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• How to Conduct a Customer Journey Analysis (Step-by-Step)

  9 mai 2024, par Erin

  Your customers are everything.

  Treat them right, and you can generate recurring revenue for years. Treat them wrong ; you’ll be spinning your wheels and dealing with churn.

  How do you give your customers the best experience possible so they want to stick around ?

  Improve their customer experience.

  How ?

  By conducting a customer journey analysis.

  When you know how your customers experience your business, you can improve it to meet and exceed customer expectations.

  In this guide, we’ll break down how the customer journey works and give you a step-by-step guide to conduct a thorough customer journey analysis so you can grow your brand.

  What is a customer journey analysis ?

  Every customer you’ve ever served went on a journey to find you.

  From the moment they first heard of you, to the point that they became a customer. 

  Everything in between is the customer journey.

  A customer journey analysis is how you track and analyse how your customers use different channels to interact with your brand.

  

  Analysing your customer journey involves identifying the customer’s different touchpoints with your business so you can understand how it impacts their experience. 

  This means looking at every moment they interacted with your brand before, during and after a sale to help you gain actionable insights into their experience and improve it to reach your business objectives.

  Your customers go through specific customer touchpoints you can track. By analysing this customer journey from a bird’s eye view, you can get a clear picture of the entire customer experience.

  4 benefits of customer journey analysis

  Before we dive into the different steps involved in a customer journey analysis, let’s talk about why it’s vital to analyse the customer journey.

  By regularly analysing your customer journey, you’ll be able to improve the entire customer experience with practical insights, allowing you to :

  Understand your customers better

  What’s one key trait all successful businesses have ?

  They understand their customers.

  By analysing your customer journey regularly, you’ll gain new insights into their wants, needs, desires and behaviours, allowing you to serve them better. These insights will show you what led them to buy a product (or not).

  For example, through conducting a customer journey analysis, a company might find out that customers who come from LinkedIn are more likely to buy than those coming from Facebook.

  Find flaws in your customer journey

  Nobody wants to hear they have flaws. But the reality is your customer journey likely has a few flaws you could improve.

  By conducting customer journey analysis consistently, you’ll be able to pinpoint precisely where you’re losing prospects along the way. 

  For example, you may discover you’re losing customers through Facebook Ads. Or you may find your email strategy isn’t as good as it used to be.

  But it’s not just about the channel. It could be a transition between two channels. For example, you may have great engagement on Instagram but are not converting them into email subscribers. The issue may be that your transition between the two channels has a leak.

  Or you may find that prospects using certain devices (i.e., mobile, tablet, desktop) have lower conversions. This might be due to design and formatting issues across different devices.

  By looking closely at your customer journey and the different customer touchpoints, you’ll see issues preventing prospects from turning into leads or customers from returning to buy again as loyal customers.

  Gain insights into how you can improve your brand

  Your customer journey analysis won’t leave you with a list of problems. Instead, you’ll have a list of opportunities.

  Since you’ll be able to better understand your customers and where they’re falling off the sales funnel, you’ll have new insights into how you can improve the experience and grow your brand.

  For example, maybe you notice that your visitors are getting stuck at one stage of the customer journey and you’re trying to find out why.

  So, you leverage Matomo’s heatmaps, sessions recordings and scroll depth to find out more.

  In the case below, we can see that Matomo’s scroll map is showing that only 65% of the visitors are reaching the main call to action (to write a review). 

  

  To try to push for higher conversions and get more reviews, we could consider moving that button higher up on the page, ideally above the fold.

  Rather than guessing what’s preventing conversions, you can use user behaviour analytics to “step in our user’s shoes” so you can optimise faster and with confidence.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Grow your revenue

  By taking charge of your customer journey, you can implement different strategies that will help you increase your reach, gain more prospects, convert more prospects into customers and turn regulars into loyal customers.

  Using customer journey analysis will help you optimise those different touchpoints to maximise the ROI of your channels and get the most out of each marketing activity you implement.

  7 steps to conduct a customer journey analysis

  Now that you know the importance of conducting a customer journey analysis regularly, let’s dive into how to implement an analysis.

  Here are the seven steps you can take to analyse the customer journey to improve your customer experience :

  

  1. Map out your customer journey

  Your first step to conducting an effective customer journey analysis is to map your entire customer journey.

  Customer journey mapping means looking at several factors :

  • Buying process
  • Customer actions
  • Buying emotions
  • Buying pain points
  • Solutions

  Once you have an overview of your customer journey maps, you’ll gain insights into your customers, their interests and how they interact with your brand. 

  After this, it’s time to dive into the touchpoints.

  2. Identify all the customer touchpoints 

  To improve your customer journey, you need to know every touchpoint a customer can (and does) make with your brand.

  This means taking note of every single channel and medium they use to communicate with your brand :

  • Website
  • Social media
  • Search engines (SEO)
  • Email marketing
  • Paid advertising
  • And more

  Essentially, anywhere you communicate and interact with your customers is fair game to analyse.

  If you want to analyse your entire sales funnel, you can try Matomo, a privacy-friendly web analytics tool. 

  You should make sure to split up your touchpoints into different customer journey stages :

  • Awareness
  • Consideration
  • Conversion
  • Advocacy

  Then, it’s time to move on to how customers interact on these channels.

  Try Matomo for Free

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

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  No credit card required

  3. Measure how customers interact on each channel

  To understand the customer journey, you can’t just know where your customers interact with you. You end up learning how they’re interacting.

  This is only possible by measuring customer interactions.

  How ?

  By using a web analytics tool like Matomo.

  With Matomo, you can track every customer action on your website.

  This means anytime they :

  • Visit your website
  • View a web page
  • Click a link
  • Fill out a form
  • Purchase a product
  • View different media
  • And more

  You should analyse your engagement on your website, apps and other channels, like email and social media.

  4. Implement marketing attribution

  Now that you know where your customers are and how they interact, it’s time to analyse the effectiveness of each channel based on your conversion rates.

  Implementing marketing attribution (or multi-touch attribution) is a great way to do this.

  Attribution is how you determine which channels led to a conversion.

  While single-touch attribution models credit one channel for a conversion, marketing attribution gives credit to a few channels.

  For example, let’s say Bob is looking for a new bank. He sees an Instagram post and finds himself on HSBC’s website. After looking at a few web pages, he attends a webinar hosted by HSBC on financial planning and investment strategies. One week later, he gets an email from HSBC following up on the webinar. Then, he decides to sign up for HSBC’s online banking.

  Single touch attribution would attribute 100% of the conversion to email, which doesn’t show the whole picture. Marketing attribution would credit all channels : social media, website content, webinars and email.

  Matomo offers multiple attribution models. These models leverage different weighting factors, like time decay or linear, so that you can allocate credit to each touchpoint based on its impact.

  Matomo’s multi-touch attribution reports give you in-depth insights into how revenue is distributed across different channels. These detailed reports help you analyse each channel’s contribution to revenue generation so you can optimise the customer journey and improve business outcomes.

  Try Matomo for Free

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

  Start for free

  No credit card required

  5. Use a funnels report to find where visitors are leaving

  Once you set up your marketing attribution, it’s time to analyse where visitors are falling off.

  You can leverage Matomo funnels to find out the conversion rate at each step of the journey on your website. Funnel reports can help you see exactly where visitors are falling through the cracks so you can increase conversions.

  6. Analyse why visitors aren’t converting

  Once you can see where visitors are leaving, you can start to understand why.

  For example, let’s say you analyse your funnels report in Matomo and see your landing page is experiencing the highest level of drop-offs.

  

  You can also use form analytics to find out why users aren’t converting on your landing pages – a crucial part of the customer journey.

  7. A/B test to improve the customer journey

  The final step to improve your customer journey is to conduct A/B tests. These are tests where you test one version of a landing page to see which one converts better, drives more traffic, or generates more revenue.

  For example, you could create two versions of a header on your website and drive 50% of your traffic to each version. Then, once you’ve got your winner, you can keep that as your new landing page.

  

  Using the data from your A/B tests, you can optimise your customer journey to help convert more prospects into customers.

  Use Matomo to improve your customer journey analysis

  Now that you understand why it’s important to conduct customer journey analysis regularly and how it works, it’s time to put this into practice.

  To improve the customer journey, you need to understand what’s happening at each stage of your funnel. 

  Matomo gives you insights into your customer journey so you can improve website performance and convert more visitors into customers.

  Used by over 1 million websites, Matomo is the leading privacy-friendly web analytics solution in the world. 

  Matomo provides you with accurate, unsampled data so you understand exactly what’s going on with your website performance.

  The best part ?

  It’s easy to use and is compliant with the strictest privacy regulations.

  Try Matomo free for 21-days and start Improving your customer journey. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• ffmpeg blend to exclude the top videos background

  2 février 2016, par user2995705

  I want to blend two videos, the top video named "top.mp4" was combined with some PNG files,but the combined video’s background was black ? why ?

  then i try to overlay the top.mp4 on another video (named bottom.mp4,was capture by android camera with QUALITY_480P)

  but when I use Overlay filter i find the top.mp4 was not transparent and have a black background on the result.mp4.

  I try to use blend filter to combine top.mp4 and bottom.mp4 to exclude the top.mp4’s black background , but i don’t know how to use the blend filter.

  here is my question :
  1.is it possible to combine PNG files to a video and the video has transparent background ? and then just use overlay filter,the result.mp4 will not have a black mask under the top.mp4 ?

  2.if 1 is impossible, how to blend top.mp4 and bottom.mp4 to exclude the black background of top.mp4

  my all output log

  02-02 19:34:04.300 7979-18573/com.demo D/LLLLLLLLLL: /data/data/com.demo/app_bin/ffmpeg -i /storage/sdcard0/baishiMagic/magic/waterfall/waterfall/waterfall_%04d.png -r 25 -vcodec copy -preset ultrafast -y /storage/sdcard0/baishiMagic/temp/anim1.mov 
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL: ffmpeg version n2.4.2 Copyright (c) 2000-2014 the FFmpeg developers
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   built on Oct  7 2014 15:05:17 with gcc 4.8 (GCC)
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   configuration: --target-os=linux --cross-prefix=/home/sb/Source-Code/ffmpeg-android/toolchain-android/bin/arm-linux-androideabi- --arch=arm --cpu=cortex-a8 --enable-runtime-cpudetect --sysroot=/home/sb/Source-Code/ffmpeg-android/toolchain-android/sysroot --enable-pic --enable-libx264 --enable-libass --enable-libfreetype --enable-libfribidi --enable-fontconfig --enable-pthreads --disable-debug --disable-ffserver --enable-version3 --enable-hardcoded-tables --disable-ffplay --disable-ffprobe --enable-gpl --enable-yasm --disable-doc --disable-shared --enable-static --pkg-config=/home/sb/Source-Code/ffmpeg-android/ffmpeg-pkg-config --prefix=/home/sb/Source-Code/ffmpeg-android/build/armeabi-v7a --extra-cflags='-I/home/sb/Source-Code/ffmpeg-android/toolchain-android/include -U_FORTIFY_SOURCE -D_FORTIFY_SOURCE=2 -fno-strict-overflow -fstack-protector-all' --extra-ldflags='-L/home/sb/Source-Code/ffmpeg-android/toolchain-android/lib -Wl,-z,relro -Wl,-z,now -pie' --extra-libs='-lpng -lexpat -lm' --extra-cxxflags=
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libavutil      54.  7.100 / 54.  7.100
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libavcodec     56.  1.100 / 56.  1.100
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libavformat    56.  4.101 / 56.  4.101
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libavdevice    56.  0.100 / 56.  0.100
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libavfilter     5.  1.100 /  5.  1.100
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libswscale      3.  0.100 /  3.  0.100
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libswresample   1.  1.100 /  1.  1.100
  
  02-02 19:34:04.345 7979-18576/com.demo D/LLLLLLLLLL:   libpostproc    53.  0.100 / 53.  0.100
  
  02-02 19:34:04.460 7979-18576/com.demo D/LLLLLLLLLL: Input #0, image2, from '/storage/sdcard0/baishiMagic/magic/waterfall/waterfall/waterfall_%04d.png':
  
  02-02 19:34:04.460 7979-18576/com.demo D/LLLLLLLLLL:   Duration: 00:00:08.00, start: 0.000000, bitrate: N/A
  
  02-02 19:34:04.460 7979-18576/com.demo D/LLLLLLLLLL:     Stream #0:0: Video: png, rgba, 480x640 [SAR 3779:3779 DAR 3:4], 25 fps, 25 tbr, 25 tbn, 25 tbc
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL: Output #0, mov, to '/storage/sdcard0/baishiMagic/temp/anim1.mov':
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL:   Metadata:
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL:     encoder         : Lavf56.4.101
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL:     Stream #0:0: Video: png (png  / 0x20676E70), rgba, 480x640 [SAR 3779:3779 DAR 3:4], q=2-31, 25 fps, 12800 tbn, 25 tbc
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL: Stream mapping:
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL:   Stream #0:0 -> #0:0 (copy)
  
  02-02 19:34:04.470 7979-18576/com.demo D/LLLLLLLLLL: Press [q] to stop, [?] for help
  
  02-02 19:34:05.565 7979-18576/com.demo D/LLLLLLLLLL: frame=   46 fps=0.0 q=-1.0 size=   12339kB time=00:00:01.84 bitrate=54936.1kbits/s    
  
  02-02 19:34:06.070 7979-18576/com.demo D/LLLLLLLLLL: frame=   59 fps= 56 q=-1.0 size=   22617kB time=00:00:02.36 bitrate=78509.1kbits/s    
  
  02-02 19:34:06.580 7979-18576/com.demo D/LLLLLLLLLL: frame=   71 fps= 45 q=-1.0 size=   32451kB time=00:00:02.84 bitrate=93605.9kbits/s    
  
  02-02 19:34:07.095 7979-18576/com.demo D/LLLLLLLLLL: frame=   88 fps= 42 q=-1.0 size=   42163kB time=00:00:03.52 bitrate=98124.1kbits/s    
  
  02-02 19:34:07.610 7979-18576/com.demo D/LLLLLLLLLL: frame=  109 fps= 42 q=-1.0 size=   52919kB time=00:00:04.36 bitrate=99428.7kbits/s    
  
  02-02 19:34:08.095 7979-18576/com.demo D/LLLLLLLLLL: frame=  128 fps= 41 q=-1.0 size=   64222kB time=00:00:05.12 bitrate=102755.2kbits/s    
  
  02-02 19:34:08.270 7979-18576/com.demo D/LLLLLLLLLL: frame=  160 fps= 44 q=-1.0 size=   74397kB time=00:00:06.40 bitrate=95227.6kbits/s    
  
  02-02 19:34:08.270 7979-18576/com.demo D/LLLLLLLLLL: frame=  200 fps= 53 q=-1.0 Lsize=   77598kB time=00:00:08.00 bitrate=79460.4kbits/s    
  
  02-02 19:34:08.270 7979-18576/com.demo D/LLLLLLLLLL: video:77596kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.002606%
  
  02-02 19:34:08.270 7979-18573/com.demo D/LLLLLLLLLL: 图片合成,exitValue=0
  
  02-02 19:34:08.295 7979-18573/com.demo D/LLLLLLLLLL: /data/data/com.demo/app_bin/ffmpeg -i /storage/sdcard0/baishiMagic/magic/waterfall/fish_isolate/fish_%04d.png -r 25 -vcodec copy -preset ultrafast -y /storage/sdcard0/baishiMagic/temp/anim2.mov 
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL: ffmpeg version n2.4.2 Copyright (c) 2000-2014 the FFmpeg developers
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   built on Oct  7 2014 15:05:17 with gcc 4.8 (GCC)
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   configuration: --target-os=linux --cross-prefix=/home/sb/Source-Code/ffmpeg-android/toolchain-android/bin/arm-linux-androideabi- --arch=arm --cpu=cortex-a8 --enable-runtime-cpudetect --sysroot=/home/sb/Source-Code/ffmpeg-android/toolchain-android/sysroot --enable-pic --enable-libx264 --enable-libass --enable-libfreetype --enable-libfribidi --enable-fontconfig --enable-pthreads --disable-debug --disable-ffserver --enable-version3 --enable-hardcoded-tables --disable-ffplay --disable-ffprobe --enable-gpl --enable-yasm --disable-doc --disable-shared --enable-static --pkg-config=/home/sb/Source-Code/ffmpeg-android/ffmpeg-pkg-config --prefix=/home/sb/Source-Code/ffmpeg-android/build/armeabi-v7a --extra-cflags='-I/home/sb/Source-Code/ffmpeg-android/toolchain-android/include -U_FORTIFY_SOURCE -D_FORTIFY_SOURCE=2 -fno-strict-overflow -fstack-protector-all' --extra-ldflags='-L/home/sb/Source-Code/ffmpeg-android/toolchain-android/lib -Wl,-z,relro -Wl,-z,now -pie' --extra-libs='-lpng -lexpat -lm' --extra-cxxflags=
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libavutil      54.  7.100 / 54.  7.100
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libavcodec     56.  1.100 / 56.  1.100
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libavformat    56.  4.101 / 56.  4.101
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libavdevice    56.  0.100 / 56.  0.100
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libavfilter     5.  1.100 /  5.  1.100
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libswscale      3.  0.100 /  3.  0.100
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libswresample   1.  1.100 /  1.  1.100
  
  02-02 19:34:08.325 7979-18674/com.demo D/LLLLLLLLLL:   libpostproc    53.  0.100 / 53.  0.100
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL: Input #0, image2, from '/storage/sdcard0/baishiMagic/magic/waterfall/fish_isolate/fish_%04d.png':
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL:   Duration: 00:00:08.00, start: 0.000000, bitrate: N/A
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL:     Stream #0:0: Video: png, rgba, 480x640 [SAR 3779:3779 DAR 3:4], 25 fps, 25 tbr, 25 tbn, 25 tbc
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL: Output #0, mov, to '/storage/sdcard0/baishiMagic/temp/anim2.mov':
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL:   Metadata:
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL:     encoder         : Lavf56.4.101
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL:     Stream #0:0: Video: png (png  / 0x20676E70), rgba, 480x640 [SAR 3779:3779 DAR 3:4], q=2-31, 25 fps, 12800 tbn, 25 tbc
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL: Stream mapping:
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL:   Stream #0:0 -> #0:0 (copy)
  
  02-02 19:34:08.410 7979-18674/com.demo D/LLLLLLLLLL: Press [q] to stop, [?] for help
  
  02-02 19:34:08.750 7979-18674/com.demo D/LLLLLLLLLL: frame=  200 fps=0.0 q=-1.0 Lsize=    6606kB time=00:00:08.00 bitrate=6764.2kbits/s    
  
  02-02 19:34:08.750 7979-18674/com.demo D/LLLLLLLLLL: video:6604kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.023112%
  
  02-02 19:34:08.750 7979-18573/com.demo D/LLLLLLLLLL: 图片合成,exitValue=0
  
  02-02 19:34:08.780 7979-18573/com.demo D/LLLLLLLLLL: /data/data/com.demo/app_bin/ffmpeg -y -i /storage/sdcard0/baishiMagic/temp/video1454412829452.mp4 -r 25 -i /storage/sdcard0/baishiMagic/temp/anim1.mov -i /storage/sdcard0/baishiMagic/temp/anim2.mov -i /storage/sdcard0/frame.ts -filter_complex transpose=1,crop=480:640:0:40,overlay=0:0:0,overlay=-2:-2:0,overlay=0:0:0 -preset ultrafast -strict -2 /storage/sdcard0/baishiMagic/result.mp4 
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL: ffmpeg version n2.4.2 Copyright (c) 2000-2014 the FFmpeg developers
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   built on Oct  7 2014 15:05:17 with gcc 4.8 (GCC)
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   configuration: --target-os=linux --cross-prefix=/home/sb/Source-Code/ffmpeg-android/toolchain-android/bin/arm-linux-androideabi- --arch=arm --cpu=cortex-a8 --enable-runtime-cpudetect --sysroot=/home/sb/Source-Code/ffmpeg-android/toolchain-android/sysroot --enable-pic --enable-libx264 --enable-libass --enable-libfreetype --enable-libfribidi --enable-fontconfig --enable-pthreads --disable-debug --disable-ffserver --enable-version3 --enable-hardcoded-tables --disable-ffplay --disable-ffprobe --enable-gpl --enable-yasm --disable-doc --disable-shared --enable-static --pkg-config=/home/sb/Source-Code/ffmpeg-android/ffmpeg-pkg-config --prefix=/home/sb/Source-Code/ffmpeg-android/build/armeabi-v7a --extra-cflags='-I/home/sb/Source-Code/ffmpeg-android/toolchain-android/include -U_FORTIFY_SOURCE -D_FORTIFY_SOURCE=2 -fno-strict-overflow -fstack-protector-all' --extra-ldflags='-L/home/sb/Source-Code/ffmpeg-android/toolchain-android/lib -Wl,-z,relro -Wl,-z,now -pie' --extra-libs='-lpng -lexpat -lm' --extra-cxxflags=
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libavutil      54.  7.100 / 54.  7.100
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libavcodec     56.  1.100 / 56.  1.100
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libavformat    56.  4.101 / 56.  4.101
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libavdevice    56.  0.100 / 56.  0.100
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libavfilter     5.  1.100 /  5.  1.100
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libswscale      3.  0.100 /  3.  0.100
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libswresample   1.  1.100 /  1.  1.100
  
  02-02 19:34:08.810 7979-18716/com.demo D/LLLLLLLLLL:   libpostproc    53.  0.100 / 53.  0.100
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL: Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/storage/sdcard0/baishiMagic/temp/video1454412829452.mp4':
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:   Metadata:
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     major_brand     : isom
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     minor_version   : 0
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     compatible_brands: isom3gp4
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     creation_time   : 2016-02-02 11:33:58
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:   Duration: 00:00:07.64, start: 0.000000, bitrate: 3099 kb/s
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     Stream #0:0(eng): Video: h264 (Baseline) (avc1 / 0x31637661), yuv420p, 640x480, 3074 kb/s, SAR 1:1 DAR 4:3, 29.82 fps, 30 tbr, 90k tbn, 180k tbc (default)
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       rotate          : 180
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       creation_time   : 2016-02-02 11:33:58
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       handler_name    : VideoHandle
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       encoder         :                                
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     Side data:
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       displaymatrix: rotation of 180.00 degrees
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     Stream #0:1(eng): Audio: aac (mp4a / 0x6134706D), 48000 Hz, mono, fltp, 124 kb/s (default)
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       creation_time   : 2016-02-02 11:33:58
  
  02-02 19:34:08.870 7979-18716/com.demo D/LLLLLLLLLL:       handler_name    : SoundHandle
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL: Input #1, mov,mp4,m4a,3gp,3g2,mj2, from '/storage/sdcard0/baishiMagic/temp/anim1.mov':
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:   Metadata:
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:     major_brand     : qt  
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:     minor_version   : 512
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:     compatible_brands: qt  
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:     encoder         : Lavf56.4.101
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:   Duration: 00:00:08.00, start: 0.000000, bitrate: 79460 kb/s
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:     Stream #1:0(eng): Video: png (png  / 0x20676E70), rgba, 480x640 [SAR 3779:3779 DAR 3:4], 79458 kb/s, 25 fps, 25 tbr, 12800 tbn, 12800 tbc (default)
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:08.920 7979-18716/com.demo D/LLLLLLLLLL:       handler_name    : DataHandler
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL: Input #2, mov,mp4,m4a,3gp,3g2,mj2, from '/storage/sdcard0/baishiMagic/temp/anim2.mov':
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL:   Metadata:
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL:     major_brand     : qt  
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL:     minor_version   : 512
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL:     compatible_brands: qt  
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL:     encoder         : Lavf56.4.101
  
  02-02 19:34:08.950 7979-18716/com.demo D/LLLLLLLLLL:   Duration: 00:00:08.00, start: 0.000000, bitrate: 6764 kb/s
  
  02-02 19:34:08.955 7979-18716/com.demo D/LLLLLLLLLL:     Stream #2:0(eng): Video: png (png  / 0x20676E70), rgba, 480x640 [SAR 3779:3779 DAR 3:4], 6762 kb/s, 25 fps, 25 tbr, 12800 tbn, 12800 tbc (default)
  
  02-02 19:34:08.955 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:08.955 7979-18716/com.demo D/LLLLLLLLLL:       handler_name    : DataHandler
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL: Input #3, mpegts, from '/storage/sdcard0/frame.ts':
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL:   Duration: 00:00:05.13, start: 1.533333, bitrate: 1006 kb/s
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL:   Program 1 
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL:       service_name    : Service01
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL:       service_provider: FFmpeg
  
  02-02 19:34:08.985 7979-18716/com.demo D/LLLLLLLLLL:     Stream #3:0[0x100]: Video: h264 (High) ([27][0][0][0] / 0x001B), yuv420p, 480x480 [SAR 1:1 DAR 1:1], 15 fps, 15 tbr, 90k tbn, 30 tbc
  
  02-02 19:34:09.000 7979-18716/com.demo D/LLLLLLLLLL: [libx264 @ 0x2b447ef0] using SAR=1/1
  
  02-02 19:34:09.020 7979-18716/com.demo D/LLLLLLLLLL: [libx264 @ 0x2b447ef0] using cpu capabilities: none!
  
  02-02 19:34:09.115 7979-18716/com.demo D/LLLLLLLLLL: [libx264 @ 0x2b447ef0] profile Constrained Baseline, level 3.0
  
  02-02 19:34:09.115 7979-18716/com.demo D/LLLLLLLLLL: [libx264 @ 0x2b447ef0] 264 - core 142 - H.264/MPEG-4 AVC codec - Copyleft 2003-2014 - 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=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=250 keyint_min=25 scenecut=0 intra_refresh=0 rc=crf mbtree=0 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=0
  
  02-02 19:34:09.170 7979-18716/com.demo D/LLLLLLLLLL: Output #0, mp4, to '/storage/sdcard0/baishiMagic/result.mp4':
  
  02-02 19:34:09.170 7979-18716/com.demo D/LLLLLLLLLL:   Metadata:
  
  02-02 19:34:09.170 7979-18716/com.demo D/LLLLLLLLLL:     major_brand     : isom
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:     minor_version   : 0
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:     compatible_brands: isom3gp4
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:     encoder         : Lavf56.4.101
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:     Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 480x640 [SAR 1:1 DAR 3:4], q=-1--1, 30 fps, 15360 tbn, 30 tbc (default)
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:       encoder         : Lavc56.1.100 libx264
  
  02-02 19:34:09.175 7979-18716/com.demo D/LLLLLLLLLL:     Stream #0:1(eng): Audio: aac ([64][0][0][0] / 0x0040), 48000 Hz, mono, fltp, 128 kb/s (default)
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:     Metadata:
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:       creation_time   : 2016-02-02 11:33:58
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:       handler_name    : SoundHandle
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:       encoder         : Lavc56.1.100 aac
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL: Stream mapping:
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:   Stream #0:0 (h264) -> transpose (graph 0)
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:   Stream #1:0 (png) -> overlay:overlay (graph 0)
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:   Stream #2:0 (png) -> overlay:overlay (graph 0)
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:   Stream #3:0 (h264) -> overlay:overlay (graph 0)
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:   overlay (graph 0) -> Stream #0:0 (libx264)
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL:   Stream #0:1 -> #0:1 (aac (native) -> aac (native))
  
  02-02 19:34:09.180 7979-18716/com.demo D/LLLLLLLLLL: Press [q] to stop, [?] for help
  
  02-02 19:34:10.250 7979-18716/com.demo D/LLLLLLLLLL: frame=    1 fps=0.0 q=0.0 size=       0kB time=00:00:01.00 bitrate=   0.4kbits/s    
  
  02-02 19:34:10.745 7979-18716/com.demo D/LLLLLLLLLL: frame=    6 fps=5.9 q=0.0 size=       0kB time=00:00:01.00 bitrate=   0.4kbits/s    
  
  02-02 19:34:11.295 7979-18716/com.demo D/LLLLLLLLLL: frame=   11 fps=7.1 q=19.0 size=      11kB time=00:00:01.00 bitrate=  92.1kbits/s    
  
  02-02 19:34:11.760 7979-18716/com.demo D/LLLLLLLLLL: frame=   16 fps=7.7 q=19.0 size=      24kB time=00:00:01.00 bitrate= 199.3kbits/s    
  
  02-02 19:34:12.335 7979-18716/com.demo D/LLLLLLLLLL: frame=   22 fps=8.5 q=20.0 size=      39kB time=00:00:01.00 bitrate= 319.8kbits/s    
  
  02-02 19:34:12.870 7979-18716/com.demo D/LLLLLLLLLL: frame=   28 fps=9.0 q=19.0 size=      58kB time=00:00:01.00 bitrate= 475.7kbits/s    
  
  02-02 19:34:13.345 7979-18716/com.demo D/LLLLLLLLLL: frame=   30 fps=8.3 q=20.0 size=      63kB time=00:00:02.00 bitrate= 258.6kbits/s    
  
  02-02 19:34:13.935 7979-18716/com.demo D/LLLLLLLLLL: frame=   34 fps=8.2 q=20.0 size=      74kB time=00:00:02.00 bitrate= 300.8kbits/s    
  
  02-02 19:34:14.480 7979-18716/com.demo D/LLLLLLLLLL: frame=   39 fps=8.2 q=20.0 size=      87kB time=00:00:02.00 bitrate= 354.0kbits/s    
  
  02-02 19:34:15.015 7979-18716/com.demo D/LLLLLLLLLL: frame=   43 fps=8.2 q=20.0 size=      98kB time=00:00:02.00 bitrate= 400.4kbits/s    
  
  02-02 19:34:15.515 7979-18716/com.demo D/LLLLLLLLLL: frame=   48 fps=8.3 q=21.0 size=     112kB time=00:00:02.00 bitrate= 459.5kbits/s    
  
  02-02 19:34:16.015 7979-18716/com.demo D/LLLLLLLLLL: frame=   54 fps=8.5 q=24.0 size=     137kB time=00:00:02.00 bitrate= 557.9kbits/s    
  
  02-02 19:34:16.575 7979-18716/com.demo D/LLLLLLLLLL: frame=   58 fps=8.5 q=24.0 size=     169kB time=00:00:02.19 bitrate= 628.2kbits/s    
  
  02-02 19:34:17.090 7979-18716/com.demo D/LLLLLLLLLL: frame=   60 fps=8.2 q=24.0 size=     189kB time=00:00:03.00 bitrate= 515.4kbits/s    
  
  02-02 19:34:17.605 7979-18716/com.demo D/LLLLLLLLLL: frame=   65 fps=8.3 q=23.0 size=     228kB time=00:00:03.00 bitrate= 621.7kbits/s    
  
  02-02 19:34:18.090 7979-18716/com.demo D/LLLLLLLLLL: frame=   70 fps=8.4 q=24.0 size=     269kB time=00:00:03.00 bitrate= 731.7kbits/s    
  
  02-02 19:34:18.655 7979-18716/com.demo D/LLLLLLLLLL: frame=   76 fps=8.5 q=25.0 size=     320kB time=00:00:03.00 bitrate= 870.6kbits/s    
  
  02-02 19:34:19.115 7979-18716/com.demo D/LLLLLLLLLL: frame=   82 fps=8.7 q=25.0 size=     374kB time=00:00:03.00 bitrate=1019.4kbits/s    
  
  02-02 19:34:19.620 7979-18716/com.demo D/LLLLLLLLLL: frame=   87 fps=8.8 q=24.0 size=     419kB time=00:00:03.00 bitrate=1142.2kbits/s    
  
  02-02 19:34:20.120 7979-18716/com.demo D/LLLLLLLLLL: frame=   89 fps=8.5 q=22.0 size=     434kB time=00:00:04.01 bitrate= 885.6kbits/s    
  
  02-02 19:34:20.685 7979-18716/com.demo D/LLLLLLLLLL: frame=   94 fps=8.6 q=22.0 size=     464kB time=00:00:04.01 bitrate= 947.8kbits/s    
  
  02-02 19:34:21.155 7979-18716/com.demo D/LLLLLLLLLL: frame=  100 fps=8.7 q=22.0 size=     495kB time=00:00:04.01 bitrate=1010.7kbits/s    
  
  02-02 19:34:21.685 7979-18716/com.demo D/LLLLLLLLLL: frame=  105 fps=8.8 q=22.0 size=     523kB time=00:00:04.01 bitrate=1067.3kbits/s    
  
  02-02 19:34:22.180 7979-18716/com.demo D/LLLLLLLLLL: frame=  110 fps=8.8 q=22.0 size=     551kB time=00:00:04.01 bitrate=1125.1kbits/s    
  
  02-02 19:34:22.685 7979-18716/com.demo D/LLLLLLLLLL: frame=  115 fps=8.9 q=22.0 size=     576kB time=00:00:04.01 bitrate=1176.9kbits/s    
  
  02-02 19:34:23.200 7979-18716/com.demo D/LLLLLLLLLL: frame=  118 fps=8.7 q=21.0 size=     592kB time=00:00:04.92 bitrate= 984.3kbits/s    
  
  02-02 19:34:23.715 7979-18716/com.demo D/LLLLLLLLLL: frame=  123 fps=8.8 q=22.0 size=     619kB time=00:00:05.01 bitrate=1011.1kbits/s    
  
  02-02 19:34:24.265 7979-18716/com.demo D/LLLLLLLLLL: frame=  127 fps=8.7 q=22.0 size=     638kB time=00:00:05.01 bitrate=1042.8kbits/s    
  
  02-02 19:34:24.765 7979-18716/com.demo D/LLLLLLLLLL: frame=  131 fps=8.7 q=23.0 size=     658kB time=00:00:05.01 bitrate=1074.5kbits/s    
  
  02-02 19:34:25.310 7979-18716/com.demo D/LLLLLLLLLL: frame=  137 fps=8.8 q=23.0 size=     700kB time=00:00:05.01 bitrate=1143.7kbits/s    
  
  02-02 19:34:25.800 7979-18716/com.demo D/LLLLLLLLLL: frame=  142 fps=8.8 q=23.0 size=     736kB time=00:00:05.01 bitrate=1202.2kbits/s    
  
  02-02 19:34:26.345 7979-18716/com.demo D/LLLLLLLLLL: frame=  147 fps=8.8 q=24.0 size=     771kB time=00:00:05.14 bitrate=1229.1kbits/s    
  
  02-02 19:34:26.895 7979-18716/com.demo D/LLLLLLLLLL: frame=  150 fps=8.7 q=24.0 size=     798kB time=00:00:06.01 bitrate=1086.7kbits/s    
  
  02-02 19:34:27.385 7979-18716/com.demo D/LLLLLLLLLL: frame=  156 fps=8.8 q=24.0 size=     847kB time=00:00:06.01 bitrate=1153.9kbits/s    
  
  02-02 19:34:27.895 7979-18716/com.demo D/LLLLLLLLLL: frame=  161 fps=8.9 q=23.0 size=     886kB time=00:00:06.01 bitrate=1206.2kbits/s    
  
  02-02 19:34:28.455 7979-18716/com.demo D/LLLLLLLLLL: frame=  167 fps=8.9 q=23.0 size=     927kB time=00:00:06.01 bitrate=1261.6kbits/s    
  
  02-02 19:34:28.905 7979-18716/com.demo D/LLLLLLLLLL: frame=  173 fps=9.0 q=23.0 size=     964kB time=00:00:06.01 bitrate=1312.4kbits/s    
  
  02-02 19:34:29.440 7979-18716/com.demo D/LLLLLLLLLL: frame=  177 fps=9.0 q=23.0 size=     987kB time=00:00:06.20 bitrate=1302.9kbits/s    
  
  02-02 19:34:29.995 7979-18716/com.demo D/LLLLLLLLLL: frame=  180 fps=8.9 q=23.0 size=    1008kB time=00:00:07.01 bitrate=1176.9kbits/s    
  
  02-02 19:34:30.490 7979-18716/com.demo D/LLLLLLLLLL: frame=  186 fps=9.0 q=21.0 size=    1048kB time=00:00:07.01 bitrate=1223.3kbits/s    
  
  02-02 19:34:31.015 7979-18716/com.demo D/LLLLLLLLLL: frame=  191 fps=9.0 q=19.0 size=    1076kB time=00:00:07.01 bitrate=1255.5kbits/s    
  
  02-02 19:34:31.495 7979-18716/com.demo D/LLLLLLLLLL: frame=  197 fps=9.0 q=18.0 size=    1103kB time=00:00:07.01 bitrate=1287.1kbits/s    
  
  02-02 19:34:32.040 7979-18716/com.demo D/LLLLLLLLLL: frame=  202 fps=9.1 q=19.0 size=    1119kB time=00:00:07.01 bitrate=1306.5kbits/s    
  
  02-02 19:34:32.555 7979-18716/com.demo D/LLLLLLLLLL: frame=  207 fps=9.1 q=20.0 size=    1138kB time=00:00:07.01 bitrate=1328.1kbits/s    
  
  02-02 19:34:33.140 7979-18716/com.demo D/LLLLLLLLLL: frame=  213 fps=9.1 q=19.0 size=    1161kB time=00:00:07.01 bitrate=1354.7kbits/s    
  
  02-02 19:34:33.695 7979-18716/com.demo D/LLLLLLLLLL: frame=  217 fps=9.1 q=19.0 size=    1176kB time=00:00:07.59 bitrate=1268.3kbits/s    
  
  02-02 19:34:34.205 7979-18716/com.demo D/LLLLLLLLLL: frame=  222 fps=9.1 q=19.0 size=    1195kB time=00:00:07.59 bitrate=1288.8kbits/s dup=0 drop=4    
  
  02-02 19:34:34.720 7979-18716/com.demo D/LLLLLLLLLL: frame=  222 fps=8.9 q=19.0 size=    1195kB time=00:00:07.59 bitrate=1288.8kbits/s dup=0 drop=13    
  
  02-02 19:34:35.075 7979-18716/com.demo D/LLLLLLLLLL: frame=  222 fps=8.7 q=19.0 size=    1195kB time=00:00:07.59 bitrate=1288.8kbits/s dup=0 drop=24    
  
  02-02 19:34:35.075 7979-18716/com.demo D/LLLLLLLLLL: frame=  222 fps=8.6 q=-1.0 Lsize=    1233kB time=00:00:07.63 bitrate=1322.5kbits/s dup=0 drop=30    
  
  02-02 19:34:35.075 7979-18716/com.demo D/LLLLLLLLLL: video:1104kB audio:122kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.615799%
  
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• Linear Attribution Model : What Is It and How Does It Work ?

  16 février 2024, par Erin

  Want a more in-depth way to understand the effectiveness of your marketing campaigns ? Then, the linear attribution model could be the answer.

  Although you can choose from several different attribution models, a linear model is ideal for giving value to every touchpoint along the customer journey. It can help you identify your most effective marketing channels and optimise your campaigns. 

  So, without further ado, let’s explore what a linear attribution model is, when you should use it and how you can get started. 

  What is a linear attribution model ?

  A linear attribution model is a multi-touch method of marketing attribution where equal credit is given to each touchpoint. Every marketing channel used across the entire customer journey gets credit, and each is considered equally important. 

  So, if a potential customer has four interactions before converting, each channel gets 25% of the credit.

  

  Let’s look at how linear attribution works in practice using a hypothetical example of a marketing manager, Sally, who is looking for an alternative to Google Analytics. 

  Sally starts her conversion path by reading a Matomo article comparing Matomo to Google Analytics she finds when searching on Google. A few days later she signs up for a webinar she saw on Matomo’s LinkedIn page. Two weeks later, Sally gets a sign-off from her boss and decides to go ahead with Matomo. She visits the website and starts a free trial by clicking on one of the paid Google Ads. 

  Using a linear attribution model, we credit each of the channels Sally uses (organic traffic, organic social, and paid ads), ensuring no channel is overlooked in our marketing analysis. 

  Are there other types of attribution models ?

  Absolutely. There are several common types of attribution models marketing managers can use to measure the impact of channels in different ways. 

  

  • First interaction : Also called a first-touch attribution model, this method gives all the credit to the first channel in the customer journey. This model is great for optimising the top of your sales funnel.
  • Last interaction : Also called a last-touch attribution model, this approach gives all the credit to the last channel the customer interacts with. It’s a great model for optimising the bottom of your marketing funnel. 
  • Last non-direct interaction : This attribution model excludes direct traffic and credits the previous touchpoint. This is a fantastic alternative to a last-touch attribution model, especially if most customers visit your website before converting. 
  • Time decay attribution model : This model adjusts credit according to the order of the touchpoints. Those nearest the conversion get weighted the highest. 
  • Position-based attribution model : This model allocates 40% of the credit to the first and last touchpoints and splits the remaining 20% evenly between every other interaction.

  Why use a linear attribution model ?

  Marketing attribution is vital if you want to understand which parts of your marketing strategy are working. All of the attribution models described above can help you achieve this to some degree, but there are several reasons to choose a linear attribution model in particular. 

  It uses multi-touch attribution

  Unlike single-touch attribution models like first and last interaction, linear attribution is a multi-touch attribution model that considers every touchpoint. This is vital to get a complete picture of the modern customer journey, where customers interact with companies between 20 and 500 times. 

  Single-touch attribution models can be misleading by giving conversion credit to a single channel, especially if it was the customer’s last use. In our example above, Sally’s last interaction with our brand was through a paid ad, but it was hardly the most important. 

  It’s easy to understand

  Attribution models can be complicated, but linear attribution is easy to understand. Every touchpoint gets the same credit, allowing you to see how your entire marketing function works. This simplicity also makes it easy for marketers to take action. 

  It’s great for identifying effective marketing channels

  Because linear attribution is one of the few models that provides a complete view of the customer journey, it’s easy to identify your most common and influential touchpoints. 

  It accounts for the top and bottom of your funnel, so you can also categorise your marketing channels more effectively and make more informed decisions. For example, PPC ads may be a more common bottom-of-the-full touchpoint and should, therefore, not be used to target broad, top-of-funnel search terms.

  Are there any reasons not to use linear attribution ?

  Linear attribution isn’t perfect. Like all attribution models, it has its weaknesses. Specifically, linear attribution can be too simple, dilute conversion credit and unsuitable for long sales cycles.

  

  It can be too simple

  Linear attribution lacks nuance. It only considers touchpoints while ignoring other factors like brand image and your competitors. This is true for most attribution models, but it’s still important to point it out. 

  It can dilute conversion credit

  In reality, not every touchpoint impacts conversions to the same extent. In the example above, the social media post promoting the webinar may have been the most effective touchpoint, but we have no way of measuring this. 

  The risk with using a linear model is that credit can be underestimated and overestimated — especially if you have a long sales cycle. 

  It’s unsuitable for very long sales cycles

  Speaking of long sales cycles, linear attribution models won’t add much value if your customer journey contains dozens of different touchpoints. Credit will get diluted to the point where analysis becomes impossible, and the model will also struggle to measure the precise ways certain touchpoints impact conversions. 

  Should you use a linear attribution model ?

  A linear attribution model is a great choice for any company with shorter sales cycles or a reasonably straightforward customer journey that uses multiple marketing channels. In these cases, it helps you understand the contribution of each touchpoint and find your best channels. 

  It’s also a practical choice for small businesses and startups that don’t have a team of data scientists on staff or the budget to hire outside help. Because it’s so easy to set up and understand, anyone can start generating insights using this model. 

  How to set up a linear attribution model

  Are you sold on the idea of using a linear attribution model ? Then follow the steps below to get started :

  

  Choose a marketing attribution tool

  Given the market is worth $3.1 billion, you won’t be surprised to learn there are plenty of tools to choose from. But choose carefully. The tool you pick can significantly impact your success with attribution modelling. 

  Take Google Analytics, for instance. While GA4 offers several marketing attribution models for free, including linear attribution, it lacks accuracy due to cookie consent rejection and data sampling. 

  Accurate marketing attribution is included as a feature in Matomo Cloud and is available as a plugin for Matomo On-Premise users. We support a full range of attribution models that use 100% accurate data because we don’t use data sampling, and cookie consent isn’t an issue (with the exception of Germany and the UK). That means you can trust our insights.

  Matomo’s marketing attribution is available out of the box, and we also provide access to raw data, allowing you to develop your custom attribution model. 

  Collect data

  The quality of your marketing attribution also depends on the quality and quantity of your data. It’s why you need to avoid a platform that uses data sampling. 

  This should include :

  • General data from your analytics platform, like pages visited and forms filled
  • Goals and conversions, which we’ll discuss in more detail in the next step
  • Campaign tracking data so you can monitor the behaviour of traffic from different referral channels
  • Behavioural data from features like Heatmaps or Session Recordings

  Set up goals and conversions

  You can’t assign conversion values to customer journey touchpoints if you don’t have conversion goals in place. That’s why the next step of the process is to set up conversion tracking in your web analytics platform. 

  Depending on your type of business and the product you sell, conversions could take one of the following forms :

  • A product purchase
  • Signing up for a webinar
  • Downloading an ebook
  • Filling in a form
  • Starting a free trial

  Setting up these kinds of goals is easy if you use Matomo. 

  Just head to the Goals section of the dashboard, click Manage Goals and then click the green Add A New Goal button. 

  Fill in the screen below, and add a Goal Revenue at the bottom of the page. Doing so will mean Matomo can automatically calculate the value of each touchpoint when using your attribution model. 

  

  If your analytics platform allows it, make sure you also set up Event Tracking, which will allow you to analyse how many users start to take a desired action (like filling in a form) but never complete the task. 

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  Test and validate

  As we’ve explained, linear attribution is a great model in some scenarios, but it can fall short if you have a long or complex sales funnel. Even if you’re sure it’s the right model for your company, testing and validating is important. 

  Ideally, your chosen attribution tool should make this process pretty straightforward. For example, Matomo’s Marketing Attribution feature makes comparing and contrasting three different attribution models easy. 

  Here we compare the performance of three attribution models—linear, first-touch, and last-non-direct—in Matomo’s Marketing Attribution dashboard, providing straightforward analysis.

  

  If you think linear attribution accurately reflects the value of your channels, you can start to analyse the insights it generates. If not, then consider using another attribution model.

  Don’t forget to take action from your marketing efforts, either. Linear attribution helps you spot the channels that contribute most to conversions, so allocate more resources to those channels and see if you can improve your conversion rate or boost your ROI. 

  Make the most of marketing attribution with Matomo

  A linear attribution model lets you measure every touchpoint in your customer journey. It’s an easy attribution model to start with and lets you identify and optimise your most effective marketing channels. 

  However, accurate data is essential if you want to benefit the most from marketing attribution data. If your web analytics solution doesn’t play nicely with cookies or uses sampled data, then your linear model isn’t going to tell you the whole story. 

  That’s why over 1 million sites trust Matomo’s privacy-focused web analytics, ensuring accurate data for a comprehensive understanding of customer journeys.

  Now you know what linear attribution modelling is, start employing the model today by signing up for a free 21-day trial, no credit card required. 

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