On June 6, 2019, Google announced its acquisition of Looker, a business intelligence and analytics startup. The acquisition is set to complete at the end of the year.
Acquisition Features:
With this acquisition, Lookerwill join the Google Cloud Platform (GCP) to provide a full end-to-end analytics solution that collects, analyzes, and visualizes data.…
For a refresher on the marketing testing tool, Google Optimize 360, and its capabilities compared to the free version Google Optimize, read my previous blog post here.
For the purposes of this post, I’ll do a deep dive into Optimize 360’s Personalization andAudience Targeting capabilities, including showing you step by step walkthrough on how to create a personalization experiment in Optimize 360.…
Retargeting turns visitor actions on your site or app into a second chance to capture a sale. It’s an excellent way to move top-of-funnel traffic from organic search to conversions.
LearnDash is one of the most well-known and well-regarded online course builder plugins for WordPress. In fact, it’s reportedly used by Fortune 500 companies, major universities, and training organizations, as well as online entrepreneurs publishing learning content online. While it’s clear LearnDash is a capable eLearning plugin, is it right for your project? That’s the... View Article
While Google is an obvious “horse to bet on,” ubiquity, UX and utility will dominate the voice-search innovation race in 2020 and beyond.
An old prediction that half of all internet searches will be carried out via voice by 2020 does not look like it’s on track to be a reality, despite making it into Mary Meeker’s Internet Trends 2016 Report.
Back then, in 2014, Baidu had just hired away Google Brain mastermind Andrew Ng to head up a massive deep learning project to help it beat Google in a new, non-text search race.
About five years later, China’s top search engine, Baidu, is still competing in the race to win in voice search dominance and secure the healthiest share of the lucrative market for voice commerce (v-commerce) that pundits predict lies ahead. In January during the annual C.E.S. show, Baidu said its answer to Alexa and Siri was already on 200 million devices. At that time, more than 100 million devices pre-installed with Alexa had been sold, and Google forecast it expected to have one billion devices with its Assistant onboard by the end of that month.
Just last month, a Harvard Business Review article by Bret Kinsella explored the race to own voice search and, in turn, the best v-commerce experience amongst the global tech giants:
“You can only understand the voice platform wars by first recognizing that voice assistants, specifically, represent both a platform and user interface (UI) shift comparable to the web and smartphones. This scenario both excites and frightens the leading tech companies that carved out enviable positions in the earlier web and smartphone platform wars.”
Google’s advantages and core challenge to winning voice search
Of the tech giants on our “home team” that are based in the U.S., Google seems like the most obvious company (versus Amazon, Microsoft or Apple) to bet on to eventually win and dominate in a new voice search age that powers v-commerce and corresponding ad dollars to support it.
Its brand is synonymous with search today. “Just Google it,” as we all constantly say. Google’s ascendance as the dominant search engine sets it up well to be a similar authority and leader in voice search. Also, its Google Maps that is the go-to navigation app used by about 70 percent of smartphone owners, sets it up to help on-the-go users find and buy needed products nearby — from an umbrella in a rainstorm to the closest place to get some work done in a nearby internet cafe. When it comes to smartphones, Google’s Android OS continues to dominate the global market with about 86 percent of current market share compared to about 14 percent using Apple iOS, per the latest numbers from IDC.
But, the future of voice search and v-commerce is not just all about smartphones. Juniper Research predicts there will be nearly eight billion digital voice assistants in use by 2023 compared to 1.5 billion assistance devices today.
The fastest growing category is connected TV-based voice assistants, and others will “live” in refrigerators, cars, smartwatches and more. The tech giants vying to own the future of v-commerce need to think about a ubiquitous presence across all types of smart devices that will be coming to market. Our virtual assistants are independent of smartphones or speakers. They can operate and serve us in many places.
It’s too early to rule out Amazon and Apple from winning the v-commerce race yet
In comparison to Google, both Amazon and Apple are less open in comparison. Both companies rely on walled gardens that provide distinct advantages and disadvantages.
For Apple, its focus on privacy for users has been popular with consumers and regulators, but when it comes to voice search paired with location-based marketing, Apple’s push to turn off access to location beacons in mobile apps via latest iOs 13 will limit how it plays in a world of v-commerce because its users won’t be able to ask their iPhone where the closest Starbucks is to them anymore. Apple’s approach also won’t lend itself to delivering a future of highly relevant, personalized offers that we’ve seen in future-forward SciFi movies such as “Minority Report.”
When it comes to eCommerce, no other player dominates like Amazon does today. Amazon has more than 80 percent of conversions compared to other eCommerce sites across a diverse set of product categories, according to Jumpshot. The planet’s largest retailer, a title that Amazon took from Walmart last month, has also surpassed Google in terms of where product searches start from on the internet. Yet, so far, Amazon has trailed behind Google in terms of an easy experience and accessibilty to bring users into a voice-activated world across devices.
Ubiquity across devices, the best UX and shopper utility will lead to dominance
The ultimate winner, in the end, will be the tech platform that delivers the ability to use voice search across the internet and the maximum number of devices. It’s also critical to deliver the simplicity of experience and speedy results that helped Google become the long-standing king of the hill for internet search with its first focus on text.
Google is doing its best to keep a step ahead in the emerging world of voice search that will enable a whole new way of controlling the online experience through our voices. That quality of experience and when voice search hits the mainstream will be driven, in large part, by the accuracy of voice recognition compared to actual human speech through advances in A.I. Two years ago, Google reached a threshold of understanding human language within 95 percent accuracy.
What promises to set Google apart, again, in voice search and give it a leg up in this very important innovation race, is the ability to create and consistently deliver a simple, elegant user experience that so far has held back consumer adoption and use of voice-powered assistants. It and Amazon both have a decent chance of converging voice-powered A.I. with a rich set of data on users that delivers the best, most personalized shopping experience.
Early on, many of the early adopters will use voice search that lead to the purchase of online items that are utilitarian in nature: such as “where can I find a late-night dinner spot that’s still open and nearby me,” or “what is the closest place to buy a laptop charger?” when you leave yours at the office during a business trip away from home. A whole new metrics category is also likely to emerge and differentiate which tech vendor(s) do it best. Perhaps we’ll measure effectiveness based on “vlicks” for voice-activated clicks as the market matures over time.
Whatever lays ahead in a brave new world of v-commerce powered by voice search, the ultimate winner(s) will be decided based on how they execute on ubiquity, UX and utility.
“On guard!” … and may the best voice search platform win. Who do you think it will be?
Gary Burtka is vice president of U.S. operations at RTB House, a global company that provides retargeting technology for global brands worldwide. Its North American headquarters are based in New York City.
In the past year, smart speaker users who own two or more devices moved from 38% to 52% Click To Tweet
Voice is the future of web search. If you’re ready get your hands dirty and begin optimizing your content for voice search, use these 4 quick tips to get started.
1. Focus on Phrases and Longtail Keywords
The search focus has shifted from terse, awkward keywords to long-tail phrases, or even entire sentences. That’s because voice searches make use of natural language. The way we talk is decidedly different than the way we type. The phrases and keywords that we use while speaking to digital assistants would therefore be different than those we use when entering text in Google search.
“What is the weather like in Miami today?” is an example of a conversational/natural language query more likely to be spoken to a digital assistant, as opposed to “weather miami,” which we would type into a search bar. Content optimized for voice SEO would therefore need to focus on this very important aspect of the nature of voice search.
2. Anticipate Specific Questions Asked in a Conversational Manner
Voice search might use entire sentences, but it’s also specific in nature. People do not ramble on when speaking to a digital assistant, possibly because a more specific question leads to a more accurate answer.
A query such as, “Find an Italian restaurant near me,” with the user’s location enabled can return precise results for users. Business owners would therefore want to optimize their websites and content for intuitive but specific queries. This can be accomplished via a detailed FAQ page or a blog containing authority content created around longtail keywords and conversational but specific questions. This would require you to research the kind of questions your target audience most frequently poses to digital assistants and produce content around those queries. It’s a good idea to take each of those questions and flesh out the answers in the form of quality blog posts.
As long as your content answers customer queries in the best and most useful manner possible, expect Google to take notice of it and rank the website/mobile site accordingly.
3. Optimize Your Website for Local SEO
The Meeker’s Internet Trends Report found that voice search is 3X times more likely to be local in nature. With this in mind, businesses should keep their profiles and contact information up to date, since this is what Google will pull for queries such as, “Where can I get the best coffee in Seattle?”
Voice search is 3X times more likely to be local in nature. Click To Tweet
For a coffee shop owner, this would mean including accurate opening hours in their profile, including the precise location of the shop, and optimizing the content on the website to be found via keywords such as “best coffee” or something more specific, such as “best spiced chai latte.”
Find out the kind of questions your target audience is most likely to pose to a digital voice assistant, and create content that provides specific answers to these queries.
4. Make Sure Your Website Is Ready for Voice Search
According to Google, micro moments (moments during which users need immediate, relevant, and ready-to-use information) are key to capitalizing on any kind of search, especially voice search. Since our smartphones are our constant companions, it is natural that with internet at our fingertips, they are going to be our first source of information. Google has therefore been encouraging businesses to be cognizant of the increasing use of mobile in internet search and accordingly optimize their sites for mobile.
We now have mobile and voice search to pay attention to. Businesses that take advantage of these micro moments stand a good chance of racing ahead of the competition:
Anticipate at which stage(s) a user is most likely to need the services your business provides.
Anticipate the nature of information they need to make a decision.
Provide users with the relevant information at that stage in order to help them make a decision, or leave them with clear further guidance.
For this to happen, businesses must ensure their websites are optimized for mobile, for local SEO, and for voice search. In order for a mobile site to be of use to someone during a micro moment, it needs to load quickly, be user-friendly, contain relevant information (local SEO), and produce the right answers in response to a voice search query. Taken together, this maximizes the chances of a user choosing your service.
Making the Leap to Voice Search
The nature of search and the evolution in search algorithms, based on changing technology and shifting consumer habits, require marketers to move in tandem with newer trends. That is the way to stay relevant and competitive.
Are you struggling to get website traffic from Google?
If so, you’re not alone. One report found that 91% of content gets zero organic traffic. It’s easy to feel frustrated if you fall within that range, especially if you’ve executed an SEO strategy you hoped would work.
However, you don’t need to overhaul your entire approach to SEO to start seeing results. Instead, you can run experiments to determine the optimization tweaks you need to make, without:
Changing your entire website
Sacrificing the (albeit small) number of rankings you’ve got
The answer? A/B SEO testing.
In this guide, we’ll explain why A/B testing is important, the SEO elements you can split test, and a handful of companies who’ve run controlled experiments that resulted in increased search traffic.
Let’s dive in.
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What Can I A/B Test?
There are various on-page SEO elements that could form the foundation of your A/B experiments, such as:
However, The Sleep Judge’s SEO Manager, Roman Kim, thinks that:
“Rewriting large parts of existing content would not be a very good test, as it would invoke Google's content freshness algorithms, so it would not be a fair test against control pages that did not receive updates.”
Let’s put that into practice. Say you’re split testing your product descriptions. You could run a controlled experiment that shows different versions of a webpage to different control groups. Group A sees your standard page with a 150-word product description; Group B is redirected to a duplicate of the page with a 400-word product description.
This type of SEO A/B test allows you to determine the ideal length of your product description before committing to writing an extra 150 words for each product — without knowing whether it’s worth it.
The same concept applies to split testing your meta titles and descriptions. You could simply group several URLs into two categories — one with the word “buy” in the meta title and one without — to see whether that phrase impacts key SEO metrics (such as CTR or ranking position).
In fact, Etsy did an SEO test similar to this. They changed the title tag for specific pages on their website, then monitored the effect on website visits:
The experiment had mixed results, with some control groups having a positive impact on visits, and others having minimal (or a negative) impact. But the point is: they wouldn’t have known unless they tested.
That’s a smart question to ask because nobody wants to put more work on their plate, right?
Well, split testing your SEO strategy saves time and money. Yes, sitewide changes is an investment, but an SEO strategy that isn’t working needs fixing. And that’s also a huge risk: Changing something across your entire site without understanding the full potential (or expected) impact could make you lose the SEO results you’ve already got. Even if you only have 50 keyword ranking positions, you don’t want to lose them.
A/B tests also protect your website from being negatively impacted by something outside of your control. That’s important in the SEO world where Google can change their “best practices” almost overnight. This happened recently with the EAT (Expertise, Authoritativeness, Trust) release — it caused several well-known sites in the healthcare industry to lose half of their organic traffic:
If you’re aware of those algorithm updates and split test the features being prioritized in the new release, you can clearly understand how your website would be impacted — rather than sit around and wait to see…by which time it might be too late.
Start by Picking Your Hypothesis
Every A/B test should start with a hypothesis — a single statement that explains the result you expect the achieve. It’s usually an if/then statement, such as:
“If my meta descriptions include a power word, then my organic CTR will increase”
“If I include more videos on my page, then my bounce rate will decrease”
A hypothesis is important for your A/B test because it gives you a thorough understanding of the results you want to see. By focusing on the expected outcome, you’re testing to see whether your initial expectations (your hypothesis) is accurate.
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How to Run an A/B SEO Test
Now that you’ve got your hypothesis, you’re ready to prove it correct. But how exactly do you run an A/B SEO test that doesn’t fail miserably or leave you with inaccurate results?
Here are three techniques you can use:
1) Clickflow
Clickflow is a tool designed to make SEO experimentation easier:
It syncs with your Google Search Console account to find opportunities where there’s a high impression count but a low click-through rate. Essentially, this means that your content is being displayed in the SERPs, but nobody’s clicking your result. They’re heading to the other listings, instead.
To start a new SEO test using Clickflow, simply head to the New Test button and select whether you’re testing a single URL or a group of URLs. You’ll then need to name your test and add the pages you want to test:
You can then run the test on those specific pages to assess your hypothesis.
For example, when you add more images onto the page, does your click-through rate increase? And when you include more internal links on a blog post, does the page rank higher in Google?
You can find the answers to your test in the reports section:
The best part about Clickflow? It suggests SEO experiments that you might’ve missed by looking at the average CTR for your top-performing pages and highlighting the potential clicks and revenue you could gain by improving important SEO metrics on one page:
2) Google Optimize
Chances are, you already have a Google Analytics account that you’re using to track the results of your SEO strategy. You could build on this by using Google Optimize, a tool that works directly with Google Analytics to run A/B tests on a specific website:
“Google Optimize is a great free tool for running A/B tests. I'm always tweaking something on pages and seeing if there is a difference. When it's small things like the copy on a button or a headline, you can make the change in Google Optimize's tool without having to edit the actual page.”
Spotify used this tool to A/B test the content of their landing pages. They used Google Analytics data to discover that customers in Germany were more interested in audiobooks than traditional playlists.
They changed a landing page using Optimize to show the collection of audiobooks they had on offer, and targeted Google Ad click traffic to drive customers from Germany towards the URL. This resulted in a 24% uplift in revenue:
SEO can take a long time to pay off, so you might be waiting a while for results to start coming in before you find the winner. But who said that SEO split testing had to rely completely on organic strategies?
Danilo Godoy of Search Evaluator uses Google Ads data to run his split tests before mapping the changes over to organic efforts:
“While the results of an A/B test for paid marketing can generally be assessed within a few days, SEO is a long game, [in] which [the] rules are more complex and constantly evolving — Google deploys minor changes in the algorithm daily.
For that reason and [because] time and budget are precious things, my recommendation is that businesses save A/B tests for PPC (e.g. Google Ads) and apply what they've learned from paid advertising in their SEO strategy.
So, for example, if you want to check which page title is more attractive (i.e. receives more user clicks), this can be easily and quickly A/B tested on Google Ads and then deployed in your SEO strategy.
Trying to apply these simple [tests directly] through SEO can be a waste of time since the multitude of variables involved in ranking could impact the results of your findings.”
The simplest way to do this is throughGoogle Ad variationsto change the text for your campaigns or swap the headlines — allowing you to A/B test which type of copy works best:
You might find that a headline containing the word “2019” has a much higher CTR and conversion rate than one without. It makes sense to edit your existing (organic) page titles to include that text, right?
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When Should You Stop Your A/B Test?
Your A/B test has been running for a while, yet you’re unsure on when you should hit the “stop” button and analyze the results. Rightly so. Drawing an end to your experiments too late or too early runs the risk of:
Wasting time (if the result is proven and reliable, there’s no need to continue testing)
Not retrieving accurate data, particularly if the sample size isn’t big enough.
You can use the following techniques to determine when to stop your A/B test:
P-Value
You’ve started your test and there’s been a small volume of people viewing the variations of your web page. To quickly understand whether the experiment will be a success, you can calculate the p-value of your A/B test.
This simply calculates the probability value of your hypothesis being accurate. The value range is between 0 and 1, with a lower score indicating that there is strong evidence against the null hypothesis.
Typically, a p-value of less than 0.05 indicates strong evidence against your hypothesis. A larger p-value of more than 0.05 means there’s a strong chance your hypothesis is true:
For example, if you’re testing to see whether the word “buy” increases the CTR in your meta titles, your null hypothesis would be an increase in CTR. Your alternative hypothesis is that it doesn’t increase CTR.
So you randomly sample a handful of meta titles and find that those including the word “buy” have a higher CTR. Your p-value turns out to be 0.005, which doesn’t really prove that the edited versions always have a higher CTR.
However, if your p-value is 0.7, it’s safe to say that your null hypothesis of an increase CTR is correct, and therefore your experiment will likely continue to show that the word “buy” does increase a meta title’s CTR.
Statistical Significance
It’s worth noting that the p-value of your experiment isn’t the end result. You’ll need to prove that the probability value is, in fact, accurate before calling out a winner. But it’s tricky to know when to call it a day.
The majority of A/B tests stop after a certain period of time — whether that’s a week, a month or a quarter. When that timeframe has been reached, the testing stops and the team behind the experiment analyzes which control won.
However, you shouldn’t run your SEO split test for a set period of time. There are thousands of things that contribute to the testing timeframe guidelines, including your website’s conversion rate and Google’s algorithm releases (if any), which makes it almost impossible to set a period of time exclusively for your test.
“In most cases we found that the impact of an experiment on traffic starts to show as early as a couple of days after launch. The difference between groups continues to grow for a week or two until it becomes steady.
Knowing this not only helps us ship successful experiments sooner, but also helps us turn off failing experiments as early as possible. We learned this the hard way.”
You could do the same thing, with the only difference being that you stop SEO A/B testing when the results are statistically significant.
An SEO experiment said to have statistical significance means that the end results aren’t biased or inaccurate. It’s a way of proving that, mathematically, the result you’ve recorded is reliable, and largely depends on your sample size. So if you run the test with a statistical significance level of 98%, you can be sure that the results are accurate and true — and that your result isn’t being skewed by an anomaly.
“The amount of time required for a reliable test will vary depending on factors like your conversion rates and how much traffic your website gets; a good testing tool should tell you when you’ve gathered enough data to draw a reliable conclusion.”
So, at what statistical significance rate should you call it a day? The answer depends massively on your sample size.
Let’s say, for example, that you ran an SEO test that measures the change in CTRs of your landing page by changing the color of your call to action buttons. Here are the results of your experiment:
Group 1 (red button): 100 visitors clicked through, but 800 visitors landed on the page. This is a CTR of 12.5%
Group 2 (green button): 3 visitors clicked through, but 15 visitors landed on the page. This is a CTR of 20%.
If you were solely using CTR as a measure success, you’d say that Group 2 had a much higher CTR — therefore, the green button wins.
The problem with that, however, is that there were significantly less people exposed to the green button than the red button. Because the sample size is small, the test is not significantly significant. The small number of people viewing the green button might be biased. So the experiment isn’t over. You need more people to be exposed to the green button before calling it a winner.
Take a look at the metric variation between each result. Before automatically labelling the control with the higher metric as the winner, take a look to see whether the test was significantly significant both in terms of sample and control size.
“Don’t just test between two, or even a few pages, as the results won’t be meaningful. Instead, test between two different groups of random pages on your website – the control group and then the group of pages you’re going to change.”
Only once each result is statistically significant can you stop the test and analyze which variation performed best.
CausalImpact Reports
You could also assess whether your A/B test has been a success by using CausalImpact reports, as Mark Edmondson explains:
“CausalImpact is a package that looks to give some statistics behind changes you may have done in a marketing campaign. It examines the time-series of data before and after an event, and gives you some idea on whether any changes were just down to random variation, or the event actually made a difference.”
This technique is more technical and you might need to enlist the help of a developer or technical SEO to assist you, but CausalImpact reports do have the ability to dive deeper into your results, and accurately prove your hypothesis.
How to Scale Your A/B SEO Tests
We’ve already touched on the fact that experiments should be stopped when the results are statistically significant. But when you’re running A/B SEO tests on a large scale — potentially a site with thousands of URLs — it can be tough to keep track of your results.
So you should focus on scalability, as one research paper states:
“The two core tenets of the platform are trustworthiness (an experiment is meaningful only if its results can be trusted) and scalability (we aspire to expose every single change in any product through an A/B experiment).”
The easiest way to do this is through Clickflow’s group URL testing feature. Instead of manually entering a bunch of URLs you want to split test, you include a string included in all of the URLs you want to test.
Before you dive in with your first split test, you’ll need to lay out some ground rules. After all, you don’t want your experiments to be a waste of time.
Here are three things you’ll need to remember throughout your tests:
1) Isolate the Elements You’re Testing
It’s easy to get carried away with your split testing and try to measure the impact of several different on-page elements. You want to see what works as quickly as possible, right? So try to avoid the rush, and isolate your tests.
Running several A/B tests at the same time could skew your results, as Dave Hermansen notes in his explanation of the approach they use at Store Coach:
“We only test one thing at a time so that there is no noise and we can tell if it was that one thing that made a positive or negative difference.
We let the test run for a decent amount of time — usually at least 1,000 impressions (the more the better) — so that we can feel more assured that any changes in rankings, click through rate or conversions were due to the thing being tested and not just a fluke.”
Sure, you can run several A/B SEO tests, but make sure you pick just one element you want to test and run that experiment in isolation.
2) Avoid Cloaking Google Bots
The entire foundation of A/B testing is displaying different versions of the same webpage to groups of people and judging how they interact with each. The page you’re showing isn’t exactly the same — which makes it easy for you to land in hot water.
Google strictly forbids cloaking — which is the act of showing different webpages (and content) to search engine spiders and your human visitors:
So how can you avoid landing in Google’s bad books when the entire point of A/B SEO testing is to create multiple versions of the same page? The answer is simple: Don’t index the duplicate pages.
When you’ve got several copies of the same page, Google bots won’t know which is the real one. They might think your original page is for humans and your test page is built solely for bots — meaning you’re cloaking them. However, de-indexing the new test page (or adding canonical tags to the original) will ensure that Google knows which page is the right one.
3) Build an A/B Testing Mindset
The truth is that running split tests can be scary, especially if you’re running them on a huge website with lots of potential for things to go wrong.
In LinkedIn’s write up of the common A/B testing challenges in social networks, they stated that:
“Running large scale A/B tests is not just a matter of infrastructure and best practices; establishing a strong experimentation culture is also key to embedding A/B testing as part of the decision making progress.”
To make SEO split-testing work for you, you need to practice. Provide your team with lots of opportunities to run experiments — so long as they’re done correctly and safely. As LinkedIn explained, you need to build an experimentation culture.
Thumbtack, who runs 30 A/B tests per month, does this effectively. They build a split-testing mindset into their team by encouraging half of their in-house engineers to own at least one A/B test within their first six months through:
Hosting hour-long, monthly workshops on A/B testing for their new team members
Intensive documentation, shared on a company drive, which details how they run tests
Providing sample size calculation tools for staff to understand the expected outcome of their experiments
Asking team members with experience in split testing to coach their new engineers as they complete their own experiments
Not only does this help boost the confidence and willingness of their team, but the constant reinforcement of “we need to test regularly” could lead to incredible results.
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Final Thoughts
As you can see, split testing various SEO strategies is complex — but worth its weight in gold when you hit the jackpot and move up from the low keyword ranking positions you’ve been stuck in.
Remember to start with your hypothesis, pick an approach that works depending on the elements you’re testing, and avoid cloaking Google bots. You don’t want to land a nasty penalty in the meantime!
I can't calculate clear-cut ROI from SEO. But I can address the activities that, in my experience, typically lead to higher rankings for ecommerce merchants. It's not a stretch to suggest that 80 percent of your daily SEO activities should be connected to the four items below.
Audiences are shouting at brands through the “silence, brand” memes to stick to their mission. Yet brands are still doing cause marketing. Unfortunately, sometimes they do it the wrong way. Here’s how the Burger King team ran amok when they did it their way. Continue reading →
Amazon has two Alexas in its family: the intelligent voice agent that is popping up everywhere, and Alexa.com, a subsidiary which provides competitive analysis about any web site.
Today, the latter Alexa is announcing new features that pushes it deeper into world of the search engine optimization (SEO) and search engine marketing (SEM).
New keyword and audience analysis
The new Alexa Site Overview service now provides keyword opportunities and audience analysis about any site or its competing sites.
A screenshot from Alexa.com, showing some of the new features.
The new keyword opportunities include words that competitors are using to drive traffic, but the site in question is not. Easy-to-rank keywords indicates popular keywords the given site could probably rank for, based on Alexa’s assessment of its Competitive Power.
Buyer keywords are those used for search by the site’s targeted audience, and which indicate a high intent to purchase by the searcher. And optimization finds popular keywords that currently drive only a small amount of traffic to the site, but could drive more traffic if they were better utilized.
Competitive analysis now shows a percentage of overall traffic from search engines to a given site compared with competing sites, the number of referral sites driving traffic compared to competitors, and top search terms for a site and competitors.
Audience Insights
In the new Audience Insights, a marketer can now see the categories of interest for a site’s audience, plus a listing of other sites in those categories that the audience visits. An audience overlap shows sites competing for that audience.
Previously, Alexa.com President Andrew Ramm told SEW via email, his site’s free Site Overview tool provided only traffic stats on an input site URL. In March of last year, the site added Competitor Keyword Matrix, to understand the keywords used by competitors.
Now, he pointed out, the free tool “automatically generates top competing sites that it uses as the basis for a more in-depth competitive analysis report.”
A comparison of search traffic, referral sources and top site keywords between a site and its competitors are free, while other features are included in the site’s subscription plan.
Griffith said that other free tools “don’t aggregate this information in a way that makes it easy to extract customized and actionable insights for a site,” based on competitors. Other tools, he added, “often focus on simply providing data about a site’s current performance [for such factors as keywords or traffic], but fall short of presenting untapped opportunities for the site.”
Social media marketing has historically been an island of its own: We seldom see it integrated into other digital marketing channels, like SEO or content.
This is unfortunate because your social media strategy can benefit a lot from data and insights that your other teams and tools can contribute. After all, there's one single goal behind all your digital marketing channels: You want to better understand and better serve your target audience to turn one-time visitors and one-time buyers into brand advocates.
The more data you can collect about your target customer's struggles, preferences and interests, the better you can serve them, regardless of the channel. And collecting data from non-social media channels makes a lot of sense – it can help you or your social media manager to better relate to and cater to your audience.
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4 Content Marketing Tools to Improve Your Social Media Strategy
Here are four content marketing tools that can provide valuable insight to inform and direct your social media strategy:
1) Find Topics to Cover on Social Media Using Keyword Research Tools
There's one thing about old-school keyword research (or keyword extensions, as it's traditionally about making your seed term longer): it's terribly under-utilized by marketing teams outside of a purely SEO department. And that's a shame because keyword research provides a goldmine of insight into the interests and struggles of your audience.
What many people fail to grasp is that there's a real breathing human being behind each search query. If you humanize keywords, they become much more useful, giving you lots of ideas on how to better engage and serve your target customer.
The logic is simple: If enough people type a search query into the search box for keyword research tools to make a record of it, this topic is likely to resonate on social media, too. When it comes to crafting social media updates, encourage your team to use keyword research to:
Generally learn which topics to cover in social media updates (like creating mini tutorials, posting “tip of the day” updates, etc.)
Come up with popular hashtags to increase your social media reach on Instagram and Twitter
The best part is that keyword research doesn't have to be paid. You can use a free tool and find lots of content ideas to implement on both your website and social media.
For example, Kparser is a freemium keyword research tool that generates huge keyword lists for free without even requiring a login. To use Kparser:
Type in your basic core term
Play with filters to the left to narrow keyword results based on a chosen angle
The only real problem behind keyword lists is that they are really messy to the point that they are unusable. Even narrow-niche company keyword lists are thousands of lines long because there are so many ways to put the same query into a search box.
This is where keyword clustering (i.e. “grouping”) comes into play. Kparser does some sort of clustering, which allows you to group keywords by a common word inside the phrase. But this is a very basic clustering technique that doesn't give you a good enough overview of the topic. This technique would group hiking shoes and hiking boots into two different categories, while essentially they belong to one.
Serpstat offers a much more effective keyword clustering functionality, which allows you get a better understanding of your niche and target your content and social media updates more effectively:
To access the clustering functionality, you'll need to upgrade your Serpstat account to at least Plan B tier ($69 monthly)
Proceed to the “clustering” section and upload your keyword list (you can copy/paste the list from your Excel file you generated from Kparser or Google Search Console)
Give it some time. Serpstat will go through your keywords, identify those queries that trigger similar/overlapping results in Google and group your keywords into clusters by relevancy:
The beauty of this clustering technique is that you can catch related terms quite easily in order to come up with a more diverse hashtag marketing strategy.
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2) Use Question Research to Discover What to Ask Your Social Media Audience
While keyword research has been around for ages, the recent voice-search-driven trend toward optimizing for natural language has created a demand for new tools, i.e. those that can be used to research niche questions.
And this is a very fortunate trend for social media marketing because asking questions is one of the best ways toimprove social media engagement, including gettingmore comments. Questions trigger the natural human instinct called “instinctive elaboration”. This instinct forces people to pause and start looking for or formulate an answer.
Adding all types of questions to your social media calendar thus makes a lot of sense and with new SEO and content marketing tools, question research has become much easier and more effective. For example, Campmor, the outdoors gear retailer, asked a simple question on social media and received 139 comments:
There are several powerful tools and platforms that make it easy to discover niche questions to include in your marketing editorial calendar. These include:
One of the newer tools that allows you to identify and research questions on any topic isText Optimizer, which uses semantic analysis to extract related and popular questions right from Google's search snippets. You can export the whole list and hand it to your social media manager to include them in your social media calendar.
The tool will use semantic analysis to identify popular questions on the topic and show you a huge list sorted by popularity:
There are many ways to include popular questions into your social media marketing, such as:
Simply ask your audience a question in a new post (you can also include a nicely branded visual version of the question)
Create a poll and encourage your users to choose their answers (and comment with more thoughts)
Invite an active social media follower or a niche influencer to host a mini-AMA event on your page (or at your Twitter chat) to answer some of those questions
Diversifying your social media content is key to engaging your audience, and incorporating niche questions will make your strategy even more effective.
3) Use On-Page Analytics to Identify the Best Calls-to-Action
Finally, we want some of our social media updates to convert, not just engage. While social media analytics provides some basic insight into how different calls-to-actions perform across different social media updates, we have much more testing and analytics flexibility when dealing with our own website.
On-site analytics provide much deeper insight into what triggers conversions and what drives people away, so it makes perfect sense to use this insight when crafting CTA wording for social media updates. too. Google Analytics offers in-depth goals tracking, but it's a high-level tool, which makes it less useful for social media marketers who are basically looking for very specific answers.
Finteza is a free analytics software that is designed to be straightforward enough for even a non-analytics person to understand. Once you install Finteza's tracking code on your site (much like you install Google analytics code), you can use Finteza's WordPress plugin to add on-page links for event tracking:
If you are not comfortable with using tracking codes, Finteza allows you to use WordPress's visual editor to create events to monitor.
Once your in-content CTAs are set up for tracking, you can easily put together funnel monitoring inside Finteza. Simply select your URLs and events to visualize which of those lead magnets work best and whether this is something worth promoting via social media ads:
It is obvious that a “Whitepaper” CTA collects more leads than a “Webinar” CTA, so your social media team may create better-converting ads if they choose the whitepaper asset to promote on social media.
Finteza is one of the most marketing-friendly analytics suits because it's incredibly easy to set up.
4) How to Put It All Together and Keep Your Team Updated
Finally, with so many data points and (remote) teams working together for the common goal, what's the best way to keep everyone updated?
ContentCal is a collaborating social media management tool that offers a “Campaigns” feature that allows you to schedule content marketing campaigns and create detailed content briefs which can be accessed and edited by other team members:
ContentCal allows you to schedule content + social media marketing campaigns so that your whole company knows what is coming and when, and lets them contribute their own data from their managed tools.
It's a great idea to record all the keywords, questions and best-working content magnets for your social media marketing managers to implement in their strategy:
Each brief can be as detailed or as concise as you choose it to be and your other team members can add their data, too. The end result is that your social media manager knows everything, from lead generation assets to use across ads to hashtags and popular questions to include in social media updates for better organic reach.
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Takeaway: 4 Content Marketing Tools for Social Media
Kparser and Serpstat – Use these two keyword research tools for crafting social media updates and coming up with hashtags.
Text Optimizer– Research popular questions using this tool and ask those questions on social media for more engagement.
Finteza – Use this tool to monitor your social media audience and understand what engages them best.
ContentCal – Use this content marketing calendar to put everything together. You can also use Google Timeline to plan your projects.
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Document your content marketing strategy with a hands-on, insightful company-wide workshop if you want to achieve big success. Follow these nine steps to lead a session to get everybody on board and working toward the same goals. Continue reading →
NLP, or natural language processing, is an area of computing that aims to help computers make sense of human (or “natural”) language. It’s on the rise, incredibly powerful and is about to have a life-changing impact on marketing.
Even though language is second nature for the vast majority of humans, it’s very difficult for computers to interpret and use correctly. The rigid, rule-bound format of spreadsheets and databases are perfect for software, but the random, context-bound and seemingly rule-less nature of human languages makes AI want to reboot!
NLP might not ring a bell for you right now, but it’s been around for the last 30 years or more – and it still has a long way to go. Experts believe that some of NLP’s next steps will be huge, centering around the move from structured data (databases) to unstructured data (text), as well as an increased ability to “understand” humans as they speak normally.
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Why Should Marketers Care About NLP?
As a marketer, you might be thinking, “That’s nice, but what has it got to do with me?” Well, if experts are to be believed, some of the biggest, most revolutionary uses of NLP center in and around its applications on marketing.
Now, bearing in mind that NLP is a scientific discipline (and one that’s not necessarily easy to understand in a single, 2,000-word article), let’s start by running through the main types NLP you may encounter on a regular basis:
Optical Character Recognition: Converting written or printed text into data a computer can read. Ever tried to edit a non-editable PDF? If you have, you have my sympathy. OCR is the tech that “helped” the process.
Speech Recognition: Converting spoken words into data a computer can understand. This is the NLP technology you use every time you ask Siri, Cortana, Echo or Google Voice a question.
Machine Translation: Translating text from one language to another. This is the tech that underlies translation apps like Google Translate.
Natural Language Generation: Outputting information as a human language. This is the tech you use every time Siri or Cortana answers your question.
Sentiment Analysis: Extracting data from topics being discussed (often “big text”) and assessing whether that data is negative or positive (or if it can detect something else).
Semantic Search: Closely linked to speech recognition, as above, this allows you to ask natural questions of an app like Siri, rather than having to formulate your question in a particular, unnatural way.
Natural Language Programming: These are tools that allow users to make apps and software using natural language commands (instead of programming in the traditional, computer-friendly way).
Affective Computing: Using NLP and other technologies to understand and replicate human emotions (this is the one that most people are scared of).
These definitions may seem high-level but, in fact, you already use them. You might have even used them today if you’ve consulted:
All these apps – and many more – use NLP so that you can interact with them and they can interact with you. Have more ideas for NLP occurred to you? If you’re the creative type, the answer should be “yes!” Let’s walk through some of the uses of NLP in marketing that aren’t just a reality; they’re highly successful as well.
The one use of NLP that you may already have heard of is big text sentiment analysis. You’ve heard of big data, right? Well, meet its cousin, big text.
Sentiment analysis is becoming sufficiently advanced these days so as to be able to give us not just an insight into what people are saying about our brand online, but also how they feel about it. As all marketers know, mentions do not equal positive mentions. With NLP, we have the power to prove it.
With NLP employing sentiment analysis, we can mine big text to find those negative mentions and reach out to try and mitigate the consequences. Likewise, sentiment analysis can help brands find instances of people with a clear intention to purchase so that you can make the moves necessary to ensure that your brand appears before their eyes.
If you’re in e-commerce, you’ll enjoy this one: Other aspects of NLP can be used to sift through product descriptions and automatically amend the HTML to include attributes that may not have been added when the product was originally uploaded. Not only does this cut down on grunt work for you, it adds context and detail to the listing, meaning that Google is even better informed when it comes to ranking your beautifully descriptive products in search.
Our final example is the use of NLP to improve the performance of chatbots. Not only can NLP help improve their usability – and their customer experience as a result – but it can also be combined with marketing psychology and targeting to actually increase conversions and sales.
As an example, last year retailer Asos reported a 300% increase in orders by using their new “fashion bot” Enki. The company used to have a chatbot (the boring-sounding “gift assistant”) and, by all accounts, it was pretty underwhelming. Using the new, all-improved Facebook Messenger chatbot, they saw a 250% return on spend while reaching 3.5x more people. Impressive, right?
Cosmetics giant Sephora has also jumped on the chatbot wagon, with not one, but three automatic assistants:
Sephora Reservation Assistant (Facebook)
Sephora Virtual Assist (Facebook)
Sephora's Kik bot
The Facebook booking bot has an 11% better conversion rate compared to any other method of booking a makeover.
One thing that might hamper your understanding of NLP and its possibilities for the future of marketing is that even though it’s not hard to understand how it works (it helps computers understand human speech and text), it can be hard to imagine the full breadth of the applications it might be used for.
One of the major challenges – and advantages – of NLP-powered systems is that they can process a HUGE amount of data. What’s more, much of this will be unstructured data that we’ve never been able to process on a large scale before. The result, from our point of view, is that we now have unimaginable amounts of data from which we can draw conclusions and influence strategy.
The problem lies in the fact that we must be able to actually draw these conclusions. In other words, we have to be able to use the data in a meaningful way. If we don’t, it’s effectively the same as not having any data at all. That’s why the first requirement and challenge of using NLP is the need to have systems in place that can take advantage of the data, in addition to systems that pass that data onto even more systems that can actually take action with it.
Many of the world’s newest NLP-enabled apps are just that: tools that take actionable data and use it to achieve a goal. The degree to which companies manage to do this is the key challenge influencing how NLP will affect the world of marketing in 2020 and beyond.
Here are some of the biggest challenges with NLP.
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Challenge #1: Presenting Raw Data Attractively
Much of the use of NLP in marketing centers around social media – using the technology to sift through the millions of casual mentions of a given topic and pull out both the most important ones and the overall “feeling” about the topic. Sometimes these apps focus on a certain social media platform like Twitter, while others are built into social media management apps, likeHootsuite:
Either way, the challenge here is to analyze the growing amount of big text. And grow it will – big data market revenues are projected to increase from $42B in 2018 to $103B in 2027 (and big text is part of big data). As the data increases, tools will need to hustle even harder to make sure such vast knowledge can actually be understood and used by humans.
Challenge #2: Presenting Raw Data in a Way that Saves Humans Time
Likewise, this avalanche of data will be much more usable if apps find a way of “triaging” the information it provides, making it not only easier to understand, but also making inroads into how much of it is left to humans to deal with after automated processes have started the job.
Apps likeMonkeyLearn, for example, analyze customer support tickets and then automatically tag and categorize tickets based on – you guessed it – sentiment analysis. Once employees interact with the data, it’s incorporated into their normal workflow, reducing the amount of effort required to get it support ready.
Challenge #3: Presenting Raw Data in a Format that Can Be Used in Real Time
The idea of having to let a computer “do its work” while you wait is an old-fashioned one. We live in a society that expects things now. Even so, getting that NLP-enhanced info to you on the fly is in the early days, and it’s definitely still got some distance to go.
We’ve already seen a great real-time use of NLP for the writer as they’re writing:the ability to examine content as it’s being written and to communicate suggestions for improvement as learned through machine learning and big text. This helps writers make decisions that will take an article from average to highly optimized, helping them spot missed opportunities.
It’s a fascinating topic, and we’re already seeing progress in this area. One app that attempts to perform this task isMarketMuse:
In exchange for an email address, they’ll take a peek at a piece of your content and suggest how it might be improved. That’s the power of NLP.
Challenge #4: Making It Easier to Interact with Tools that Use NLP
Although marketing and customer experience aren’t the same, they are related, and we’ve seen already how improving automated bot experiences can offer major marketing benefits in terms of conversions and sales.
Chatbots, knowledge bases, and customer support resources can all be optimized by helping people access the information they need more quickly (data mining), allowing them a more natural state of interaction with the tools that can help them (natural language processing), and by streamlining the human-led section of the customer support process (by automatically categorizing, tagging or triaging inquiries).
Stress-free technology interactions are a key to happy customers, and, as we all know, happy customers make the whole company smile.
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The Future of NLP in Marketing
If you’re in marketing, you should be very excited about the possibilities of NLP. If the sheer opportunities it presents aren’t enough, then at the very least, the journey that it’s already made and its possibilities for the future – many of which haven’t yet been discovered – should get you excited. If you ever tried using Google Translate back in 2006 when it was first launched, I’m sure you’d more than agree!
As we move towards the future of NLP in marketing, keep an eye on the evolution of the NLP-powered tools that will be made available. No matter what you’re marketing and no matter if you’re a big business or a small player, you’ll be able to make use of some of the most exciting and practical uses of big data we’ve ever seen.
Since one of the keys to modern marketing seems to be the analysis and application of big data insights, anything that helps us better manage this big data should be welcomed. NLP may be one of the best tools we have to do this in a sustainable, scalable and real-time way, making it one tech buzzword you can’t afford to ignore.
