Tag Archives: marketers

10 great reasons to book your ticket NOW for SMX West!

The largest gathering of the search marketing community is happening March 13-15 in San Jose, California. Search marketers from around the globe are flocking to SMX West for three days of unrivaled training alongside industry experts in a warm, welcoming environment. But that’s not the only reason...

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Looking back at 2018 in search: A time traveler’s year in review

What does 2018 have in store for search marketers? Columnist (and time-traveler) Dave Davies pays a visit from the future to share what this year's major search developments will be. The post Looking back at 2018 in search: A time traveler’s year in review appeared first on Search Engine...

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How the latest Google Analytics updates will benefit marketers

Google has announced a range of significant new updates to its Analytics product, all of which should help marketers to understand their individual customers at a deeper level. Below, we assess the impact each of these four enhancements will have on search marketing analysis. 

The ongoing implementation of machine learning into all Google products has benefited GA, with the addition of Analytics Intelligence a particular highlight from the last 12 months.

Simultaneously, Google wants to provide site owners with insight into the impact of their marketing activities across all channels. This has always been the aim, but it is a challenging one from a tracking perspective. The partnership between GA 360 and Salesforce is a reflection of Google’s willingness to work alongside other companies to achieve this goal and ensure it keeps its dominant position.

The four latest updates to GA all exhibit some elements of these trends, with machine learning and user-level analysis never far from the foreground.

Users in standard reports

The underpinnings of the standard report dashboard have been adapted to include more insight into user-level behavior.

This is a significant shift from the historical focus on sessions, as an individual user could have multiple sessions even within the same day. The implications of this hierarchical system (User>Session>Hit) were discussed in a previous post, where we assessed some common GA misunderstandings.

Marketers will undoubtedly welcome the default option to analyze users alongside sessions and we should expect Google to continue improving the accuracy of user-level data. As it does so, more options for assessment and targeting will follow.

How marketers can use this feature:

  • Go to Admin > Property Settings in your GA account, then select the option for Enable Users in Reporting.
  • Combine with other (relatively new) features like Cohort Analysis to get a clearer picture of how groups of users arrive at – and interact with – your site.

User Explorer: Lifetime metrics and dimensions

User Explorer, which allows marketers to isolate user behavior down to the session level, has huge potential as an analytical tool. It is already available in all GA accounts and draws its data from the lifetime of a user’s cookie.

Google has recently revamped this feature with the addition of lifetime metrics and dimensions for individual users.

As can be seen in the screenshot below, this is displayed in a dashboard that contains a variety of information about past, present, and predicted future behaviors.

Taken in isolation, this level of granularity may appeal to little more than our curiosity. However, the ability to scale this and identify patterns across a large set of Client Id numbers could reap significant rewards for marketers. Once we group together similar users, we can tailor our marketing activities and messaging, both for prospecting and remarketing.

How the latest Google Analytics updates will benefit marketers

How marketers can use this feature:

  • Identify patterns in the channels that lead valuable clients to arrive at your site. This can be of use when prospecting for new customers who share the same attributes.
  • Maximize the value of current customers with a high projected lifetime value, through remarketing and tailored messaging.

Audience reporting

This is a logical and much-needed update to Analytics, making it a particularly welcome addition. Users can now create audiences within GA and then publish them within the platform for analysis.

Up to now, we have been able to create audiences and publish them to other Google properties, such as AdWords. This has been very useful for remarketing, but it was not possible to create a report for these audiences within GA.

This new feature uses ‘Audience’ as its primary dimension and permits users to compare performance across different segments.

For example, we could create an audience for customers that have purchased more than 5 times in the last 6 months, and compare this group with visitors that consume a lot of our content but do not make purchases.

How the latest Google Analytics updates will benefit marketers

How marketers can use this feature:

  • Create audiences based on the behaviors that matter to your business and monitor their interactions over time. These can then be compared to derive insights about the characteristics of our most valuable customers.
  • Given that these same lists can be uploaded to AdWords, we can draw a more direct line from analysis to action. If we notice trends within specific customer groups that we would like to enhance or reverse in our GA reports, we can do this seamlessly by targeting that same audience group through AdWords.
  • Use audience lists as the basis for conversion rate optimization tests.

Conversion probability

This is perhaps the most exciting of the four updates and has the highest potential to have a positive impact on marketers’ ROI.

By analyzing your site’s historical data and automatically identifying the patterns between variables within sets of high-value customers, Google can identify the recent site visitors with the highest probability of a future conversion.

This has been achievable in the past through a variety of means, notably through the use of Google Analytics Premium data, logistic regression analysis, and Google BigQuery. Many paid media management platforms also employ this type of machine learning to help with bid management, as does Google AdWords.

However, by incorporating this technology into the standard Google Analytics platform, a much wider user base will now have access to predictive analytics about their customers.

Combined with the updates listed above, we can see how this fits into the broader picture. Google uses machine learning to identify future customers, which site owners can then use to create audiences for analysis and remarketing.

This feature is rolling out to all accounts in beta over the next few months, so it is worth looking out for.

How marketers can use this feature:

  • Identify the quality of traffic that is driven by your marketing activities. The ‘Average % Conversion Probability’ metric will reveal this within your Conversion reports.
  • For remarketing, Google offers a few pointers of its own:

The advantages are clear: Marketers can create remarketing lists that target users who have a high likelihood to purchase and then reach those users through either advertising campaigns in AdWords and DoubleClick or site experiments in Optimize.

Viewed together as a group of updates, the key takeaway here is self-evident: Google is at pains to use its machine learning capabilities to create a deeper understanding of individual users. The field of predictive analytics can be a particularly profitable one, especially for a company with targeting technology as effective as Google’s.

The latest enhancements to GA should see these capabilities extended to a much wider audience than ever before.

Artificial intelligence and machine learning: What are the opportunities for search marketers?

Did you know that by 2020 the digital universe will consist of 44 zettabytes of data (source: IDC), but that the human brain can only process the equivalent of 1 million gigabytes of memory?

The explosion of big data has meant that humans simply have too much data to understand and handle daily.

For search, content and digital marketers to make the most out the valuable insights that data can provide, it is essential to utilize artificial intelligence (AI) applications, machine learning algorithms and deep learning to move the needle of marketing performance in 2018.

In this article, I will explain the advancements and differences between artificial intelligence (AI), machine learning and deep learning while sharing some tips on how SEO, content and digital marketers can make the most of the insights – especially from deep learning – that these technologies bring to the search marketing table.

I studied artificial intelligence in college and after graduating took a job in the field. It was an exciting time, but our programming capabilities, when looking back now, were rudimentary. More than intelligence, it was algorithms and rules that did their best to mimic how intelligence solves problems with best-guess recommendations.

Fast forward to today and things have evolved significantly.

The Big Bang: The big data explosion and the birth of AI

Since 1956, AI pioneers have been dreaming of a world where complex machines possess the same characteristics as human intelligence.

In 1996, the industry reached a major milestone when the IBM’s Deep Blue computer defeated a chess grandmaster by considering 200,000,000 chessboard patterns a second to make optimal moves.

Between 2000 and 2017, there were many developments that enabled great leaps forward. Most important were the geometric increases in the amount data collected, stored, and made retrievable. That mountain of data, which came to be known as big data, ushered in the advent of AI.

And it keeps growing exponentially: in 2016 IBM estimated that 90% of the world’s data had been generated over the last few years.

When thinking about AI, machine learning and deep learning, I find it helps to simplify and visualize how the 3 categories work and relate to each other –  this framework also works from a chronological, sub-set development and size perspective.

Artificial intelligence is the science of making machines do things requiring human intelligence. It is human intelligence in machine format where computer programs develop data-based decisions and perform tasks normally performed by  humans.

Machine learning takes artificial intelligence a step further in the sense that algorithms are programmed to learn and improve without the need for human data input and reprogramming.

Machine learning can be applied to many different problems and data sets. Google’s RankBrain algorithm is a great example of machine learning that evaluates the intent and context of each search query, rather than just delivering results based on programmed rules about keyword matching and other factors.

Deep learning is a more detailed algorithmic approach, taken from machine learning, that uses techniques based on logic and exposing data to neural networks (think human brain) so that the technology trains itself to perform tasks such as speech and image recognition.

Massive data sets are combined with pattern recognition capabilities to automatically make decisions, find patterns, emulate previous decisions, etc. Self-learning comes from here as the machine gets better from the more data that it is supplied.

Driverless cars, Netflix movie recommendations and IBMs Watson are all great examples of deep learning applications that break down tasks to make machine actions and assists possible.

Organic search, content and digital performance: Challenge and opportunity

Organic search (SEO) drives 51% of all website traffic and hence in this section it is only natural to explain the key benefits that deep-learning brings to SEO and digital marketers.

Organic search is a data-intensive business. Companies value and want their content to be visible on thousands or even millions of keywords in one to dozens of languages. Search best practices involve about 20 elements of on-page and off-page tactics. The SERPs themselves now come in more than 15 layout varieties.

Organic search is your market-wide voice of the customer, telling you what customers want at scale. However, marketers are faced with the challenge of making sense of so much data, having limited resources to mine insights and then actually act on the right and relevant insight for their business.

To succeed in highly demanding markets against your competitors’ many brands now requires the expertise of an experienced data analyst, and this is where machine learning and deep learning layers help recommend optimizations to content.

Connecting the dots with deep learning: Data and machine learning

The size of the organic data and the number of potential patterns that exist on that data make it a perfect candidate for deep learning applications. Unlike simple machine learning, deep-learning works better when it can analyse a massive amount of relevant data over long periods of time.

Deep learning and its ability to identify or prioritize material changes in interests and consumption behavior allows organic search marketers to gain a competitive advantage, be at the forefront of their industry, and produce the material that people need before their competitors, boosting their reputation.

In this way, marketers can begin to understand the strategies put forth by their competitors. They will see how well they perform compared to others in their industry and can then adjust their strategies to address the strengths or weaknesses that they find.

  • The insights derived from deep learning technologies blend the best of search marketing and content marketing practices to power the development, activation, and automated optimization of smart content, content that is self-aware and self-adjusting, improving content discovery and engagement across all digital marketing channels.
  • Intent data offers in-the-moment context on where customers want to go and what they want to know, do, or buy. Organic search data is the critical raw material that helps you discover consumer patterns, new market opportunities, and competitive threats.
  • Deep learning is particularly important in search, where data is copious and incredibly dynamic. Identifying patterns in data in real-time makes deep learning your best first defense in understanding customer, competitor, or market changes – so that you can immediately turn these insights into a plan to win.

To propel content and organic search success in 2018 marketers should let the machines does more of the leg work to provide the insights and recommendations that allow marketers to focus on the creation of smart content.

Below are a just a few examples of the benefits for the organic search marketer:

Site analysis

Pinpoint and fix critical site errors that drive the greatest benefits to a brand’s bottom line. Deep learning technology can be used to incorporate website data, detect anomalies tying site errors to estimated marketing impact so that marketers can prioritize fixes for maximum results.

Without a deep learning application to help you, you might be staring at a long list of potential fixes which typically get postponed to later.

Competitive strategy

Identifying patterns in real-time makes deep learning a brands’ best first defense in understanding customer, competitor, or market changes– so that marketers can immediately turn these insights into a plan to win.

Content discovery

Surface high-value topics that target different content strategies, such as stopping competitive threats or capitalizing on local demand.

Deep learning technology can be used to assess the ROI of new content items and prioritize their development by unveiling insights such as topic opportunity, consumer intent, characteristics of top competing content, and recommendations for improving content performance.

Content development

Score the quality and relevance of each piece of content produced. Deep learning technology can help save time with automated tasks of content production, such as header tags, cross-linking, copy optimization, image editing, highly optimized CTAs that drive performance, and embedded performance tracking of website traffic and conversion.

Content activation

Deep learning technology can help ensure that each piece of content is optimized for organic performance and customer experience—such as schema for structure, AMP for better mobile experiences, and Open Graph for Facebook. Technology can help marketers can amplify their content in social networks for greater visibility.

Automation

Automation helps marketers do more with less and execute more quickly. It allows marketers to manage routine tasks with little effort, so that they can focus on high-impact activities and accomplish organic business goals at scale.

Note: To make the most of the insights and recommendations from deep learning marketers need to take action and make the relevant changes to web page content to keep website visitors engaged and ultimately converting.

Additionally, because the search landscape changes so frequently, deep learning fuels the development of smart content and can be used to automatically adjust to changes in content formats and standards.

Deep learning in action

An example of deep learning in organic search is DataMind. BrightEdge (disclosure, my employer) Data Mind is like a virtual team of data scientists built into the platform, that combines massive volumes of data with immediate, actionable insights to inform marketing decisions.

In this case the deep learning engine analyzes huge, complex, and dynamic data sets (from multiple sources that include 1st and 3rd party data) to determine patterns and derive the insights marketers need. Deep learning is used to detect anomalies in a site’s performance and interpret the reasons, such as industry trends, while making recommendations about how to proceed.

Conclusion

Think of deep learning applications as your own personal data scientist – here to help and assist and not to replace. The adoption of AI, machine learning and now deep learning technologies allows faster decisions, more accurate and smarter insights.

Brands compete in the content battleground to ensure their content is optimized and found, engages audiences and ultimately drives conversions and digital revenue. When armed with these insights from deep learning, marketers get a new competitive weapon and a massive competitive edge.

Best of 2017: Our top 5 articles in PPC

As we come to the end of 2017, we’ve decided to take a look back at some of our most-read articles throughout the year. For the rest of this week, we’ll be highlighting the top five most popular articles in various categories across the site.

Yesterday, we kicked things off with a look at our top 5 articles about SEO, and if you missed that one, it’s definitely worth a read. Today, we’ll be turning our attention to the other great staple of Search Engine Watch content: PPC.

We covered some fun ground with our PPC articles this year, from emoji in AdWords ad titles to the psychology of ad copy, to the impact of Google’s new ‘Ad’ label on marketers. Let’s not waste any more time – here are our top 5 articles from 2017 about PPC.

#1: Emoji appear in Google AdWords ad titles

This was an interesting one. Just a couple of weeks after we wrote about Google’s decision to bring emoji back to the SERPs, emoji were spotted in the wild in AdWords ad titles, suggesting that Google had decided to go the whole hog in embracing emoji in both organic search and paid search ads.

Sadly, the test doesn’t seem to have lasted in the case of paid search, as Google’s official stance is still that emoji are “invalid characters” – but there have also been recent reports of people being able to bid on emoji in AdWords. Either way, the combination of fun emoji news with a potential big change for search marketers makes it no surprise that this was our most-read article about PPC in 2017.

Emoji appear in Google AdWords ads titles

#2: The psychology of language for paid search

When it comes to PPC best practice, there’s a vast amount of ground you can cover, from keyword bidding to demographic targeting, AdWords reports, landing page optimization and everything in between. But how often do we talk about the actual copy of the ads that are supposed to get consumers’ attention?

According to Sophie Turton, Head of Content and PR at Bozboz, people don’t buy what you do; they buy why you do it. In her presentation at Brighton SEO in April 2017, she explained how search marketers can use psychology to make their paid search ads more effective. Tereza Litsa sums up the key highlights in an informative piece for Search Engine Watch.

The psychology of language for paid search

#3: 10 online marketing strategies to make you a unicorn [infographic]

It’s hard to go wrong with a good infographic, and Larry Kim of Wordstream has a great one which brings together 10 online marketing strategies to make you a unicorn – one of those magical campaigns that’s so effective, it performs in the top 1-3% of all marketing campaigns.

Sound like a dream come true? Check out Larry’s infographic, whose points he expands on in further detail in his post, and find out why you need to forget everything you know about Conversion Rate Optimization.

10 online marketing strategies to make you a unicorn [infographic]

#4: How to target high-income consumers with AdWords

There are many industries in which being able to target high net worth individuals with your paid search campaigns is extremely useful. If you think that AdWords doesn’t have this function, you might want to think again.

Wesley Parker reveals the secret behind a “deeply hidden gem within AdWords”, currently available for U.S. locations only, which allows you to target people based on their household income. With step-by-step instructions and screenshots, he explains exactly how to set this up, as well as how you can use layered targeting to pull in multiple different demographics.

How to target high-income consumers with AdWords

#5: How will Google’s new ‘Ad’ label impact marketers?

In a major development for PPC, Google began testing a new look for its ad labels in January of this year, and in late February confirmed that this would be rolled out globally.

The new white label with green text and a green outline replaced the green label that was launched in June 2016, and blends much more seamlessly with the rest of the ad placement, perhaps creating less of a contrast between organic and paid search results. Clark Boyd considered Google’s motivation for the change, and the possible impact on search marketers.

How will Google’s new ‘Ad’ label impact marketers?

The changing SERP: Understanding and adapting to dynamic search results

Search results have become more personalized and dynamic over the years, creating a more challenging SEO environment for search and content marketers. But columnist Jim Yu shows how these changes can create opportunities for those willing to do the work. The post The changing SERP: Understanding...

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7 tips to ramp up your holiday advertising

Search marketers, are you ready for the holiday shopping season? Columnist Mona Elesseily shares tips for getting the most out of your holiday paid search ads. The post 7 tips to ramp up your holiday advertising appeared first on Search Engine Land.

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SEO is Dead! – The Biggest SEO Myth That Needs to Die [POLL] by @A_Ninofranco

Which SEO myth do search marketers hear most often – and most wish would die. Find out their answers here.

The post SEO is Dead! – The Biggest SEO Myth That Needs to Die [POLL] by @A_Ninofranco appeared first on Search Engine Journal.

Siri, Safari and Google Search: What does it mean for marketers?

Columnist Jim Yu explains how Apple's recent announcements and updates to Siri and Safari have had a major impact on the search marketing industry. The post Siri, Safari and Google Search: What does it mean for marketers? appeared first on Search Engine Land.

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9 reasons why search marketers have been at the cutting edge of marketing technology

Scott Brinker on why search marketers are uniquely qualified to lead in the new "martech era." The post 9 reasons why search marketers have been at the cutting edge of marketing technology appeared first on Search Engine Land.

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