Tag Archive | "Reporting"

Google now gives more preference to original reporting in search

Google has updated its algorithms to show more original reporting in search and Google News results.



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Microsoft Advertising says it’s keeping average position reporting

Position-based impression share metrics are now available.



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These are the Google Ads reporting metrics still affected by May 2 bug

The company is still working to fully correct reporting for May 1 and 2.



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Google Ads store visits, store sales reporting data partially corrected

Google says it is making progress, but there are still days for which reporting is inaccurate.



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All the ABM metrics to measure for your quarterly reporting

The traditional demand funnel doesn’t cut it for account-based marketing — so report on these KPIs instead.



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SearchCap: Reporting delays in Google Search Console, navigate in search & structure data

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.



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Automating Technical Reporting for SEO

Posted by petewailes

As the web gets more complex, with JavaScript framework and library front ends on websites, progressive web apps, single-page apps, JSON-LD, and so on, we’re increasingly seeing an ever-greater surface area for things to go wrong. When all you’ve got is HTML and CSS and links, there’s only so much you can mess up. However, in today’s world of dynamically generated websites with universal JS interfaces, there’s a lot of room for errors to creep in.

The second problem we face with much of this is that it’s hard to know when something’s gone wrong, or when Google’s changed how they’re handling something. This is only compounded when you account for situations like site migrations or redesigns, where you might suddenly archive a lot of old content, or re-map a URL structure. How do we address these challenges then?

The old way

Historically, the way you’d analyze things like this is through looking at your log files using Excel or, if you’re hardcore, Log Parser. Those are great, but they require you to know you’ve got an issue, or that you’re looking and happen to grab a section of logs that have the issues you need to address in them. Not impossible, and we’ve written about doing this fairly extensively both in our blog and our log file analysis guide.

The problem with this, though, is fairly obvious. It requires that you look, rather than making you aware that there’s something to look for. With that in mind, I thought I’d spend some time investigating whether there’s something that could be done to make the whole process take less time and act as an early warning system.

A helping hand

The first thing we need to do is to set our server to send log files somewhere. My standard solution to this has become using log rotation. Depending on your server, you’ll use different methods to achieve this, but on Nginx it looks like this:

# time_iso8601 looks like this: 2016-08-10T14:53:00+01:00
if ($  time_iso8601 ~ "^(\d{4})-(\d{2})-(\d{2})") {
        set $  year $  1;
        set $  month $  2;
        set $  day $  3;
}
<span class="redactor-invisible-space">
</span>access_log /var/log/nginx/$  year-$  month-$  day-access.log;

This allows you to view logs for any specific date or set of dates by simply pulling the data from files relating to that period. Having set up log rotation, we can then set up a script, which we’ll run at midnight using Cron, to pull the log file that relates to yesterday’s data and analyze it. Should you want to, you can look several times a day, or once a week, or at whatever interval best suits your level of data volume.

The next question is: What would we want to look for? Well, once we’ve got the logs for the day, this is what I get my system to report on:

30* status codes

Generate a list of all pages hit by users that resulted in a redirection. If the page linking to that resource is on your site, redirect it to the actual end point. Otherwise, get in touch with whomever is linking to you and get them to sort the link to where it should go.

404 status codes

Similar story. Any 404ing resources should be checked to make sure they’re supposed to be missing. Anything that should be there can be investigated for why it’s not resolving, and links to anything actually missing can be treated in the same way as a 301/302 code.

50* status codes

Something bad has happened and you’re not going to have a good day if you’re seeing many 50* codes. Your server is dying on requests to specific resources, or possibly your entire site, depending on exactly how bad this is.

Crawl budget

A list of every resource Google crawled, how many times it was requested, how many bytes were transferred, and time taken to resolve those requests. Compare this with your site map to find pages that Google won’t crawl, or that it’s hammering, and fix as needed.

Top/least-requested resources

Similar to the above, but detailing the most and least requested things by search engines.

Bad actors

Many bots looking for vulnerabilities will make requests to things like wp_admin, wp_login, 404s, config.php, and other similar common resource URLs. Any IP address that makes repeated requests to these sorts of URLs can be added automatically to an IP blacklist.

Pattern-matched URL reporting

It’s simple to use regex to match requested URLs against pre-defined patterns, to report on specific areas of your site or types of pages. For example, you could report on image requests, Javascript files being called, pagination, form submissions (via looking for POST requests), escaped fragments, query parameters, or virtually anything else. Provided it’s in a URL or HTTP request, you can set it up as a segment to be reported on.

Spiky search crawl behavior

Log the number of requests made by Googlebot every day. If it increases by more than x%, that’s something of interest. As a side note, with most number series, a calculation to spot extreme outliers isn’t hard to create, and is probably worth your time.

Outputting data

Depending on what the importance is of any particular section, you can then set the data up to be logged in a couple of ways. Firstly, large amounts of 40* and 50* status codes or bad actor requests would be worth triggering an email for. This can let you know in a hurry if something’s happening which potentially indicates a large issue. You can then get on top of whatever that may be and resolve it as a matter of priority.

The data as a whole can also be set up to be reported on via a dashboard. If you don’t have that much data in your logs on a daily basis, you may simply want to query the files at runtime and generate the report fresh each time you view it. On the other hand, sites with a lot of traffic and thus larger log files may want to cache the output of each day to a separate file, so the data doesn’t have to be computed. Obviously the type of approach you use to do that depends a lot on the scale you’ll be operating at and how powerful your server hardware is.

Conclusion

Thanks to server logs and basic scripting, there’s no reason you should ever have a situation where something’s amiss on your site and you don’t know about it. Proactive notifications of technical issues is a necessary thing in a world where Google crawls at an ever-faster rate, meaning that they could start pulling your rankings down thanks to site downtime or errors within a matter of hours.

Set up proper monitoring and make sure you’re not caught short!

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Let Data Take the Wheel – Using API-Integrated Reporting Dashboards

Posted by IanWatson

Some say the only constant thing in this world is change — and that seems to go double for the online marketing and SEO industry. At times this can seem daunting and sometimes insurmountable, but some have found ways to embrace the ambiguity and even thrive on it. Their paths and techniques may all differ slightly, but a commonality exists among them.

That commonality is the utilization of data, mainly via API-driven custom tools and dashboards. APIs like Salesforce’s Chatter, Facebook’s Graph, and our very own Mozscape all allow for massive amounts of useful data to be integrated into your systems.

So, what do you do with all that data?

The use cases are limitless and really depend on your goals, business model, and available resources. Many in our industry, including myself, still rely heavily upon spreadsheets to manage large data sets.

However, the amount of native data and data within reach has grown drastically, and can quickly become unwieldy.

An example of a live reporting dashboard from Klipfolio.

Technology to the rescue!

Business intelligence (BI) is a necessary cog in the machine when it comes to running a successful business. The first step to incorporating BI into your business strategy is to adopt real-time reporting. Much like using Google Maps (yet another API!) on your phone to find your way to a new destination, data visualization companies like Klipfolio, Domo, and Tableau have built live reporting dashboards to help you navigate the wild world of online marketing. These interactive dashboards allow you in integrate data from several sources to better assist you in making real-time decisions.

A basic advertising dashboard.

For example, you could bring your ad campaign, social, and web analytics data into one place and track key metrics and overall performance in real-time. This would allow you to delegate extra resources towards what’s performing best, pulling resources from lagging activities in the funnel as they are occurring. Or perhaps you want to be ahead of the curve and integrate some deep learning into your analysis? Bringing in an API like Alchemy or a custom set-up from Algorithmia could help determine what the next trends are before they even happen. This is where the business world is heading; you don’t want to fall behind.

Resistance is futile.

The possibilities of real-time data analysis are numerous, and the first step towards embracing this new-age necessity is to get your first, simple dashboard set up. We’re here to help. In fact, our friends at Klipfolio were nice enough to give us step-by-step instructions on integrating our Mozscape data, Hubspot data, and social media metrics into their live reporting dashboard — even providing a live demo reporting dashboard. This type of dash allows you to easily create reports, visualize changes in your metrics, and make educated decisions based on hard data.

Create a live reporting dashboard featuring Moz, Hubspot and social data

1. First, you’ll need to create your Mozscape API key. You’ll need to be logged into your existing Moz account, or create a free community or pro Moz account. Once you’re logged in and on the API key page, press “Generate Key.”

2. This is the key you’ll use to access the API and is essentially your password. This is also the key you’ll use for step 6, when you’re integrating this data into Klipfolio.

3. Create a free 14-day Klipfolio trial. Then select “Add a Klip.”

4. The Klip Gallery contains pre-built widgets for your whatever your favorite services might be. You can find Klips for Facebook, Instagram, Alexa, Adobe, Google Adwords and Analytics, and a bunch of other useful integrations. They’re constantly adding more. Plus, in Klipfolio, you can build your own widgets from scratch.

For now, let’s keep it simple. Select “Moz” in the Klip Gallery.

5. Pick the Klip you’d like to add first, then click “Add to Dashboard.”

6. Enter your API key and secret key. If you don’t have one already, you can get your API key and secret ID here.

7. Enter your company URL, followed by your competitors’ URLs.

8. Voilà — it’s that easy! Just like that, you have a live look at backlinks on your own dash.

9. From here, you can add any other Moz widgets you want by repeating steps 5–8. I chose to add in MozRank and Domain Authority Klips.

10. Now let’s add some social data streams onto our dash. I’m going to use Facebook and Twitter, but each of the main social media sites have similar setup processes.

11. Adding in other data sources like Hubspot, Searchmetrics, or Google Analytics simply requires you to bet set up with those parties and to allow Klipfolio access.

12. Now that we have our Klips set up, the only thing left to do is arrange the layout to your liking.

After you have your preferred layout, you’re all set! You’ve now entered the world of business intelligence with your first real-time reporting dashboard. After the free Klipfolio trial is complete, it’s only $ 20/month to continue reporting like the pros. I haven’t found many free tools in this arena, but this plan is about as close as you’ll come.

Take a look at a live demo reporting dash, featuring all of the sources we just went over:

Click to see a larger version.

Conclusion

Just like that, you’ve joined the ranks of Big SEO, reporting like the big industry players. In future posts we’ll bring you more tutorials on building simple tools, utilizing data, and mashing it up with outside sources to better help you navigate the ever-changing world of online business. There’s no denying that, as SEO and marketing professionals, you’re always looking for that next great innovation to give you and your customers a competitive advantage.

From Netflix transitioning into an API-centric business to Amazon diving into the API management industry, the largest and most influential companies out there realize that utilizing large data sets via APIs is the future. Follow suit: Let big data and business intelligence be your guiding light!

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SearchCap: Google Mobile Usability Reporting, Flash Warnings Expand & Groupon Pages

Below is what happened in search today, as reported on Search Engine Land and from other places across the web. From Search Engine Land: Groupon Pages Part Of Company Evolution Into Local Search Site Depending on your viewpoint, Groupon’s new Pages offering is either a helpful new tool for…



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CRO Statistics: How to Avoid Reporting Bad Data

Posted by CraigBradford

Without a basic understanding of statistics, you can often present misleading results to your clients or superiors. This can lead to underwhelming results when you roll out new versions of a page which on paper look like they should perform much better. In this post I want to cover the main aspects of planning, monitoring and interpreting CRO results so that when you do roll out new versions of pages, the results are much closer to what you would expect. I’ve also got a free tool to give away at the end, which does most of this for you.

Planning

A large part running a successful conversion optimisation campaign starts before a single visitor reaches the site. Before starting a CRO test it’s important to have:

  1. A hypothesis of what you expect to happen
  2. An estimate of how long the test should take
  3. Analytics set up correctly so that you can measure the effect of the change accurately

Assuming you have a hypothesis, let’s look at predicting how long a test should take.

How long will it take?

As a general rule, the less traffic that your site gets and/or the lower the existing conversion rate, the longer it will take to get statistically significant results. There’s a great tool by Evan Miller that I recommend using before starting any CRO project. Entering the baseline conversion rate and the minimum detectable effect (i.e. What is the minimum percentage change in conversion rate that you care about, 2%? 5%? 20%?) you can get an estimate of how much traffic you’ll need to send to each version. Working backwards from the traffic your site normally gets, you can estimate how long your test is likely to take. When you arrive on the site, you’ll see the following defaults:

Notice the setting that allows you to swap between ‘absolute’ and ‘relative’. Toggling between them will help you understand the difference, but as a general rule, people tend to speak about conversion rate increases in relative terms. For example:

Using a baseline conversion rate of 20%

  • With a 5% absolute improvement – the new conversion rate would be 25%
  • With a 5% relative improvement - the new conversion would be 21%

There’s a huge difference in the sample size needed to detect any change as well. In the absolute example above, 1,030 visits are needed to each branch. If you’re running two test versions against the original, that looks like this:

  • Original – 1,030
  • Version A – 1,030
  • Version B – 1,030

Total 3,090 visits needed.

If you change that to relative, that drastically changes: 25,255 visits are needed for each version. A total of 75,765 visits.

If your site only gets 1,000 visits per month and you have a baseline conversion rate of 20%, it’s going to take you 6 years to detect a significant relative increase in conversion rate of 5% compared to only around 3 months for an absolute change of the same size.

This is why the question of whether or not small sites can do CRO often comes up. The answer is yes, they can, but you’ll want to aim higher than a 5% relative increase in conversions. For example, If you aim for a 35% relative increase (with 20% baseline conversion), you’ll only need 530 visits to each version. In summary, go big if you’re a small site. Don’t test small changes like button changes, test complete new landing pages, otherwise it’s going to take you a very long time to get significantly better results.

Analytics

A critical part of understanding your test results is having appropriate tracking in place. At Distilled we use Optimizely so that’s what I’ll cover today; fortunately Optimizely makes testing and tracking really easy. All you need is a Google analytics account that has a custom variable (custom dimension in universal analytics) slot free. For either Classic or Universal Analytics, begin by going to the Optimizely Editor, then clicking Options > Analytics Integration. Select enable and enter the custom variable slot that you want to use, that’s it. For more details, see the help section on the Optimizely website here.

With Google analytics tracking enabled, now when you go to the appropriate custom variable slot in Google Analytics, you should see a custom variable named after the experiment name. In the example below the client was using custom variable slot 5:

This is a crucial step. While you can get by by just using Optimizely goals like setting a thankyou page as a conversion, it doesn’t give you the full picture. As well as measuring conversions, you’ll also want to measure behavioral metrics. Using analytics allows you to measure not only conversions, but other metrics like average order value, bounce rates, time on site, secondary conversions etc.

Measuring interaction

Another thing that’s easy to measure with Optimizely is interactions on the page, things like clicking buttons. Even if you don’t have event tracking set up in Google Analytics, you can still measure changes in how people interact with the site. It’s not as simple as it looks though. If you try and track an element in the new version of a page, you’ll get an error message saying that no items are being tracked. See the example from Optimizely below:

Ignore this message, as long as you’ve highlighted the correct button before selecting track clicks, the tracking should work just fine. See the help section on Optimizely for more details.

Interpreting results

Once you have a test up and running, you should start to see results in Google Analytics as well as Optimizely. At this point, there’s a few things to understand before you get too disappointed or excited.

Understanding statistical significance

If you’re using Google analytics for conversion rates, you’ll need something to tell you whether or not your results are statistically significant – I like this tool by Kiss Metrics which looks like this:

It’s easy to look at the above and celebrate your 18% increase in conversions – however you’d be wrong. It’s easier to explain what this means with an example. Let’s imagine you have a pair of dice that we know are exactly the same. If you were to roll each die 100 times, you would expect to see each of the numbers 1-6 the same number of times on both die (which works out at around 17 times per side). Let’s say on this occasion though we are trying to see how good each die is at rolling a 6. Look at the results below:

  • Die A – 17/100 = 0.17 conversion rate
  • Die B – 30/100 = 0.30 conversion rate

A simplistic way to think about Statistical significance is it’s the chance that getting more 6s on the second die was just a fluke and that it hasn’t been optimised in some way to roll 6s.

This makes sense when we think about it. Given that out of 100 rolls we expect to roll a 6 around 17 times, if the second time we rolled a 6 19/100 times, we could believe that we just got lucky. But if we rolled a 6 30/100 times (76% more), we would find it hard to believe that we just got lucky and the second die wasn’t actually a loaded die. If you were to put these numbers into a statistical significance tool (2 sided t-test), it would say that B performed better than A by 76% with 97% significance.

In statistics, statistical significance is the complement of the P value. The P value in this case is 3% and the complement therefore being 97% (100-3 = 97). This means there’s a 3% chance that we’d see results this extreme if the die are identical.

When we see statistical significance in tools like Optimizely, they have just taken the complement of the P-value (100-3 = 97%) and displayed it as the chance to beat baseline. In the example above, we would see a chance to beat baseline of 97%. Notice that I didn’t say there’s a 97% chance of B being 76% better – it’s just that on this occasion the difference was 76% better.

This means that if we were to throw each dice 100 times again, we’re 97% sure we would see noticeable differences again, which may or may not be by as much as 76%. So, with that in mind here is what we can accurately say about the dice experiment:

  • There’s a 97% chance that die B is different to die A

Here’s what we cannot say:

  • There’s a 97% chance that die B will perform 76% better than die A

This still leaves us with the question of what we can expect to happen if we roll version B out. To do this we need to use confidence intervals.

Confidence intervals

Confidence intervals help give us an estimate of how likely a change in a certain range is. To continue with the dice example, we saw an increase in conversions by 76%. Calculating confidence intervals allow us to say things like:

  • We’re 90% sure B will increase the number of 6s you roll by between 19% to 133%
  • We’re 99% sure B will increase the number of 6s you roll by between -13% to 166%

Note: These are relative ranges. That being -13% less than 17% and 166% greater than 17%.

The three questions you might be asking at this point are:

  1. Why is the range so large?
  2. Why is there a chance it could go negative?
  3. How likely is the difference to be on the negative side of the range?

The only way we can reduce the range of the confidence intervals is by collecting more data. To decrease the chance of the difference being less than 0 (we don’t want to roll out a version that performs worse than the original) we need to roll the dice more times. Assuming the same conversion rate of A (0.17%) and B (0.3%) – look at the difference increasing the sample size makes on the range of the confidence intervals.

As you can see, with a sample size of 100 we have a 99% confidence range of -13% to 166%. If we kept rolling the dice until we had a sample size of 10,000 the 99% confidence range looks much better, it’s now between 67% better and 85% better.

The point of showing this is to show that even if you have a statistically significant result, it’s often wise to keep the test running until you have tighter confidence intervals. At the very least I don’t like to present results until the lower limit of the 90% interval is greater than or equal to 0.

Calculating average order value

Sometimes conversion rate on its own doesn’t matter. If you make a change that makes 10% fewer people buy, but those that do buy spend 10x more money, then the net effect is still positive.

To track this we need to be able to see the average order value of the control compared to the test value. If you’ve set up Google analytics integration like I showed previously, this is very easy to do.

If you go into Google analytics, select the custom variable tab, then select the e-commerce view, you’ll see something like:

  • Version A 1000 visits – 10 conversions – Average order value $ 50
  • Version B 1000 visits – 10 conversions – Average order value $ 100

It’s great that people who saw version B appear to spend twice as much, but how do we know if we just got lucky? To do that we need to do some more work. Luckily, there’s a tool that makes this very easy and again this is made by Evan Miller: Two sample t-test tool.

To find out if the change in average order value is significant, we need a list of all the transaction amounts for version A and version B. The steps to do that are below:

1 - Create an advanced segment for version A and version B using the custom variable values.

2 - Individually apply the two segments you’ve just created, go to the transactions report under e-commerce and download all transaction data to a CSV.

3 - Dump data into the two-sample t-test tool

The tool doesn’t accept special characters like $ or £ so remember to remove those before pasting into the tool. As you can see in the image below, I have version A data in the sample 1 area and the transaction values for version B in the sample 2 area. The output can be seen in the image below:

Whether or not the difference is significant is shown below the graphs. In this case the verdict was that sample 1 was in fact significantly different. To find out the difference, look at the “d” value where is says “difference of means”. In the example above the transactions of those people that saw the test version were on average $ 19 more than those that saw the original.

A free tool for reading this far

If you run a lot of CRO tests you’ll find yourself using the above tools a lot. While they are all great tools, I like to have these in one place. One of my colleagues Tom Capper built a spreadsheet which does all of the above very quickly. There’s 2 sheets, conversion rate and average order value. The only data you need to enter in the conversion rate sheet is conversions and sessions, and in the AOV sheet just paste in the transaction values for both data sets. The conversion rate sheet calculates:

  1. Conversion rate
  2. Percentage change
  3. Statistical significance (one sided and two sided)
  4. 90,95 and 99% confidence intervals (Relative and absolute)

There’s an extra field that I’ve found really helpful (working agency side) that’s called “Chance of <=0 uplift”.

If like the example above, you present results that have a potential negative lower range of a confidence interval:

  • We’re 90% sure B will increase the number of 6s you roll by between 19% and 133%
  • We’re 99% sure B will increase the number of 6s you roll by between -13% and 166%

The logical question a client is going to ask is: “What chance is there of the result being negative?”

That’s what this extra field calculates. It gives us the chance of rolling out the new version of a test and the difference being less than or equal to 0%. For the data above, the 99% confidence interval was -13% to +166%. The fact that the lower limit of the range is negative doesn’t look great, but using this calculation, the chance of the difference being <=0% is only 1.41%. Given the potential upside, most clients would agree that this is a chance worth taking.

You can download the spreadsheet here: Statistical Significance.xls

Feel free to say thanks to Tom on Twitter.

This is an internal tool so if it breaks, please don’t send Tom (or me) requests to fix/upgrade or change.

If you want to speed this process up even more, I recommend transferring this spreadsheet into Google docs and using the Google Analytics API to do it automatically. Here’s a good post on how you can do that.

I hope you’ve found this useful and if you have any questions or suggestions please leave a comment.

If you want to learn more about the numbers behind this spreadsheet and statistics in general, some blog posts I’d recommend reading are:

Why your CRO tests fail

How not to run an A/B test

Scientific method: Statistical errors

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