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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.

• Processing a single frame of audio and image in FFmpeg

  21 juillet 2015, par James F

  Currently we have an implementation of FFmpeg which is triggered from an ActionScript 3 (AS3) application, via CrossBridge (formerly Flascc). In this implementation, we write the entire audio track into the CModule’s memory, using malloc from the AS3 application. Once written, the application starts to process each of the image frames we would like to combine with our audio. This process begins by the AS3 application calling the CModule’s write_frame public method.

  C :
  int write_frame(struct Session *s, uint8_t *buffer, int bufferSize){}

  AS3 :
  var ret:int = writeFrame(_sessionPtr, _pixelBytesPtr, _pixelBytes.length);

  Once the video output has been created, it is retrieved from the CModule to AS3 as a byte array.

  With this implementation, a long duration video or audio track - the application runs out of memory (there’s a memory limit within our CrossBridge sandbox environment). The largest portion of this memory is currently our audio track, as it’s uncompressed PCM data (raw float values).

  Ideally, we would like to write a single audio frame and video frame together, with the AS3 application writing the 1 x audio frame byte array to the CModule’s memory. I have attempted to do this, by allocating the memory requirement for a single frame of audio using malloc, and then overwriting this memory, each time write_frame is called. However, this results in the video file containing a single frame of audio at the start of the video, and no other audio.

  I’m convinced that the audio frame is being constructed correctly, but I believe this approach is conflicting with some of the code within our Muxing.c file. It’s a little different to FFmpeg’s example file (https://ffmpeg.org/doxygen/trunk/muxing_8c-source.html), as this file has been modified by several people. Here’s the methods calls from within write_frame :

  fill_audio_buffer(s->audio_input, s->audio_input_length, s->audio_input_index, s->audio_input_frame_size * 2, s->audio_frame_buffer);

  retval = av_samples_alloc(converted_buffer, NULL, 2, out_samples, audio_st->codec->sample_fmt, 0);

  out_samples = swr_convert(s->audio_swr_context, converted_buffer, out_samples (void *) &s->audio_frame_buffer, in_samples);

  retval = write_audio_frame(s, s->oc, s->audio_st, s->audio_input_frame_size (uint16_t *) converted_buffer[0]);

  s->audio_input_index += s->audio_input_frame_size * 2;

  Is it possible to move to procedural muxing of 1 x frame of audio and 1 x frame of image approach ? Even if it’s slightly slower - it’ll mean we’re not hold the entire audio track in memory. Any suggestions to the required approach would be great, thanks in advance !

  @VC. One - The PCM data is made outside of FFmpeg and then written to the memory that FFmpeg has access to. (using malloc, and then the pointer to this address is sent to the FFmpeg).

  The FFmpeg output file can either be a .WMV file, or .AVi file - the codecs WMV2 and DIVX are used in each case. I have made some modifications since posting the original question, but you’re correct in thinking that the first chunk was being used and then the last frame size increased, meaning the next read of the buffer would yield nothing as it exceeded the buffer.

  I’ve now made some progress by resetting the index audio_input_index back to ’0’ at the start of each write_frame call. However, i’ll need to check whether this is the correct approach, as between each audio frame (1 second at 1fps), there is a slight blip/audio pop noise. In addition to this - the last few frames of audio seem to overlap, causing some of the audio to be repeated. Is it safe practice with C/FFmpeg to recycle a buffer in this way ? It seems that the length of each audio frame changes - at AS3 level my current calculation of the audio frame byte length is (44,100 kHz sample rate * 8) / Frames per second. It’s * 8 as it’s two channel, and each float value is 4 bytes.

  Thanks again for your help

• Merge video with ffmpeg(or alternatives) with extra properties(an overlay)

  28 juin 2021, par dconixDev

  I'll be scraping clips from twitch and merging them to create a single video file.
I already figured out the scraping of twitch clip links(but i only get 16-20 videos because i need to scroll with selenium but i dont really mind it, if you have a working solution then make an answer about it) and also the simple merging videos.

  


  I'm scraping links with :

  


  #!/usr/bin/python3.9
import bs4
import requests
import time
from datetime import datetime
from selenium import webdriver
from selenium.webdriver.firefox.options import Options

# Initialize driver and run it headless
options = Options()
options.headless = True
driver = webdriver.Firefox(options=options)

def extract_source(url):
     agent = {"User-Agent":"Mozilla/5.0 (Windows NT 10.0; rv:78.0) Gecko/20100101 Firefox/78.0"}
     source=requests.get(url, headers=agent).text
     return source

def extract_data(source):
     soup=bs4.BeautifulSoup(source, 'html.parser')
     names=soup.find_all('a', attrs={'data-a-target':'preview-card-image-link'})
     return names

driver.get('https://www.twitch.tv/directory/game/League%20of%20Legends/clips?range=24hr')

# I wait 3 seconds for the clips to get pulled in
# I'd like here to scroll down a bit so i can scrape more clips, but even after i tried some solutions my firefox(was debugging in GUI mode, not headless as it is now) wasnt scrolling
time.sleep(3)
extract_links=extract_data(driver.page_source)
for a in extract_links:
    print(a.get('href'))

driver.quit()

# I tried scrolling using this but didnt work, not sure why
# this script is supposed to scroll until youre at the end of the page
# SCROLL_PAUSE_TIME = 0.5

# # Get scroll height
# last_height = driver.execute_script("return document.body.scrollHeight")

# for i in range(3):
    # # Scroll down to bottom
    # driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")

    # # Wait to load page
    # time.sleep(SCROLL_PAUSE_TIME)

    # # Calculate new scroll height and compare with last scroll height
    # new_height = driver.execute_script("return document.body.scrollHeight")
    # if new_height == last_height:
        # break
    # last_height = new_height


  


  I'm joining videos together after downloading(with youtube-dl) with ffmpeg :

  


  ffmpeg -safe 0 -f concat -segment_time_metadata 1 -i videos.txt -vf select=concatdec_select -af aselect=concatdec_select,aresample=async=1 out.mp4

  


  Where videos.txt is as follows :

  


  file 'video_file1.mp4'
file 'video_file2.mp4'
...


  


  I can't really find answers on how to add a watermark(different for each video, although i found this it doesnt explain how to add a unique watermark to individual videos but the same watermark to two videos) without having to render each and every video twice but doing so in one go.

  


  I think I stumbled upon some people who made their videos.txt as follows in purpose of adding extra options to each video :

  


  file 'video_file1.mp4'
option 1(for video_file1.mp4)
option 2(for video_file1.mp4)
file 'video_file2.mp4'
option 1(for video_file2.mp4)
option 2(for video_file2.mp4)
...


  


  Would this work for unique watermarks for each videos(lets suppose watermarks are named video_file1.png, ... meaning the same as the videos, also the watermark is transparent in case that needs more configuration)