Using Amplitude for Product & Web Analytics

I’ve previously published this blog post in Dutch on Webanalisten.nl.

What if you are looking for a product for web analytics but have a lot of events, a complicated product and sending more and more data over time. Sometimes it wouldn’t just work to go with Google Analytics (360), Adobe Analytics and maybe integrating your custom build solution or Snowplow might be too complicated for your organization. In this post I’d like to show you another tool that might be interesting to you: Amplitude. I’ve been working with it for the last year and it provides some great flexibility over other products in the industry.

What is Amplitude?

“Analytics for modern product teams”

Or in normal language, you can track any event and connect that to a user. All the events that you send can have properties, just like the user can have properties. You can filter by all these data points and move your data around to turn it into multiple types of charts: funnels, event hits, revenue, etc. In this article we’ll be running through how you can be using Amplitude and what it’s good for. Let’s go ahead and dive in!

Why would you be using Amplitude?

You want to measure what is happening within your product and what users are doing. Keeping in mind that all this data can help you improve their workflows and measure the impact certain changes have on their behaviour. In that aspect, Amplitude is directly competing with mostly tools outside of web analytics, like: Google Analytics for Firebase, Snowplow, KISSmetrics, Mixpanel, etc. In the next section we’ll explain why, as a lot of features are interpreted differently from regular web analytics but can still help you a lot in your daily work:

What’s the difference?

 

  • Instant Reporting/DIY: While most analytics tools provide you with a lot of pre configured dashboards. Amplitude will let you do this all on your own, which can be a time consuming task but in my opinion it also lets you think a bit more about the way you set-up your analytics infrastructure.
  • No default metrics: Bounce rate doesn’t exist as any event can be triggered to influence it (plus, would that be your most useful metric anyway?)
  • Funnels: Anything can be a  funnel, in my opinion that makes it very powerful as it doesn’t require you to create any predefined goals and also will make sure you can create funnels retroactively (recognize this pain point in Google Analytics?). If you added events a few weeks ago and now is the time to start creating a funnel of it, you’re able to. Want to change the funnel and add more/less events. You can.

 

 

  • User/Session: Sessions (on Web) don’t really exist in Amplitude. While in my opinion this is a metric that has a very loosely defined definition anyway it can come in handy from time to time to measure retention. It will provide this data on mobile where sessions are way easier to be defined (app open/close).
  • Channel/Traffic Source: If you’re looking for an easy way to track where your visitors are coming from with detailed reports that would be associated with costs. That’s just not what Amplitude is for. While it can easily save the UTM data that you’re sending along it won’t offer your great reporting around it. That’s why there focus is mostly on product analytics.
  • Merging Events/Linking Events: At the beginning of this section we talked about the need for setting up all the dashboards yourself. As you won’t have a very defined plan with your tracking for what’s to come over the next few years it can be hard to follow a certain naming convention from scratch. Usually turning your analytics data into an unstructured chaos over time. Within Amplitude you’re able to merge certain event names and link them together. So you can easily change your old event names to something new and still connect the data together. One of the features I really miss sometimes in other tools when I’m trying to redefine naming conventions and clean them up.

Why data governance is even more important

The role of data governance is becoming more important by using tools like this in combination with having the need for good documentation. If you come into an organization that is already sending hundreds of different events it can be really hard to get started with making a more deep analysis as you’re not always familiar with the

    • Naming conventions: You want to make sure that you’re using the right names for the events and that you’re making sure that their logical in order to send the data. It would be good to give this article, on creating measurement plans, that I wrote for online-behavior.com a read. We’ll talk later about how Amplitude can still help you if you would like to make changes to the events you sent.

 

  • Segments/Cohorts: As most of the data for users can be saved in event or user properties this will also mean that you need to make sure that the data in there doesn’t change too often as it might affect how you’ve set up your segments and cohorts.

 

  • Also funnels and many reports can be impacted by the way you save data.

Overview of features

  • Dashboarding/Charts: As we talked about the flexibility that Amplitude can provide you with, this mostly shows in the way you’re working with charts and adding them to dashboards. You can create dozens of different charts and add them to a dashboard. The dashboards will then, for example, give you the ability to change the date range. If you don’t like that you can still make all the changes from the actual chart.
  • A/B Testing – Significance Calculator: Are you running an A/B test on your site and sending the data to Amplitude. Within certain charts you can segment out the results and immediately calculate if they’re significant for what you’re analyzing. Saves time trying to find a significance calculator.

      • Custom Metrics: Just as many other web analytics tools, Amplitude will give the ability to create custom formulas within a chart to calculate other metrics.

     

  • Retroactive reporting: You have added tracking months ago but only today you’ve figured out that an event should be measure as a goal? You can set up a goal funnel really easily with old data and have all the old data being available to you.
  • Realtime: The fact that all of the events that you send to Amplitude are processed in real time makes it very powerful. Basically within minutes of launching a new feature you can start analyzing the data to see what’s going on or if it’s broken. Gone are the times were you need to wait for hours to have all the data that you are collecting be fully available.
  • Unlimited event/user properties & ‘dimensions’: Every event can have properties that can be related to the event. In addition to that a user can have properties that can be used too. So if I want to mark certain users with an action I can easily send that along with an event to update the records for this.
  • CLTV: Measuring the lifetime value of users will obviously require you to start identifying users (relatively easy to set up). But this will enable you to look into how you’re users are performing over time and if you have high retention for what that means for their customer lifetime value. This is an example report that would provide me with the performance of a segment of users over the last 12 weeks and what they’re worth to the business.

Chart for CLTV

What’s missing?

Google integrations? Obviously some things are missing, while the Cohort feature’ abilities are very powerful and Amplitude can provide you with some cool integrations with other software it still can’t make the connection with the audience data from Google. Which is obviously always going to be a big upside of the Google Analytics Suite.

Transactions/Purchase: The way Amplitude is tracking a conversion is a bit weird. You send all the products that you purchase as different revenue events. There is no concept of a purchase, which seems strange. Also it’s really hard to identify what the Xth purchase was, these are events that you need to setup yourself.

UTM/Traffic Source Reporting: It does exist but it isn’t great and definitely not as powerful as you’re used to in your regular web analytics tools. Does it get the job done for product analytics, Yes it does I’d say. If you’re looking to do anything more advanced with the data you should be building additional capabilities on your own end.

Use Cases

  • Funnels: Every event can be part of a funnel and that makes it very flexible and useful if you want to compare user behaviour. For example connecting certain user actions before a purchase funnel can be the case too.
  • Customer Lifetime Value/Retention:
  • Cohorts: Where you would have segments & audiences in Google Analytics you have the ability to also create cohorts of users to measure the impact of certain properties/events on their behaviour over time. For example this is a cohort that we used often at Postmates where we would look at what users that have come in with the sign up referrer that includes google, yahoo, bing (an organic search user). We would use this cohorts either to export them from other marketing purposes (email/push campaigns) or to analyze their behaviour against other cohorts.
    • How do organic search users in the last month behave different if they have used x feature?
    • How do users who have touched feature x find the other features?

Segmenting users with its Cohort feature.

Conclusion

Overall I’m pretty satisfied with Amplitude and how it can help you with its flexibility in adding/creating events and figuring out later what kind of dashboarding/charts you’ll create on top of this. But it’s likely (for now) not going to replace most of the data that you’re used to in web analytics as that would require a lot of additional setup and tracking. You can use it very effectively within organizations to track certain aspects and user behaviour. All in all a great addition to most analytics teams. All in all I would advise most companies to use these tools together as they can be very useful in providing more insights into what the user is doing specifically.

If you’ve worked with Amplitude and want to share more about your experiences, leave a comment! Currently I’m working on exporting Amplitude data to Google BigQuery for ‘big data’ analysis, in a future post I hope to share with you on how you can set that up yourself.


Dealing with SEO within your company / internally

“My company/manager/CEO doesn’t understand SEO, my engineers have no idea on how to implement X, I don’t get the buy-in that I need.” Just some of the comments that I hear in real life and see pass by on Twitter. That’s why in late 2017 I asked this question on Twitter. So that’s why I thought it would be time for a write up on the scenarios that I’ve seen over the last few years in SEO and the other ways that I’ve seen that help for getting input. I’ll try to share some insights into how I’m/we’re dealing with explaining and dealing with SEO internally.

Is there any believe in SEO?

For a successful SEO strategy and great results the first thing that you’ll need is somebody in the organization needs to support SEO. If that person or role isn’t there it’s going to be really hard to get things moving forward. But how do you know that support is there. In my case I’ve been lucky, my first job at Springest I had a guy who started a SEO agency himself, TNW was such a big online tech player I didn’t even need to explain what SEO was and these days at Postmates I got specifically asked if I wanted to focus on SEO when I joined. All with the believe that SEO would help move the business forward. But in the end you don’t always need to have people that know what SEO is and know everything inside out. It will help if they’re able to help you out when you have questions of SEO and at least know the good and bad parts about it. If the person that’s asking you to help out is also talking about link buying I would probably reconsider my decisions to work for them a few times  (and probably decide not to).

Explaining the value

In most organizations you’re going to need to explain the value of SEO, in the end it remains a black/dark grey box in which it’s hard to explain what kind of impact you can deliver on. But what you can explain in my opinion is the following:

  • What is the opportunity, how big is your industry/nice in terms of search volume?
  • What is our current position? How good/bad are we performing against our competition?
  • Who’s our competition really? It’s most likely not the companies that offer the same product but probably the ones that are beating your *ss in the search results.
  • Increase conversion rate while they’re at it, increase awareness while they’re at it, increase referral traffic while they’re at it.

These are just a few that additional values that you can bring to the table as an SEO (team) in a company. Most of all, if you do a great job on keyword research you can tell your internal organization a lot about the keywords and intent of the users within your industry/niche.

But how do you proceed, to excel even more. In the end you want your whole organization to be supportive and help the cause of SEO. The more people that work on it the more you can hopefully grow your visibility and with that your relevant business metrics (clicks, leads, sign ups, etc.).

Creating Buy In

What kind of support do you need, why do you need it and what can you do to get it? Let’s talk a little bit about that:

Team Support

Does your team know what SEO is, how it can help the company and what their contribution to it could be/mean? Very often I don’t see SEOs talk with the IT team/engineers/developers or whatever job title they’ll have in your organization. The often talked about phrase: “Sometimes you should just have a beer with them to build up a good relation” most often is incorrect. You’ll build up a better relationship, that I agree with. But that doesn’t always cover an actual understanding of the problem which is still going to be essential.

Building up understanding on what you’re/they’re working on

Does your boss, his boss and the CEO what you work on for SEO? Pretty sure that they don’t. So it’s not surprising that you don’t have all the support or resources that you would need. Start educating them, on what you’re working on and what the results are. If you’re in doubt about something there are multiple paths to go: run an experiment, launch an MVP as soon as possible or create the business case/technical documentation so you’re aware that you know what you’re building.

My {fill in random job title} doesn’t understand SEO

“Why didn’t my engineer think of adding redirects?”, “Why didn’t our content team use the right keywords?”, “Why doesn’t my boss understand what SEO is really about?”. Questions that you must have asked yourself and I can’t blame you. But the answer to all of them is easy: “Because you haven’t told them”. In the end all these things matter to SEO and your success, so why don’t you explain it once more. Repetition makes it easier to have these answers printed on their minds.

What can help me build up an understanding of SEO?

Besides working with your team it’s even more important to work in a nice way with the other teams in a company. It’s very likely that there are more people outside of SEO in your company then there are working with it on a daily basis. All of these folks can help you out with SEO too. I remember the times where I was in desperate need for a copywriter after hiring dramas that continued for months and our receptionist turned out to be an English major and able to help out immediately.

Internal Decks/Meeting

The previous two companies that I’ve worked for were relatively small (<75) and as I was an ‘early’ employee at both I saw a lot of people come in. In the end that made it easier to explain SEO to them as I either hired them or they had a manager that I already worked with within the organization. At Postmates that is quite different, I came in years after founding the company at the point where we had over 400+ people and a growing organization.

That’s why early on, when I was formulating the SEO strategy I started creating a slide deck explaining SEO for the rest of the organization and also telling them more about the projects that we already worked on or would be working on in the next months. Whenever a new team would be formulated or somebody would join the Growth team I tried to keep up with setting up a meeting with them and seeing if there would be any overlap or room to work together. In the end your Comms teams, Support teams have probably some interest in SEO or you can help them with their work with the tools, resources and/or products that you have available.

Weekly Status Meeting/Email/Update

When you work with multiple people on a team it’s hard to keep them all up to date with everything that is going on. Regular status updates, either in person or via VC, email can help with that. As we have multiple engineers working on SEO at the same time they can already get behind on what’s going on easily. That’s why on a weekly basis we send out updates on the work that they’ve done to the organization but also to the the bigger team. With this it’ makes it easier to show progress, list down what we’re planning on working on and provide early results. So things that we list in the email are:

  • Experiments started/finished: Do we have any results, or what did we launch this week?
  • What did we do last week? What are the tickets that we worked on, what kind of early results do we have, is this already being picked up by a search engine?
  • What are we going to work on this week? What issues will we work on, what kind of results might that provide, why do we work on this.
  • What did we learn last week? What kind of results die we see, what kind of growth did we achieve.

Monthly Updates

On top of that we send some headlines once a month for the bigger projects that we launched so we know what kind of progress we have made for the quarterly targets that we have. This will give a more birds eye view on what we’re achieving and if that’s on track with what we planned upfront. It’s a similar update to the Monthly one, but a bit more high level and readable for the whole team and people that don’t work with SEO on a day to day basis.

What’s next?

This is still not good enough, even when you have internal support you always have new questions rise and even when they all support you it’s probably going to happen that they start asking deeper questions that you need to keep explaining. This is something I now endure, questions basically are more relevant to your own work. Which is awesome, as it makes up for great debates that in the end will only improve products and SEO strategy. Even better, this will all help streamline the process of SEO and usually speed up the output.


Explaining SEO Experimentation

What is SEO Experimentation?

If you’ve been reading some of my blog posts in the past you’ll have noticed that I worked a lot on analytics, experimentation, and SEO. Having been able to combine these areas together has led to the point that for both Postmates and The Next Web previously, we worked on setting up a framework for SEO Experimentation. In my opinion, over time you’d like to learn more about what Google appreciates and how it can help you future wise, to think about what you should be focusing your attention on == prioritization. Lately, I read a blog post on the SEMrush blog with the title: SEO Experiments that will blow your mind (clickbait alert! so I won’t link to it). Hoping there would be a lot of great ideas in that blog post I started reading realizing that over 50% of examples weren’t even close to being an experiment. They were just research, from over a year ago (which was alarming too!).

Which pushed me to write this essay to tell you more about what SEO experimentation really is and how you can learn more about it as it’s still a relatively new area in SEO that is only been explored and exposed by a few big companies.

Update: since the original publication date I’ve become even more excited about SEO Experimentation and its possibilities. That’s why I’ve updated this post with more information and some frequently asked questions that I get about the subject.

Last update: January 2020 – Updated the blog post with some new learnings and rewrote some parts.

What really is, SEO Experimentation?

You’re testing 2-X variations of a template on your site (all product pages, all category pages) and measure over a period of time what the impact is in organic search traffic of the pages (usually a template) that have seen changes in your experiment. You want to isolate your changes as much as possible (example: change Title tag template), you set a certain level of significance and you calculate in a mathematical way how your results have changed in the period before and after the change.

It’s not:

  • Research (I  compare data between periods too, but it doesn’t make it an experiment).
  • Guesswork: I’ve seen my pageviews go up with X% after optimizing my title on this page. This is just guess work as you’re not using any data that is statistically siginificant to prove the statements.

I would encourage you to also read this post on Moz by Will Critchlow in which he shared how they built Distilled ODN (we worked with their platform when I was at The Next Web) and how you can start testing SEO yourself.

How is SEO Experimentation different from user testing (aka A/B Testing)?

Measurement: Instead of measuring conversion rates or other business metrics you’re mostly focused on tracking the number of sessions that are coming in from organic search. If the variants that you’ve worked on increase you’ll start calculating the impact on this. On the side, you’re still making sure that the conversion rate of these pages doesn’t decline as, in the end, it will still be the business metrics that will count.

Bucketing: Instead of bucketing users and sending user A into bucket A you’re doing this with pages. In the end, you want to make sure that the same pages (just like you do with users) end up in the same bucket. How you usually do that is that you sort by alphabet or category to have some kind of logic. What’s important though is to make sure that these buckets are in balance. Which is harder than you would do with user testing.

The difference between bucketing for users experimentation and SEO experiments

Bucketing works differently with SEO Experimentation

What are some examples of SEO Experiments?

Examples of things that you could be testing? Let’s list a few examples that I can think of:

  • Updating Title tags: does a certain keyword help, do I need to make them longer/shorter?
  • Adding/Removing Schema.org, what is the impact of adding structured data to a page?
  • Adding content: does adding more content to a page help with its ranking? What if I add an additional heading to my content.
  • Internal link structures: do you want to add more relevant links to other pages (how many, what anchor texts)?
  • Testing Layouts, what layout does help better for the SEO on this page? Ever noticed why Amazon has so many different variations of product pages 😉

How do I start doing SEO Experimentation?

Let me give you a very much shortened and a simplified idea of how an SEO experiment works:

  1. Think about what you want to be testing (adding descriptions, setting a canonical), write down a hypothesis, we’ll talk about this shortly. 
  2. Figure out if you have enough traffic (500+ per variation per day) would be a minimum I’d say.
  3. Figure out how you can serve different content/HTML per page. Are you able to serve different areas on your site based on a randomized factor, still making sure that your buckets are just as big (50/50)?
  4. Setup the experiment and let it run for as long as needed to see valid results (usually at least 3-4+ weeks). You want to make sure that Google has picked up on all the changes on your page and has re-indexed the impacted pages.
  5. Analyze the results after this period and look at how these buckets of pages performed before your experiment and after. Are these results valid, you didn’t make any other changes to these pages in the meantime? Are the results per variant significant?
  6. You have found the winner + loser is. Time to start iterating and launch another experiment.

How to document an SEO Experiment?

You want to make sure that what you do around SEO experimentation is well documented, this will help you in the future with figuring out what kind of experiments are working and what you learned. When you can run over 25 experiments a year you probably won’t know after a year how many of these were successful and how to interpret the results. For documenting SEO experiments I’ve created a template that we filled in with the data on the actual experiment.  You can find it here and copy it in your own Google Drive for own use:

How to analyze an SEO Experiment?

You want to make sure before an SEO experiment is running that you know what has happened with it before it starts. It’s basically the ‘control’, you want to make sure your bucket is providing stable results so you can clearly analyze the difference when the new variants are being launched.

Creating buckets for SEO Experimentation

Bucketing needs to ensure that there are additional buckets so you can measure the baseline and take care of anomalies in your data sets.

Bucketing: Your bucketing needs to make sure that it’s sending the right variant to your analytics account (most often used: Google Analytics). When the experiment starts, make an annotation so you know from when you start analyzing the results. I would recommend going with at least 4 buckets, of which A + AA are the same and so are B + BB. This way you can ensure that there won’t be any anomalies in your bucket due to 1-2 pages outperforming others due to other reasons. See my comments below on anomolies.

Logs: Logs can come into play when you start analyzing the results. In most cases, your experiment won’t generate results in the first week as the changes in your variant haven’t been picked up in the experiment. That’s why you want to look at your log files to ensure that the new variants have been crawled.

Measuring & Analyzing impact: For measuring the impact you’re segmenting down the buckets and measure what happened before the experiment and after. To see if the changes are significant or not, you need to rely on the CausalImpact library to see what has happened or not. You want to send the data for different buckets in a way that can be visualized like this:

Sending dataLayer events for measuring SEO experiments

Send data about the buckets and elements (de)activated to web analytics

Anomalies: Analyze your buckets individually! Do you see any spikes in traffic that might be hurting the data quality? You need to be sure that is not the case. For example, what if one of the buckets contain pages about political topics that all of a sudden see a spike in search volume. This doesn’t mean that your page has been performing better, it means there was just more demand so the data for that variant might be invalid.


Examples of SEO Experiments

As I mentioned both at The Next Web and Postmates I was responsible for running SEO experiments. Most of them were around optimizing <title> tags. As changes to this have, in most cases, a direct connection to the CTR within the SERPS. The title is, in the end, used as the headline for a specific search result. So let me walk you through an example of an SEO experiment as we ran it at Postmates.

Updating Titles with additional keywords

The problem: We noticed a lot of search traffic for terms around food delivery in which a zip code, like 91615 was mentioned. As we could ‘easily’ create pages for zip codes we wanted to know if that was worth it, so: “What can we do to drive more additional searches around zip codes without building new landing pages and wasting valuable engineering resources doing so”.

The solution: As we knew for restaurants in what specific zip codes they were active we had the ability to mention the zip code in the title. As we were doing this across tens of thousands of restaurants we knew that we had enough of a sample size.

  • Old:
    • {Restaurant Name} {Street Address} in ({City}} – Postmates On-Demand Delivery
    • Paxti’s Pizza 176 Fillmore Street in San Francisco – Postmates On-Demand Delivery
  • New:
    • {Restaurant Name} {Street Address} in ({City}} ({Zip Code}) – Postmates On-Demand Delivery
    • Paxti’s Pizza 176 Fillmore Street in San Francisco (97521) – Postmates On-Demand Delivery

The result: It was inconclusive, in the end, that wasn’t likely the outcome that you were hoping for. But I want to paint a realistic picture of what can happen when you run experiments. In a lot of cases, you don’t see enough changes in the data to be certain that it’s an improvement. In this case, we expected that a title change wasn’t good enough to actually compete for zip code related queries. The food delivery industry is one of the most competitive in the world for SEO so we knew it was always possible to have an outcome like this.


Frequently Asked Questions + Answers

There are a lot of questions that come up when I talk about this subject with people. So I’ll try to keep up this blog post with any new questions that might arise:

Isn’t this cloaking? Doesn’t this hurt for my Google rankings?

Not really, you’re not changing anything based on who’s looking at the page. You’re changing this only on certain pages that are being served and the search engine + user will see the same thing. Ever looked at Amazon’s product pages and wonder why they all have a different layout? Because they’re testing for both user experience as SEO.

Do you want to learn more about this subject?

Great, when we were setting up our own SEO experimentation framework at Postmates about 1,5 years ago I tried to find all the articles related to it and talked to as many people as possible on this. These were mostly the articles I would refer you to if you want to learn more.

Resources

When I wanted to learn more about SEO experimentation I started to figure out what was already written on the web, most of these resources are from teams & companies that I worked with before. So if you’re enthusiastic about this subject, read more here:


Let’s really start & keep innovating in the SEO industry and let’s get rid of terrible clickbait headlines. SEO Experimentation is like I mentioned something that we should be embracing as it’s a more scientific approach that is going to lead to finding new insights.

If you want to talk more about this, feel free to reach out. Like I said, I have been updating this post for over two years now, and will keep on doing so in the future.


What books am I reading in 2018?

For the last two years I wrote blog posts (2017 & 2016) listing the books that I read in the past year and that I wanted to be reading in that specific year. As always, the past year I didn’t read all the books that I’ve listed out in the blog post as I discovered some new ones and changed my focus during the year. Also moving to another country (hi San Francisco!) made it tough to keep up with the goals I set for myself. So that’s why I didn’t make it to the goal to read 20+ books last year and had to leave it at 14.

So what will I (at least) be reading in 2018:

So what does this tell you? The guy wants to know more about branding in 2018 and is in desperate need for some cool new personal development books. Over the last year I read a lot of popular books (Elon Musk, High Output Management, etc.) that have provided me with a lot of inspiration on great managers + techniques. In 2018 I’d like to dive a bit more into brand building, although I have an SEO job most of what we think about everyday is building out the Postmates brand and luckily we get a ton of freedom to do that + in the end I remain a marketer.

As always, leave your recommendations in my Twitter feed (@MartijnSch) as I’d love to know from others what I should be reading and what you recommend should be on the list or removed from the list.


Finding & Dealing with Related Keywords

How do you go from 1 keyword and find another 10.000 that might also be relevant to your business/site. One of the things that I’ve been thinking about and worked on for some sites recently. It’s fun as with smaller sites it makes it easy to get more insights into what an estimated size can be of an industry/niche that a company operates in. This ain’t rocket science and hopefully, after this blog posts, you’ll get some new ideas on how to deal with this.

How to get started?

Pick 1 keyword, preferably short-head: coffee mug, black rug, Tesla Roadster. They’re keywords that can create a good start for your keyword research as they’re more generic. In the research itself, we’ll talk about ways to get more insights into the long tail based on this 1 keyword.

From 1 to 10.000

Start finding related keywords for the keyword(s) you picked that you consider relevant. Use the tools that we’re going to talk about after this and repeat the process for all the keywords that you get back after the first run: 1 = 100 results = 10.000 results. Depending on the industry/niche that you operate in you might be able to find even more keywords using this method. When I started doing research for a coffee brand within 30 mins I ended up with data for 3 big niches within that space and over 25k keywords.

What tools are out there?

Obviously, you can’t do this without any tools. For my own research, I use the tools that are listed beneath. They’re a mix of different tools but they have the same output eventually. Getting to know more keywords but at the same time also get different input on intent. Focused on search (I’m looking for.. {topic_name}) and other search intent (I have a question around {topic_name}).

Besides the tools that I’ve listed there are many more that you could be using that I want you to benefit from:

    • Google Adwords Keyword Tool: The best source for related keywords by a keyword.
    • SEMRush: The second best source likely as they’re using all sorts of ways to figure out what keywords are related to each other. Also a big database of keywords.
    • AnswerThePublic: Depending on why/what/where/who you’re looking for AnswersThePublic can help you find keywords that are related to a user question.

Suggested Searches:

    • Google, Bing, Yahoo: The biggest search engines in the world are all using different ways to calculate related searches through their suggestions. So they’re all worth looking into.
    • Google Trends: Is a keyword trending or not and what keywords are related to a trending topic. Mostly useful when you’re going after topics that might have (had) some popularity.
    • YouTube: Everything video related, need I say more.
    • Wikipedia: You really are looking for some in-depth information in the topic, Wikipedia can likely tell you more about the topic and the related topics that are out there.
    • Instagram: Everything related to pictures and keywords, their hashtags might mislead you from time to time.
    • Reddit: The weirdest place to find keywords and topics.
    • Quora: Users have questions, you can answer them. The most popular questions on Quora on a topic are usually the biggest questions on your customer’s minds too.
    • Yahoo Answers: Depending on the keyword the data can be a bit old, who still uses Yahoo? But it can be useful to get the real hardcore keywords with a question intent.
    • Synonyms: The easiest relevance, find the keywords that have the same intention.
    • Amazon: Find keywords that people are using in a more transactional intent and that you might search for when you’re looking for a product. Great for e-commerce.

Grouping Keywords

When you’ve found your related keyword data set it’s time for the second phase, grouping them together. In the end, 1 keyword never comes alone and there is a ton you can do with them if you group them together in a way that makes sense for you….

By name/relevance/topical: Doing this at scale is hard, but I’m pretty sure that you see the similarity between the keywords: coffee mug and: black coffee mug. In both ‘coffee mug’ is the keyword that is overlapping (bigram). If you start splitting up keywords with different words relatively fast you’re able to find the top words and word combinations that your audience is using most. If you’re wanting to find out more on how to group them, check out KeywordClarity.io where you can group keywords together based on word groupings.

By keyword volume: If you have the right setup you can retrieve the keyword volumes for all of these keywords and start bucketing the keywords together based on short-head and the long tail. This will enable you to get better insights into the total size of the volume in your industry/niche.

By ranking/ aka opportunity: It would be great if you can combine your keywords with data from rankings. So you know what opportunity is and for what words you’re still missing out on some additional search volume.

What’s next?

Did you read the last part? What if you would start combining all three ways of grouping them? In that case, you’ll get more insights into the opportunity, your current position in the group and what kind of topical content you should be serving your audience. Food for thought for future blog posts around this topic.