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.


Using Keyword Rankings In SEO

A few weeks ago I gave a talk at an SEO Meetup in San Francisco. It was a great opportunity to get some more feedback on a product/tool that I’m working on (and that we are already using at Postmates). You’ll hear more on this in the upcoming months (hopefully). In a previous blog post at TNW I talked about using dozens of GBs of data to get better insights in search performance. Over the last years I kept working on the actual code around this to also provide myself with more insights into the world around a set of keywords.

Because billions of searches are done on a daily basis and ~20% of queries haven’t been searched for in the past 30-90 days it means that there is always something new to find out. I’m on the hunt to explore these new keyword areas/segment & opportunities as fast as possible to get an idea on how important they can be.

That means two things:

  1. The keyword might be absolutely new and has never been searched for.
  2. The keyword has never come up on the radar of the company, it was never a related keyword or never got an impression simply because content didn’t rank for it.

Usually the next thing you want to know is what their ranking is so you can start improving on it, obviously that can be done in thousands of ways. But hopefully the process would usually work something like this. Moving up from an insane ranking (read: nowhere to be found) to the first position within a dozen weeks (don’t we all wish that can happen in that amount of time?).

Obviously what you’re looking for is hopefully a graph for a keyword that will look something like this:

What am I talking about?

Back at TNW my team was tracking 30.000 keywords on a weekly basis to get better insights into what was happening with our search volume & our rankings. It has multiple benefits:

  1. Get insights into your own performance for specific keywords.
  2. Get insights in your actual performance in search engines (are 100 keywords increasing/stable/decreasing?).
  3. Get insights into your competitors performance.

Besides that there is a great opportunity to learn more about the flux/delta of changes in the search results. You’re likely familiar with Mozcast & SERPMetrics Flux and other ‘weather’ radars that monitor the flux in rankings for tons of keywords to see what is changing and if they’re noticing an update. With your own toolset you’ll be able to get insights into that immediately. I started thinking about this whole concept years ago after this Mozcon talk from Martin McDonald in 2013. One of the things that are particularly interesting:

Share of Voice

You’ve also likely heard of the concept of Share of Voice in search. In this case we’re talking about it in the concept of rankings. If you rank #100 in the search results, you’ll get 1 point. If you’ll rank #1 you would assign it 100 points. Which basically means that you will get more points the higher you’ll rank. If you bundle all the keywords together, let’s say 100 you can get: 100 x 100 = 10.000 in total. Over time this will help you to see how a lot of rankings will be influenced and where you’re growing instead of being focused on just the rankings of 1 keyword (always a bad idea in my opinion).

In addition to measuring this for yourself, there will also be other useful ways you can use Share of Voice:

  • Who are my competitors: Obviously you know your direct competitors, but most of the times that doesn’t mean that they’re the same as you’re going against in search results. Get the top 10-20-50-100 (whatever works for you) and count the URLs for the same domain in all of the keywords in a group and multiply that by their Share of Voice. The ones that raise to the top will be the competitors that are annoying you most.
  • Competitors: You’re familiar now with the concept, so if you apply the same thing to your competitors you’re able to figure out how they’re growing compared to you and what their coverage is in search for a set of keywords. Basically providing you with the data you otherwise would have to dig up somewhere else.

How can you combine it with other data sets?

In a future blog posts I’m hoping to tell you more about how to do the actual work to connect your data to other sets in order for it to make sense. But the heading I’m going for right now is to also look more at competitors/ or at least other people in the same space. There is probably a big overlap with them but there also will be a lot of keywords missing.

What’s next?

I’m nearing the end of the first alpha version to use, it will enable users to track their rankings wherever they want. Don’t dozens of tools already do that? Yes! I’m just trying to make the process more useful for bigger companies and provide users with more opportunities to expand their keyword arsenal. All with the goal to increase innovation in this space and to lower costs. It doesn’t have to be expensive to track thousands of keywords whenever you want.


20 Reasons Why Most Experiment Programs Are Setup for Failure

Over the course of the last few years I worked on over 200+ experiments, from a simple change to a Call To Action (CTA) up to complete design overhauls and full feature integrations into products. So far it taught me a lot about how to set up an experiment program and what you can mess up along the way that could have a major impact (good and/or bad). As I get a lot of questions these days on how to set up a new testing program or people asking me how to get started I created a slide deck that I gave a couple times this year at conferences about all the failures that I see & made myself running an experimentation program.

The (well known) process of A/B Testing

You’ve all seen this ‘circle’ process before. It shows the different stages of an experiment, you start with a ton of ideas, you create their hypothesis, you go on to designing & building them (with or without engineers), you do the appropriate Quality Assurance checks before launching, you run an analyze the results of your test. If all goes well you’re able to repeat this process endlessly. Sounds relatively easy, right? It could be, although along the way I’ve made mistakes in all of these steps. In this blog post I’d like to run you through the top 20 mistakes that I’ve (seen being) made.

You can also go through the slidedeck that I’ve presented at LAUNCH SCALE and Growth Marketing Conference:

Bad Experiments: The #18 Ways You’re A/B Tests are Going Wrong. from Martijn Scheijbeler

Ideas

1. They just launch, they just test.

One of the easiest to spot mistakes, as you’re basically not experimenting but putting features/products live without figuring out if they’re really going to have an impact on what you’re doing. That’s why you basically always want to give a certain feature a test run on a small percentage of your traffic, if your audience is big enough that could be just as little as 1% or for smaller companies run it 50%/%50. In that case it’s easier for you to isolate what the impact is, that’s the solution to this problem.

2. Companies that believe they’re wasting money with experimentation.

One of the most fun arguments to run into in my opinion. Whenever organisations think that by running so many experiments that don’t provide a winner it might kill their bottom line there are still some steps to take that will help them better understand experimentation. Most of the times this is easy to over come, ask them what they think the right way to go is with experimentation and let them pick the winners for a few experiments. Chances are about 100% that at least one of their answers will be proven wrong. Point being that whenever they would have made the decision based on gut feeling or experience it also would cost the organization money (and in most cases even way more money). That’s why it’s still important to quickly overcome this argument and get the buy-in of the whole organization to make sure people believe in experimentation.

3. Expect Big Wins.

It depends in what stage you are with your experimentation program, at the beginning it’s likely that you’ll pick up a lot of low hanging fruit that will provide you with some easy wins. But I promise it won’t get easier of time (read more about the local maximum here). You won’t be achieving big results all the time. But don’t give up, if you can still achieve a lot of small wins over time it will also sum up to a lot of results. If you expect that every test will double your business as you might read in (bad) blog posts, you won’t.

4. My Competitor is Doing X, so that’s why we’re testing X.

Wrong! Chances are your competitor also has no clue what they’re doing, just like you! So focus on what you should be doing best, know your own customers and focus on your own success. Even when you see your competition is running experiments, chances are high that they’re also not sure what will become a winner and what will be a loser. So focusing on repeating their success will only put you behind them as you need to spend maybe even longer then them figuring out what’s working and what’s not.

5. Running tests when you don’t have (enough) traffic.

Probably the most asked question around experimentation: How much traffic do I need to run a successful experiment on my site? Usually followed by: I don’t have that much traffic, should I still be focused on running experiments. What I’d recommend most of the time is figure out if you can successfully launch more than ~20 experiments yearly. If you have to wait too long on results for your experiments you might run into trouble with your analysis (see one of the items on this laster). This is combined most of the time with the fact that these teams are relatively small and don’t always have the capacity to do more with this it might be better to focus first on converting more users or focus on the top of the funnel (acquisition).

Hypothesis

6. They don’t create a hypothesis.

I can’t explain writing a hypothesis better than this blog post by Craig Sullivan. Where he lays out the frameworks for a simple and more advanced hypothesis. If you don’t have a hypothesis, you can’t use it to verify later on that your test has been successful or not. That’s why you want to make sure that you have documented how you are going to measure the impact and how you’ll be evaluating that the impact was big enough that you’ll deploy it.

7. Testing multiple variables at the same time, 3 changes require 3 tests.

Great, you realize that you need to test more. That’s a good step in the right direction. But over time changing too many elements on a specific page or across pages can make it hard to figure out what is leading to an actual change in results for an experiment. But if you need to show real results in an experiment you could turn this failure into a winner by running 1 experiment where you change a lot and seeing what the impact is. Which after you do you run more experiments that will prove what specific element brought most of the value. I’d like to do this from time to time, sometimes when you make small incremental changes time after time it could be that there is no clear winner. Running a big experiment will help in that case to see if you can impact the results with that. Once you do that, go back and experiment with smaller changes to see what exactly led to that result so you know going forward what potential areas are for experimentation that will provide big changes.

8. Use numbers as the basis of your research, not your gut feeling.

We like our green buttons more than our red ones. In the early days of experimentation an often heard reply. These days I still hear many variations of the same line. But what you want to make sure is that you use data as the basis for your experiment instead of a gut feeling. If you know based on research that you need to improve the submission rate for a form. You usually won’t be asking more questions but want to make sure that the flow of the form is getting more optimal to boost results. If you noticed in your heat maps or surveys that users are clicking in a certain area or can’t find the answer on a particular question they have you might want to add more buttons or a FAQ. By adding and testing you’re building on top of a hypothesis, like we discussed, before that is data driven.

Design & Engineering

9. Before and After is not an A/B test. We launched, let’s see what the impact is.

The most dangerous way of testing that I see companies do is testing: before > after. You’re testing what the impact is of a certain change by just launching it, which is dangerous considering that many surrounding factors are changing with that as well. With experiments like this it’s near impossible to really isolate the impact on the change, making it basically not an experiment but just a change where you’re hoping to see what the impact is.

10. They go over 71616 revisions for the design.

You want to follow your brand and design guidelines, I get that. It’s important as you don’t want to run something that is not going to open up to the world if it’s a winner. But if you’re trying to figure out what the perfect design solution is to a problem you’re probably wasting your time as that’s exactly why you’re running an experiment, to find the actual best variant. That’s why I would advise to come up with a couple of design ideas that you can experiment with and run the test as soon as possible to learn and adapt to the results as soon as possible.

Quality Assurance

11. They don’t Q&A their tests. Even your mother can have an opinion this time.

Most of the time your mother shouldn’t be playing a role in your testing program. The chances that she can tell you more about two tiered tests and how you should be interpreting your results then you do as an upcoming testing expert are very minimal. But what she can help you with is make sure that your tests are functionally working. Just make sure she’s segmented in your new variant and run her through the flow of your test. Is everything working as expected? Is nothing breaking? Does your code work in all browsers and across devices? With more complex tests I noticed that usually at least 1 element when you put it through some extensive testing, that’s why this step is so important in your program. Every test that is not working can be a waste of testing days in the years and one not spend on actually optimizing for positive returns.

Run & Analysis

12. Running your tests not long enough, calling the results early.

Technically you can run your test for 1 hour and achieve significance if you had the right amount of users + conversions in your tests. But that doesn’t always mean you should call the results of the test. A lot of business deal with longer lead/sales times which could influence the results, also weekends, weekdays whatever can influence your business is something that might have your results be different. You want to take all of this into account to make sure your results are as trustworthy as possible.

13. Running multiple tests with overlap.. it’s possible, but segment the sh*t out of your tests.

If you have the traffic to run multiple experiments at the same time you’ll likely run into the issue that your tests will overlap. If you run a test on the homepage and at the same time one on your product pages it’s likely that a user might end up in both experiments at the same time. Most people don’t realize that this is influencing the results of the experiment for both tests as theoretically you just ended running a Multivariate test across multiple pages. That’s why it’s important to also use this in your analysis, by creating the right segments where you audience is overlapping in multiple experiments but also by isolating the users in 1 segment.

14. Data is not sent to your main analytics tool, or you’re comparing your A/B testing tool to analytics, good luck.

You’re likely already using a tool for your Web Analytics; Google Analytics, Clicky, Adobe Analytics, Omniture, Amplitude, etc.. chances are that they’re tracking the core metrics that matter to your business. As most A/B testing tools are also measuring similar metrics that are relevant for your tests you’ll likely run into a discrepancy between the metrics, either on revenue (sales, revenue, conversion rate)  or regular visitor metrics (clicks, session, users). They’re loading before/after your main analytics tool and/or the definition of the metrics are different, that’s why you’ll always end up with some difference that can’t be explained. What I usually tried was making sure that all the information on an experiment is also captured in your main analytics tool (GA was usually the tool of my liking). Then you don’t have to worry about any discrepancies as you’re using your main analytics tool (which should be tracking everything related to your business) to analyze the impact of an experiment.

15. Going with your results without significance.

Your results are improving with 10% but the significance is only 75%. That’s a problem, it means that 25% of the time you don’t know for sure that the experiment is going to provide the results that you have so far (although you still would never know for sure as reaching 100% is impossible). With experimentation it’s a problem, in simple words: it basically means that you can’t trust the results of your experiment as they aren’t significant enough to say it’s a winner or a loser just yet. When you want to know if your results are significant make sure that you’re using a tool that can calculate this for you, one of these tools is this significance calculator. You enter the data from your experiment and you’ll find out what the impact was.

16. You run your tests for too long… more than 4 weeks is not to be advised, cookie deletion.

For smaller sites that don’t have a ton of traffic it can be hard to reach significance, they just need a lot of data to make a decision that is supported by it. But also for smaller sites that are running experiments on a smaller segment this could become an issue. If your test is running for multiple weeks, let’s say 4+ weeks, it’s going to be hard to measure the impact for this in a realistic way as it could be that people are deleting their cookies and a lot of surrounding variables might be changing during that period of time. What that means is that over time the context of the experiment might change too much which could have an effect on how you’re analyzing the results.

Repeat

17. Not deploying your winner fast enough, it takes 2 months to launch.

One of the aspects of experimentation is that you have to move fast (and not break things). When you find a winning variant in your experiment you want to have the benefits from it as soon as possible. That’s how you make a testing program worth it for your business. Too often I see companies (usually the bigger ones) having to deal with the rough implementation process to get something implemented for production purposes. A great failure because they can’t get the upside of the experiment and likely by the time they can finally launch the winning variant circumstances have changed so much that it might already need a re-test.

18. They’re not keeping track of their tests. No documentation.

Can you tell me what the variants looked like of the test that ran two months ago and what the significance level was for that specific test? You probably can’t as you didn’t keep track of your testing documentation. Definitely in bigger organizations and when you’re company is testing with multiple teams at the same time this is a big issue. As you’re collecting so many learnings over time it can be super valuable to keep track of them, so document what you’re doing. You don’t want to make the mistake that another team is implementing a clear loser that you’ve tested months ago. You want to prove to them that you’ve already ran the test before. Your testing documentation will help you with that, in addition it can be very helpful in organizing the numbers. If you want to know what you’ve optimized on a certain page it can probably tell you over time changing what elements brought most return.

19. They’re not retesting their previous ideas.

You tested something 5 months ago, but as so many variables changed it might be time to come up a new experiment that is re-testing your original evaluation. This also goes for experiments that did provide a clear winner, over time you still want to know if the uplift that noticed before is still going on or if the results have flattened over time. A retest is great for this as you’re testing your original hypothesis again to see what has been changed. It will provide you usually with even more learnings.

20. They give up.

Never give up, there is so much to learn about your audience when you keep on testing. You’ve never reached the limits! Keep on going whenever a new experiment doesn’t provide a new winner. The compound effect: incremental improvements is what lets most companies win!

That’s it, please don’t make all these mistakes anymore! I already made them for you..

What did I miss, what kind of failures did you have while setting up your experimentation program and what did you learn from them?


Measuring SEO Progress: From Start to Finish – Part 2: From Creation to Getting Links

How to measure (and over time forecast) the impact of features that you’re building for SEO and how to measure this from start to finish. In this series I already provided some more information on how to measure progress: from creation to traffic (part 1). This blog post (part 2) will go deeper into another aspect of SEO: getting more links and how you can measure the impact of that. We’ll go a bit more into depth on how you can easily (through 4 steps, 1 bonus step) get insights into the links that you’ve acquired and how to measure their impact.

1. Launch

You’ve spent a lot of time writing a new article or working on a new feature/product with your team, so the last thing you want is not to receive search traffic for it and not start ranking. For most keywords you’ll need to do some additional authority building to make sure you’ll get the love that you might be needing. But it’s going to be important to keep track of what’s happening around that to measure the impact of your links on your organic search traffic.

2. Monitor

So the first thing you’d like to know if your new page is getting any links, there are multiple ways to track this. For this you can use the regular link research tools, that we’ll talk about more in depth later in this piece. But one of the easiest ways for a link to show real impact is to figure out if you’re receiving traffic from it and when that time was. Just simple and easy to figure out in Google Analytics. Head to the traffic sources report and see for that specific page if you’re getting any referral traffic. Is that the case? Then try to figure out when the first visit was, you’ll be able to monitor more closely then since when you’ll have this link or look at the obvious thing, the published date if you can find it.

How to measure success?

Google Alerts, Mention, Just-Discovered Links (Moz) and as described Google Analytics. They’re are all tools that can be used to identify links that are coming in and might be relatively new. As they’re mentions in the news media or just the newest being picked up by a crawler. It’s important to know more about that as you don’t’ want to be dependent on a link index that is updating on an irregular basis.

3. Analyze

Over a longer period of time you want to know how your authority through links is increasing. While I’m not a huge fan of the ‘core metrics’ like Domain Authority, Page Authority, etc. as they can change without providing any context I rather look at the graphs and new and incoming root domains to see how fast that is growing. In the end it is a numbers game (usually more quality + quantity) so that’s the best way to see it. One of my favorite reports in Majestic is the cumulated links + domains so I can get an easy grasp of what’s happening. Are you rapidly growing up and to the right or is progress slow?

How to measure success?

One suggestion that I would have is to look at the cached pages for your links: So by now you’ve figured out what kind of links are sending traffic, so that’s a good first sign. But are they also providing any value for your SEO? Put the actual link into Google and see if the page is being indexed + cached. It is? Good for you, that means the page is of good enough quality and being cached for Google’s sake. It’s not, hmm then there is work to do for no and your actual page might need some authority boosting on its own.

4. Impact

Are you links really impacting what’s happening to the authority and ranking of the page. You would probably want to know. It’s one of the harder tasks to figure out as you have a lot of variables that can be playing a role in this. It’s basically a combination of the value of these links, which you could use one of the link research tools’ metrics for or just looking at the actual changes for search traffic for your landing page. Do you see any changes there?

5. Collect all the Links

In addition to getting insights into what kind of links might be impacting your rankings for a page you’ll likely want to know where all of your links can be find. That’s relatively simple, it’s just a matter of connecting all the tools together and using them in the most efficient way.

So sign up for at least the first three tools, as Google Search Console and Bing Webmaster Tools are free, you can use them to download your link profiles. When you sign up for Majestic you’re able to verify your account with your GSC account and get access to your own data when you connect your properties. So you just unlocked three ways of getting more data.

That’s still not enough? Think about getting a (paid) account at three other services so you can download their data and combine it with the previous data sets, you’re not going to be able to retrieve much more data and get a better overview as you’re now leveraging 6 different indexes.

(P.S. Take notice that all of them grow their indexes over time, a growing link profile might not always mean that you’re getting more links, it might be that they’re just getting better at finding them.)

How to measure success?

Download all the data on a regular basis (weekly, monthly, quarterly) and combine the data sets, as they’re all providing links and root domains you can easily add the sheets together and remove the duplicate values. You won’t have all the metrics per domain + link that way but still can get a pretty good insight into what your most popular linking root domains + links are.
In the previous part I talked more about measuring the impact from creation to getting traffic. Hopefully the next part will provide more information on how to measure business impact & potentially use the data for forecasting. In the end when you merge all these different areas you should be able to measure impact in any stage independently. What steps did I miss in this analysis and could use some more clarification?