Recherche avancée

Sur d’autres sites (9351)

• How to use Behavioural Analytics to Improve Website Performance

  20 septembre 2021, par Ben Erskine — Analytics Tips, Plugins, Heatmap
  

  User behavioural analytics (UBA) give your business unique insights into your customers. 

  Where traditional website metrics track what actions are completed or how many visitors you have, user behaviour shows the driving factors behind those actions. UBA tools such as website heatmap software provide an easy-to-read visualisation of this data. 

  Ultimately, user behaviour analysis improves website performance and conversions by boosting customer engagement, optimising positive customer experiences, and focusing on the most important part of your sales : the people who are actually buying from you. 

  What is user behaviour analytics ?

  User behaviour analytics (UBA) is data that shows how customers and website visitors interact with your brand online. 

  UBA is tracked using tools such as heatmaps, session recordings and data visualisation software. 

  Where traditional web analytics track metrics such as page views and bounce rates, behavioural analytics provide an even more in-depth picture of your website or funnel success. 

  For example, UBA tracks actions like 

  • How far users are scrolling down the page 
  • Which CTA’s and copy they are focusing on (or not focusing on) 
  • Which design elements, links or buttons they are interacting with 
  • What is happening in between each action

  Tracking user behaviour metrics help keep visitors on your website longer because they analyse where customers may be confused or unclear so you can fix it. 

  What’s the difference between data and behavioural analytics ?

  There are a few key differences between data and behavioural analytics. While data analytics are beneficial to improving website performance, using UBA creates a more customer-centric approach to funnel building. 

  The biggest difference between data and behavioural analytics ? Metric data shows which actions are happening. Behavioural analytics show you WHY they are happening. 

  For example, data can show you that a customer bounced or clicked away. Behaviour analytics show you that a page took a long time to load, they tried to click a link several times and then maybe got frustrated and clicked away. 

  Key differences between data analytics and behavioural analytics : 

  • What is happening versus what is driving it 
  • Track an action (e.g. click-through) versus tracking inaction (e.g. hover without clicking) 
  • Measuring completion of an action versus the flow of actions to complete action 
  • Source of traffic versus individual actions 
  • What happens when someone takes an action versus what happens in between taking action 

  Matomo heatmaps offer both website analytics and user behaviour for a comprehensive analysis.

  Why do behavioural analytics help improve website performance ?

  User behaviour is important because it doesn’t matter how many website visitors you have if they don’t convert. 

  If you have a lot of traffic on mobile devices, but a low CTR, heatmaps show you what is causing the low conversions. Perhaps there is a button that isn’t optimised for mobile scrolling, or a pop up that covers important copy. 

  Analysing the driving factors behind each decision means that you can increase sign-ups and conversions without losing money on website traffic that never actually buys. 

  

  How do heatmap tools show website user behaviour analytics ? 

  Heatmap tools provide a visual representation of user behaviour. 

  There are several key ways that heatmap tracking can improve website performance and therefore your overall conversions.

  Firstly, heatmaps show where to optimise website structure. It uses real visitor experiences to indicate whether customers have to scroll to reach important content, whether important messages are being missed, and whether CTAs are clear. 

  Secondly, heatmaps provide always-on UX and useability testing for your website, identifying user frustrations and optimising their experience over time.

  They also show valuable user experience insights for A/B versions of a landing page. Not only will you see the raw conversion data, but you will also understand why one page converts more than another.

  Ultimately, heatmaps increase ROI on marketing by optimising the traffic that you are sending to your website.

  

  5 ways heatmaps and user behaviour analytics improve website performance and conversions

  #1. Improve customer experience

  One of the most important uses for UBA is to improve your customer experience. 

  Imagine you had a physical store. If there was something blocking customers from getting to the counter you could easily see and fix the problem. 

  It is just as important for an online store to find and fix these “roadblocks”. 

  Not only does it reduce friction in the sales funnel and make it easy for customers to buy from you, it improves their overall experience. And when 86% of buyers are willing to pay more for a great customer experience, UBA should be one of your number one priorities for growing your bottom line. 

  #2. Improve customer engagement

  Customer engagement is any interaction between a customer/product user and your business. 

  User behaviour analytics increase engagement at each customer journey touch point. 

  Using data from heatmaps will improve customer engagement because it gives you insights into how you can make your website more user friendly. This reduces friction and increases customer loyalty by making sure customers :

  • See important content 
  • Are not distracted by unnecessary elements 
  • Can easily access information or pages no matter what device they are using 
  • Are clicking on important page elements that take them further through the customer journey 

  For example, say a customer is on a sales page. A heatmap might show that pop ups or design elements like links to another page are pulling their attention away from the primary focus (i.e. the sales copy). 

  #3. Focus on customer-centric approach 

  A customer-centric approach means putting your customers at the centre of everything that you do. There is a lot of competition for your customers’ hard earned dollars, so you need to stand out. A good product or service is not enough on its own anymore. 

  User behaviour analytics are at the heart of customer-centric strategies. Instead of guessing how customers interact with your online presence, tools like heatmaps give insight into exactly what customers need. 

  This matched with an effective customer feedback strategy gives a holistic and effective approach to improving your customer experiences. 
  

  #4. Capture customer data across multiple channels

  Most customers won’t convert on their very first visit to a website. They might interact with your business across many channels and research your product multiple times before purchasing. 

  Multi Channel Conversion Attribution, also known as Cross Channel Attribution, lets you assign a value to each visit prior to a conversion or prior to a sale. By applying different attribution models, you get a better view on which channels actually lead to a conversion.

  User behaviour analytics like the multi channel conversion attribution that Matomo offers can show you exactly where you should focus your money to acquire new customers. 

  #5. Track and measure business objectives

  User behaviour analytics like heatmaps can show you whether you are actually hitting your targets. 

  Setting goals helps track your website performance against business objectives. 

  These include objectives such as lead generation, online sales and increased brand exposure. Matomo has a specific function for tracking goals and measuring analytics.

  Using a combination of UBA and data metrics will produce the most effective conversions. 

  For example, a customer reaching the payment confirmation page is a common objective to measure conversions. However, it is only tracked if they actually complete the action. Measuring on-page customer activity with heatmaps shows why they do or do not convert so you can fix issues. 

  Final thoughts on user behaviour analytics 

  User behavioural analytics (UBA) provide a unique and in-depth insight into your customers and their needs. Unlike traditional data metrics that track completed actions, UBA like heatmaps show you what happens in between each action and help fix any critical issues. 

  Heatmaps are your secret weapon to improving website performance while staying customer-centric ! 

  Want to know how heatmap analytics increase conversions and improve customer experience without spending more on traffic or marketing ? Check out some of the other in depth guides below. 

  The Ultimate Guide to Heatmap Software

  10 Proven Ways Heatmap Software Improves Website Conversions

  Heatmap Video

  Session Recording Video

• An introduction to reverse engineering

  22 janvier 2011

  (This blog is still in hibernation, but I needed somewhere to post this)

  Reverse engineering is one of those wonderful topics, covering everything from simple "guess how this program works" problem solving, to poking at silicon with scanning electron microscopes. I’m always hugely fascinated by articles that walk through the steps involved in these types of activities, so I thought I’d contribute one back to the world.

  In this case, I’m going to be looking at the export bundle format created by the Tandberg Content Server, a device for recording video conferences. The bundle is intended for moving recordings between Tandberg devices, but it’s also the easiest way to get all of the related assets for a recorded conference. Unfortunately, there’s no parser available to take the bundle files (.tcb) and output the component pieces. Well, that just won’t do.

  For this type of reverse engineering, I basically want to learn enough about the TCB format to be able to parse out the individual files within it. The only tools I’ll need in this process are a hex editor, a notepad, and a way to convert between hex and decimal (the OS X calculator will do fine if you’re not the type to do it in your head).

  Step 1 : Basic Research
  After Googling around to see if this was a solved issue, I decided to dive into the format. I brought a sample bundle into my trusty hex editor (in this case Hex Fiend).

  

  A few things are immediately obvious. First, we see the first four bytes are the letters TCSB. Another quick visit to Google confirms this header type isn’t found elsewhere, and there’s essentially no discussion of it. Going a few bytes further, we see "contents.xml." And a few bytes after that, we see what looks like plaintext XML. This is a pretty good clue that the TCB file consists of a . Let’s scan a bit further and see if we can confirm that.
  
  In this segment, we see the end of the XML, and something that could be another filename - "dbtransfer" - followed by what looks like gibberish. That doesn’t help too much. Let’s keep looking.
  
  Great - a .jpg ! Looking a bit further, we see the letters "JFIF," which is recognizable as part of a JPEG header. If you weren’t already familiar with that, a quick google for "jpg hex header" would clear up any confusion. So, we’ve got the basics of the file format down, but we’ll need a little bit more information if we’re going to write a parser.

  Step 2 : Finding the pattern
  We can make an educated guess that a file like this has to provide a few hints to a decoder. We would either expect a table of contents, describing where in the bundle each individual file was located, some sort of stop bit marking the boundary between files, byte offsets describing the locations of files, or a listing of file lengths.

  There isn’t any sign of a table of contents. Let’s start looking for a stop bit, as that would make writing our parser really easy. Want I’m going to do is pull out all of the data between two prospective files, and I want two sets to compare.
  I’ve placed asterisks to flag the bytes corresponding to the filenames, since those are known.

  1E D1 70 4C 25 06 36 4D 42 E9 65 6A 9F 5D 88 38 0A 00 *64 62 74 72 61 6E 73 66 65 72* 42 06 ED 48 0B 50 0A C4 14 D6 63 42 F2 BF E3 9D 20 29 00 00 00 00 00 00 DE E5 FD

  01 0C 00 *63 6F 6E 74 65 6E 74 73 2E 78 6D 6C* 9E 0E FE D3 C9 3A 3A 85 F4 E4 22 FE D0 21 DC D7 53 03 00 00 00 00 00 00

  The first line corresponds to the "dbtransfer" entry, the second to the "contents.xml" entry. Let’s trim the first entry to match the second.

  38 0A 00 *64 62 74 72 61 6E 73 66 65 72* 42 06 ED 48 0B 50 0A C4 14 D6 63 42 F2 BF E3 9D 20 29 00 00 00 00 00 00

  01 0C 00 *63 6F 6E 74 65 6E 74 73 2E 78 6D 6C* 9E 0E FE D3 C9 3A 3A 85 F4 E4 22 FE D0 21 DC D7 53 03 00 00 00 00 00 00

  It looks like we’ve got three bytes before the filename, followed by 18 bytes, followed by six bytes of zero. Unfortunately, there’s no obvious pattern of bits which would correspond to a "break" between segments. However, looking at those first three bytes, we see a 0x0A, and a 0x0C, two small values in the same place. 10 and 12. Interesting - the 12 entry corresponds with "contents.xml" and the 10 entry corresponds with "dbtransfer". Could that byte describe the length of the filename ? Let’s look at our much longer JPG entry to be sure.

  70 4A 00 *77 77 77 5C 73 6C 69 64 65 73 5C 64 37 30 64 35 34 63 66 2D 32 39 35 62 2D 34 31 34 63 2D 61 38 64 66 2D 32 66 37 32 64 66 33 30 31 31 35 65 5C 74 68 75 6D 62 6E 61 69 6C 73 5C 74 68 75 6D 62 6E 61 69 6C 30 30 2E 6A 70 67*

  0x4A - 74, corresponding to a 74 character filename. Looks like we’re in business.

  At this point, it’s worth an aside to talk about endianness. I happen to know that the Tandberg Content Server runs Windows on Intel, so I went into this with the assumption that the format was little-endian. However, if you’re not sure, it’s always worth looking at words backwards and forwards, just in case.

  So we know how to find our filename. Now how do we find our file data ? Let’s go back to our JPEG. We know that JPEGs start with 0xFFD8FFE0, and a quick trip to Google also tells us that they end with 0xFFD9. We can use that to pull a sample jpeg out of our TCB, save it to disk, and confirm that we’re on the right track.
  

  This is one of those great steps in reverse engineering - concrete proof that you’re on the right track. Everything seems to go quicker from this point on.

  So, we know we’ve got a JPEG file in a continuous 2177 byte segment. We know that the format used byte lengths to describe filenames - maybe it also uses byte lengths to describe file lengths. Let’s look for 2177, or 0x8108, near our JPEG.

  

  Well, that’s a good sign. But, it could be coincidental, so at this point we’d want to check a few other files to be sure. In fact, looking further in some file, we find some larger .mp4 files which don’t quite match our guess. It turns out that file length is a 32bit value, not a 16bit value - with our two jpegs, the larger bytes just happened to be zeros.

  Step 3 : Writing a parser

  "Bbbbbut...", I hear you say ! "You have all these chunks of data you don’t understand !"

  True enough, but all I care about is getting the files out, with the proper names. I don’t care about creation dates, file permissions, or any of the other crud that this file format likely contains.

  

  Let’s look at the first two files in this bundle. A little bit of byte counting shows us the pattern that we can follow. We’ll treat the first file as a special case. After that, we seek 16 bytes from the end of file data to find the filename length (2 bytes), then we’re at the filename, then we seek 16 bytes to find the file length (4 bytes) and seek another 4 bytes to find the start of the file data. Rinse, repeat.

  I wrote a quick parser in PHP, since the eventual use for this information is part of a larger PHP-based application, but any language with basic raw file handling would work just as well.

  tcsParser.txt
  This was about the simplest possible type of reverse engineering - we had known data in an unknown format, without any compression or encryption. It only gets harder from here...

• Recursively convert images in each subfolder into individual videos using FFmpeg

  17 décembre 2024, par arutan edram

  I am using this script to convert all images in a folder into a video. Each image is shown for 4 seconds and the script runs from a bat file.

  


  `ffmpeg -framerate 1/4 -i %%03d.jpg -pix_fmt yuv420p video.mp4`


  


  I have hundreds of subfolders each containing images with the same resolution and I want to convert them into one moive per subfolder.

  


  I might be close to a solution but it still does not do the job

  


  @echo off
setlocal enabledelayedexpansion

:: Set the frame rate and file format
set "framerate=1/4"
set "image_format=%%03d.jpg"
set "output_video=video.mp4"

:: Traverse all subfolders
for /d /r %%F in (*) do (
    echo Processing folder: %%F
    cd "%%F"
    :: Check if images exist
    if exist "%image_format%" (
        echo Converting images in %%F to video...
        ffmpeg -framerate %framerate% -i "%image_format%" -pix_fmt yuv420p "%%~nxF.mp4"
    ) else (
        echo No images found in %%F, skipping...
    )
    cd ..
)


  


  Any help is appreciated