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Using STAT: How to Uncover Additional Value in Your Keyword Data

Posted by TheMozTeam

Changing SERP features and near-daily Google updates mean that single keyword strategies are no longer viable. Brands have a lot to keep tabs on if they want to stay visible and keep that coveted top spot on the SERP.

That’s why we asked Laura Hampton, Head of Marketing at Impressionto share some of the ways her award-winning team leverages STAT to surface all kinds of insights to make informed decisions.

Snag her expert tips on how to uncover additional value in your keyword data — including how Impression’s web team uses STAT’s API to improve client reporting, how to spot quick wins with dynamic tags, and what new projects they have up their sleeves. Take it away, Laura!

Spotting quick wins 

We all remember the traditional CTR chart. It suggests that websites ranking in position one on the SERPs can expect roughly 30 percent of the clicks available, with position two getting around 12 percent, position three seeing six percent, and so on (disclaimer: these may not be the actual numbers but, let’s face it, this formula is way outdated at this point anyway).

Today, the SERP landscape has changed, so we know that the chances of any of the above-suggested numbers being correct are minimal — especially when you consider the influence of elements like featured snippets on click-through rates.

But the practical reality remains that if you can improve your ranking position, it’s highly likely you’ll get at least some uplift in traffic for that term. This is where STAT’s dynamic tags can really help. Dynamic tags are a special kind of tag that automatically populates keywords based on changeable filter criteria.

We like to set up dynamic tags based on ranking position. We use this to flag keywords which are sitting just outside of the top three, top five, or top 10 positions. Layer into this some form of traffic benchmark, and you can easily uncover keywords with decent traffic potential that just need an extra bit of work to tip them into a better position.

Chasing position zero with featured snippets and PAAs 

There’s been a lot of chat in our industry about the growing prevalence of SERP features like featured snippets and “People also ask” (PAA) boxes. In fact, STAT has been instrumental in leading much of the research into the influence of these two SERP features on brand visibility and CTRs.

If your strategy includes a hunt for the coveted position zero, you’re in luck. We like to use STAT’s dynamic tagging feature to monitor the keywords that result in featured snippets. This way, we can track keywords where our client owns the snippet and where they don’t. We can also highlight new opportunities to create optimized content and attempt to capture the spot from their competitors.

This also really helps guide our overall content strategy, since STAT is able to provide quick feedback on the type of content (and, therefore, the assumed intent) that will perform best amongst a keyword set.

Making use of data views 

Data views are one of the most fundamental elements of STAT. They are tools that allow you to organize your data in ways that are meaningful to you. Holding multiple keyword segments (tags) and producing aggregate metrics, they make it possible for us to dissect keyword information and then implement strategically driven decisions.

For us at Impression, data views are essential. They reflect the tactical aspirations of the client. While you could create a single templated dashboard for all your clients with the same data views, our strategists will often set up data views that mirror the way each client and account work.

Even if we’re not yet actively working on a keyword set, we usually create data views to enable us to quickly spot opportunities and report back on the strategic progression.

Here are just some of the data views we’ve grouped our keyword segments into:

The conversion funnel

Segmenting keywords into the stages of the conversion funnel is a fairly common strategy for search marketers — it makes it possible to focus in on and prioritize higher intent queries and then extrapolate out.

Many of our data views are set up to monitor keywords tagged as “conversion,” “education,” and “awareness.”

Client goals

Because we believe successful search marketing is only possible when it integrates with wider business goals, we like to spend time getting to know our clients’ audiences, as well as their specific niches and characteristics.

This way, we can split our keywords into those which reflect the segments that our clients wish to target. For example, in some cases, this is based on sectors, such as our telecommunications client who targets audiences in finance, marketing, IT, and general business. In others, it’s based on locations, in which case we’ll leverage STAT’s location capabilities to track the visibility of our clients to different locales.

Services and/or categories

For those clients who sell online — whether it’s products or services — data views are a great way to track their visibility within each service area or product category.

Our own dashboard (for Impression) uses this approach to split out our service-based keywords, so our data view is marked “Services” and the tags we track within are “SEO,” “PPC,” “web,” and so on. For one of our fashion clients, the data view relates to product categories, where the tracked tags include “footwear,” “accessories,” and “dresses.”

At-a-glance health monitoring

A relatively new feature in STAT allows us to see the performance of tags compared to one another: the Tags tab.

Because we use data views and tags a lot, this has been a neat addition for us. The ability to quickly view those tags and how the keywords within are progressing is immensely valuable.

Let’s use an example from above. For Impression’s own keyword set, one data view contains tags that represent different service offerings. When we click on that data view and choose “Tags” in the tabbed options, we can see how well each service area is performing in terms of its visibility online.

This means we can get very quick strategic insights that say our ranking positions for SEO are consistently pretty awesome, while those around CRO (which we are arguably less well known for), tend to fluctuate more. We can also make a quick comparison between them thanks to the layout of the tab.

Identifying keyword cannibalization risk through duplicate landing pages 

While we certainly don’t subscribe to any notion of a content cannibalization penalty per se, we do believe that having multiple landing pages for one keyword or keyword set is problematic.

That’s where STAT can help. We simply filter the keywords table to show a given landing page and we’re able to track instances where it’s ranking for multiple keywords.

By exporting that information, we can then compare the best and worst ranking URLs. We can also highlight where the ranking URL for a single keyword has changed, signaling internal conflict and, therefore, an opportunity to streamline and improve.

Monitoring the competitive landscape 

No search strategy is complete without an understanding of the wider search landscape. Specifically, this means keeping track of your and/or your client’s rankings when compared to others ranking around them.

We like to use STAT’s Competitive Landscape tab to view this information for a specific data view, or across the whole account. In particular, the Share of Voice: Current Leaders board tells us very quickly who we’re up against for a keyword set.

This leads to insights such as the competitiveness of the keyword set, which makes it easier to set client expectations. It also surfaces relevance of the keywords tracked, where, if the share of voice is going to brands that aren’t your own, it may indicate the keywords you’re targeting are not that relevant to your own audience.

You can also take a look at the Share of Voice: Top 10 Trending to see where competitors are increasing or decreasing their visibility. This can be indicative of changes on the SERPs for that industry, or in the industry as a whole.

Creating a custom connector for GDS 

Reporting is a fundamental part of agency life. Our clients appreciate formalized insights into campaign progression (on top of regular communications throughout the month, of course) and one of our main challenges in growing our agency lies in identifying the best way to display reports.

We’ll be honest here: There was a point where we had started to invest in building our own platform, with all sorts of aspirations of bespoke builds and highly branded experiences that could tie into a plethora of other UX considerations for our clients.

But at the same time, we’re also big believers that there’s no point in trying to reinvent the wheel if an appropriate solution already exists. So, we decided to use Google Data Studio (GDS) as it was released in Beta and moved onto the platform in 2017.

Of course, ranking data — while we’d all like to reserve it for internal insight to drive bigger goals — is always of interest to clients. At the time, the STAT API was publicly available, but there was no way to pull data into GDS.

That’s why we decided to put some of our own time into creating a GDS connector for STAT. Through this connector, we’re able to pull in live data to our GDS reports, which can be easily shared with our clients. It was a relatively straightforward process and, because GDS caches the data for a short amount of time, it doesn’t hammer the STAT API for every request.

Though our clients do have access to STAT (made possible through their granular user permissions), the GDS integration is a simpler way for them to see top-level stats at a glance.

We’re in the process of building pipelines through BigQuery to feed into this and facilitate date specific tracking in GDS too — keep an eye out for more info and get access to the STAT GDS connector here.

Want more? 

Ready to learn how to get cracking and tracking some more? Reach out to our rad team and request a demo to get your very own tailored walkthrough of STAT. 

If you’re attending MozCon this year, you can see the ins and outs of STAT in person — grab your ticket before they’re all gone! 

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How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

“Our acquisition of Dynamic Yield has brought us a lot of excitement,” says McDonald’s CEO Steve Easterbrook. “Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website will automatically suggest two or three things to go along with it. We’re the first business that we’re aware of that can bring that into the physical world. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.”

Steve Easterbrook, CEO of McDonald’s, discusses how the company is using technology to elevate the customer experience and accelerate growth in an interview on CNBC:

Continue To See How We Can Elevate the Customer Experience

As we’ve executed the growth plan we’ve spent the first two years, three or four years ago, turning the business around. Now we’ve had a couple of years of growth. We’re confident now that we’re beginning to identify further opportunities to further accelerate growth. That takes a little bit of research and development cost. It means you’ve got to bring some expertise into the business to help us do that. We’re still managing to effectively run the business. G&A is staying the same and we’re putting a little bit more into innovation.

We continue to see how can we help continue to elevate the experience for customers. With this pace of change in the world and with different technology and different innovations, whether it’s around food, technology, or design, we’re seeing opportunities that we think can either make the experience more fun and enjoyable or smoother for customers. If we can find that we’re going to go hard at it.

We need to continue growing. If where we are investing that money is helping drive growth across 38,000 restaurants then I think the shareholders and investors would be satisfied. We want to bring our owner-operators along with us as well. They’re investing their hard-earned dollars so that always means we got a business case. The owner-operators will want to see a return on their investment just the same as a shareholder would. We’ve got a wonderful check and balance in the system to help us make sure we spend that innovative money in the right way.

Using Data, Machine Learning, and AI to Accelerate Growth

Our acquisition of Dynamic Yield has brought us a lot of excitement. It was our first acquisition for 20 years. It was an acquisition in a way that was different from the past. It wasn’t looking at different restaurant businesses to try and expand our footprint. It’s bringing a capability, an IP and some talent, into our business that can help us accelerate the growth model. We completed the deal mid-April and within two weeks we had that technical capability in 800 drive-throughs here in the U.S. It’s a very rapid execution and implementation.

Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website we’re on these days will automatically suggest two or three things to go along with it. People who buy that tend to like these things as well. We’re the first business that we’re aware of that can bring that into the physical world. As customers are at the menu board, maybe they’re ordering a coffee and we can suggest a dessert or they’re ordering a quarter pounder with cheese and we can suggest making that into a meal. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.

Mining All of the Data Will Improve the Business

The best benefit for customers is we’re more likely to suggest things they do want and less likely to suggest things they don’t. It’ll just be a nicer experience for the customer. But yes, for the restaurant itself, because we can put our drive-thru service lines in there, for example, the technical capability by mining all of the data will be to suggest items are easier to make at our busier times. That’ll help smooth the operation as well. The immediate result will be some ticket (increases). But frankly, if the overall experience is better customers come back more often. That’s ultimately where the success will be, driving repeat visits and getting people back more often.

Across the entire sector, traffic is tight right now and people are eating out less. They have been progressively eating out less for a number of years. Whether it’s the advent of home delivery, for example, which is something we participate in, but at the moment it’s just a little bit tight out there. It’s a fight for market share. Anyone who is getting growth, typically it’s because they’re adding new units. People are finding it hard to (increase) guest count growth. It’s something that we have stated as an ambition of ours. We think that’s a measure of the true health of the business. Last quarter, we did grow traffic and we’ve grown traffic for the last couple of years, but only modestly. We want to be stronger than that.

How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

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How WeWork is Using Technology to Revolutionize Office Space Worldwide

“We open 15 to 20 buildings a month,” says WeWork CTO Shiva Rajaraman. “Anything we can use to automate or augment a person through machine learning we’re taking all that data in one central place and starting to create an engine around that. That’s key to successful scaling today. When we think about enterprise we sort of step back and say what’s our Google Analytics for commercial space?”

Shiva Rajaraman, Chief Technology Officer of WeWork, discusses how WeWork is using technology to revolutionize office space worldwide in an interview on Bloomberg:

How Do We Offer Space As a Service?

There are three capabilities when we think about WeWork. One is how do we offer space as a service? If you just think about it’s really basic. Are you looking for what location do you need? Where do you need it? How long do you need it? Are there different pricing models for it? One of the things we’ve done is effectively taken all of this space and put it into a big database and we start to shape it based on what we see out there in the market. Some of that is just pricing automation at the end of the day. Some of it is how do we automate that supply chain of delivering a building?

We open 15 to 20 buildings a month. Anything we can use to automate or augment a person through machine learning we’re taking all that data in one central place and starting to create an engine around that. That’s key to successful scaling today. The biggest technically challenging thing is operational scale. If you step back you don’t want a lot of variability. You want to step back and say, “Hey, can I deliver this building on time at quality as people need it?” That’s where you need operational technology that really works in a way that normally construction has not worked in the past.

What’s Our Google Analytics for Commercial Space?

One of the key things on the strategy side is that as we see this demand and we start to get critical mass in different areas can we disrupt the business model a little bit? Let me give you an example. If you take someone like GE Health in Seoul, South Korea, they had underutilized real estate. We redesigned that so they can use it in a more flexible way. We also created a new membership called the City Pass which gives all of their employee’s access to WeWork throughout Seoul. Now they can go where they’re more productive. One of the key things we’re looking at right now is what’s a density that translates to interesting memberships that allow people to be more productive?

Let’s talk about the M&A that’s created a fabric that we can start to offer to enterprises. When we think about enterprise we sort of step back and say, “What’s our Google Analytics for commercial space?” Can we help these enterprises create a good workplace experience through things like room booking (service) all the way to understand how they use space so they can come and use WeWork on demand if they need it. We can also help them grow in the future if they’re looking at new markets to expand into.

How WeWork is Using Technology to Revolutionize Office Space Worldwide

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How IBM is Using AI to Improve Hiring and to Retain and Retrain Employees

“We are the number one destination for Gen Z on Glassdoor,” says IBM CEO Ginni Rometty. “I get 8,000 resumes a day. I don’t make them go hunt for jobs. The AI talks to them and we ask very nicely and get permission, share this with me, share that with me, share this LinkedIn review with me, share this resume, and instead of you looking for jobs I’ll serve up jobs to you that actually match you. Our match rate of applying is 30 percent. With anybody else, it’s about nine percent.”

Ginni Rometty, CEO of IBM, discusses how they are using AI to improve hiring and to retain and retrain employees in an interview on CNBC:

AI Will Change 100 Percent of Jobs

The original genesis of this was a belief that AI will change 100 percent of jobs. But if you’re going to really get the benefit of it you have to change how the work is done. We chose to make HR, my HR leader chose to make HR, really the role model example of that. She has done a fantastic job putting AI in end to end. She tracks (the value of this AI approach) and we have now just from the AI alone, my HR function has saved $ 300 million from just doing that piece of it. In part, it helps the employees, because it completely makes HR employee centric. You don’t do things to people, you do it for them. It’s consumer-centric because of how we apply the AI. The other part of it is there’s productivity on the other side. Both are important right now.

Our experience has been and I’ll just use HR as an example. On the one hand, we were able to replace a lot of routine work. In the case of HR, our HR staffing went down by 30 percent. However, the people then doing the job of HR, they do far more non-routine work, their salaries all went up or their skills went up with it. You’re going to have this trade-off where technology will drive productivity but then it will also drive you and me to do our job different. It sits at that intersection.

Good for the Employee and Really Good for Business

This includes how we recruit today. We are the number one destination for Gen Z on Glassdoor. I get 8,000 resumes a day. I don’t make them go hunt for jobs. The AI talks to them and we ask very nicely and get permission, share this with me, share that with me, share this LinkedIn review with me, share this resume, and instead of you looking for jobs I’ll serve up jobs to you that actually match you. Our match rate of applying is 30 percent. With anybody else, it’s about nine percent.

It just shows this effectiveness for using the AI for things like a manager who says I’m doing salary. We do something to be sure salaries are fair, no unconscious biases that are in there, and then as well, proactive retention. That is the ability to use many pieces of data to say this person is likely to quit in the next six months, so do something now so that never enters their mind. We’re 95 percent accurate and have saved $ 300 million in replacement costs from that. These are both good for the employee and it’s really good for business.

We’ve Got to Make This Era of Technology More Inclusive

It’s not just driven by that (job demand driven by booming economy). I think you’ve got married here this idea that technology is going to change everyone’s job. It means reskilling of your current population. This is also so they’ve got the skills that apply for the future. I think this point of the word transparency, being clear with every employee, is their skill in the market hot or not so needed (based on) demand? Also, for your strategy, is it needed or won’t be needed for the future? We update that every quarter, that matrix, and we share it with employees. They know where they are and they say yes, I’ve got to move here and we use AI to help them move to a new area.

What’s happening in the market, whether or not there were IPOs, this would be happening anyways, this remake of skills. It means reskilling your current population. It means a strong belief that we’ve got to make this era of technology more inclusive. Six-year high schools where community colleges and high schools are combined together. We’ve been working with 500 other companies and with those schools and there’s a pipeline of 125,000 kids coming through. Now, 15 percent of our hiring was of less than 4-year college graduates. If you’re going to make this era inclusive, the technology is moving so fast, you’ve got to make it so more people can have a job in this world.

I just shared with the CHROs, one of the number one issues we see is we as employers over-spec the jobs that we go to hire for. We write down so many credentials they should have and it’s not true. If you’re your cyber analyst, which there’s going to be two million open jobs, let me tell you how many people can actually fill that that don’t have to have all those credentials. If I just talked about making this era for this country inclusive it’s that. It’s 15 percent and particularly the middle of the country is where we’ve done that hiring.

How IBM is Using AI to Improve Hiring and to Retain and Retrain Employees

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Using insights to find new audiences to test for awareness campaigns

Learn how to test new audience combinations with the Audience Insights tool in Google Ads Audience Manager.



Please visit Search Engine Land for the full article.


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Using automation to boost PPC performance

Learn how automating bid management, scripts, error checking, reporting and ad copy can help marketers harness the power of automation to succeed in the fast-paced world of PPC.



Please visit Search Engine Land for the full article.


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Using STAT for Content Strategy – Whiteboard Friday

Posted by DiTomaso

Search results are sophisticated enough to show searchers not only the content they want, but in the format they want it. Being able to identify searcher intent and interest based off of ranking results can be a powerful driver of content strategy. In this week’s Whiteboard Friday, we warmly welcome Dana DiTomaso as she describes her preferred tools and methods for developing a modern and effective content strategy.

Click on the whiteboard image above to open a high resolution version in a new tab!

Video Transcription

Hi, everyone. Welcome to Whiteboard Friday. My name is Dana DiTomaso. I’m President and partner of Kick Point, which is a digital marketing agency based way up in Edmonton, Alberta. Come visit sometime.

What I’m going to be talking about today is using STAT for content strategy. STAT, if you’re not familiar with STAT Search Analytics, which is in my opinion the best ranking tool on the market and Moz is not paying me to say that, although they did pay for STAT, so now STAT is part of the Moz family of products. I really like STAT. I’ve been using it for quite some time. They are also Canadian. That may or may not influence my decision.

But one of the things that STAT does really well is it doesn’t just show you where you’re ranking, but it breaks down what type of rankings and where you should be thinking about rankings. Typically I find, especially if you’ve been working in this field for a long time, you might think about rankings and you still have in your mind the 10 blue links that we used to have forever ago, and that’s so long gone. One of the things that’s useful about using STAT rankings is you can figure out stuff that you should be pursuing other than, say, the written word, and I think that that’s something really important again for marketers because a lot of us really enjoy reading stuff.

Consider all the ways searchers like to consume content

Maybe you’re watching this video. Maybe you’re reading the transcript. You might refer to the transcript later. A lot of us are readers. Not a lot of us are necessarily visual people, so sometimes we can forget stuff like video is really popular, or people really do prefer those places packs or whatever it might be. Thinking outside of yourself and thinking about how Google has decided to set up the search results can help you drive better content to your clients’ and your own websites.

The biggest thing that I find that comes of this is you’re really thinking about your audience a lot more because you do have to trust that Google maybe knows what it’s doing when it presents certain types of results to people. It knows the intent of the keyword, and therefore it’s presenting results that make sense for that intent. We can argue all day about whether or not answer boxes are awesome or terrible.

But from a visitor’s perspective and a searcher’s perspective, they like them. I think we need to just make sure that we’re understanding where they might be showing up, and if we’re playing by Google rules, people also ask is not necessarily going anywhere.

All that being said, how can we use ranking results to figure out our content strategy? The first thing about STAT, if you haven’t used STAT before, again check it out, it’s awesome.

Grouping keywords with Data Views

But one of the things that’s really nice is you can do this thing called data views. In data views, you can group together parts of keywords. So you can do something called smart tags and say, “I want to tag everything that has a specific location name together.”

Opportunities — where are you not showing up?

Let’s say, for example, that you’re working with a moving company and they are across Canada. So what I want to see here for opportunities are things like where I’m not ranking, where are there places box showing up that I am not in, or where are the people also ask showing up that I am not involved in. This is a nice way to keep an eye on your competitors.

Locations

Then we’ll also do locations. So we’ll say everything in Vancouver, group this together. Everything in Winnipeg, group this together. Everything in Edmonton and Calgary and Toronto, group all that stuff together.

Attributes (best, good, top, free, etc.)

Then the third thing can be attributes. This is stuff like best, good, top, free, cheap, all those different things that people use to describe your product, because those are definitely intent keywords, and often they will drive very different types of results than things you might consider as your head phrases.

So, for example, looking at “movers in Calgary” will drive a very different result than “top movers in Calgary.” In that case, you might get say a Yelp top 10 list. Or if you’re looking for “cheapest mover in Calgary,”again a different type of search result. So by grouping your keywords together by attributes, that can really help you as well determine how those types of keywords can be influenced by the type of search results that Google is putting out there.

Products / services

Then the last thing is products/services. So we’ll take each product and service and group it together. One of the nice things about STAT is you can do something called smart tags. So we can, say, figure out every keyword that has the word “best” in it and put it together. Then if we ever add more keywords later, that also have the word “best,”they automatically go into that keyword group. It’s really useful, especially if you are adding lots of keywords over time. I recommend starting by setting up some views that make sense.

You can just import everything your client is ranking for, and you can just take a look at the view of all these different keywords. But the problem is that there’s so much data, when you’re looking at that big set of keywords, that a lot of the useful stuff can really get lost in the noise. By segmenting it down to a really small level, you can start to understand that search for that specific type of term and how you fit in versus your competition.

A deep dive into SERP features

So put that stuff into STAT, give it a little while, let it collect some data, and then you get into the good stuff, which is the SERP features. I’m covering just a tiny little bit of what STAT does. Again, they didn’t pay me for this. But there’s lots of other stuff that goes on in here. My personal favorite part is the SERP features.

Which features are increasing/decreasing both overall and for you?

So what I like here is that in SERP features it will tell you which features are increasing and decreasing overall and then what features are increasing and decreasing for you.

This is actually from a real set for one of our clients. For them, what they’re seeing are big increases in places version 3, which is the three pack of places. Twitter box is increasing. I did not see that coming. Then AMP is increasing. So that says to me, okay, so I need to make sure that I’m thinking about places, and maybe this is a client who doesn’t necessarily have a lot of local offices.

Maybe it’s not someone you would think of as a local client. So why are there a lot more local properties popping up? Then you can dive in and say, “Okay, only show me the keywords that have places boxes.” Then you can look at that and decide: Is it something where we haven’t thought about local SEO before, but it’s something where searchers are thinking about local SEO? So Google is giving them three pack local boxes, and maybe we should start thinking about can we rank in that box, or is that something we care about.

Again, not necessarily content strategy, but certainly your SEO strategy. The next thing is Twitter box, and this is something where you think Twitter is dead. No one is using Twitter. It’s full of terrible people, and they tweet about politics all day. I never want to use it again, except maybe Google really wants to show more Twitter boxes. So again, looking at it and saying, “Is Twitter something where we need to start thinking about it from a content perspective? Do we need to start focusing our energies on Twitter?”

Maybe you abandoned it and now it’s back. You have to start thinking, “Does this matter for the keywords?” Then AMP. So this is something where AMP is really tricky obviously. There have been studies where it said, “I implemented AMP, and I lost 70% of my traffic and everything was terrible.” But if that’s the case, why would we necessarily be seeing more AMP show up in search results if it isn’t actually something that people find useful, particularly on mobile search?

Desktop vs mobile

One of the things actually that I didn’t mention in the tagging is definitely look at desktop versus mobile, because you are going to see really different feature sets between desktop and mobile for these different types of keywords. Mobile may have a completely different intent for a type of search. If you’re a restaurant, for example, people looking for reservations on a desktop might have different intent from I want a restaurant right now on mobile, for example, and you’re standing next to it and maybe you’re lost.

What kind of intent is behind the search results?

You really have to think about what that intent means for the type of search results that Google is going to present. So for AMP, then you have to look at it and say, “Well, is this newsworthy? Why is more AMP being shown?” Should we consider moving our news or blog or whatever you happen call it into AMP so that we can start to show up for these search results in mobile? Is that a thing that Google is presenting now?

We can get mad about AMP all day, but how about instead if we actually be there? I don’t want the comment section to turn into a whole AMP discussion, but I know there are obviously problems with AMP. But if it’s being shown in the search results that searchers who should be finding you are seeing and you’re not there, that’s definitely something you need to think about for your content strategy and thinking, “Is AMP something that we need to pursue? Do we have to have more newsy content versus evergreen content?”

Build your content strategy around what searchers are looking for

Maybe your content strategy is really focused on posts that could be relevant for years, when in reality your searchers are looking for stuff that’s relevant for them right now. So for example, things with movers, there’s some sort of mover scandal. There’s always a mover who ended up taking someone’s stuff and locking it up forever, and they never gave it back to them. There’s always a story like that in the news.

Maybe that’s why it’s AMP. Definitely investigate before you start to say, “AMP everything.” Maybe it was just like a really bad day for movers, for example. Then you can see the decreases. So the decrease here is organic, which is that traditional 10 blue links. So obviously this new stuff that’s coming in, like AMP, like Twitter, like places is displacing a lot of the organic results that used to be there before.

So instead you think, well, I can do organic all day, but if the results just aren’t there, then I could be limiting the amount of traffic I could be getting to my website. Videos, for example, now it was really interesting for this particular client that videos is a decreasing SERP for them, because videos is actually a big part of their content strategy. So if we see that videos are decreasing, then we can take a step back and say, “Is it decreasing in the keywords that we care about? Why is it decreasing? Do we think this is a test or a longer-term trend?”

Historical data

What’s nice about STAT is you can say “I want to see results for the last 7 days, 30 days, or 60 days.” Once you get a year of data in there, you can look at the whole year and look at that trend and see is it something where we have to maybe rethink our video strategy? Maybe people don’t like video for these phrases. Again, you could say, “But people do like video for these phrases.” But Google, again, has access to more data than you do.

If Google has decided that for these search phrases video is not a thing they want to show anymore, then maybe people don’t care about video the way that you thought they did. Sorry. So that could be something where you’re thinking, well, maybe we need to change the type of content we create. Then the last one is carousel that showed up for this particular client. Carousel, there are ones where they show lots of different results.

I’m glad that’s dropping because that actually kind of sucks. It’s really hard to show up well there. So I think that’s something to think about in the carousel as well. Maybe we’re pleased that that’s going away and then we don’t have to fight it as much anymore. Then what you can see in the bottom half are what we call share of voice.

Share of voice

Share of voice is calculated based on your ranking and all of your competitors’ ranking and the number of clicks that you’re expected to get based on your ranking position.

So the number 1 position obviously gets more ranks than the number 100 position. So the share of voice is a percentage calculated based on how many of these types of items, types of SERP features that you own versus your competitors as well as your position in these SERP features. So what I’m looking at here is share of voice and looking at organic, places, answers, and people also ask, for example.

So what STAT will show you is the percentage of organic, and it’s still, for this client — and obviously this is not an accurate chart, but this is vaguely accurate to what I saw in STAT — organic is still a big, beefy part of this client’s search results. So let’s not panic that it’s decreasing. This is really where this context can come in. But then you can think, all right, so we know that we are doing “eeh” on organic.

Is it something where we think that we can gain more? So the green shows you your percentage that you own of this, and then the black is everyone else. Thinking realistically, you obviously cannot own 100% of all the search results all the time because Google wouldn’t allow that. So instead thinking, what’s a realistic thing? Are we topping out at the point now where we’re going to have diminishing returns if we keep pushing on this?

Identify whether your content efforts support what you’re seeing in STAT

Are we happy with how we’re doing here? Maybe we need to turn our attention to something else, like answers for example. This particular client does really well on places. They own a lot of it. So for places, it’s maintain, watch, don’t worry about it that much anymore. Then that can drop off when we’re thinking about content. We don’t necessarily need to keep writing blog post for things that are going to help us to rank in the places pack because it’s not something that’s going to influence that ranking any further.

We’re already doing really well. But instead we can look at answers and people also ask, which for this particular client they’re not doing that well. It is something that’s there, and it is something that it may not be one of the top increases, but it’s certainly an increase for this particular client. So what we’re looking at is saying, “Well, you have all these great blog posts, but they’re not really written with people also ask or answers in mind. So how about we go back and rewrite the stuff so that we can get more of these answer boxes?”

That can be the foundation of that content strategy. When you put your keywords into STAT and look at your specific keyword set, really look at the SERP features and determine what does this mean for me and the type of content I need to create, whether it’s more images for example. Some clients, when you’re looking at e-commerce sites, some of the results are really image heavy, or they can be product shopping or whatever it might be.

There are really specific different features, and I’ve only shown a tiny subset. STAT captures all of the different types of SERP features. So you can definitely look at anything if it’s specific to your industry. If it’s a feature, they’ve got it in here. So definitely take a look and see where are these opportunities. Remember, you can’t have a 100% share of voice because other people are just going to show up there.

You just want to make sure that you’re better than everybody else. Thanks.

Video transcription by Speechpad.com

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How LinkedIn is Using Machine Learning to Determine Skills

One of the more interesting reveals that Dan Francis, Senior Product Manager for LinkedIn Talent Insights, provided in a recent talk about the Talent Insights tool is how LinkedIn is using machine learning to determine skills of people. He says that there are now over 575 million members in the LinkedIn database and there are 35,000 standardized skills in LinkedIn’s skills taxonomy. The way LinkedIn is figuring out what skills a member has is via machine learning technology.

Dan Francis, Senior Product Manager, LinkedIn Talent Insights, discussed Talent Insights in a recent LinkedIn video embedded below:

LinkedIn Using Machine Learning to Determine Skills

The skills data in Talent Insights comes from a variety of sources, mainly from a member’s profile. There are over 35,000 standardized skills that we have in LinkedIn’s skills taxonomy, and the way we’re figuring out what skills a member has is using machine learning. We can identify skills that a member has that’s based on things that they explicitly added to their profile.

The other thing that we’ll do is look at the text of the profile. There’s a field of machine learning called natural language processing and we’re basically using that. It’s scanning through all the words that are on a member’s profile, and when we can determine that it’s pertaining to the member, as oppose the company or another subject, we’ll say okay, we think that this member has this skill. We also look at other attributes, like their title or the company, to make sure they actually are very likely to have that skill.

The last thing that we’ll do is look at the skills a member has and figure out what are skill relationships. So as an example, let’s say that a member has Ember, which is a type of JavaScript framework, since we know that they know Ember, they also know JavaScript. So if somebody’s running a search like that, we’ll surface them in the results. I think that the most important reason why this is helpful and the real benefit to users of the platform is when you’re searching, you want to get as accurate a view of the population as possible. What we’re trying to do is look at all the different signals that we possibly have to represent that view.  

575 Million People on LinkedIn Globally and Adding 2 Per Second

Today, LinkedIn has over 575 million members that are on the platform globally. This is actually growing at a pretty rapid clip, so we’re adding about two members per second. One of the great things about LinkedIn is that we’re actually very well represented in terms of the professional workforce globally. If you look at the top 30 economies around the world, we actually have the majority of professionals in all of those economies.

LinkedIn is the World’s Largest Aggregator of Jobs

I think there’s often a perception that most of the data’s directly from LinkedIn, stuff that’s posted on LinkedIn and job status is one notable exception to that. Plenty of companies and people will post jobs on LinkedIn, and that’s information that does get surfaced. However, we’re also the world’s largest aggregator of jobs. At this point there are over 20 million jobs that are on LinkedIn.

The way that we’re getting that information is we’re working with over 40,000 partners. These are job boards, ATS’s, and direct customer relationships. We’re collecting all of those jobs, standardizing them, and showing them on our platform. The benefit is not just for displaying the data in Talent Insights, the benefit is also when members are searching on LinkedIn.com, we’re giving them as representative a view of the job market as possible.

The post How LinkedIn is Using Machine Learning to Determine Skills appeared first on WebProNews.

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Sparrho CEO: Using Augmented Intelligence to Build Trust in Brands

Many companies are working to build authentic and trusted brands with consumers. This is especially true with pharmaceuticals, biotech, and med-tech companies. The CEO of Sparrho, Dr. Vivian Chan, says that their approach combines artificial intelligence and 400,000 Ph.D.’s to deliver scientific data to companies. This data helps companies back up their marketing messages which enables them to more effectively build that vital trust with their customers.

Dr. Vivian Chan, Sparrho CEO, recently discussed on CNBC their unique hybrid AI approach to helping companies use science and information to back up their brands messaging:

AI Enables Humans to Make Better-Informed Decisions

Artificial intelligence is really about algorithms and how we can use data that we collect to enable humans to make better-informed decisions. I not at all about having computers make decisions on behalf of humans. In a way, I think it’s machines that will be helping evolve the tasks and not actually replacing the human roles. Human roles themselves will be evolving also as the technology improves. This allows humans to have more headspace to be thinking about things that machines can’t do right now.

Machines can’t necessarily summarize a lot of pieces of contextual analysis very well yet to a 100 percent accuracy and humans are still better at making nonlinear connection points. For example, being able to say that this mathematical equation is super relevant to an agricultural problem. If we don’t have the tagging and reference and citations humans are still better at making those nonlinear new connection points than machines.

Humans are still good at coming up with the questions. If you actually pose the right question and you train the data and the algorithms you might actually get the right answer. However, you still need to have the humans to be thinking about what the questions are in order to ultimately get the answers.

It’s About Using AI as a Means to an End

I  think the angle is really thinking about using AI as a means to an end and not just the end. Ultimately, this is a hybrid approach and various different people are calling it differently. Even MIT professors are calling it a hybrid approach. We’re calling it augmented intelligence. We need to come up with a good relationship between humans and machines. Marketing is about building relationships. It’s about building relationships between brands and consumers and now how do we build that relationship digitally?

Using Science to Build an Authenticated Brand

In this digital age, consumers are a lot more tech savvy but are also information savvy. They want to know what the is science behind certain things. Even if you’re talking about CPG, consumer packaged goods, what is the science behind a shampoo product right now when it claims 98 percent prevention of hair loss? What is the real science behind that and how do we actually bring that simplified science-oriented message to the consumer? How can consumers educate themselves and make informed decisions based on the products and thereby build a stronger brand relationship?

Ultimately what we’re trying to do at Sparrow is simplify science to build trust in brands. Especially for marketing departments and brands, it’s really allowing them to have the evidence-based science and the facts because building a very authenticated brand is what is meaningful to consumers. Research says that about 71 percent of consumers immediately reject content that looks like a sales pitch. Building a relationship and having an authenticated brand and content is super important in building that relationship between brand and consumers.

Sparrho Provides Content as a Service On Demand

We’re going even wider with that by providing what we call content as a service or relevant content on demand. We then integrate that into the digital platforms or the brands. We have what we call augmented intelligence with over 16 million pieces of content that is augmented by a network of more than 400,000 monthly active PhDs in a150 countries. They curate and summarize what’s actually happening in the latest of science.

We know that in about 60 percent of pharmaceuticals, biotech, and even med-tech companies, are spending more than $ 50 million per year just in content. Content has been the major driver for a lot of their marketing. In pharmaceuticals, they’re trying to really bring that relationship that they have offline to online. It’s at the heart of this digital transformation age that we are going through. This is really helping bring that relationship online by using the right engaging content. Our goal with Sparrow is to drive more engagement and ultimately more sales.

The post Sparrho CEO: Using Augmented Intelligence to Build Trust in Brands appeared first on WebProNews.

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How to Stop Drowning in Data and Begin Using Your Metrics Wisely

Digital marketers have a problem: We’ve got too much data. It sounds like a ridiculous complaint coming from a data…

The post How to Stop Drowning in Data and Begin Using Your Metrics Wisely appeared first on Copyblogger.


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