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• Top Conversion Metrics to Track in 2024

  22 janvier 2024, par Erin

  2023 boasts  2.64 billion online shoppers worldwide ; that’s more than a third of the global population. With these numbers on an upward trajectory in 2024, conversion metrics are more important than ever to help marketers optimise the online shopping experience. 

  In this article, we’ll provide predictions for the most important conversion metrics you should keep track of in 2024. We’ll also examine how social media can make or break your brand engagement strategy. Keep reading to stay ahead of the competition for 2024 and gain tips and tricks for improving conversion performance.

  What are conversion metrics ?

  In technical terms, conversion metrics are the quantifiable measurements used to track the success of specific outcomes on a website or marketing campaign. Conversion metrics demonstrate how well your website prompts visitors to take desirable actions, like signing up for a newsletter, making a purchase, or filling out a form, for instance.

  Let’s say you’re running a lemonade stand, and you want to compare the number of cups sold to the number of people who approached your stand (your conversion rate). This ratio of cups sold to the total number of people can help you reassess your sales approach. If the ratio is low, you might reconsider your approach ; if it’s high, you can analyse what makes your technique successful and double down.

  

  In 2023, we saw the average conversion rate for online shopping grow by 5.53% compared to the previous year. An increase in conversion rate typically indicates a higher percentage of website visitors converting to buyers. It can also be a good sign for marketing teams that marketing campaigns are more effective, and website experiences are more user-friendly than the previous year. 

  Conversion metrics are a marketers’ bread and butter. Whether it’s through measuring the efficacy of campaigns, honing in on the most effective marketing channels or understanding customer behaviour — don’t underestimate the power of conversion metrics. 

  Conversion rate vs. conversion value 

  Before we dive into the top conversion metrics to track in 2024, let’s clear up any confusion about the difference between conversion rate and conversion value. Conversion rate is a metric that measures the ratio of website visitors/users who complete a conversion action to the total number of website visitors/users. Conversion rates are communicated as percentages.

  A conversion action can mean many different things depending on your product or service. Some examples of conversion actions that website visitors can take include : 

  • Making a purchase
  • Filling out a form
  • Subscribing to a newsletter
  • Any other predefined goal

  Conversion rate is arguably one of the most valuable conversion metrics if you want to pinpoint areas for improvement in your marketing strategy and user experience (UX).

  A good conversion rate completely depends on the type of conversion being measured. Shopify has reported that the average e-commerce conversion rate will be 2.5%-3% in 2023, so if you fall anywhere in this range, you’re in good shape. Below is a visual aid for how you can calculate conversion rate depending on which conversion actions you decide to track :

  

  Conversion value is also a quantifiable metric, but there’s a key difference : conversion value assigns a numerical value to each conversion based on the monetary value of the completed conversion action. Conversion value is not calculated with a formula but is assigned based on revenue generated from the conversion. Conversion value is important for calculating marketing efforts’ return on investment (ROI) and is often used to allocate marketing budgets better. 

  Both conversion rate and conversion value are vital metrics in digital marketing. When used in tandem, they can provide a holistic perspective on your marketing efforts’ financial impact and success. 

  9 important conversion metrics to track in 2024

  Based on research and results from 2023, we have compiled this list of predictions for the most important metrics to track in 2024. 

  

  1. Conversion rate 

  To start things out strong, we’ve got the timeless and indispensable conversion rate. As we discussed in the previous section, conversion rate measures how successfully your website convinces visitors to take important actions, like making purchases or signing up for newsletters. 

  An easy-to-use web analytics solution like Matomo can help in tracking conversion rates. Matomo automatically calculates conversion rates of individual pages, overall website and on a goal-by-goal basis. So you can compare the conversion rate of your newsletter sign up goal vs a form submission goal on your site and see what is underperforming and requires improvement.

  

  In the example above in Matomo, it’s clear that our goal of getting users to comment is not doing well, with only a 0.03% conversion rate. To improve our website’s overall conversion rate, we should focus our efforts on improving the user commenting experience.

  For 2024, we predict that the conversion rate will be just as important to track as in 2023. 

  2. Average visit duration

  This key metric tracks how long users spend on your website. A session typically starts when a user lands on your website and ends when they close the browser or have been inactive for some time ( 30 minutes). Tracking the average visit duration can help you determine how well your content captures users’ attention or how engaged users are when navigating your website. 

  Average Visit Duration = Total Time Spent / Number of Visits

  

  Web analytics tools like Matomo help in monitoring conversion rate metrics like average visit duration. Timestamps are assigned to each interaction within a visit, so that average visit duration can be calculated. Analysing website visit information like average visit duration allows you to evaluate the relevance of your content with your target audience. 

  3. Starter rate

  If your business relies on getting leads through forms, paying attention to Form Analytics is crucial for improving conversion rates. The “starter rate” metric is particularly important—it indicates the number of who people start filling out the form, after seeing it. 

  When you’re working to increase conversion rates and capture more leads, keeping an eye on the starter rate helps you understand where users might encounter issues or lose interest early in the form-filling process. Addressing these issues can simplify the form-filling experience and increase the likelihood of successful lead captures.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Concrete CMS tripled their leads using Form Analytics in Matomo—see how in their case study.

  4. Bounce rate

  Bounce rate reflects the percentage of visitors who exit your site after interacting with a single page. Bounce rate is an important metric for understanding how relevant your content is to visitors or how optimised your user experience is. A high bounce rate can indicate that visitors are having trouble navigating your website or not finding what they’re looking for. 

  Matomo automatically calculates bounce rate on each page and for your overall website.

  

  Bounce Rate = (# of Single-Page Sessions / Total # of Sessions) * 100

  5. Cost-per-conversion

  This metric quantifies the average cost incurred for each conversion action (i.e., sale, acquired lead, sign-up, etc.). Marketers use cost-per-conversion to assess the cost efficiency of a marketing campaign. You want to aim for a lower cost-per-conversion, meaning your advertising efforts aren’t breaking the bank. A high cost-per-conversion could be acceptable in luxury industries, but it often indicates a low marketing ROI. 

  Cost-per-Conversion = Ad Spend / # of Conversions

  By connecting your Matomo with Google Ads through Advertising Conversion Export feature in Matomo, you can keep tabs on your conversions right within the advertising platform. This feature also works with Microsoft Advertising and Yandex Ads.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  6. Average order value (AOV)

  AOV is a conversion metric that calculates the average monetary value of each order. AOV is crucial for helping e-commerce businesses understand the value of their transactions. A high AOV means buyers spend more per transaction and could be more easily influenced by upselling or cross-selling. Low AOV isn’t necessarily bad — you can compensate for a low AOV by boosting transaction volume. 

  

  AOV = Total Revenue / Total # of Orders 

  Matomo automatically tracks important e-commerce metrics such as AOV, the percentage of visits with abandoned carts and the conversion rate for e-commerce orders.

  7. Exit rate

  Exit rate measures the percentage of visitors who leave a specific webpage after viewing it. Exit rate differs from bounce rate in that it focuses on the last page visitors view before leaving the site. A high exit rate should be examined to identify issues with visitors abandoning the specific page. 

  Exit Rate = (# of Exits from a Page / Total # of Pageviews for that Page) * 100

  

  In the Matomo report above, it’s clear that 77% of visits to the diving page ended after viewing it (exit rate), while 23% continued exploring. 

  On the other hand, our products page shows a lower exit rate at 36%, suggesting that more visitors continue navigating through the site after checking out the products.

  How to improve your conversion performance 

  If you’re curious about improving your conversion performance, this section is designed to guide you through that exact process.

  

  Understand your target audience and their behaviour

  You may need to return to the drawing board if you’re noticing high bounce rates or a lack of brand engagement. In-depth audience analysis can unveil user demographics, preferences and behaviours. This type of user data is crucial for building user personas, segmenting your visitors and targeting marketing campaigns accordingly.

  You can segment your website visitors in a number of web analytics solutions, but for the example below, we’ll look at segmenting in Matomo. 

  

  In this instance, we’ve segmented visitors by mobile users. This helps us see how mobile users are doing with our newsletter signup goal and identify the countries where they convert the most. It also shows how well mobile users are doing with our conversion goal over time.

  It’s clear that our mobile users are converting at a very low rate—just 0.01%. This suggests there’s room for improvement in the mobile experience on our site.

  Optimise website design, landing pages, page loading speed and UX

  A slow page loading speed can result in high exit rates, user dissatisfaction and lost revenue. Advanced web analytics solutions like Matomo, which provides heatmaps and session recordings, can help you find problems in your website design and understand how users interact with it.

  Making a website that focuses on users and has an easy-to-follow layout will make the user experience smooth and enjoyable.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Create compelling calls-to-action (CTA)

  Research shows that a strategically placed and relevant CTA can significantly increase your revenue. CTAs guide prospects toward conversion and must have a compelling and clear message. 

  You can optimise CTAs by analysing how users interact with them — this helps you tailor them to better resonate with your target audience. 

  A/B testing

  A/B testing can improve your conversion performance by allowing you to experiment with different versions of a web page. By comparing the impact of different web page elements on conversions, you can optimise your website with confidence. 

  Key conversion metrics takeaways

  Whether understanding user behaviour to develop a more intuitive user experience or guessing which marketing channel is the most effective, conversion metrics can be a marketer’s best friend. Conversion metrics help you save time, money and headaches when making your campaigns and website as effective as possible. 

  Make improving conversion rates easier with Matomo, a user-friendly all-in-one solution. Matomo ensures reliable insights by delivering accurate data while prioritising compliance and privacy.

  Get quality insights from your conversion metrics by trying Matomo free for 21 days. No credit card required.

• Revision 30332 : On passe la génération de la file d’attente et donc des inserts en base ...

  29 juillet 2009, par kent1@… — Log

  On passe la génération de la file d’attente et donc des inserts en base dans l’action spipmotion_ajouter_file
  On ajoute un suffixe au nom de fichier -encoded pour éviter de boucler sur les encodages
  On clean aussi un chouilla

• JavaCV Create Compatible MP4

  15 juin 2017, par Hamish258

  Trying to modify JavaCV 3.2.0 sample https://github.com/bytedeco/javacv/blob/master/samples/WebcamAndMicrophoneCapture.java, to produce an mp4 that quicktime on macOS 10.12.5 can use. Output mp4 works fine with VLC but for the software I’m producing I’d like to minimise users having to install additional products.

  The sample code produces the following output

  [libx264 @ 0x7f927794e200] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 0x7f927794e200] profile Constrained Baseline, level 3.1
[libx264 @ 0x7f927794e200] 264 - core 148 - H.264/MPEG-4 AVC codec - Copyleft 2003-2016 - http://www.videolan.org/x264.html - options: cabac=0 ref=1 deblock=0:0:0 analyse=0:0 me=dia subme=0 psy=1 psy_rd=1.00:0.00 mixed_ref=0 me_range=16 chroma_me=1 trellis=0 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=0 threads=4 lookahead_threads=4 sliced_threads=1 slices=4 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=60 keyint_min=6 scenecut=0 intra_refresh=0 rc=crf mbtree=0 crf=28.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=0
Output #0, mp4, to 'alatest.mp4':
 Metadata:
   encoder         : Lavf57.56.100
   Stream #0:0: Video: h264 (Constrained Baseline) ([33][0][0][0] / 0x0021), yuv420p, 1280x720, q=2-31, 2000 kb/s, 15360 tbn
   Stream #0:1: Audio: aac (LC) ([64][0][0][0] / 0x0040), 44100 Hz, stereo fltp, 192 kb/s

  The constrained baseline to 3.1 should produce usable mp4 according to https://trac.ffmpeg.org/wiki/Encode/H.264

  If I use the command line ffmpeg -i alatest.mp4 -pix_fmt yuv420p alantest.mp4 it produces a usable mo4, even though as you can see above yuv420p is the pixel format in use so there’s something else I don’t understand, the code snippet from the sample link is :

     FFmpegFrameRecorder recorder = new FFmpegFrameRecorder("alatest.mp4",1280, 720, 2);
   recorder.setInterleaved(true);
   recorder.setVideoOption("tune", "zerolatency");
   recorder.setVideoOption("preset", "ultrafast");
   recorder.setVideoOption("crf", "28");
   recorder.setVideoBitrate(2000000);
   recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
   recorder.setPixelFormat(avutil.AV_PIX_FMT_YUV420P);
   recorder.setFormat("flv");
   recorder.setFrameRate(30);
   recorder.setGopSize(60);

  The command line ffmpeg is displaying

  [libx264 @ 0x7ffca4019400] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 0x7ffca4019400] profile High, level 3.1
[libx264 @ 0x7ffca4019400] 264 - core 148 - H.264/MPEG-4 AVC codec - Copyleft 2003-2016 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'alantest.mp4':
 Metadata:
   encoder         : Lavf57.71.100
   Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 1280x720, q=-1--1, 30 fps, 15360 tbn, 30 tbc
   Metadata:
     encoder         : Lavc57.89.100 libx264
   Side data:
     cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
   Stream #0:1: Audio: aac (LC) ([64][0][0][0] / 0x0040), 44100 Hz, stereo, fltp, 128 kb/s
   Metadata:
     encoder         : Lavc57.89.100 aac
frame=  179 fps= 54 q=-1.0 Lsize=     570kB time=00:00:05.94 bitrate= 786.2kbits/s dup=93 drop=0 speed= 1.8x
video:470kB audio:93kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.341890%

  This is using a High profile and I see q being set differently and tbc. I can’t figure out how to mimic this behaviour in JavaCV, all help greatly appreciated !