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• Multivariate Testing vs A/B Testing (Quick-Start Guide)

  7 March 2024, by Erin

  Traditional advertising (think Mad Men) was all about slogans, taglines and coming up with a one-liner that was meant to change the world.

  But that type of advertising was extremely challenging to test, so it was hard to know if it worked. Most of the time, nobody knew if they were being effective with their advertising.

  Enter modern marketing: the world of data-driven advertising.

  Thanks to the internet and web analytics tools like Matomo, you can quickly test almost anything and improve your site.

  The question is, should you do multivariate testing or A/B testing?

  While both have their advantages, each has a specific use case.

  In this guide, we’ll break down the differences between multivariate and A/B testing, offer some pros and cons of each and show you some examples so you can decide which one is best for you.

  What is A/B testing?

  A/B testing, or split testing, is testing an individual element in a medium against another version of the same element to see which produces better results.

  

  A/B tests are conducted by creating two different versions of a digital landmark: a website, landing page, email, or advertisement.

  The goal? Figure out which version performs better.

  Let’s say, for example, you want to drive more sales on your core product page.

  You test two call-to-action buttons: “Buy Now” and “Add to Cart.”

  After running the test for two weeks, you see that “Buy Now” produced 1.2% conversions while “Add to Cart” produced 7.6%.

  In this scenario, you’ve found your winner: version B, “Add to Cart.”

  By conducting A/B tests regularly, you can optimise your site, increase engagement and convert more visitors into customers.

  Keep in mind that A/B testing isn’t perfect; it doesn’t always produce a win.

  According to Noah Kagan, founder of AppSumo, only 1 out of 8 A/B tests his company conducts produces significant change.

  Advantages of A/B testing

  A/B testing is great when you need to get an accurate result fast on a specific element of your marketing efforts.

  Whether it’s a landing page or product page, you can get quick results without needing a lot of traffic.

  A/B testing is one of the most widely accepted and used testing methods for marketers and business owners.

  When you limit the number of tracked variables used in a test, you can quickly deliver reliable data, allowing you to iterate and pivot quickly if necessary.

  This is a great way to test your marketing methods, especially if you’re a newer business or you don’t have substantial traffic yet.

  Splitting up your traffic into a few segments (like with multivariate testing) will be very challenging to gain accurate results if you have lower daily traffic.

  One final advantage of A/B testing is that it’s a relatively easy way to introduce testing and optimising to a team, decision-maker, or stakeholder since it’s easy to implement. You can quickly demonstrate the value with a simple change and tangible evidence.

  Disadvantages of A/B testing

  So, what are the downsides to A/B testing?

  Although A/B testing can get you quick results on small changes, it has limitations.

  A/B testing is all about measuring one element against another.

  This means you’re immediately limited in how many elements you can test. If you have to test out different variables, then A/B testing isn’t your best option since you’ll have to run test after test to get your result.

  If you need specific information on how different combinations of elements interact with one another on a web page, then multivariate is your best option.

  What is multivariate testing?

  If you want to take your testing to the next level, you’ll want to try multivariate testing.

  Multivariate testing relies on the same foundational mechanism of A/B testing, but instead of matching up two elements against one another, it compares a higher number of variables at once.

  Multiple + variations = multivariate.

  Multivariate testing looks at how combinations of elements and variables interact.

  Like A/B testing, traffic to a page is split between different web page versions. Multivariate testing aims to measure each version’s effectiveness against the other versions.

  Ultimately, it’s about finding the winning combination.

  

  When to use multivariate testing

  The quick answer on when to use multivariate testing is if you have enough traffic.

  Just how much traffic, though?

  While there’s no set number, you should aim to have 10,000 visitors per month or more, to ensure that each variant receives enough traffic to produce meaningful results within a reasonable time frame.

  Once you meet the traffic requirement, let’s talk about use cases.

  Let’s say you want to introduce a new email signup.

  But you want to create it from scratch and aren’t sure what will make your audience take action.

  So, you create a page with a signup form, a header, and an image.

  To run a multivariate test, you create two lengths of signup forms, four headlines, and two images.

  Next, you would create a test to split traffic between these sixteen combinations.

  Advantages of multivariate testing

  If you have enough traffic, multivariate testing can be an incredible way to speed up your A/B testing by testing dozens of combinations of your web page.

  This is handy when creating a new landing page and you want to determine if specific parts of your design are winners — which you can then use in future campaigns.

  Disadvantages of multivariate testing

  The main disadvantage of multivariate testing is that you need a lot of traffic to get started.

  If you try to do a multivariate analysis but you’re not getting much traffic, your results won’t be accurate (and it will take a long time to see accurate data).

  Additionally, multivariate tests are more complicated. They’re best suited for advanced marketers since more moving parts are at play.

  Key differences between multivariate and A/B testing

  Now that we’ve covered what A/B and multivariate tests are, let’s look at some key differences to help clarify which is best for you.

  

  1. Variation of combinations

  The major difference between A/B and multivariate testing is the number of combinations involved.

  With A/B testing, you only look at one element (no combinations). You simply take one part of your page (i.e., your headline copy) and make two versions.

  With multivariate testing, you’re looking at combinations of different elements (i.e., headline copy, form length, images).

  2. Number of pages to test

  The next difference lies in how many pages you will test.

  With an A/B test, you are splitting traffic on your website to two different pages: A and B.

  However, with multivariate testing, you will likely have 4-16 different test pages.

  This is because dozens of combinations can be created when you start testing a handful of elements at once.

  For example, if you want to test two headlines, two form buttons and two images on a signup form, then you have several combinations:

  • Headline A, Button A, Image A
  • Headline A, Button A, Image B
  • Headline A, Button B, Image A
  • Headline A, Button B, Image B
  • Headline B, Button A, Image A
  • Headline B, Button A, Image B
  • Headline B, Button B, Image A
  • Headline B, Button B, Image B

  In this scenario, you must create eight pages to send traffic to.

  3. Traffic requirements

  The next major difference between the two testing types is the traffic requirements.

  With A/B testing, you don’t need much traffic at all.

  Since you’re only testing two pages, you can split your traffic in half between the two types.

  However, if you plan on implementing a multivariate test, you will likely be splitting your traffic at least four or more ways.

  This means you need to have significantly more traffic coming in to get accurate data from your test. If you try to do this when your traffic is too low, you won’t have a large enough sample size.

  4. Time requirements

  Next up, just like traffic, there’s also a time requirement.

  A/B testing only tests two versions of a page against each other (while testing a single element). This means you’ll get accurate results faster than a multivariate test — usually within days.

  However, for a multivariate test, you might need to wait weeks. This is because you’re splitting your traffic by 4, 8, 12, or more web page variations. This could take months since you need a large enough sample size for accuracy.

  5. Big vs. small changes

  Another difference between A/B testing and multivariate testing is the magnitude of changes.

  With an A/B test, you’re looking at one element of a page, which means changing that element to the winning version isn’t a major overhaul of your design.

  But, with multivariate testing, you may find that the winning combination is drastically different than your control page, which could lead to a significant design change.

  6. Accuracy of results

  A/B tests are easier to decipher than multivariate testing since you only look at two versions of a single element on a page.

  You have a clear winner if one headline yields a 5% conversion rate and another yields a 1.2% conversion rate.

  But multivariate testing looks at so many combinations of a page that it can be a bit trickier to decipher what’s moving the needle.

  Pros and cons: Multivariate vs. A/B testing

  Before picking your testing method of choice, let’s look at some quick pros and cons.

  

  A/B testing pros and cons

  Here are the pros and cons of A/B testing:

  Pros

  • Get results quickly
  • Results are easier to interpret
  • Lower traffic requirement
  • Easy to get started

  Cons

  • You need to be hyper-focused on the right testing element
  • Requires performing test after test to optimise a web page

  Multivariate testing pros and cons

  Here are the pros and cons of multivariate testing:

  Pros

  • Handy when redesigning an entire web page
  • You can test multiple variables at once
  • Significant results (since traffic is higher)
  • Gather multiple data insights at once

  Cons

  • Requires substantial traffic
  • Harder to accurately decipher results
  • Not as easy to get started (more advanced)

  Use Matomo to start testing and improving your site

  

  You need to optimise your website if you want to get more leads, land more conversions and grow your business.

  A/B testing and multivariate testing are proven testing methods you can lean on to improve your website and create a better user experience.

  You may prefer one testing method now over the other, and that’s okay.

  The main thing is you’re starting to test. The best marketers and analysts in the world find what works through testing and double down on their winning tactics.

  If you want to start improving your website with testing today, get started with Matomo for free.

  With Matomo, you can conduct A/B tests and multivariate tests easily, accurately, and ethically. Unlike other web analytics tools, Matomo prioritises privacy, providing
  100% accurate data without sampling, and eliminates the need for cookie consent
  banners (except in the UK and Germany).

  Try Matomo free for 21-days. No credit card required.

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• Encoding AV1 to h264 error: Assertion pkt failed at D:/code/ffmpeg/src/fftools/ffmpeg_dec.c:518 [closed]

  1 March 2024, by KyonGiang

  I used below code to convert from AV1 to x264 mp4, and I got error message: "Assertion pkt failed at D:/code/ffmpeg/src/fftools/ffmpeg_dec.c:518"

  


  ffmpeg -i input.mp4 -c:v h264_nvenc -c:a copy output.mp4 


  


  

  • I'm using Windows 10 64b, Command Prompt, ffmpeg version 2024-01-20-git-6c4388b468-full_build-www.gyan.dev

  


  


  I removed h264_nvenc, Run as Administrator but the result is same.

  


  ffmpeg -i input.mp4 -c:v -c:a copy output.mp4

  


• Node 18 or Node 20 break ffmpeg (in google cloud functions -> ffprobe was killed with signal SIGSEGV)

  10 January 2024, by user20206929

  Please see below, the code is working on node js 16, but not when upgrading to node 18 or 20.

  


  const ffmpeg = require("fluent-ffmpeg");

// Following is inside a .https.onRequest Google Cloud function with enough memory

try {
  const duration = new Promise((resolve, reject) => {
  ffmpeg.ffprobe(videoUrl, async (err, metadata) => {
    if (err) {
      if (res.headersSent) {
        console.error("Response already sent");
        return;
      } else {
        console.log("Metadata:", metadata);
        console.log("err: " + err);
        res.status(400).send("Error getting video metadata");
        return;
      }
    }
  const duration = metadata.format.duration;
  console.log("video duration in second: " + duration);
  resolve(duration);
  });
});
  videoDuration = await duration;
} catch (err) {
  console.log(err);
  throw err;
}


  


  When upgrading to node 18/20 (No other change than upgrading node), the error "ffprobe not found" appears.

  


  But setting the path manually using ffmpeg.setFfprobePath(ffprobePath);
trigger the error: Error: ffprobe was killed with signal SIGSEGV

  


  So it seem its a permissions issue.

  


  However, I tried a lot of different solutions, none of them made this work.
For instance i tried to download manually the ffprobe from the official website https://ffbinaries.com/downloads. Then manually add it to the code.

  


  I tried to use https://www.npmjs.com/package/@ffprobe-installer/ffprobe or others package like https://www.npmjs.com/package/ffprobe-static

  


  I also tried to download the ffprobe file to the temporary folder of google cloud, and change the permission of this folder.

  


  All of those was doing the same error.

  


  None of what i could think of made any difference.

  


  Please help because i need to update node 16 to 18 or 20 before google remove node 16 on january 31 2024 and for now i don't see a solution.

  


  I also looked for other solution to get this duration from a video file url, but using ffmpeg seem to be the only one that should work out of the box. As it is working on node 16.

  


  Thank you,

  


  UPDATE - 11/26/2023

  


  GCP Functions NodeJS 16 runtime uses Ubuntu 18.04 with FFMpeg installed.
NodeJS 18/20 use Ubuntu 22.04, and Google decided not to include FFMpeg.

  


  https://cloud.google.com/functions/docs/runtime-support#node.js
https://cloud.google.com/functions/docs/reference/system-packages

  


  No workaround or solutions found as of now

  


  UPDATE - 01/10/2024

  


  Google added back ffmpeg to latest version, this is working as before now.