorite features is the force-directed visualization of the link profile. It allows you to visually highlight the nofollow vs. dofollow links, unnatural vs. natural and also isolate links using search. Visualizations like this make it a lot easier for clients to understand what’s at play.

I also really love their spam classification system. It’s driven off human input in that it requires the user to classify anchor text first and then run the spam classification algorithm. Once that’s done you get a detailed report of what they algorithmically determine to be spam links. While the human classification process can be very time-intensive, there is typically a very high payoff and you definitely shave several hours off of reviewing every link by hand.


Again, on the thread of innovation, I get excited due to the rate at which new APIs are popping up. I frequently click around on ProgrammableWeb to see what new datasets are available. What I don’t have time for is fiddling with libraries or reconciling code against outdated API documentation versus how things actually work. So Postman is a great Chrome Extension for me to play with an API, see what types of data it will return and how I can work it into something else we’ve built.  It’s a great tool for non-coders to get insights on APIs to better communicate with developers what data they want to use.

I love the fact that it allows me to quickly prototype an idea, understand the idiosyncrasies of the API’s responses and develop functional requirements.


I think it’s funny that you can put the word “science” after anything and it sounds way more interesting than it actually is. Think about it; Computer Science. Marketing Science. And of course, Data Science. That being said, I still haven’t gotten around to deep fluency in Python or R, so Orange is an incredibly helpful tool for me to use in data mining. You can visually set up your analysis, set the options and let it rip. Of course, you need an understanding of statistics to begin with, but once you’ve got that, there’s no coding involved. Orange is a data mining tool that for Windows, Linux and MacOS that allows you to visually set up data analysis. Effectively, it’s a GUI for data science tasks in Python.

I love it because complicated computations are drag and drop. You can drag in your dataset, drag in linear regression and the method of visualization and voila! It’s that simple to get a result, although it’s certainly not that simple to interpret and ensure you get the right results. As a data miner, I find it incredibly valuable for running models and digging into data.


There’s been a lot of talk in recent years about topic modeling, entities and the usage of co-relevant or co-occurring terms, but I find it hard to believe many people are truly putting these ideas into practice. Virante on the other hand has done the research and built an otherwise ugly and heuristic defying tool called nTopic to support the practice.

Simply, nTopic identifies the keywords your content should feature to be more relevant when considered for ranking for your target keywords. For example, taking the first page of our magnificent GTM Guide, nTopic tells me that while the page is a 99.87% for the keyword “google tag manager” there are some words to consider when editing the copy to have the page perform better.

I love it because, as opposed to Searchmetrics’ version of the tool, I don’t need to setup a campaign to access the analysis. It’s easy to do it on an ad hoc basis. They also have a plugins for WordPress and Chrome that makes it easy for these optimizations to happen within a writer’s normal workflow.

Keyword Studio

Our approach to keyword research is perhaps one of the most thoughtful, audience-driven, time consuming and, most importantly, actionable methodologies available. As an agency, time comes at a premium though so it’s up to us to stay on top of ways to automate where possible to achieve quality and scale. The most time-intensive part of the process is the segmentation of keywords into categories, personas and need states.

While any tool for the latter two would require machine learning, Keyword Studio leverages a synonymy engine to greatly improve speed of categorization. It’s the only true all-in-one keyword research tool in that it pulls keyword opportunities, rankings, search volumes and CPCs. Basically it’s what Google’s Keyword Tool would be if they supported Organic the same way they support Paid Search.

That’s All Folks

There you have it. Some other lesser known tools that are becoming incredibly critical to our various online marketing analyses and workflows. Hope they can help you with your speed and scale.

Your turn, what tools do you use that people aren’t talking about?

How to Run Screaming Frog and URL Profiler on Amazon Web Services

I’ve been a huge fan of Screaming Frog SEO Spider for a number of years now. One would be hard-pressed to find a finite number of use cases for the tool. I also very much appreciate Dan Sharp and his team’s continued focus on innovation and improvement with the tool as well.

I also love a lot of the other crawler tools that have popped up in its wake like DeepCrawl and URLProfiler. Now I’m also getting to know as well and I encourage you to give their free trial a spin.

URL Profiler though has planted itself as the go to tool for our content auditing process. Although, I’d encourage you to check out Moz’s new content auditing tool as well.

From what I know of each of these tools is that they all have their own strengths, weaknesses and use cases. For example, if we’re doing a population (vs. sample-based) content audit on millions of pages, we’d typically use DeepCrawl then batches of 50k URLs in URLProfiler.

However, despite how awesome the SaaS crawlers are, I always feel like I “know” a website better when I do a Screaming Frog or URLProfiler crawl. Also one of our team members has built to bring headless browsing features to Screaming Frog, so that is an added incentive for us to make it work. I’m well aware that this is more a reflection of how well I know these products than the shortcomings of the other products. Nonetheless, it’s more important to do what it takes to do work that we’re PROUD of than to use the most sophisticated tool.

All that said, how many times have you been frustrated by this dialog box?

Why Does That Happen?

Technologically, cloud-based crawlers have a distinct advantage over desktop crawlers. Typically, cloud-based crawlers operate using a series of nodes that distribute the crawl. Each of these nodes runs a small application managed by another centralized application that makes the crawling fault-tolerant. Also cloud-based crawlers are saving their crawl data to a database so memory overhead can be kept very low. Finally, cloud-based crawlers have a virtually infinite set of computing resources to pull from to facilitate the crawl. In summation, cloud-based crawlers can be distributed, faster and more resistant to failure. The diagram below from an eBay patent gives a visual representation on of how a cloud-based distributed crawling system typically works.

Conversely, desktop crawlers are limited by the specs of your computer and they run in memory. If your machine has 4 CPU cores, 8 GB of RAM, you’re running Windows 8, have 50 tabs open in Chrome and have a bunch of TSRs running, the Frog is very likely to actually be screaming in pain while it’s crawling for you. A desktop crawl is inherently a resource limited crawl; this is why it’s prone to crash or run out of memory when it crawls too many pages.

Screaming Frog’s advantage over URL Profiler is that, once it reaches the resource limitation, it will ask you if you’d like to save you crawl and then keep going. URL Profiler on the other hand will just crash and all of that data is gone. Typically, I watch the usage of processes in Task Manager and start closing other applications when CPU or memory get too close to 100%.

Sounds like the odds are against you for big sites with desktop tools? Sure, they certainly can be, but none of the cloud-based tools get me the combination of data I want just the way I want it. So what can we do?

Enter Amazon Web Services

What we’re going to do now is run Screaming Frog and URLProfiler on Amazon Web Services. This will allow us to run the tools on an isolated machine that is has far more resources and likely more consistent speed than anything you or I have in our respective offices. My own machine, which is a fantastic Samsung ATIV-9, has 2 cores, 8 GB RAM and 256 SSD. On AWS we can configure a machine that has 40 cores, 160 GB and virtually infinite space. We won’t, because that’s overkill, but you get the point.

Odds are that you’ve heard of Amazon Web Services (AWS) and you may throw it around as an option for how you can do fancy things on the web. Or perhaps you’ve read about how it powers many of the apps that we all use every day. Whatever the case, the long and short of it is Amazon Web Services gives you virtual computing resources in a variety of different ways. Effectively, you can host a series of servers, databases, storage space, et al in myriad configurations and manipulate them programmatically on-demand. For example, when you fire up a crawl in DeepCrawl, it takes a few minutes for it to get started because it has to launch a number of EC2 instances to facilitate that crawl.

That use case doesn’t apply to what we’re doing here, but you now have a picture of how those tools use AWS to their advantage. In this case, we’ll spin up one box and configure it to just run exactly what we need.

As you can see below, there are numerous different services that Amazon offers. The one we will be focusing on most is Elastic Computing Cloud, commonly referred to as EC2.

You’ll also need to know a little bit about VPC to get access to your servers remotely, but we won’t go too deep into that.

Although the list of services above can appear daunting, I promise you the process of getting setup will be pretty painless. Shall we?

How to Set Up a Windows Box on AWS with Screaming Frog and URLProfiler

To get yourself going on Amazon Web Services, we’ll effectively be setting up an instance of a Windows Server, installing the programs on it, running our crawls, saving an image of that instance and shutting it down. Here we go!

  1. Login to Amazon Web ServicesYou’ll be using you Amazon account for this. Amazon gives a free 12 months of AWS service to first time users. Be advised that the free tier only applies to certain usage types. Instances in the free tier won’t be adequate for what we’re looking to accomplish, but pricing beyond those usage types is quite reasonable.
  2. Launch your Instance – First, make sure you’re in the right availability zone (in the upper right, next to my name). North Virginia is the cheapest of the data centers. After that click Launch Instance.
  3. Choose your AMI – An Amazon Machine Image (AMI) is a pre-installed set of configured software. Rather than setting up a blank machine and needing to install an operating system, Amazon allows you to clone a fresh machine with an Operating System of you choice already installed. You could set up your own configurations and create your own AMIs as well, but we won’t. In this case we’ll be choosing the Windows Server 2012 R2 Base AMI.
  4. Choose an Instance Type – This is where you get to choose your computing power. As you can see the free tier (t2.micro) only gives you one core and one GB of RAM. That’d be fine, for a single node, if you’re writing a script that did your crawling, but you’re not, you’re running a full featured memory-hungry Windows application. Go with the r3.4xlarge instance type with 16 cores and 122 GB of RAM and let those programs breathe. You can find out more information on the instance types that AWS offers here. Spoiler alert: The R3 instances are “memory-optimized” and suggested specifically for running analytics programs.
  5. Configure Instance Details – You can pretty much leave these all as defaults. Well, this being your first instance, you’ll have to set up a VPC and configure a network interface so that you can actually login to your Windows server. You should also check protect from automatic shut down since this is your first time playing with AWS; that way you’re sure to not lose any data.

    Read this for more information on configuring a VPC.

  6. Configure Security Group – AWS is annoyingly secure. You’re going to need to configure a security group using the launch wizard. Security groups allow you to give access to users based on their IP addresses. However, since you’re not storing anything significant on this box you can go ahead and give the security group access from any IP. Should you start saving anything of value, I’d recommend locking it down to the IPs that only you and your team can access.
  7. Review Instance Launch – As with any tool that uses a wizard, you are just making a final check of your configuration at this point. Double check that your screen looks pretty close to this. You should see the two warning indicators at the top if you’ve configured it as I have. Your instance type will be reflective of whatever options you’ve set.
  8. Create a New Key Pair – A key pair is a public and private key that AWS uses for logging in. For Windows Server, AWS uses this so you can retrieve the administrator password. Create the key pair and download the file.

  9. Connect to Your Instance – AWS will give you a configuration file to download in order to connect to your instance using the Remote Desktop application. You’ll also need to upload your key pair first to get the administrator password here. Once you do this, the admin password does not change so as long as you keep it, you won’t need to connect via this interface again. So go ahead and save your password and login using the Remote Desktop Connection app directly. You’ll want to save the file and password to make it easy to share login details with your colleagues.

    Once you’ve logged in, you’ll get a window of Windows that looks like this (minus Chrome, URL Profiler and my Screaming Frog crawls directory):

    Naturally Windows Server has a different features from the Home versions, but it will operate fundamentally the same as Windows 8. RDC will take over hot keys whenever the window is maximized. If this is your first time use the Remote Desktop application, check out this post on how to map your drives so you can access your local files on the remote machine.

  10. Install Chrome – The first thing you will want to do is install Chrome so you are not saddled with the abomination that is Internet Explorer.
  11. Change Internet Security Settings – You’re going to run into some issues trying to install Java on this annoyingly “secure” install of Windows Server. Go to Security Settings and configure the custom level by enabling everything. You can go ahead and change it back after Java is installed.
  12. Install Java 64-bit – You’ll want to install Windows Offline 64-bit from the manual install page on 64-bit is an important because the allocation option breaks Screaming Frog otherwise.

  13. Install Screaming Frog SEO Spider – Because Screaming Frog requires a little more configuration to get it supercharged, let’s start with that first. Download Screaming Frog and input your license key.

  14. Maximize Screaming Frog’s Memory Allocation – Screaming Frog has a configuration file that allows you to specify how much memory it allocates for itself at runtime. This ScreamingFrogSEOSpider.I4j file is located with the executable application files. Open it in Notepad and change its default 512MB memory allocation to 120GB. For those that want to know what this does, this value is an JVM environment variable that tells Java to allocate the specified amount of space to Screaming Frog. Screaming Frog simply passes this through to Java when it runs.
  15. Ramp up the threads – By default Screaming Frog only uses 5 threads at a time to be nice to webmasters. Let’s ramp that up to 15 so we can get this job done quicker.

  16. Install URL Profiler – Download URL Profiler, install it and put in your license key.

  17. Setup your API Keys – Setup your API keys for all of the services that you want it to use.
  18. Create an AMI Image – Now that your instance is completely configured, we’ll want to create an image of it just in case anything goes wrong or you want to create several instances of your box if you need to run multiple high-octane crawls at once.

    Give your image a name.

Now You’re Ready to Roll

While I don’t know the limitations of this configuration, I’m currently looking at it in the middle of a 20 million URL crawl. If you run into any problems you can always go to the bigger instance for more memory. Ideally, you’d be able to add bigger volumes (hard drives) to the instances the programs could lean on virtual memory, but from tests and the documentation it appears that Screaming Frog and URLProfiler only use physical memory. Effectively, you are limited to whatever the maximum memory configuration (244 GB in case you’re wondering) can hold at once. For reference, Screaming Frog’s documentation specifies that “Generally speaking with the standard memory allocation of 512mb the spider can crawl between 10K-100K URI of a site. You can increase the SEO spider’s memory and as a very rough guide, a 64bit machine with 8gb of RAM will generally allow you to crawl a couple of hundred thousand URLs.” While I’m skeptical of that number based on those specs, assuming 8GB gets you 200k URLs, then 122GB should get you 3.05 million URLs.

Additionally, the beauty of Remote Desktop is that you can start the crawl, close the window and then remote back in later and it will have kept running the entire time. Remember that Amazon Web Services charges you by the hour, so don’t forget that you’re running an instance if you’re concerned with what you’re spending. Which brings me to my next point…

What’s this Going to Cost Me?

Amazon’s pricing is completely dependent upon your configuration and they have a price calculator as well as the spending alert system to help you stay on top of it.

Based on the configuration that we’ve chosen, if we left it up for 100 hours (a little over 4 consecutive days) per month, it’d cost $237.33. Providing you could crawl 3 million URLs in that time period (site speed and throttling dependent) that’s far cheaper than the $2980 that DeepCrawl charges for 3 million URLs with their pay as you go plan.

Wrapping Up

Naturally, there are different plans that cloud-based crawlers offer and they do a lot of the work for you or you could just build a maxed out machine that just runs Screaming Frog and URLProfiler and save money. Or you could run Screaming Frog on a linux box to save more overhead and potentially run on a smaller instance, but I’m guessing that if you could, you’re probably not reading this post. Either way hosting Screaming and URLProfiler on AWS is a great short term solution when your Desktop crawl needs more power.

Now it’s your turn. I’d like to hear how you’ve overcome the limitations of desktop crawling in the comments below!

*** UPDATE: Fili Weise actually beat me the punch in this. Check out his discussion on how to run Screaming Frog on Google Gloud Servers! ***

How to Uncover 100s of New Longtail Keywords in Minutes

Hey, I’m Andrew Breen. I run Outshine Online Marketing.  We’re a small company with big ambitions. Let’s connect on Twitter: @breenandrew.

By now you’re probably well acquainted with Google Suggest. Its Google’s search tool that gives you Search suggestions as you type your query into the Google search box.

Google Canada Prepaid Credit Cards Screenshot

And while Google Suggest can generate some hilarious and weird results, you can also you it to quickly generate a massive longtail keyword list in minutes.

This article will show you how to combine two awesome Google Suggest scrape tools to generate a list of hundreds or thousands of related keywords in minutes. Then I’ll show you how to turn that huge list of keywords into something you can actually use.

What the heck do I want all those keywords for?

If you’re wondering why you’d want such a list of longtail keywords, wonder no more. I use this Google Suggest scrape method to find keywords for two things:

  1. New content ideas– Stuck on topics to write about in your niche? Scraping Google Suggest will give you content ideas you’re not going to find anywhere else.The results Google Suggest shows you are a reflection of search activity on the web. Sure, the keyword phrases it suggests may not get a lot of searches, but they are getting some. You can use this scrape method to unearth great longtail phrases that are easy to rank for and still generate traffic.
  2. PPC campaigns– Think you’ve found all the right keywords to bid on in Adwords? Maybe not.By scraping Google Suggest keywords, you can find keywords to add to your campaign that you would have never thought of.Just as importantly, Google Suggest scraping is a great way to find negative match keywords to target before you waste your money on them.

Now that you know why you want to scrape Google Suggest, let’s get into my method of actually doing it.

Step 1

Start at Ubersuggest. It’s a free web-based tool that lets you export lists of Google Suggest phrases based on a keyword you enter. Kudos to Ken Jurina for showing me this.

Enter your keyword and select your language. You can choose to scrape Google Suggest phrases from the web, news, or products searches. In this case I’ll use the web results.

Now check the txt box – that will let you download the results in a text file.

Click “suggest” and a suggestion.txt file will download to your computer. Open it in NotePad++, regular Notepad screws up the spacing.

Now I have a list of 242 “prepaid credit card” related keywords that Ubersuggest has extracted from Google Suggest.

Prepaid Credit Cards Keywords

Step 2

Here’s where the fun starts. We are going to take all 242 keywords from the previous step and look for even more Google Suggest results using ScrapeBox. While UberSuggest lets you scrape the results for one keyword, ScrapeBox lets you scrape the results for hundreds of keywords at a time.

Not familiar with ScrapeBox?  You should be. It’s a powerful way to speed up SEO tasks like keyword research and link building.

I should fully disclose here that ScrapeBox is typically a black hat SEO tool. Sure, it’s popular for mass blog comment spamming, but it’s also a versatile tool that can be used for white hat SEO too. Think of Scrapebox as a weapon – in the wrong hands it’s deadly, but it can be used for good too. And we’re all about the good.

Once ScrapeBox is open, drop that list of keywords from Step 1 into ScrapeBox’s Keyword Scraper Tool.

Select your scrape sources and search engines. There are a number of options here, and your choices will depend on the type of site you are doing keyword research for. The product and shopping suggestions are handy for ecommerce research, but for content ideas I just focus on the main search engines:

Scrapebox Keyword Scraper Options Screenshot

Click “Scrape” and kick back as ScrapeBox does its thing. This can take a few minutes depending on the number of scrape sources you picked, the number of keywords in your main list, and the speed of your proxies.

When ScrapeBox finishes running, I click “Remove Duplicate Keywords” and I am left with a list of 574 keywords related to prepaid credit cards.

Want even more keywords? Transfer the list you just scraped back into the main keyword list and run another scrape.

What do I do with all these keywords?

So now you have 100’s of keywords. Are they all useful? Of course not. But with a few minutes of work in Excel, you can turn this unmanageable mass of keywords to a targeted list that you can actually use. Here’s two different ways you can refine the list.

    1. If I’m using the list for PPC keyword ideas, I rely on Excel’s Conditional Formatting and Sort & Filter functions to hone in on the keywords I’m interested.Let’s say I’m running an Adwords campaign offering prepaid credit cards from a major credit card provider. The client is sensitive about their brand image – they don’t want to appear to be marketing to minors.With Excel, I can use the “Text That Contains” Formatting option to highlight uses of keywords like “teen,” “kid” and “child.”This highlights all keyword phrases that contain my specified text. But the highlighted keywords are still mixed in the regular keywords, so I’d then filter the list using the “Sort by Color” option. The “Sort by Color” option brings all the highlighted keywords to the top of the list so I can review them all at once.

      The Google Suggest scrape method is great for finding new negative match keywords you didn’t consider. In this case I found people were using phrases I hadn’t considered, like “under 13,” which I immediately add to my negative match list in Adwords.

    2. If you’re doing keyword research for content ideas, you’re going to love what I am about to tell you: With Richard Baxter’s Google Adwords API Extension for Excel you can take your list of keywords from Scrapebox and, from within Excel, grab Adwords search volume data.That’s right – no more flipping back and forth between the web-based Adwords Keyword Tool and your Excel sheet.  Talk about a timesaver. Now you can sort all your content idea keywords by search volume, which will show you where you’re best off investing your content-creation time.The Excel extension is free; all you pay for is the API costs to Google, which are negligible. High-five to John Doherty for showing me this.

So now that you know how to build a huge list of longtail keywords using Google Suggest scrapers, what are you waiting for? Get cracking! Test it out now and start generating new ideas for your website or PPC campaign.