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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.

• extracting video and data streams from MPEG2 TS over RTP in real-time

  10 janvier 2024, par Tejal Barnwal

  I have H264 video stream and KLV meta data encapsulated inside MPEG2 TS container which are sent over an RTP over UDP from a camera.
I intend to do the following :

  


  

  1. Extract both video and data streams from RTP

  


  2. Process video feed using opencv in a seperate thread

  


  3. process klv metadata in a seperate thread

  


  


  My problem what exact arguments should I provide to ffmpeg so as to read h264 video stream and show the images frame by frame using opencv ?

  


  With the help of some previous posts like Simultaneously map video and data streams to one subprocess pipeline in real-time, I was able to get some idea about how could I proceed to procees the stream over RTP.

  


  I started out by using the following script :

  


  #!/usr/bin/env python3
from asyncio import streams
from logging.handlers import QueueListener
import klvdata
import subprocess as sp
import shlex
import threading
import numpy as np
import cv2
import time
from io import BytesIO

# Video reader thread.
def video_reader(pipe):
    cols, rows = 1280, 720  # Assume we know frame size is 1280x720

    counter = 0
    while True:
        print("read image")
        raw_image = pipe.read(cols*rows*3)  # Read raw video frame

        # Break the loop when length is too small
        if len(raw_image) < cols*rows*3:
            break

        if (counter % 10) == 0:
            # Show video frame evey 60 frames
            image = np.frombuffer(raw_image, np.uint8).reshape([rows, cols, 3])
            cv2.imshow('Video', image) # Show video image for testing
            cv2.waitKey(1)
        counter += 1
        print("image showed on window")
        time.sleep(0.25)



# https://github.com/paretech/klvdata/tree/master/klvdata
def bytes_to_int(value, signed=False):
    """Return integer given bytes."""
    return int.from_bytes(bytes(value), byteorder='big', signed=signed)


# Data reader thread (read KLV data).
def data_reader(pipe):
    key_length = 16  # Assume key length is 16 bytes.

    f = open('data.bin', 'wb')  # For testing - store the KLV data to data.bin (binary file)

    while True:
        # https://en.wikipedia.org/wiki/KLV
        # The first few bytes are the Key, much like a key in a standard hash table data structure.
        # Keys can be 1, 2, 4, or 16 bytes in length.
        # Presumably in a separate specification document you would agree on a key length for a given application.
        key = pipe.read(key_length)  # Read the key
        
        if len(key) < key_length:
            break  # Break the loop when length is too small
        f.write(key)  # Write data to binary file for testing

        # https://github.com/paretech/klvdata/tree/master/klvdata
        # Length field
        len_byte = pipe.read(1)

        if len(len_byte) < 1:
            break  # Break the loop when length is too small
        f.write(len_byte)  # Write data to binary file for testing

        byte_length = bytes_to_int(len_byte)

        # https://github.com/paretech/klvdata/tree/master/klvdata                                                
        if byte_length < 128:
            # BER Short Form
            length = byte_length
            ber_len_bytes = b''
        else:
            # BER Long Form
            ber_len = byte_length - 128
            ber_len_bytes = pipe.read(ber_len)

            if len(ber_len_bytes) < ber_len:
                break  # Break the loop when length is too small
            f.write(ber_len_bytes)  # Write ber_len_bytes to binary file for testing

            length = bytes_to_int(ber_len_bytes)

        # Read the value (length bytes)
        value = pipe.read(length)
        if len(value) < length:
            break  # Break the loop when length is too small
        f.write(value)  # Write data to binary file for testing

        klv_data = key + len_byte + ber_len_bytes + value  # Concatenate key length and data
        klv_data_as_bytes_io = BytesIO(klv_data)  # Wrap klv_data with BytesIO (before parsing)

        # Parse the KLV data
        for packet in klvdata.StreamParser(klv_data_as_bytes_io): 
            metadata = packet.MetadataList()
            for key, value in metadata.items():
                print(key, value)
                
            print("\n") # New line

# Execute FFmpeg as sub-process
# Map the video to stderr and map the data to stdout
process = sp.Popen(shlex.split('ffmpeg -hide_banner -loglevel quiet '                        # Set loglevel to quiet for disabling the prints ot stderr
                               '-i "rtp://192.168.0.141:11024" '                                        # Input video "Day Flight.mpg"
                               '-map 0:v -c:v rawvideo -pix_fmt bgr24 -f:v rawvideo pipe:2 ' # rawvideo format is mapped to stderr pipe (raw video codec with bgr24 pixel format)
                               '-map 0:d -c copy -copy_unknown -f:d data pipe:1 '            # Copy the data without ddecoding.
                               '-report'),                                                   # Create a log file (because we can't the statuses that are usually printed to stderr).
                                stdout=sp.PIPE, stderr=sp.PIPE)


# Start video reader thread (pass stderr pipe as argument).
video_thread = threading.Thread(target=video_reader, args=(process.stderr,))
video_thread.start()

# Start data reader thread (pass stdout pipe as argument).
data_thread = threading.Thread(target=data_reader, args=(process.stdout,))
data_thread.start()


# Wait for threads (and process) to finish.
video_thread.join()
data_thread.join()
process.wait()



  


  With the above script, I was facing two issues :

  


  

  1. The second thread resulted in an attribute error

  


  


  Exception in thread Thread-2:
Traceback (most recent call last):
  File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.8/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "video_data_extraction.py", line 97, in data_reader
    print(packet.MetadataList())
AttributeError: 'UnknownElement' object has no attribute 'MetadataList'



  


  

  2. With this though I continuously able to see following output on the terminal regarding reading the images

  


  


  read image
image showed on window
read image
image showed on window
read image
image showed on window
read image
image showed on window
read image
image showed on window
read image
image showed on window


  


  The imshow windows wasnt updating properly ! It seemed stuck after a few frames.

  


  Further diving into the lane with the help of following command, I concluded that the video stream that I am reading has H264 encoding

  


  ffprobe -i rtp://192.168.0.141:11024 -show_streams -show_formats


  


  Output of the above command :

  


  ffprobe version 4.2.7-0ubuntu0.1 Copyright (c) 2007-2022 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)
  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/aarch64-linux-gnu --incdir=/usr/include/aarch64-linux-gnu --arch=arm64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
[rtp @ 0xaaaac81ecce0] PES packet size mismatch
    Last message repeated 62 times
[NULL @ 0xaaaac81f09b0] non-existing PPS 0 referenced
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[rtp @ 0xaaaac81ecce0] PES packet size mismatch
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[rtp @ 0xaaaac81ecce0] PES packet size mismatch
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] non-existing PPS 0 referenced
    Last message repeated 1 times
[h264 @ 0xaaaac81f09b0] decode_slice_header error
[h264 @ 0xaaaac81f09b0] no frame!
[rtp @ 0xaaaac81ecce0] PES packet size mismatch
    Last message repeated 187 times
Input #0, rtp, from 'rtp://192.168.0.141:11024':
  Duration: N/A, start: 1317.040656, bitrate: N/A
  Program 1 
    Stream #0:1: Video: h264 (Constrained Baseline) ([27][0][0][0] / 0x001B), yuv420p(progressive), 1280x720, 25 fps, 25 tbr, 90k tbn
    Stream #0:0: Data: klv (KLVA / 0x41564C4B)
Unsupported codec with id 100356 for input stream 0
[STREAM]
index=0
codec_name=klv
codec_long_name=SMPTE 336M Key-Length-Value (KLV) metadata
profile=unknown
codec_type=data
codec_tag_string=KLVA
codec_tag=0x41564c4b
id=N/A
r_frame_rate=0/0
avg_frame_rate=0/0
time_base=1/90000
start_pts=118533659
start_time=1317.040656
duration_ts=N/A
duration=N/A
bit_rate=N/A
max_bit_rate=N/A
bits_per_raw_sample=N/A
nb_frames=N/A
nb_read_frames=N/A
nb_read_packets=N/A
DISPOSITION:default=0
DISPOSITION:dub=0
DISPOSITION:original=0
DISPOSITION:comment=0
DISPOSITION:lyrics=0
DISPOSITION:karaoke=0
DISPOSITION:forced=0
DISPOSITION:hearing_impaired=0
DISPOSITION:visual_impaired=0
DISPOSITION:clean_effects=0
DISPOSITION:attached_pic=0
DISPOSITION:timed_thumbnails=0
[/STREAM]
[STREAM]
index=1
codec_name=h264
codec_long_name=H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10
profile=Constrained Baseline
codec_type=video
codec_time_base=1/50
codec_tag_string=[27][0][0][0]
codec_tag=0x001b
width=1280
height=720
coded_width=1280
coded_height=720
has_b_frames=0
sample_aspect_ratio=N/A
display_aspect_ratio=N/A
pix_fmt=yuv420p
level=31
color_range=unknown
color_space=unknown
color_transfer=unknown
color_primaries=unknown
chroma_location=left
field_order=progressive
timecode=N/A
refs=1
is_avc=false
nal_length_size=0
id=N/A
r_frame_rate=25/1
avg_frame_rate=25/1
time_base=1/90000
start_pts=118533659
start_time=1317.040656
duration_ts=N/A
duration=N/A
bit_rate=N/A
max_bit_rate=N/A
bits_per_raw_sample=8
nb_frames=N/A
nb_read_frames=N/A
nb_read_packets=N/A
DISPOSITION:default=0
DISPOSITION:dub=0
DISPOSITION:original=0
DISPOSITION:comment=0
DISPOSITION:lyrics=0
DISPOSITION:karaoke=0
DISPOSITION:forced=0
DISPOSITION:hearing_impaired=0
DISPOSITION:visual_impaired=0
DISPOSITION:clean_effects=0
DISPOSITION:attached_pic=0
DISPOSITION:timed_thumbnails=0
[/STREAM]
[FORMAT]
filename=rtp://192.168.0.141:11024
nb_streams=2
nb_programs=1
format_name=rtp
format_long_name=RTP input
start_time=1317.040656
duration=N/A
size=N/A
bit_rate=N/A
probe_score=100
[/FORMAT]


  


  Further, in the log output, I see a lot of statements in regard to missed packets and PES packet mismatch

  


  [rtp @ 0xaaaaf31896c0] max delay reached. need to consume packet
[rtp @ 0xaaaaf31896c0] RTP: missed 98 packets
[rtp @ 0xaaaaf31896c0] Continuity check failed for pid 40 expected 14 got 10
[rtp @ 0xaaaaf31896c0] PES packet size mismatch
rtp://192.168.0.141:11024: corrupt input packet in stream 0
frame=  124 fps=2.6 q=-0.0 size=  334800kB time=00:00:05.32 bitrate=515406.0kbits/s dup=97 drop=0 speed=0.111x 


  


  What arguments do I provide to ffmpeg and in what order because my stream 0 is metadata and stream 1 is video so as to display image frame by frame with opencv ?
I would be grateful for any help that you could provide.

  


  Further, I also have a query regarding how does ffmpeg know to that it has to first convert the rtp packets into mpeg2 TS packets before segregating video stream and data stream ?

  


• Conversion Rate Optimisation Statistics for 2024 and Beyond

  21 novembre 2023, par Erin — Analytics Tips

  Driving traffic to your website is only half the battle. The real challenge — once you’ve used a web analytics solution to understand how users behave — is turning more of those visitors into customers.

  That doesn’t happen by accident. You need to employ conversion rate optimisation strategies and tools to see even a small lift in conversion rates. The good news is that it doesn’t take much to see massive results. Raising your conversion rate from 1% to 3% can triple your revenue. 

  In even better news, you don’t have to guess at the best ways to improve your conversion rate. We’ve done the hard work and collected the most recent and relevant conversion rate optimisation statistics to help you. 

  General conversion rate optimisation statistics

  It appears the popularity of conversion rate optimisation is soaring. According to data collected by Google Trends, there were more people searching for the term “conversion rate optimization” in September 2023 than ever before. 

  As you can see from the chart below, the term’s popularity is on a clear upward trajectory, meaning even more people could be searching for it in the near future. (Source)

  

  Do you want to know what the average landing page conversion rate is ? According to research by WordStream, the average website conversion rate across all industries is 2.35%. 

  That doesn’t paint the whole picture, however. Better-performing websites have significantly higher conversion rates. The top 25% of websites across all industries convert at a rate of 5.31% or higher. (Source)

  Let’s break things down by industry now. The Unbounce Conversion Benchmark Report offers a detailed analysis of how landing pages convert across various industries.

  First, we have the Finance and Insurance industry, which boasts a conversion rate of 15.6%. 

  On the other end, agencies appears to be one of the worst-performing. Agencies’ landing pages convert at a rate of 8.8%. (Source)

  

  What about the size of the conversion rate optimisation industry ? Given the growth in popularity of the term in Google, surely the industry is experiencing growth, right ?

  You’d be correct in that assumption. The conversion rate optimisation software market was valued at $771.2 million in 2018 and is projected to reach $1.932 billion by 2026 — a compound annual growth rate (CAGR) of 9.6%.

  Statistics on the importance of conversion rate optimisation

  If you’re reading this article, you probably think conversion rate optimisation is pretty important. But do you know its importance and where it ranks in your competitors’ priorities ? Read on to find out. 

  Bounce rate — the number of people who leave your website without visiting another page or taking action — is the scourge of conversion rate optimisation efforts. Every time someone bounces from your site, you lose the chance to convert them.

  The questions, then, are : how often do people bounce on average and how does your bounce rate compare ? 

  Siege Media analysed over 1.3 billion sessions from a range of traffic sources, including 700 million bounces, to calculate an average bounce rate of 50.9%. (Source)

  

  Bounce rates vary massively from website to website and industry to industry, however. Siege Media’s study unveils an array of average bounce rates across industries :

  • Travel – 82.58%
  • B2B – 65.17%
  • Lifestyle – 64.26%
  • Business and Finance – 63.51%
  • Healthcare – 59.50%
  • eCommerce – 54.54%
  • Insurance – 45.96%
  • Real Estate – 40.78%

  It won’t come as much of a surprise to learn that marketers are determined to reduce bounce rates and improve lead conversion. Today’s marketers are highly performance-based. When asked about their priorities for the coming year, 79% of marketers said their priority was generating quality qualified leads — the most popular answer in the survey. (Source)

  Just because it is a priority for marketers doesn’t mean that everyone has their stuff together. If you have a conversion rate optimisation process in place, you’re in the minority. According to research by HubSpot, less than one in five marketers (17%) use landing page A/B tests to improve their conversion rates. (Source)

  When it comes to personalisation strategies – a common and effective tool to increase conversion rates — the picture isn’t any rosier. Research by Salesforce found just over one-quarter of markets are confident their organisation has a successful strategy for personalisation. (Source)

  Conversion rate optimisation tactics statistics

  There are hundreds of ways to improve your website’s conversion rates. From changing the color of buttons to the structure of your landing page to your entire conversion funnel, in this section, we’ll look at the most important statistics you need to know when choosing tactics and building your own CRO experiments. 

  If you are looking for the best method to convert visitors, then email lead generation forms are the way to go, according to HubSpot. This inoffensive and low-barrier data collection method boasts a 15% conversion rate, according to the marketing automation company’s research. (Source)

  Where possible, make your call-to-actions personalised. Marketing personalisation, whether through behavioral segmentation or another strategy, is an incredibly powerful way of showing users that you care about their specific needs. It’s no great surprise, then, that HubSpot found personalised calls-to-actions perform a whopping 202% better than basic CTAs. (Source)

  If you want to boost conversion rates, then it’s just as important to focus on quantity as well as quality. Yes, a great-looking, well-written landing page will go a long way to improving your conversion rate, but having a dozen of these pages will do even more. 

  Research by HubSpot found companies see a 55% increase in leads when they increase the number of landing pages from 10 to 15. What’s more, companies with over 40 landing pages increase conversion by more than 500%. (Source)

  

  User-generated content (UGC) should also be high on your priority list to boost conversion rates. Several statistics show how powerful, impactful and persuasive social proof like user reviews can be. 

  Research shows that visitors who scroll to the point where they encounter user-generated content increase the likelihood they convert by a staggering 102.4%. (Source)

  Other trust signs can be just as impactful. Research by Trustpilot found that the following four trust signals make consumers more likely to make a purchase when shown on a product page :

  • Positive star rating and reviews (85% more likely to make a purchase)
  • Positive star rating (78%)
  • Positive customer testimonials (82%)
  • Approved or authorised seller badge (76%)

  (Source)

  Showing ratings and reviews has also increased conversion rates by 38% on home appliances and electronics stores. (Source)

  And no wonder, given that consumers are more likely to buy from brands they trust than brands they love, according to the 2021 Edelman Trust Barometer Special Report. (Source) 

  A lack of trust is also one of the top four reasons consumers abandon their shopping cart at checkout. (Source) 

  Traffic source conversion rate statistics

  What type of traffic works the best when it comes to conversions, or how often you should be signing up users to your mailing list ? Let’s look at the stats to find out. 

  Email opt-ins are one of the most popular methods for collecting customer information — and an area where digital marketers spend a lot of time and effort when it comes to conversion rate optimisation. So, what is the average conversion rate of an email opt-in box ?

  According to research by Sumo — based on 3.2 billion users who have seen their opt-in boxes — the average email opt-in rate is 1.95%. (Source)

  Search advertising is an effective way of driving website traffic, but how often do those users click on these ads ?

  WordStream’s research puts the average conversion of search advertising for all industries at 6.11%. (Source)

  The arts and entertainment industry enjoys the highest clickthrough rates (11.78%), followed by sports and recreation (10.53%) and travel (10.03%). Legal services and the home improvement industry have the lowest clickthrough rates at 4.76% and 4.8%, respectively.

  

  If you’re spending money on Google ads, then you’d better hope a significant amount of users convert after clicking them. 

  Unfortunately, conversion rates from Google ads decreased year-on-year for most industries in 2023, according to research by WordStream — in some cases, those decreases were significant. The only two industries that didn’t see a decrease in conversion rates were beauty and personal care and education and instruction. (Source)

  The average conversion rate for search ads across all industries is 7.04%. The animal and pet niche has the highest conversion rate (13.41%), while apparel, fashion and jewelry have the lowest conversion rate (1.57%). (Source)

  What about other forms of traffic ? Well, there’s good reason to try running interstitial ads on smartphone apps if you aren’t already. Ads on the iOS app see a 14.3 percent conversion rate on average. (Source)

  E-commerce conversion rate optimisation statistics (400 words)

  Conversion rate optimisation can be the difference between a store that sets new annual sales records and one struggling to get by. 

  The good news is that the conversion rate among US shoppers was the highest it’s ever been in 2021, with users converting at 2.6%. (Source)

  If you have a Shopify store, then you may find conversion rates a little lower. A survey by Littledata found the average conversion rate for Shopify was 1.4% in September 2022. (Source)

  What about specific e-commerce categories ? According to data provided by Dynamic Yield, the consumer goods category converted at the highest rate in September 2023 (4.22%), a spike of 0.34% from August. 

  Generally, the food and beverage niche boasts the highest conversion rate (4.87%), and the home and furniture niche has the lowest conversion rate (1.44%). (Source)

  If you’re serious about driving sales, don’t focus on mobile devices at the expense of consumers who shop on desktop devices. The conversion rate among US shoppers tends to be higher for desktop users than for mobile users. 

  

  In the second quarter of 2022, for instance, desktop shoppers converted at a rate of 3% on average compared to smartphone users who converted at an average rate of 2%. (Source)

  Increase your conversions with Matomo

  Conversion rate optimisation can help you grow your subscriber list, build your customer base and increase your revenue. Now, it’s time to put what you’ve learned into practice.

  Use the advice above to guide your experiments and track everything with Matomo. Achieve unparalleled data accuracy while harnessing an all-in-one solution packed with essential conversion optimisation features, including Heatmaps, Session Recordings and A/B Testing. Matomo makes it easier than ever to analyse conversion-focused experiments.

  Get more from your conversion rate optimisations by trying Matomo free for 21 days. No credit card required.