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• How to Increase Conversions With Form Analysis

  30 January 2024, by Erin

  Forms are one of the most important elements of your website. They are also one of the most difficult elements to analyse and improve. 

  Unlike a webpage, forms aren’t all that easy to analyse with standard web analytics tools. You need to learn how to conduct form analysis if you want to improve your forms’ conversion rates and increase revenue. 

  In this article, we’ll explain what form analysis is and why conducting a thorough form analysis is so important. 

  What is form analysis?

  Form analysis is a process that measures the effectiveness of your forms. Form analysis uses several tools and techniques like a form analytics platform, heatmaps, and session recordings to collect user data and understand how visitors behave when filling in forms. 

  The goal is to improve the design and effectiveness of your forms, reducing abandonment rate and encouraging more users to submit them. 

  There are plenty of reasons visitors could be having trouble with your forms, from confusing form fields to poor design and lengthy verification processes. Form analytics can help you pinpoint why your form’s conversion rate is so low or why so many users abandon your form halfway through filling it in. 

  Why is form analysis important?

  Website forms have some of the highest bounce rates and abandonments of any website element. By analysing your forms, you can achieve the following outcomes:

  

  Reduce form abandonment

  When it’s tough enough to get users to start filling in your form, the last thing you want them to do is abandon it halfway through. But that’s probably what your users are doing more than you’d like to think. 

  Why are they abandoning it? Even if you’re humble enough to admit you didn’t create the greatest form the world’s ever seen, it can still be incredibly difficult to pin down why users give up on your form.

  That’s unless you conduct a form analysis. By analysing metrics and user behaviour, you can pinpoint and rectify the issues that cause users to abandon your form. 

  Improve the user experience

  Best practices will only take you so far. How users behave when filling in a form on your website may be completely different to how they behave on another site. That’s why you need to use form analysis to understand how users behave specifically on your website — and then use that information to optimise the design, layout, and content of the form to better suit them. 

  If one field is regularly left empty, for example, you can delete it. If users spend several minutes filling out a form with a high abandonment rate, you could shorten it. 

  The goal isn’t to make the best form ever but to make the best form for your audience. 

  Increase conversions

  Ultimately, form analysis helps you improve your form’s most important metric: conversions. Reducing your abandonment rate will naturally lead to more completions, but so will taking advantage of other optimisation opportunities that only become clear with form analysis. This can include optimisations like:

  • Moving the form higher up on the page
  • Shortening the form
  • Changing the heading and CTAs
  • Renaming field labels 

  A thorough form analysis process can ensure your forms generate as many conversions as possible. 

  Why do users abandon forms?

  Are you already suffering from high form abandonment rates? Don’t worry, you’re not alone. Marketers regularly make the same mistakes when creating forms that cause users to give up halfway through completion.

  Here are some of the most common reasons for form abandonment:

  • There are too many steps. If you’re telling users they’ve just completed step 2 of 12, you can bet they won’t bother finishing your form. 
  • They ask for too much information. No one wants to fill out a long form, and often, users won’t have the information on hand if you ask for too much. Just look at the rate left blank from the Unneeded Fields report in the screenshot below:

  

  • The form is confusing. Unclear form fields or directions can put users off. 
  • All the fields are free text and time-consuming. Filling out forms with long text fields takes too much time. To speed things up, use dropdown options in the fields, but keep the options to a minimum. This not only helps users finish the form faster but also makes it easier to analyse the data later because it keeps the data format consistent so you can organise the information more efficiently. 
  • Users don’t trust the form. This is a particular problem on checkout pages where users are entering sensitive information.

  How to conduct form analysis

  You need to collect user behaviour data to effectively analyse your forms. But a lot of traditional website analytics tools won’t have the required functionality. 

  Matomo is different. Our web analytics solution offers comprehensive web analytics as well as additional features like Heatmaps, Session Recordings, A/B Testing, and Form Analytics to provide all the functionality you need. 

  Now if you don’t use Matomo, you can try it free for 21 days (no credit card required) to see if it’s the right tool for you.

  Whether you use Matomo or not is up to you. But, once you have a suitable tool in place, just follow the steps below to conduct a form analysis. 

  Check your analytics

  Tracking and analysing specific form metrics should be the first place you start. We recommend collecting data on the following metrics:

  • Form starter rate: the percentage of visitors who actually start to fill in your form
  • Completion rate: the percentage of visitors who complete the form
  • Form abandonment rate: the percentage of users who gave up filling in your form
  • Time spent completing your form: the average length of time users spend on your form

  Let’s look at these metrics are in Matomo’s Form Analytics:

  

  The dashboard shows an overview of these metrics over a given period, allowing you to see at a glance whether there are issues you need to rectify. 

  Next, deep dive into the performance of each form to see things like:

  • Drop off fields
  • Unused fields
  • Entry field
  • Most corrected fields 

  You can even use Matomo’s visitor log to see who’s behind every submission.

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  Use a heatmap

  A heatmap is a colour-based graphical representation of data. A heatmap will show what users to do on your website, including: 

  • How far they scroll
  • Which buttons they click on
  • Where they focus their attention

  When used on a webpage with a form, you’ll be able to see how often users interact with your form based on the heatmap colour, with warmer colours representing greater engagement levels.

  Let’s look at a heatmap in Matomo:

  

  This heatmap is showing us how far down users have scrolled. It’s clear that only 63% of visitors are reaching the point above our call to action to see all features. We might want to consider moving that call to action up in order to get more engagement. 

  A heatmap is a great way to see whether your form’s placement gets the level of attention you want from visitors and to what extent visitors interact with your field.

  Record user sessions

  Session replays go even further than heatmaps, recording a real-life user interacting with your site. It’s like looking over a visitor’s shoulder while they use your site.

  

  With Matomo, you can record any sessions where the user takes a certain action (like starting to fill in a form), allowing you to build a rich library of qualitative data. 

  You can then replay a recorded session at your leisure to understand exactly how users interact with your forms.

  Segment users

  If you really want to understand how visitors use your forms, then it’s essential to segment your data. 

  You can segment all Form Analytics reports by over 100 pre-built segments in Matomo.

  

  One way to segment your data is by comparing the average time on form of those who completed the form with those who abandoned it. 

  If users abandon a form quickly, that could indicate your form is irrelevant to this audience or too long. If users spend a lot of time on the form, however, it’s probably safe to assume that it is relevant but there is something wrong with the form itself. 

  Looking at the Field Timings report will help you pinpoint which field visitors are spending the most time on and causing frustration. 

  

  The Field Timings example report in Matomo above, it’s evident that the “Overview of your needs” field takes up the most time (avg. time spent is 1 min 40s). To improve this, we might want to change it to a dropdown field. This way, users can quickly select options, and if necessary, provide additional details.

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  Another way is to segment data by traffic source and compare each source’s conversion rate. This will show whether one traffic source converts better than another or if one source isn’t interested in your form at all.

  How to optimise web forms

  Want to implement what you’ve learnt from your form analysis? Follow these steps to optimise your existing web forms. 

  Define your form’s purpose

  The first step in optimising your existing web forms is to give a clear and definitive purpose to every single one. 

  When you have a defined goal, creating a form users will complete is much easier. After all, if you don’t know why people should fill in one of your forms, how would a visitor possibly know?

  Take a look at one of our forms below:

  

  The purpose of this form is to get users to sign up for a free trial of our web analytics platform, and every element works towards that goal:

  • The headline directs the user to take action
  • The copy explains that it’s a free trial that doesn’t require credit card details
  • The green call-to-action button reinforces the action and benefit 
  • There is validation to support this under the form – “Trusted on over 1 million websites in over 190+ countries”

  Our clear instructions leave users no doubt about why they should fill in the form or what will happen. 

  Choose the right type of form

  You can use several forms on your website, each with different designs, form fields, and goals.

  For example:

  • Registration forms are fairly minimalist and designed to collect the least amount of data possible. 
  • Contact forms are concise so that it’s easy for potential customers to reach your team. 
  • Checkout forms balance a need to collect important data with a streamlined design that doesn’t put users off.
  • Lead generation forms are compelling and usually include qualifying questions so sales teams can score leads.

  Make sure you are using the right type of form to avoid abandonments and other issues. For example, requiring users to fill in a lengthy lead generation-style form when you want them to sign up for a free trial will probably kill your conversion rate. 

  Test form elements

  If your form analysis has shed light on one or two issues, you can use A/B or multivariate testing to trial new elements or designs and see how they compare.

  There’s no shortage of elements you can test, including the form’s:

  • Headline
  • Placement
  • Design
  • CTA button
  • Colour-scheme
  • Length
  • Form fields

  

  Matomo makes it easy to create and run A/B tests on your website’s forms. 

  Move your form above the fold

  One of the simplest ways to optimise your web form is to move it above the fold — that’s the section of the screen users see when they load your page. 

  Why? Well, the more people who see your form, the more people will fill it in. And when it’s above the fold, users can’t help but see it.

  Conclusion

  Forms are one of the most important elements on your website, so why not treat them as such and regularly run a thorough form analysis? By doing so, you’ll identify ways to optimise your form, improve the user experience, and improve conversions. 

  Matomo is the best platform for conducting form analysis. Our combination of web analytics, Form Analytics, Session Recordings, and Heatmaps means you have all the tools you need to learn exactly how visitors interact with your forms. 

  See just how powerful Matomo’s tools are by starting a free 21-day trial, no credit card required. 

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  21 day free trial. No credit card required.

  
  

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• ffmepg Video Encoding got a error with "invalid plane data size"

  28 October 2020, by Mrguo

  ffmpeg version:4.0.3; video size: 611G; Duration:01:34:03

  


  

  • encoding command is:

  


  


  ffmpeg -i 2131.MOV -vcodec libx264 -preset ultrafast -pix_fmt yuv420p -b:v 600k -vf scale=640:-1 small.mp4

  


  

  • video info:

  


  


  ffprobe version 4.0.3 Copyright (c) 2007-2018 the FFmpeg developers
  built with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-17)
  configuration: --enable-shared --enable-libx264 --enable-gpl
  libavutil      56. 14.100 / 56. 14.100
  libavcodec     58. 18.100 / 58. 18.100
  libavformat    58. 12.100 / 58. 12.100
  libavdevice    58.  3.100 / 58.  3.100
  libavfilter     7. 16.100 /  7. 16.100
  libswscale      5.  1.100 /  5.  1.100
  libswresample   3.  1.100 /  3.  1.100
  libpostproc    55.  1.100 / 55.  1.100
[mov,mp4,m4a,3gp,3g2,mj2 @ 0xca7ac0] overread end of atom '�mak' by 2 bytes
[mov,mp4,m4a,3gp,3g2,mj2 @ 0xca7ac0] overread end of atom '�swr' by 3 bytes
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '04487162221cb76b0a4465037f4a3d7d.MOV':
  Metadata:
    major_brand     : qt  
    minor_version   : 537199360
    compatible_brands: qt  
    creation_time   : 2024-04-16T19:59:27.000000Z
    make            : Atomos
    make-eng        : Atomos
    encoder         : ShogunSumo - 9.11Cn 
    encoder-eng     : ShogunSumo - 9.11Cn 
    com.atomos.hdr.gamut: Rec709
    com.atomos.hdr.gamma: Rec709
    com.apple.proapps.image.{TIFF}.Make: Atomos
    com.apple.proapps.image.{TIFF}.Model: ShogunSumo
    com.apple.proapps.image.{TIFF}.Software: 9.11Cn
    timecode        : 00:00:00:00
  Duration: 01:34:03.86, start: 0.000000, bitrate: 929321 kb/s
    Stream #0:0(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, 4 channels, s32 (24 bit), 4608 kb/s (default)
    Metadata:
      creation_time   : 2024-04-16T19:59:27.000000Z
    Stream #0:1(eng): Video: prores (apcn / 0x6E637061), yuv422p10le(bt709, progressive), 3840x2160, 924704 kb/s, SAR 1:1 DAR 16:9, 50 fps, 50 tbr, 5k tbn, 5k tbc (default)
    Metadata:
      creation_time   : 2024-04-16T19:59:27.000000Z
      encoder         : Apple ProRes 422
    Stream #0:2(eng): Data: none (tmcd / 0x64636D74) (default)
    Metadata:
      creation_time   : 2024-04-16T19:59:27.000000Z
      timecode        : 00:00:00:00
Unsupported codec with id 0 for input stream 2


  


  

  • log with error:

  


  


  frame=166986 fps=9.8 q=24.0 size=  350976kB time=00:55:39.46 bitrate= 861.0kbits/s speed=0.196x    
frame=166991 fps=9.8 q=24.0 size=  350976kB time=00:55:39.56 bitrate= 861.0kbits/s speed=0.196x    
[prores @ 0x94f840] invalid plane data size
    Last message repeated 85 times
[prores @ 0x94f840] ac tex damaged 1024, 1024
[prores @ 0x94f840] invalid plane data size
    Last message repeated 1723 times
frame=166997 fps=9.8 q=24.0 size=  350976kB time=00:55:39.68 bitrate= 860.9kbits/s speed=0.196x    
[prores @ 0x94f840] invalid frame header
Error while decoding stream #0:1: Invalid data found when processing input


  


  how can I solve the problem?

  


• Why does every encoded frame's size increase after I had use to set one frame to be key in intel qsv of ffmpeg

  22 April 2021, by TONY

  I used intel's qsv to encode h264 video in ffmpeg. My av codec context settings is like as below:

  


   m_ctx->width = m_width;
    m_ctx->height = m_height;
    m_ctx->time_base = { 1, (int)fps };
    m_ctx->qmin = 10;
    m_ctx->qmax = 35;
    m_ctx->gop_size = 3000;
    m_ctx->max_b_frames = 0;
    m_ctx->has_b_frames = false;
    m_ctx->refs = 2;
    m_ctx->slices = 0;
    m_ctx->codec_id = m_encoder->id;
    m_ctx->codec_type = AVMEDIA_TYPE_VIDEO;
    m_ctx->pix_fmt = m_h264InputFormat;
    m_ctx->compression_level = 4;
    m_ctx->flags &= ~AV_CODEC_FLAG_CLOSED_GOP;
    AVDictionary *param = nullptr;
    av_dict_set(&param, "idr_interval", "0", 0);
    av_dict_set(&param, "async_depth", "1", 0);
    av_dict_set(&param, "forced_idr", "1", 0);


  


  and in the encoding, I set the AVFrame to be AV_PICTURE_TYPE_I when key frame is needed:

  


    if(key_frame){
        encodeFrame->pict_type = AV_PICTURE_TYPE_I;
    }else{
        encodeFrame->pict_type = AV_PICTURE_TYPE_NONE;
    }
    avcodec_send_frame(m_ctx, encodeFrame);
    avcodec_receive_packet(m_ctx, m_packet);
   std::cerr<<"packet size is "<size<<",is key frame "<code>

  


  The strange phenomenon is that if I had set one frame to AV_PICTURE_TYPE_I, then every encoded frame's size after the key frame would increase. If I change the h264 encoder to x264, then it's ok.

  


  The packet size is as below before I call "encodeFrame->pict_type = AV_PICTURE_TYPE_I":

  


  packet size is 26839
packet size is 2766
packet size is 2794
packet size is 2193
packet size is 1820
packet size is 2542
packet size is 2024
packet size is 1692
packet size is 2095
packet size is 2550
packet size is 1685
packet size is 1800
packet size is 2276
packet size is 1813
packet size is 2206
packet size is 2745
packet size is 2334
packet size is 2623
packet size is 2055


  


  If I call "encodeFrame->pict_type = AV_PICTURE_TYPE_I", then the packet size is as below:

  


  packet size is 23720,is key frame 1
packet size is 23771,is key frame 0
packet size is 23738,is key frame 0
packet size is 23752,is key frame 0
packet size is 23771,is key frame 0
packet size is 23763,is key frame 0
packet size is 23715,is key frame 0
packet size is 23686,is key frame 0
packet size is 23829,is key frame 0
packet size is 23774,is key frame 0
packet size is 23850,is key frame 0