Actually Accurate Analytics – Whiteboard Friday

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RuthBurrReedy

Clean, useful Google Analytics data is all-important — both for you, and for the clients and colleagues that will be working on the site in the future. Ruth Burr Reedy shares her absolute best tips for getting your Analytics data accurate, consistent, and future-proof in this week’s Whiteboard Friday.

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Video Transcription

Hi, Moz fans. I’m Ruth Burr Reedy, and I am the Vice President of Strategy at UpBuild. We’re a technical marketing agency specializing in technical SEO and advanced web analytics. One of the things I wanted to talk about today, Whiteboard Friday, is about analytics.

So when I talk to SEOs about analytics and ask them, “When it comes to analytics, what do you do? What do you do first? When you’re taking on a new client, what do you do?” SEOs are often really eager to tell me, “I dive into the data. Here’s what I look like.Here are the views that I set up. Here’s how I filter things. Here’s where I go to gain insights.”

But what I often don’t hear people talk about, that I think is a super important first step with a new client or a new Analytics account, or really any time if you haven’t done it, is making sure your Analytics data is accurate and consistent. Taking the time to do some basic Analytics housekeeping is going to serve you so far into the future and even beyond your time at that given client or company.

The people who come after you will be so, so, so thankful that you did these things. So today we’re going to talk about actually accurate analytics. 

Is your Analytics code on every page?

So the first question that you should ask yourself is: Is your Analytics code on every page? Is it?

Are you sure? There are a lot of different things that can contribute to your Analytics code not actually being on every single page of your website. One of them is if portions of your site have a different CMS from the main CMS that’s driving your site. 

Forums, subdomains, landing pages

We see this a lot with things like subdomains, with things like forums. A really common culprit is if you’re using a tool like Marketo or HubSpot or Unbounce to build landing pages, it’s really easy to forget to put Analytics on those pages.

Over time those pages are out there in the world. Maybe it’s just one or two pages. You’re not seeing them in Analytics at all, which means you’re probably not thinking about them, especially if they’re old. But that doesn’t mean that they don’t still exist and that they aren’t still getting views and visits. 

Find orphan pages

So, okay, how do we know about these pages? Well, before you do anything, it’s important to remember that, because of the existence of orphan pages, you can’t only rely on a tool like Screaming Frog or DeepCrawl to do a crawl of your site and make sure that code is on every page, because if the crawler can’t reach the page and your code is not on the page, it’s kind of in an unseeable, shrouded in mystery area and we don’t want that.

Export all pages

The best way, the most sure way to make sure that you are finding every page is to go to your dev team, to go to your developers and ask them to give you an export of every single URL in your database. If you’re using WordPress, there’s actually a really simple tool you can use. It’s called Export All URLs in the grand tradition of very specifically named WordPress tools.

But depending on your CMS and how your site is set up, this is something that you can almost certainly do. I need a list of every single URL on the website, every single URL in our database. Your dev team can almost certainly do this. When you get this, what you can do, you could, if you wanted, simply load that list of URLs. You’d want to filter out things like images and make sure you’re just looking at the HTML documents.

Dedupe with Screaming Frog

Once you had that, you could load that whole thing into Screaming Frog as a list. That would take a while. What you could do instead, if you wanted, is run a Screaming Frog crawl and then dedupe that with Screaming Frog. So now you’ve got a list of your orphan pages, and then you’ve got a list of all of the pages that Screaming Frog can find. So now we have a list of every single page on the website.

We can use either a combination of crawler and list or just the list, depending on how you want to do it, to run the following custom search. 

What to do in Screaming Frog

Configuration > Custom > Search

So in Screaming Frog, what you can do is you can go to Configuration and then you go to Custom Search. It will pop up a custom search field. What this will allow you to do is while the crawler is crawling, it will search for a given piece of information on a page and then fill that in a custom field within the crawler so that you can then go back and look at all of the pages that have this piece of information.

What I like to do when I’m looking for Analytics information is set up two filters actually — one for all of the pages that contain my UA identifier and one for all of the pages that don’t contain it. Because if I just have a list of all the pages that contain it, I still don’t know which pages don’t contain it. So you can do this with your unique Google Analytics identifier.



If you’re deploying Google Analytics through Google Tag Manager, instead you would look for your GTM Number, your GTM ID. So it just depends how you’ve implemented Analytics. You’re going to be looking for one of those two numbers. Almost every website I’ve worked on has at least a few pages that don’t have Analytics on them.

What you’ll sometimes also find is that there are pages that have the code or that should have the code on them, but that still aren’t being picked up. So if you start seeing these errors as you’re crawling, you can use a tool like Tag Assistant to go in and see, “Okay, why isn’t this actually sending information back to Google Analytics?” So that’s the best way to make sure that you have code on every single page. 

Is your code in the <head> and as high as possible?

The other thing you want to take a look at is whether or not your Analytics code is in the head of every page and as close to the top of the head as possible. Now I know some of you are thinking like, “Yeah, that’s Analytics implementation 101.” But when you’re implementing Analytics, especially if you’re doing so via a plug-in or via GTM, and, of course, if you’re doing it via GTM, the implementation rules for that are a little bit different, but it’s really easy for over time, especially if your site is old, other things to get added to the head by other people who aren’t you and to push that code down.

Now that’s not necessarily the end of the world. If it’s going to be very difficult or time-consuming or expensive to fix, you may decide it’s not worth your time if everything seems like it’s firing correctly. But the farther down that code gets pushed, the higher the likelihood that something is going to go wrong, that something is going to fire before the tracker that the tracker is not going to pick up, that something is going to fire that’s going to prevent the tracker from firing.

It could be a lot of different things, and that’s why the best practice is to have it as high up in the head as possible. Again, whether or not you want to fix that is up to you. 

Update your settings:

Once you’ve gotten your code firing correctly on every single page of your website, I like to go into Google Analytics and change a few basic settings. 

1. Site Speed Sample Rate

The first one is the Site Speed Sample Rate.

So this is when you’re running site speed reports in Google Analytics. Typically they’re not giving you site timings or page timings for the site as a whole because that’s a lot of data. It’s more data than GA really wants to store, especially in the free version of the tool. So instead they use a sample, a sample set of pages to give you page timings. I think typically it’s around 1%.

That can be a very, very small sample if you don’t have a lot of traffic. It can become so small that the sample size is skewed and it’s not relevant. So I usually like to bump up that sample size to more like 10%. Don’t do 100%. That’s more data than you need. But bump it up to a number that’s high enough that you’re going to get relevant data.

2. Session and Campaign Timeout

The other thing that I like to take a look at when I first get my hands on a GA account is the Session and Campaign Timeout. So session timeout is basically how long somebody would have to stay on your website before their first session is over and now they’ve begun a new session if they come back and do something on your site where now they’re not being registered as part of their original visit.

Historically, GA automatically determined session timeout at 30 minutes. But this is a world where people have a million tabs open. I bet you right now are watching this video in one of a million tabs. The longer you have a tab open, the more likely it is that your session will time out. So I like to increase that timeout to at least 60 minutes.

The other thing that Google automatically does is set a campaign timeout. So if you’re using UTM parameters to do campaign tracking, Google will automatically set that campaign timeout at six months. So six months after somebody first clicks that UTM parameter, if they come back, they’re no longer considered part of that same campaign.

They’re now a new, fresh user. Your customer lifecycle might not be six months. If you’re like a B2B or a SaaS company, sometimes your customer lifecycle can be two years. Sometimes if you’re like an e-com company, six months is a really long time and you only need 30 days. Whatever your actual customer lifecycle is, you can set your campaign timeout to reflect that.

I know very few people who are actually going to make that window shorter. But you can certainly make that longer to reflect the actual lifecycle of your customers. 

3. Annotations

Then the third thing that I like to do when I go into a Google Analytics account is annotate what I can. I know a lot of SEOs, when you first get into a GA account, you’re like, “Well, no one has been annotating.Ho-hum. I guess going forward, as of today, we’re going to annotate changes going forward.”

That’s great. You should definitely be annotating changes. However, you can also take a look at overall traffic trends and do what you can to ask your coworkers or your client or whatever your relationship is to this account, “What happened here?” Do you remember what happened here? Can I get a timeline of major events in the company, major product releases, press releases, coverage in the press?

Things that might have driven traffic or seen a spike in traffic, product launches. You can annotate those things historically going back in time. Just because you weren’t there doesn’t mean it didn’t happen. All right. So our data is complete. It’s being collected the way that we want to, and we’re tracking what’s happening.

Account setup

Cool. Now let’s talk about account setup. I have found that many, many people do not take the time to be intentional and deliberate when it comes to how they set up their Google Analytics account. It’s something that just kind of happens organically over time. A lot of people are constrained by defaults. They don’t really get what they’re doing.

What we can do, even if this is not a brand-new GA account, is try to impose some structure, order, consistency, and especially some clarity, not only for ourselves as marketers, but for anybody else who might be using this GA account either now or in the future. So starting out with just your basic GA structure, you start with your account.

Your Account Name is usually just your company name. It doesn’t totally matter what your Account Name is. However, if you’re working with a vendor, I know they’d prefer that it be your company name as opposed to something random that only makes sense to you internally, because that’s going to make it easier for them. But if you don’t care about that, you could conceivably name your account whatever you want. Most of the time it is your company name.

Then you’ve got your property, and you might have various properties. A good rule of thumb is that you should have one property per website or per group of sites with the same experience. So if you have one experience that goes on and off of a subdomain, maybe you have mysite.com and then you also have store.mysite.com, but as far as the user experience is concerned it’s one website, that could be one property.

That’s kind of where you want to delineate properties is based on site experiences. Then drilling down to views, you can have as many views as you want. When it comes to naming views, the convention that I like to use is to have the site or section name that you’re tracking in that specific view and then information about how that view is set up and how it’s intending to be used.

Don’t assume that you’re going to remember what you were doing last year a year from now. Write it down. Make it clear. Make it easy for people who aren’t you to use. You can have as many views as you want. You can set up views for very small sections of your site, for very specific and weird filters if there are some customizations you want to do. You can set up as many views as you need to use.

Must-have views

1. Raw data – Unfiltered, Don’t Touch

But I think there are three views that you should make sure you have. The first is a Raw Data view. This is a view with no filters on it at all. If you don’t already have one of these, then all of your data in the past is suspect. Having a view that is completely raw and unfiltered means if you do something to mess up the filtering on all your other views, you at least have one source of total raw data.

I know this is not new information for SEOs when it comes to GA account setup, but so many people don’t do it. I know this because I go into your accounts and I see that you don’t have it. If you don’t have it, set it up right now. Pause this video. Go set it up right now and then come back and watch the rest, because it’s going to be good. In addition to naming it “Raw Data Unfiltered,” I like to also add something like “Don’t Touch” or “For Historical Purposes Only,” if you’re not into the whole brevity thing, something that makes it really clear that not only is this the raw data, but also no one should touch it.

This is not the data we’re using. This is not the data we’re make decisions by. This is just our backup. This is our backup data. Don’t touch it. 

2. Primary view – Filtered, Use This One

Then you’re going to want to have your Primary view. So however many views you as a marketer set up, there are going to be other people in your organization who just kind of want the data.

So pick a view that’s your primary filtered view. You’re going to have a lot of your basic filters on this, things like filtering out your internal IP range, filtering out known bots. You might set up some filtering to capture the full hostname if you’re tracking between subdomains, things like that. But it’s your primary view with basic filtering. You’re going to want to name that something like “Use This One.”

Sometimes if there’s like one person and they won’t stop touching your raw data, you can even say like, “Nicole Use This One.” Whatever you need to label it so that even if you got sick and were in the hospital and unreachable, you won the lottery, you’re on an island, no one can reach you, people can still say, “Which of these 17 views that are set up should I use? Oh, perhaps it’s the one called ‘Use This One.'” It’s a clue. 

3. Test view – Unfiltered

Then I like to always have at least one view that is a Test view. That’s usually unfiltered in its base state. But it’s where I might test out filters or custom dimensions or other things that I’m not ready to roll out to the primary view. You may have additional views on top of those, but those are the three that, in my opinion, you absolutely need to have.

4. All Website Data

What you should not have is a view called “All Website Data.” “All Website Data” is what Google will automatically call a view when you’re first setting up GA. A lot of times people don’t change that as they’re setting up their Analytics. The problem with that is that “All Website Data” means different things to different people. For some people, “All Website Data” means the raw data.

For some people, “All Website Data” means that this is the “Use This One” view. It’s unclear. If I get into a GA account and I see that there is a view named “All Website Data,” I know that this company has not thought about how they’re setting up views and how they’re communicating that internally. Likely there’s going to be some filtering on stuff that shouldn’t have been filtered, some historical mishmash.

It’s a sign that you haven’t taken the time to do it right. In my opinion, a good SEO should never have a view called “All Website Data.” All right. Great. So we’ve got our views set up. Everything is configured the way that we want it. How that’s configured may be up to you, but we’ve got these basic tenets in place.

Goals

Let’s talk about goals. Goals are really interesting. I don’t love this about Google Analytics, but goals are forever. Once you set a goal in GA, information that is tracked to that number or that goal number within that goal set will always be tracked back to that. What that means is that say you have a goal that’s “Blue Widget Sales” and you’re tracking blue widget sales.

Goals are forever

Over time you discontinue the blue widget and now you’re only tracking red widget sales. So you rename the “Blue Widget Sales” widget to now it’s called “Red Widget Sales.” The problem is renaming the goal doesn’t change the goal itself. All of that historical blue widget data will still be associated with that goal. Unless you’re annotating carefully, you may not have a good idea of when this goal switched from tracking one thing to be tracking another thing.

This is a huge problem when it comes to data governance and making decisions based on historical data. 

The other problem is you have a limited number of goals. So you need to be really thoughtful about how you set up your goals because they’re forever. 

Set goals based on what makes you money

A basic rule is that you should set goals based on what makes you money.

You might have a lot of micro conversions. You might have things like newsletter sign-ups or white paper downloads or things like that. If those things don’t make you money, you might want to track those as events instead. More on that in a minute. Whatever you’re tracking as a goal should be related to how you make money. Now if you’re a lead gen biz, things like white paper downloads may still be valuable enough that you want to track them as a goal.

It just depends on your business. Think about goals as money. What’s the site here to do? When you think about goals, again, remember that they’re forever and you don’t get that many of them. 

Group goals efficiently

So any time you can group goals efficiently, take some time to think about how you’re going to do that. If you have three different forms and they’re all going to be scheduling a demo in some way or another, but they’re different forms, is there a way that you can have one goal that’s “Schedule a Demo” and then differentiate between which form it was in another way?

Say you have an event category that’s “Schedule a Demo” and then you use the label to differentiate between the forms. It’s one goal that you can then drill down. A classic mistake that I see with people setting up goals is they have the same goal in different places on the website and they’re tracking that differently. When I say, “Hey, this is the same goal and you’re tracking it in three different places,” they often say, “Oh, well, that’s because we want to be able to drill down into that data.”

Great. You can do that in Google Analytics. You can do that via Google Analytics reporting. You can look at what URLs and what site sections people completed a given goal on. You don’t have to build that into the goal. So try to group as efficiently as possible and think long term. If it at any time you’re setting up a goal that you know is someday going to be part of a group of goals, try to set it up in such a way that you can add to that and then drill down into the individual reports rather than setting up new goals, because those 20 slots go quick.

Name goals clearly

The other thing you’re going to want to do with goals and with everything — this is clearly the thesis for my presentation — is name them clearly. Name them things where it would be impossible not to understand exactly what it is. Don’t name your goal “Download.” Don’t name your goal “Thank You Page.”

Name your goal something specific enough that people can look at it at a glance. Even people who don’t work there right now, people in the future, the future people can look at your goals and know exactly what they were. But again, name them not so specifically that you can’t then encompass that goal wherever it exists on the site. So “Download” might be too broad.

“Blue Widget White Paper Download” might be too specific. “White Paper Download” might be a good middle ground there. Whatever it is for you, think about how you’re going to name it in such a way that it’ll make sense to somebody else, even if you don’t work there anymore and they can’t ask you. Now from talking about goals it kind of segues naturally into talking about events, event tracking.

Events

Event tracking is one of the best things about Google Analytics now. It used to be that to track an event you had to add code directly to a page or directly to a link. That was hard to do at scale and difficult to get implemented alongside conflicting dev possibilities. But now, with Google Tag Manager, you can track as many events as you want whenever you want to do them.

You can set them up all by yourself, which means that now you, as the marketer, as the Analytics person, become the person who is in charge of Google Analytics events. You should take that seriously, because the other side of that coin is that it’s very possible to get event creep where now you’re tracking way too many events and you’re tracking them inefficiently and inconsistently in ways that make it difficult to extract insights from them on a macro level.

What do you want and why?

So with events, think about what you want and why. Any time somebody is like, “I want to track this,” ask them, “Okay, what are we going to do with that information?” If they’re like, “I don’t know. I just want to know it.” That might not be a good case to make to track an event. Understand what you’re going to do with the data. Resist the urge to track just for tracking’s sake.

Resist data for data’s sake. I know it’s hard, because data is cool, but try your best. 

Naming conventions

As you take over, now that you are the person in charge of events, which you are, you’re taking this on, this is yours now, develop naming conventions for your events and then become the absolute arbiter of those conventions. Do not let anybody name anything unless it adheres to your conventions.

Category

Now how you name things is up to you. Some suggestions, for category, I like that to be the site section that something is in or maybe the item type. So maybe it’s product pages. Maybe it’s forms. Maybe it’s videos. However you are going to group these events on a macro level, that should be your category.

Action

The action is the action. So that’s click, submit, play, whatever the action is doing. 

Label

Then the label is where I like to get unique and make sure that I’m drilling down to just this one thing. So maybe that’s where I’ll have the actual CTA of the button, or which form it was that people filled out, or what product it was that they purchased. Again, think about information that you can get from other reports.

So for example, you don’t need to capture the URL that the event was recorded on as part of the label, because you can actually go in and look at all of your events by URL and see where that happened without having to capture it in that way. The important thing is that you have rules, that those rules are something that you can communicate to other people, and that they would then be able to name their own categories, actions, and labels in ways that were consistent with yours.

Over time, as you do this and as you rename old events, you’re going to have a more and more usable body of data. You’re going to be increasingly comparing apples to apples. You’re not going to have some things where Click is the action and some things where Click is the label, or things that should be in one category that are in two or three categories. Over time you’re going to have a much more usable and controllable body of event data.

Be consistent

Then you need to be ruthless about consistency with usage of these naming conventions. There will be no just setting up an event real quick. Or, in fact, there will be just setting up an event real quick, but it will be using these rules that you have very thoroughly outlined and communicated to everybody, and that you are then checking up to make sure everything is still tracking the same way. A big thing to watch for when you’re being ruthless about consistency is capitalization.

Capitalization in category action and label and event tracking will come back as two different things. Capital “C” and lowercase “c” category are two different things. So make sure as you’re creating new events that you have some kind of standardization. Maybe it’s the first letter is always capitalized. Maybe it’s nothing is ever capitalized.

It doesn’t matter what it is as long as it’s all the same. 

Think about the future!

Then think about the future. Think about the day when you win the lottery and you move to a beautiful island in the middle of the sea and you turn off your phone and you never think about Google Analytics again and you’re lying in the sand and no one who works with you now can reach you. If you never came back to work again, could the people who work there continue the tracking work that you’ve worked so hard to set up?

If not, work harder to make sure that’s the case. Create documentation. Communicate your rules. Get everybody on the same page. Doing so will make this whole organization’s data collection better, more actionable, more usable for years to come. If you do come back to work tomorrow, if in fact you work here for the next 10 years, you’ve just set yourself up for success for the next decade.

Congratulations. So these are the things that I like to do when I first get into a GA account. Obviously, there are a lot of other things that you can do in GA. That’s why we all love GA so much. 

Homework

But to break it down and give you all some homework that you can do right now.

Check for orphan pages

Tonight, go in and check for orphan pages.

When it comes to Analytics, those might be different or they might be the same as orphan pages in the traditional sense. Make sure your code is on every page. 

Rename confusing goals and views (and remove unused ones)

Rename all your confusing stuff. Remove the views that you’re not using. Turn off the goals that you’re not using. Make sure everything is as up to date as possible. 

Guard your raw data

Don’t let anybody touch that raw data. Rename it “Do Not Touch” and then don’t touch it. 

Enforce your naming conventions

Create them. Enforce them. Protect them. They’re yours now.

You are the police of naming conventions. 

Annotate everything

Annotate as much as you can. Going forward you’re going to annotate all the time, because you can because you’re there, but you can still go back in time and annotate. 

Remove old users

One thing that I didn’t really talk about today but you should also do, when it comes to the general health of your Analytics, is go in and check who has user permissions to all of your different Analytics accounts.

Remove old users. Take a look at that once a quarter. Just it’s good governance to do. 

Update sampling and timeouts

Then you’re going to update your sampling and your timeouts. If you can do all of these things and check back in on them regularly, you’re going to have a healthy, robust, and extremely usable Analytics ecosystem. Let me know what your favorite things to do in Analytics are. Let me know how you’re tracking events in GTM.

I want to hear all about everything you all are doing in Analytics. So come holler at me in the comments. Thanks.

Video transcription by Speechpad.com

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Better Content Through NLP (Natural Language Processing) – Whiteboard Friday

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RuthBurrReedy

Gone are the days of optimizing content solely for search engines. For modern SEO, your content needs to please both robots and humans. But how do you know that what you’re writing can check the boxes for both man and machine?

In today’s Whiteboard Friday, Ruth Burr Reedy focuses on part of her recent MozCon 2019 talk and teaches us all about how Google uses NLP (natural language processing) to truly understand content, plus how you can harness that knowledge to better optimize what you write for people and bots alike.

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

Video Transcription

Howdy, Moz fans. I’m Ruth Burr Reedy, and I am the Vice President of Strategy at UpBuild, a boutique technical marketing agency specializing in technical SEO and advanced web analytics. I recently spoke at MozCon on a basic framework for SEO and approaching changes to our industry that thinks about SEO in the light of we are humans who are marketing to humans, but we are using a machine as the intermediary.

Those videos will be available online at some point. [Editor’s note: that point is now!] But today I wanted to talk about one point from my talk that I found really interesting and that has kind of changed the way that I approach content creation, and that is the idea that writing content that is easier for Google, a robot, to understand can actually make you a better writer and help you write better content for humans. It is a win-win. 

The relationships between entities, words, and how people search

To understand how Google is currently approaching parsing content and understanding what content is about, Google is spending a lot of time and a lot of energy and a lot of money on things like neural matching and natural language processing, which seek to understand basically when people talk, what are they talking about?

This goes along with the evolution of search to be more conversational. But there are a lot of times when someone is searching, but they don’t totally know what they want, and Google still wants them to get what they want because that’s how Google makes money. They are spending a lot of time trying to understand the relationships between entities and between words and how people use words to search.

The example that Danny Sullivan gave online, that I think is a really great example, is if someone is experiencing the soap opera effect on their TV. If you’ve ever seen a soap opera, you’ve noticed that they look kind of weird. Someone might be experiencing that, and not knowing what that’s called they can’t Google soap opera effect because they don’t know about it.

They might search something like, “Why does my TV look funny?” Neural matching helps Google understand that when somebody is searching “Why does my TV look funny?” one possible answer might be the soap opera effect. So they can serve up that result, and people are happy. 

Understanding salience

As we’re thinking about natural language processing, a core component of natural language processing is understanding salience.

Salience, content, and entities

Salience is a one-word way to sum up to what extent is this piece of content about this specific entity? At this point Google is really good at extracting entities from a piece of content. Entities are basically nouns, people, places, things, proper nouns, regular nouns.

Entities are things, people, etc., numbers, things like that. Google is really good at taking those out and saying, “Okay, here are all of the entities that are contained within this piece of content.” Salience attempts to understand how they’re related to each other, because what Google is really trying to understand when they’re crawling a page is: What is this page about, and is this a good example of a page about this topic?

Salience really goes into the second piece. To what extent is any given entity be the topic of a piece of content? It’s often amazing the degree to which a piece of content that a person has created is not actually about anything. I think we’ve all experienced that.

You’re searching and you come to a page and you’re like, “This was too vague. This was too broad. This said that it was about one thing, but it was actually about something else. I didn’t find what I needed. This wasn’t good information for me.” As marketers, we’re often on the other side of that, trying to get our clients to say what their product actually does on their website or say, “I know you think that you created a guide to Instagram for the holidays. But you actually wrote one paragraph about the holidays and then seven paragraphs about your new Instagram tool. This is not actually a blog post about Instagram for the holidays. It’s a piece of content about your tool.” These are the kinds of battles that we fight as marketers. 

Natural Language Processing (NLP) APIs

Fortunately, there are now a number of different APIs that you can use to understand natural language processing: 

Is it as sophisticated as what they’re using on their own stuff? Probably not. But you can test it out. Put in a piece of content and see (a) what entities Google is able to extract from it, and (b) how salient Google feels each of these entities is to the piece of content as a whole. Again, to what degree is this piece of content about this thing?

So this natural language processing API, which you can try for free and it’s actually not that expensive for an API if you want to build a tool with it, will assign each entity that it can extract a salient score between 0 and 1, saying, “Okay, how sure are we that this piece of content is about this thing versus just containing it?”

So the higher or the closer you get to 1, the more confident the tool is that this piece of content is about this thing. 0.9 would be really, really good. 0.01 means it’s there, but they’re not sure how well it’s related. 

A delicious example of how salience and entities work

The example I have here, and this is not taken from a real piece of content — these numbers are made up, it’s just an example — is if you had a chocolate chip cookie recipe, you would want chocolate cookies or chocolate chip cookies recipe, chocolate chip cookies, something like that to be the number one entity, the most salient entity, and you would want it to have a pretty high salient score.

You would want the tool to feel pretty confident, yes, this piece of content is about this topic. But what you can also see is the other entities it’s extracting and to what degree they are also salient to the topic. So you can see things like if you have a chocolate chip cookie recipe, you would expect to see things like cookie, butter, sugar, 350, which is the temperature you heat your oven, all of the different things that come together to make a chocolate chip cookie recipe.

But I think that it’s really, really important for us as SEOs to understand that salience is the future of related keywords. We’re beyond the time when to optimize for chocolate chip cookie recipe, we would also be looking for things like chocolate recipe, chocolate chips, chocolate cookie recipe, things like that. Stems, variants, TF-IDF, these are all older methodologies for understanding what a piece of content is about.

Instead what we need to understand is what are the entities that Google, using its vast body of knowledge, using things like Freebase, using large portions of the internet, where is Google seeing these entities co-occur at such a rate that they feel reasonably confident that a piece of content on one entity in order to be salient to that entity would include these other entities?

Using an expert is the best way to create content that’s salient to a topic

So chocolate chip cookie recipe, we’re now also making sure we’re adding things like butter, flour, sugar. This is actually really easy to do if you actually have a chocolate chip cookie recipe to put up there. This is I think what we’re going to start seeing as a content trend in SEO is that the best way to create content that is salient to a topic is to have an actual expert in that topic create that content.

Somebody with deep knowledge of a topic is naturally going to include co-occurring terms, because they know how to create something that’s about what it’s supposed to be about. I think what we’re going to start seeing is that people are going to have to start paying more for content marketing, frankly. Unfortunately, a lot of companies seem to think that content marketing is and should be cheap.

Content marketers, I feel you on that. It sucks, and it’s no longer the case. We need to start investing in content and investing in experts to create that content so that they can create that deep, rich, salient content that everybody really needs. 

How can you use this API to improve your own SEO? 

One of the things that I like to do with this kind of information is look at — and this is something that I’ve done for years, just not in this context — but a prime optimization target in general is pages that rank for a topic, but they rank on page 2.

What this often means is that Google understands that that keyword is a topic of the page, but it doesn’t necessarily understand that it is a good piece of content on that topic, that the page is actually solely about that content, that it’s a good resource. In other words, the signal is there, but it’s weak.

What you can do is take content that ranks but not well, run it through this natural language API or another natural language processing tool, and look at how the entities are extracted and how Google is determining that they’re related to each other. Sometimes it might be that you need to do some disambiguation. So in this example, you’ll notice that while chocolate cookies is called a work of art, and I agree, cookie here is actually called other.

This is because cookie means more than one thing. There’s cookies, the baked good, but then there’s also cookies, the packet of data. Both of those are legitimate uses of the word “cookie.” Words have multiple meanings. If you notice that Google, that this natural language processing API is having trouble correctly classifying your entities, that’s a good time to go in and do some disambiguation.

Make sure that the terms surrounding that term are clearly saying, “No, I mean the baked good, not the software piece of data.” That’s a really great way to kind of bump up your salience. Look at whether or not you have a strong salient score for your primary entity. You’d be amazed at how many pieces of content you can plug into this tool and the top, most salient entity is still only like a 0.01, a 0.14.

A lot of times the API is like “I think this is what it’s about,” but it’s not sure. This is a great time to go in and bump up that content, make it more robust, and look at ways that you can make those entities easier to both extract and to relate to each other. This brings me to my second point, which is my new favorite thing in the world.

Writing for humans and writing for machines, you can now do both at the same time. You no longer have to, and you really haven’t had to do this in a long time, but the idea that you might keyword stuff or otherwise create content for Google that your users might not see or care about is way, way, way over.

Now you can create content for Google that also is better for users, because the tenets of machine readability and human readability are moving closer and closer together. 

Tips for writing for human and machine readability:

Reduce semantic distances!

What I’ve done here is I did some research not on natural language processing, but on writing for human readability, that is advice from writers, from writing experts on how to write better, clearer, easier to read, easier to understand content.Then I pulled out the pieces of advice that also work as pieces of advice for writing for natural language processing. So natural language processing, again, is the process by which Google or really anything that might be processing language tries to understand how entities are related to each other within a given body of content.

Short, simple sentences

Short, simple sentences. Write simply. Don’t use a lot of flowery language. Short sentences and try to keep it to one idea per sentence. 

One idea per sentence

If you’re running on, if you’ve got a lot of different clauses, if you’re using a lot of pronouns and it’s becoming confusing what you’re talking about, that’s not great for readers.

It also makes it harder for machines to parse your content. 

Connect questions to answers

Then closely connecting questions to answers. So don’t say, “What is the best temperature to bake cookies? Well, let me tell you a story about my grandmother and my childhood,” and 500 words later here’s the answer. Connect questions to answers. 

What all three of those readability tips have in common is they boil down to reducing the semantic distance between entities.

If you want natural language processing to understand that two entities in your content are closely related, move them closer together in the sentence. Move the words closer together. Reduce the clutter, reduce the fluff, reduce the number of semantic hops that a robot might have to take between one entity and another to understand the relationship, and you’ve now created content that is more readable because it’s shorter and easier to skim, but also easier for a robot to parse and understand.

Be specific first, then explain nuance

Going back to the example of “What is the best temperature to bake chocolate chip cookies at?” Now the real answer to what is the best temperature to bake chocolate cookies is it depends. Hello. Hi, I’m an SEO, and I just answered a question with it depends. It does depend.

That is true, and that is real, but it is not a good answer. It is also not the kind of thing that a robot could extract and reproduce in, for example, voice search or a featured snippet. If somebody says, “Okay, Google, what is a good temperature to bake cookies at?” and Google says, “It depends,” that helps nobody even though it’s true. So in order to write for both machine and human readability, be specific first and then you can explain nuance.

Then you can go into the details. So a better, just as correct answer to “What is the temperature to bake chocolate chip cookies?” is the best temperature to bake chocolate chip cookies is usually between 325 and 425 degrees, depending on your altitude and how crisp you like your cookie. That is just as true as it depends and, in fact, means the same thing as it depends, but it’s a lot more specific.

It’s a lot more precise. It uses real numbers. It provides a real answer. I’ve shortened the distance between the question and the answer. I didn’t say it depends first. I said it depends at the end. That’s the kind of thing that you can do to improve readability and understanding for both humans and machines.

Get to the point (don’t bury the lede)

Get to the point. Don’t bury the lead. All of you journalists who try to become content marketers, and then everybody in content marketing said, “Oh, you need to wait till the end to get to your point or they won’t read the whole thing,”and you were like, “Don’t bury the lead,” you are correct. For those of you who aren’t familiar with journalism speak, not burying the lead basically means get to the point upfront, at the top.

Include all the information that somebody would really need to get from that piece of content. If they don’t read anything else, they read that one paragraph and they’ve gotten the gist. Then people who want to go deep can go deep. That’s how people actually like to consume content, and surprisingly it doesn’t mean they won’t read the content. It just means they don’t have to read it if they don’t have time, if they need a quick answer.

The same is true with machines. Get to the point upfront. Make it clear right away what the primary entity, the primary topic, the primary focus of your content is and then get into the details. You’ll have a much better structured piece of content that’s easier to parse on all sides. 

Avoid jargon and “marketing speak”

Avoid jargon. Avoid marketing speak. Not only is it terrible and very hard to understand. You see this a lot. I’m going back again to the example of getting your clients to say what their products do. You work with a lot of B2B companies, you will you will often run into this. Yes, but what does it do? It provides solutions to streamline the workflow and blah, blah. Okay, what does it do? This is the kind of thing that can be really, really hard for companies to get out of their own heads about, but it’s so important for users, for machines.

Avoid jargon. Avoid marketing speak. Not to get too tautological, but the more esoteric a word is, the less commonly it’s used. That’s actually what esoteric means. What that means is the less commonly a word is used, the less likely it is that Google is going to understand its semantic relationships to other entities.

Keep it simple. Be specific. Say what you mean. Wipe out all of the jargon. By wiping out jargon and kind of marketing speak and kind of the fluff that can happen in your content, you’re also, once again, reducing the semantic distances between entities, making them easier to parse. 

Organize your information to match the user journey

Organize it and map it out to the user journey. Think about the information somebody might need and the order in which they might need it. 

Break out subtopics with headings

Then break it out with subheadings. This is like very, very basic writing advice, and yet you all aren’t doing it. So if you’re not going to do it for your users, do it for machines. 

Format lists with bullets or numbers

You can also really impact skimmability for users by breaking out lists with bullets or numbers.

The great thing about that is that breaking out a list with bullets or numbers also makes information easier for a robot to parse and extract. If a lot of these tips seem like they’re the same tips that you would use to get featured snippets, they are, because featured snippets are actually a pretty good indicator that you’re creating content that a robot can find, parse, understand, and extract, and that’s what you want.

So if you’re targeting featured snippets, you’re probably already doing a lot of these things, good job. 

Grammar and spelling count!

The last thing, which I shouldn’t have to say, but I’m going to say is that grammar and spelling and punctuation and things like that absolutely do count. They count to users. They don’t count to all users, but they count to users. They also count to search engines.

Things like grammar, spelling, and punctuation are very, very easy signals for a machine to find and parse. Google has been specific in things, like the “Quality Rater Guidelines,”that a well-written, well-structured, well-spelled, grammatically correct document, that these are signs of authoritativeness. I’m not saying that having a greatly spelled document is going to mean that you immediately rocket to the top of the results.

I am saying that if you’re not on that stuff, it’s probably going to hurt you. So take the time to make sure everything is nice and tidy. You can use vernacular English. You don’t have to be perfect “AP Style Guide” all the time. But make sure that you are formatting things properly from a grammatical standpoint as well as a technical standpoint. What I love about all of this, this is just good writing.

This is good writing. It’s easy to understand. It’s easy to parse. It’s still so hard, especially in the marketing world, to get out of that world of jargon, to get to the point, to stop writing 2,000 words because we think we need 2,000 words, to really think about are we creating content that’s about what we think it’s about.

Use these tools to understand how readable, parsable, and understandable your content is

So my hope for the SEO world and for you is that you can use these tools not just to think about how to dial in the perfect keyword density or whatever to get an almost perfect score on the salience in the natural language processing API. What I’m hoping is that you will use these tools to help yourself understand how readable, how parsable, and how understandable your content is, how much your content is about what you say it’s about and what you think it’s about so you can create better stuff for users.

It makes the internet a better place, and it will probably make you some money as well. So these are my thoughts. I’d love to hear in the comments if you’re using the natural language processing API now, if you’ve built a tool with it, if you want to build a tool with it, what do you think about this, how do you use this, how has it gone. Tell me all about it. Holla atcha girl.

Have a great Friday.

Video transcription by Speechpad.com

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Lead Volume vs. Lead Quality By RuthBurrReedy

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RuthBurrReedy

Ruth Burr Reedy is an SEO and online marketing consultant and speaker and the Vice President of Strategy at UpBuild, a technical marketing agency specializing in SEO, web analytics, and conversion rate optimization. This is the first post in a recurring monthly series and we’re excited! 


When you’re onboarding a new SEO client who works with a lead generation model, what do you do?

Among the many discovery questions you ask as you try to better understand your client’s business, you probably ask them, “What makes a lead a good lead?” That is, what are the qualities that make a potential customer more likely to convert to sale?

A business that’s given some thought to their ideal customer might send over some audience personas; they might talk about their target audience in more general terms. A product or service offering might be a better fit for companies of a certain size or budget, or be at a price point that requires someone at a senior level (such as a Director, VP, or C-level employee) to sign off, and your client will likely pass that information on to you if they know it. However, it’s not uncommon for these sorts of onboarding conversations to end with the client assuring you: “Just get us the leads. We’ll make the sales.”

Since SEO agencies often don’t have access to our clients’ CRM systems, we’re often using conversion to lead as a core KPI when measuring the success of our campaigns. We know enough to know that it’s not enough to drive traffic to a site; that traffic has to convert to become valuable. Armed with our clients’ assurances that what they really need is more leads, we dive into understanding the types of problems that our client’s product is designed to solve, the types of people who might have those problems, and the types of resources they might search for as they tend to solve those problems. Pretty soon, we’ve fixed the technical problems on our client’s site, helped them create and promote robust resources around their customers’ problems, and are watching the traffic and conversions pour in. Feels pretty good, right?

Unfortunately, this is often the point in a B2B engagement where the wheels start to come off the bus. Looking at the client’s analytics, everything seems great — traffic is up, conversions are also up, the site is rocking and rolling. Talk to the client, though, and you’ll often find that they’re not happy.

“Leads are up, but sales aren’t,” they might say, or “yes, we’re getting more leads, but they’re the wrong leads.” You might even hear that the sales team hates getting leads from SEO, because they don’t convert to sale, or if they do, only for small-dollar deals.

What happened?

At this point, nobody could blame you for becoming frustrated with your client. After all, they specifically said that all they cared about was getting more leads — so why aren’t they happy? Especially when you’re making the phone ring off the hook?

A key to client retention at this stage is to understand things from your client’s perspective — and particularly, from their sales team’s perspective. The important thing to remember is that when your client told you they wanted to focus on lead volume, they weren’t lying to you; it’s just that their needs have changed since having that conversation.

Chances are, your new B2B client didn’t seek out your services because everything was going great for them. When a lead gen company seeks out a new marketing partner, it’s typically because they don’t have enough leads in their pipeline. “Hungry for leads” isn’t a situation any sales team wants to be in: every minute they spend sitting around, waiting for leads to come in is a minute they’re not spending meeting their sales and revenue targets. It’s really stressful, and could even mean their jobs are at stake. So, when they brought you on, is it any wonder their first order of business was “just get us the leads?” Any lead is better than no lead at all.

Now, however, you’ve got a nice little flywheel running, bringing new leads to the sales team’s inbox all the livelong day, and the team has a whole new problem: talking to leads that they perceive as a waste of their time. 

A different kind of lead

Lead-gen SEO is often a top-of-funnel play. Up to the point when the client brought you on, the leads coming in were likely mostly from branded and direct traffic — they’re people who already know something about the business, and are closer to being ready to buy. They’re already toward the middle of the sales funnel before they even talk to a salesperson.

SEO, especially for a business with any kind of established brand, is often about driving awareness and discovery. The people who already know about the business know how to get in touch when they’re ready to buy; SEO is designed to get the business in front of people who may not already know that this solution to their problems exists, and hopefully sell it to them.

A fledgling SEO campaign should generate more leads, but it also often means a lower percentage of good leads. It’s common to see conversion rates, both from session to lead and from lead to sale, go down during awareness-building marketing. The bet you’re making here is that you’re driving enough qualified traffic that even as conversion rates go down, your total number of conversions (again, both to lead and to sale) is still going up, as is your total revenue.

So, now you’ve brought in the lead volume that was your initial mandate, but the leads are at a different point in their customer journey, and some of them may not be in a position to buy at all. This can lead to the perception that the sales team is wasting all of their time talking to people who will never buy. Since it takes longer to close a sale than it does to disqualify a lead, the increase in less-qualified leads will become apparent long before a corresponding uptick in sales — and since these leads are earlier in their customer journey, they may take longer to convert to sale than the sales team is used to.

At this stage, you might ask for reports from the client’s CRM, or direct access, so you can better understand what their sales team is seeing. To complicate matters further, though, attribution in most CRMs is kind of terrible. It’s often very rigid; the CRM’s definitions of channels may not match those of Google Analytics, leading to discrepancies in channel numbers; it may not have been set up correctly in the first place; it’s opaque, often relying on “secret sauce” to attribute sales per channel; and it still tends to encourage salespeople to focus on the first or last touch. So, if SEO is driving a lot of traffic that later converts to lead as Direct, the client may not even be aware that SEO is driving those leads.

None of this matters, of course, if the client fires you before you have a chance to show the revenue that SEO is really driving. You need to show that you can drive lead quality from the get-go, so that by the time the client realizes that lead volume alone isn’t what they want, you’re prepared to have that conversation.

Resist the temptation to qualify at the keyword level

When a client is first distressed about lead quality, It’s tempting to do a second round of keyword research and targeting to try to dial in their ideal decision-maker; in fact, they may specifically ask you to do so. Unfortunately, there’s not a great way to do that at the query level. Sure, enterprise-level leads might be searching “enterprise blue widget software,” but it’s difficult to target that term without also targeting “blue widget software,” and there’s no guarantee that your target customers are going to add the “enterprise” qualifier. Instead, use your ideal users’ behaviors on the site to determine which topics, messages, and calls to action resonate with them best — then update site content to better appeal to that target user

Change the onboarding conversation

We’ve already talked about asking clients, “what makes a lead a good lead?” I would argue, though, that a better question is “how do you qualify leads?” 

Sit down with as many members of the sales team as you can (since you’re doing this at the beginning of the engagement — before you’re crushing it driving leads, they should have a bit more time to talk to you) and ask how they decide which leads to focus on. If you can, ask to listen in on a sales call or watch over their shoulder as they go through their new leads. 

At first, they may talk about how lead qualification depends on a complicated combination of factors. Often, though, the sales team is really making decisions about who’s worth their time based on just one or two factors (usually budget or title, although it might also be something like company size). Try to nail them down on their most important one.

Implement a lead scoring model

There are a bunch of different ways to do this in Google Analytics or Google Tag Manager (Alex from UpBuild has a writeup of our method, here). Essentially, when a prospect submits a lead conversion form, you’ll want to:

  • Look for the value of your “most important” lead qualification factor in the form,
  • And then fire an Event “scoring” the conversion in Google Analytics as e.g. Hot, Warm, or Cold.

This might look like detecting the value put into an “Annual Revenue” field or drop-down and assigning a score accordingly; or using RegEx to detect when the “Title” field contains Director, Vice President, or CMO and scoring higher. I like to use the same Event Category for all conversions from the same form, so they can all roll up into one Goal in Google Analytics, then using the Action or Label field to track the scoring data. For example, I might have an Event Category of “Lead Form Submit” for all lead form submission Events, then break out the Actions into “Hot Lead — $5000+,” “Warm Lead — $1000–$5000,” etc.

Note: Don’t use this methodology to pass individual lead information back into Google Analytics. Even something like Job Title could be construed as Personally Identifiable Information, a big no-no where Google Analytics is concerned. We’re not trying to track individual leads’ behaviors, here; we’re trying to group conversions into ranges.

How to use scored leads

Drive the conversation around sales lifecycle. The bigger the company and the higher the budget, the more time and touches it will take before they’re ready to even talk to you. This means that with a new campaign, you’ll typically see Cold leads coming in first, then Hot and Warm trickling in overtime. Capturing this data allows you to set an agreed-upon time in the future when you and the client can discuss whether this is working, instead of cutting off campaigns/strategies before they have a chance to perform (it will also allow you to correctly set Campaign time-out in GA to reflect the full customer journey).

Allocate spend. How do your sales team’s favorite leads tend to get to the site? Does a well-timed PPC or display ad after their initial visit drive them back to make a purchase? Understanding the channels your best leads use to find and return to the site will help your client spend smarter.

Create better-targeted content. Many businesses with successful blogs will have a post or two that drives a great deal of traffic, but almost no qualified leads. Understanding where your traffic goals don’t align with your conversion goals will keep you from wasting time creating content that ranks, but won’t make money.

Build better links. The best links don’t just drive “link equity,” whatever that even means anymore — they drive referral traffic. What kinds of websites drive lots of high-scoring leads, and where else can you get those high-quality referrals?

Optimize for on-page conversion. How do your best-scoring leads use the site? Where are the points in the customer journey where they drop off, and how can you best remove friction and add nurturing? Looking at how your Cold leads use the site will also be valuable — where are the points on-site where you can give them information to let them know they’re not a fit before they convert?

The earlier in the engagement you start collecting this information, the better equipped you’ll be to have the conversation about lead quality when it rears its ugly head.

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