Recherche avancée

Sur d’autres sites (8254)

• Improve ffmpeg x11grab screen capture performance

  10 janvier 2020, par Toby Eggitt

  I have been doing screen-only (no sound) capture using ffmpeg with libx264 for the encoding quite successfully on an old machine built around a Core2 Quad Q6600 processor. I now need to include audio in this, but the fans on this ancient machine are too loud. So, I found a fanless motherboard (https://www.asrock.com/mb/Intel/J5005-ITX/index.asp) that has an Intel Pentium Silver J5005 processor and decided to use this instead. The CPU’s benchmarks put it in a similar bracket to the Q6600, and the general performance seems to be significantly better, presumably at least in part because it’s now using DDR4 memory that’s faster access.

  However, the machine fails horribly at the screen capture. It’s missing frames all over the place ; I actually end up with video that’s missing almost half the frames, and plays back at about double speed. Also, any audio is just messed up so badly I can hardly think how to describe it, best I can come up with is that I get perhaps a quarter second of sound then a few seconds pause (the video meanwhile is actually still playing back, albeit with no sense of time).

  Some things occur to me that might be the cause, or cure, of my troubles, some of which I might be able to fix, others not so much. What other things should I try ? (I’d prefer to avoid simply throwing money at the issue with random ideas that are baseless !)

  1) perhaps the CPU lacks some "extensions" to the instruction set (I recall years ago some CPUs gaining MMX extensions") so that the CPU is fast at mundane computing but sucks at video encoding.

  2) perhaps the fact that the old machine had a dedicated graphics card, while this new one is sharing main memory with the graphics system means that reading the screen pixels is much slower.

  3) perhaps the fact that this new machine has a single DDR4 memory stick in it means that I’m forcing all the memory reads and writes for the computations through the same memory as is holding the screen, and that’s too much (implying that adding an additional memory stick might jus possibly help ?)

  4) perhaps there’s some bios setting that would allow more efficient sharing of video memory ?

  5) my favorite, perhaps there’s a better compression library that I could use to get decent quality screen capture with much less CPU usage.

  I should also note that I have tried this with -threads 0, and the CPU usage hovers between 100% and 200% ; around 100% when the screen is static, and rising as I move windows around and otherwise create more output.

  6) the motherboard claims to have some kind of hardware video encoder built into it. I haven’t paid this any attention to this point, as I assumed it was for the purpose of taking HDMI input and encoding it, but maybe there’s a way to use this, if so, what libraries might I need to get ffmpeg to do this.

  Edits :

  • This is an off the shelf ffmpeg. I’m certainly willing to try building it myself if I have some idea what I should do different.
  • The motherboard claims to have hardware encoders, but I’m struggling to find out what they are (seems like it’s an Intel chip called "UHD Graphics 605" but nothing I can find suggests ffmpeg can work with that)

  • command line right now has been (without audio) :

    ffmpeg  -video_size 1280x720   -f x11grab  -i ${DISPLAY}+100,100  -vcodec libx264  -f alsa -i pulse -acodec ac3 -threads 0  ./video$(date +%F-%H-%M-%S).mp4

  Log from a short recording session is :

  ffmpeg version 3.4.6-0ubuntu0.18.04.1 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 7 (Ubuntu 7.3.0-16ubuntu3)
  
    configuration: --prefix=/usr --extra-version=0ubuntu0.18.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared
  
    libavutil      55. 78.100 / 55. 78.100
  
    libavcodec     57.107.100 / 57.107.100
  
    libavformat    57. 83.100 / 57. 83.100
  
    libavdevice    57. 10.100 / 57. 10.100
  
    libavfilter     6.107.100 /  6.107.100
  
    libavresample   3.  7.  0 /  3.  7.  0
  
    libswscale      4.  8.100 /  4.  8.100
  
    libswresample   2.  9.100 /  2.  9.100
  
    libpostproc    54.  7.100 / 54.  7.100
  
  [x11grab @ 0x561a723e5ac0] Stream #0: not enough frames to estimate rate; consider increasing probesize
  
  Input #0, x11grab, from ':0+100,100':
  
    Duration: N/A, start: 1578693116.465807, bitrate: N/A
  
      Stream #0:0: Video: rawvideo (BGR[0] / 0x524742), bgr0, 1280x720, 29.97 fps, 1000k tbr, 1000k tbn, 1000k tbc
  
  Unknown decoder 'libx264'
  
  simon@studio:~$ ffmpeg  -video_size 1280x720   -f x11grab  -i ${DISPLAY}+100,100  -vcodec libx264 -threads 0  ./video$(date +%F-%H-%M-%S).mp4
  
  ffmpeg version 3.4.6-0ubuntu0.18.04.1 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 7 (Ubuntu 7.3.0-16ubuntu3)
  
    configuration: --prefix=/usr --extra-version=0ubuntu0.18.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared
  
    libavutil      55. 78.100 / 55. 78.100
  
    libavcodec     57.107.100 / 57.107.100
  
    libavformat    57. 83.100 / 57. 83.100
  
    libavdevice    57. 10.100 / 57. 10.100
  
    libavfilter     6.107.100 /  6.107.100
  
    libavresample   3.  7.  0 /  3.  7.  0
  
    libswscale      4.  8.100 /  4.  8.100
  
    libswresample   2.  9.100 /  2.  9.100
  
    libpostproc    54.  7.100 / 54.  7.100
  
  [x11grab @ 0x558225bc29a0] Stream #0: not enough frames to estimate rate; consider increasing probesize
  
  Input #0, x11grab, from ':0+100,100':
  
    Duration: N/A, start: 1578693132.513351, bitrate: N/A
  
      Stream #0:0: Video: rawvideo (BGR[0] / 0x524742), bgr0, 1280x720, 29.97 fps, 1000k tbr, 1000k tbn, 1000k tbc
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))
  
  Press [q] to stop, [?] for help
  
  [libx264 @ 0x558225bcd360] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
  
  [libx264 @ 0x558225bcd360] profile High 4:4:4 Predictive, level 3.1, 4:4:4 8-bit
  
  [libx264 @ 0x558225bcd360] 264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
  
  Output #0, mp4, to './video2020-01-10-14-52-12.mp4':
  
    Metadata:
  
      encoder         : Lavf57.83.100
  
      Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv444p, 1280x720, q=-1--1, 29.97 fps, 30k tbn, 29.97 tbc
  
      Metadata:
  
        encoder         : Lavc57.107.100 libx264
  
      Side data:
  
        cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
  
  Past duration 0.806847 too large     256kB time=00:00:00.43 bitrate=4835.3kbits/s dup=16 drop=0 speed=0.207x    
  
  frame=  371 fps= 29 q=-1.0 Lsize=     639kB time=00:00:12.27 bitrate= 426.6kbits/s dup=16 drop=14 speed=0.971x    
  
  video:634kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.813096%
  
  [libx264 @ 0x558225bcd360] frame I:2     Avg QP:18.16  size:221502
  
  [libx264 @ 0x558225bcd360] frame P:93    Avg QP:14.97  size:  2007
  
  [libx264 @ 0x558225bcd360] frame B:276   Avg QP:20.13  size:    69
  
  [libx264 @ 0x558225bcd360] consecutive B-frames:  0.8%  0.0%  0.0% 99.2%
  
  [libx264 @ 0x558225bcd360] mb I  I16..4: 44.6%  0.0% 55.4%
  
  [libx264 @ 0x558225bcd360] mb P  I16..4:  0.2%  0.0%  0.3%  P16..4:  0.7%  0.1%  0.1%  0.0%  0.0%    skip:98.5%
  
  [libx264 @ 0x558225bcd360] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  1.0%  0.0%  0.0%  direct: 0.0%  skip:99.0%  L0:50.9% L1:49.0% BI: 0.1%
  
  [libx264 @ 0x558225bcd360] coded y,u,v intra: 41.3% 37.5% 37.4% inter: 0.1% 0.0% 0.0%
  
  [libx264 @ 0x558225bcd360] i16 v,h,dc,p: 58% 41%  1%  0%
  
  [libx264 @ 0x558225bcd360] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 33% 30% 14%  2%  4%  4%  5%  3%  5%
  
  [libx264 @ 0x558225bcd360] Weighted P-Frames: Y:0.0% UV:0.0%
  
  [libx264 @ 0x558225bcd360] ref P L0: 59.2%  8.8% 25.5%  6.5%
  
  [libx264 @ 0x558225bcd360] ref B L0: 59.4% 39.0%  1.6%
  
  [libx264 @ 0x558225bcd360] ref B L1: 96.5%  3.5%
  
  [libx264 @ 0x558225bcd360] kb/s:419.29
  
  Exiting normally, received signal 2.

• How to Use Analytics & Reports for Marketing, Sales & More

  28 septembre 2023, par Erin — Analytics Tips

  By now, most professionals know they should be using analytics and reports to make better business decisions. Blogs and thought leaders talk about it all the time. But most sources don’t tell you how to use analytics and reports. So marketers, salespeople and others either skim whatever reports they come across or give up on making data-driven decisions entirely. 

  But it doesn’t have to be this way.

  In this article, we’ll cover what analytics and reports are, how they differ and give you examples of each. Then, we’ll explain how clean data comes into play and how marketing, sales, and user experience teams can use reports and analytics to uncover actionable insights.

  What’s the difference between analytics & reports ? 

  Many people speak of reports and analytics as if the terms are interchangeable, but they have two distinct meanings.

  A report is a collection of data presented in one place. By tracking key metrics and providing numbers, reports tell you what is happening in your business. Analytics is the study of data and the process of generating insights from data. Both rely on data and are essential for understanding and improving your business results.

  

  A science experiment is a helpful analogy for how reporting and analytics work together. To conduct an experiment, scientists collect data and results and compile a report of what happened. But the process doesn’t stop there. After generating a data report, scientists analyse the data and try to understand the why behind the results.

  In a business context, you collect and organise data in reports. With analytics, you then use those reports and their data to draw conclusions about what works and what doesn’t.

  Reports examples 

  Reports are a valuable tool for just about any part of your business, from sales to finance to human resources. For example, your finance team might collect data about spending and use it to create a report. It might show how much you spend on employee compensation, real estate, raw materials and shipping.

  On the other hand, your marketing team might benefit from a report on lead sources. This would mean collecting data on where your sales leads come from (social media, email, organic search, etc.). You could collect and present lead source data over time for a more in-depth report. This shows which sources are becoming more effective over time. With advanced tools, you can create detailed, custom reports that include multiple factors, such as time, geographical location and device type.

  

  Analytics examples 

  Because analytics requires looking at and drawing insights from data and reports to collect and present data, analytics often begins by studying reports. 

  In our example of a report on lead sources, an analytics professional might study the report and notice that webinars are an important source of leads. To better understand this, they might look closely at the number of leads acquired compared to how often webinars occur. If they notice that the number of webinar leads has been growing, they might conclude that the business should invest in more webinars to generate more leads. This is just one kind of insight analytics can provide.

  For another example, your human resources team might study a report on employee retention. After analysing the data, they could discover valuable insights, such as which teams have the highest turnover rate. Further analysis might help them uncover why certain teams fail to keep employees and what they can do to solve the problem.

  The importance of clean data 

  Both analytics and reporting rely on data, so it’s essential your data is clean. Clean data means you’ve audited your data, removed inaccuracies and duplicate entries, and corrected mislabelled data or errors. Basically, you want to ensure that each piece of information you’re using for reports and analytics is accurate and organised correctly.

  If your data isn’t clean and accurate, neither will your reports be. And making business decisions based on bad data can come at a considerable cost. Inaccurate data might lead you to invest in a channel that appears more valuable than it actually is. Or it could cause you to overlook opportunities for growth. Moreover, poor data maintenance and the poor insight it provides will lead your team to have less trust in your reports and analytics team.

  The simplest way to maintain clean data is to be meticulous when inputting or transferring data. This can be as simple as ensuring that your sales team fills in every field of an account record. When you need to import or transfer data from other sources, you need to perform quality assurance (QA) checks to make sure data is appropriately labelled and organised. 

  Another way to maintain clean data is by avoiding cookies. Most web visitors reject cookie consent banners. When this happens, analysts and marketers don’t get data on these visitors and only see the percentage of users who accept tracking. This means they decide on a smaller sample size, leading to poor or inaccurate data. These banners also create a poor user experience and annoy web visitors.

  Matomo can be configured to run cookieless — which, in most countries, means you don’t need to have an annoying cookie consent screen on your site. This way, you can get more accurate data and create a better user experience.

  Marketing analytics and reports 

  Analytics and reporting help you measure and improve the effectiveness of your marketing efforts. They help you learn what’s working and what you should invest more time and money into. And bolstering the effectiveness of your marketing will create more opportunities for sales.

  

  One common area where marketing teams use analytics and reports is to understand and improve their keyword rankings and search engine optimization. They use web analytics platforms like Matomo to report on how their website performs for specific keywords. Insights from these reports are then used to inform changes to the website and the development of new content.

  As we mentioned above, marketing teams often use reports on lead sources to understand how their prospects and customers are learning about the brand. They might analyse their lead sources to better understand their audience. 

  For example, if your company finds that you receive a lot of leads from LinkedIn, you might decide to study the content you post there and how it differs from other platforms. You could apply a similar content approach to other channels to see if it increases lead generation. You can then study reporting on how lead source data changes after you change content strategies. This is one example of how analysing a report can lead to marketing experimentation. 

  Email and paid advertising are also marketing channels that can be optimised with reports and analysis. By studying the data around what emails and ads your audience clicks on, you can draw insights into what topics and messaging resonate with your customers.

  

  Marketing teams often use A/B testing to learn about audience preferences. In an A/B test, you can test two landing page versions, such as two different types of call-to-action (CTA) buttons. Matomo will generate a report showing how many people clicked each version. From those results, you may draw an insight into the design your audience prefers.

  Sales analytics and reports 

  Sales analytics and reports are used to help teams close more deals and sell more efficiently. They also help businesses understand their revenue, set goals, and optimise sales processes. And understanding your sales and revenue allows you to plan for the future.

  One of the keys to building a successful sales strategy and team is understanding your sales cycle. That’s why it’s so important for companies to analyse their lead and sales data. For business-to-business (B2B) companies in particular, the sales cycle can be a long process. But you can use reporting and analytics to learn about the stages of the buying cycle, including how long they take and how many leads proceed to the next step.

  Analysing lead and customer data also allows you to gain insights into who your customers are. With detailed account records, you can track where your customers are, what industries they come from, what their role is and how much they spend. While you can use reports to gather customer data, you also have to use analysis and qualitative information in order to build buyer personas. 

  

  Many sales teams use past individual and business performance to understand revenue trends. For instance, you might study historical data reports to learn how seasonality affects your revenue. If you dive deeper, you might find that seasonal trends may depend on the country where your customers live. 

  

  Conversely, it’s also important to analyse what internal variables are affecting revenue. You can use revenue reports to identify your top-performing sales associates. You can then try to expand and replicate that success. While sales is a field often driven by personal relationships and conversations, many types of reports allow you to learn about and improve the process.

  Website and user behaviour analytics and reports 

  More and more, businesses view their websites as an experience and user behaviour as an important part of their business. And just like sales and marketing, reporting and analytics help you better understand and optimise your web experience. 

  Many web and user behaviour metrics, like traffic source, have important implications for marketing. For example, page traffic and user flows can provide valuable insights into what your customers are interested in. This can then drive future content development and marketing campaigns.

  

  You can also learn about how your users navigate and use your website. A robust web analytics tool, like Matomo, can supply user session recordings and visitor tracking. For example, you could study which pages a particular user visits. But Matomo also has a feature called Transitions that provides visual reports showing where a particular page’s traffic comes from and where visitors tend to go afterward. 

  

  As you consider why people might be leaving your website, site performance is another important area for reporting. Most users are accustomed to near-instantaneous web experiences, so it’s worth monitoring your page load time and looking out for backend delays. In today’s world, your website experience is part of what you’re selling to customers. Don’t miss out on opportunities to impress and delight them.

  Dive into your data

  Reporting and analytics can seem like mysterious buzzwords we’re all supposed to understand already. But, like anything else, they require definitions and meaningful examples. When you dig into the topic, though, the applications for reporting and analytics are endless.

  Use these examples to identify how you can use analytics and reports in your role and department to achieve better results, whether that means higher quality leads, bigger deal size or a better user experience.

  To see how Matomo can collect accurate and reliable data and turn it into in-depth analytics and reports, start a free 21-day trial. No credit card required.

• Attribution Tracking (What It Is and How It Works)

  23 février 2024, par Erin

  Facebook, TikTok, Google, email, display ads — which one is best to grow your business ? There’s one proven way to figure it out : attribution tracking.

  Marketing attribution allows you to see which channels are producing the best results for your marketing campaigns.

  In this guide, we’ll show you what attribution tracking is, why it’s important and how you can leverage it to accelerate your marketing success.

  What is attribution tracking ?

  By 2026, the global digital marketing industry is projected to reach $786.2 billion.

  With nearly three-quarters of a trillion U.S. dollars being poured into digital marketing every year, there’s no doubt it dominates traditional marketing.

  The question is, though, how do you know which digital channels to use ?

  By measuring your marketing efforts with attribution tracking.

  

  So, what is attribution tracking ?

  Attribution tracking is where you use software to keep track of different channels and campaign efforts to determine which channel you should attribute conversion to.

  In other words, you can (and should) use attribution tracking to analyse which channels are pushing the needle and which ones aren’t.

  By tracking your marketing efforts, you’ll be able to accurately measure the scale of impact each of your channels, campaigns and touchpoints have on a customer’s purchasing decision.

  If you don’t track your attribution, you’ll end up blindly pouring time, money, and effort into activities that may or may not be helpful.

  Attribution tracking simply gives you insight into what you’re doing right as a marketer — and what you’re doing wrong.

  By understanding which efforts and channels are driving conversions and revenue, you’ll be able to properly allocate resources toward winning channels to double down on growth.

  Matomo lets you track attribution across various channels. Whether you’re looking to track your conversions through organic, referral websites, campaigns, direct traffic, or social media, you can see all your conversions in one place.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Why attribution tracking is important

  Attribution tracking is crucial to succeed with your marketing since it shows you your most valuable channels.

  It takes the guesswork out of your efforts.

  You don’t need to scratch your head wondering what made your campaigns a success (or a failure).

  While most tools show you last click attribution by default, using attribution tracking, or marketing attribution, you can track revenue and conversions for each touchpoint.

  For example, a Facebook ad might have no led to a conversion immediately. But, maybe the visitor returned to your website two weeks later through your email campaign. Attribution tracking will give credit over longer periods of time to see the bigger picture of how your marketing channels are impacting your overall performance.

  Here are five reasons you need to be using attribution tracking in your business today :

  

  1. Measure channel performance

  The most obvious way attribution tracking helps is to show you how well each channel performs.

  When you’re using a variety of marketing channels to reach your audience, you have to know what’s actually doing well (and what’s not).

  This means having clarity on the performance of your :

  • Emails
  • Google Ads
  • Facebook Ads
  • Social media marketing
  • Search engine optimisation (SEO)
  • And more

  Attribution tracking allows you to measure each channel’s ROI and identify how much each channel impacted your campaigns.

  It gives you a more accurate picture of the performance of each channel and each campaign.

  With it, you can easily break down your channels by how much they drove sales, conversions, signups, or other actions.

  With this information, you can then understand where to further allocate your resources to fuel growth.

  2. See campaign performance over longer periods of time

  When you start tracking your channel performance with attribution tracking, you’ll gain new insights into how well your channels and campaigns are performing.

  The best part — you don’t just get to see recent performance.

  You get to track your campaign results over weeks or months.

  For example, if someone found you through Google by searching a question that your blog had an answer to, but they didn’t convert, your traditional tracking strategy would discount SEO.

  But, if that same person clicked a TikTok ad you placed three weeks later, came back, and converted — SEO would receive some attribution on the conversion.

  Using an attribution tracking tool like Matomo can help paint a holistic view of how your marketing is really doing from channel to channel over the long run.

  Try Matomo for Free

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

  Start for free

  No credit card required

  3. Increase revenue

  Attribution tracking has one incredible benefit for marketers : optimised marketing spend.

  When you begin looking at how well your campaigns and your channels are performing, you’ll start to see what’s working.

  Attribution tracking gives you clarity into the performance of campaigns since it’s not just looking at the first time someone clicks through to your site. It’s looking at every touchpoint a customer made along the way to a conversion.

  By understanding what channels are most effective, you can pour more resources like time, money and labour into those effective channels.

  By doubling down on the winning channels, you’ll be able to grow like never before.

  Rather than trying to “diversify” your marketing efforts, lean into what’s working.

  This is one of the key strategies of an effective marketer to maximise your campaign returns and experience long-term success in terms of revenue.

  4. Improve profit margins

  The final benefit to attribution tracking is simple : you’ll earn more profit.

  Think about it this way : let’s say you’re putting 50% of your marketing spend into Facebook ads and 50% of your spend into email marketing.

  You do this for one year, allocating $500,000 to Facebook and $500,000 to email.

  Then, you start tracking attribution.

  You find that your Facebook ads are generating $900,000 in revenue. 

  That’s a 1,800% return on your investment.

  Not bad, right ?

  Well, after tracking your attribution, you see what your email revenue is.

  In the past year, you generated $1.7 million in email revenue.

  That’s a 3,400% return on your investment (close to the average return of email marketing across all industries).

  In this scenario, you can see that you’re getting nearly twice as much of a return on your marketing spend with email.

  So, the following year, you decide to go for a 75/25 split.

  Instead of putting $500,000 into both email and Facebook ads and email, you put $750,000 into email and $250,000 into Facebook ads.

  You’re still diversifying, but you’re doubling down on what’s working best.

  The result is that you’ll be able to get more revenue by investing the same amount of money, leaving you with higher profit margins.

  Different types of marketing attribution tracking

  There are several types of attribution tracking models in marketing.

  Depending on your goals, your business and your preferred method, there are a variety of types of attribution tracking you can use.

  Here are the six main types of attribution tracking :

  

  1. Last interaction

  Last interaction attribution model is also called “last touch.”

  It’s one of the most common types of attribution. The way it works is to give 100% of the credit to the final channel a customer interacted with before they converted into a customer.

  This could be through a paid ad, direct traffic, or organic search.

  One potential drawback of last interaction is that it doesn’t factor in other channels that may have assisted in the conversion. However, this model can work really well depending on the business.

  2. First interaction

  This is the opposite of the previous model.

  First interaction, or “first touch,” is all about the first interaction a customer has with your brand.

  It gives 100% of the credit to the channel (i.e. a link clicked from a social media post). And it doesn’t report or attribute anything else to another channel that someone may have interacted with in your marketing mix.

  For example, it won’t attribute the conversion or revenue if the visitor then clicked on an Instagram ad and converted. All credit would be given to the first touch which in this case would be the social media post. 

  The first interaction is a good model to use at the top of your funnel to help establish which channels are bringing leads in from outside your audience.

  3. Last non-direct

  Another model is called the last non-direct attribution model. 

  This model seeks to exclude direct traffic and assigns 100% credit for a conversion to the final channel a customer interacted with before becoming a customer, excluding clicks from direct traffic.

  For instance, if someone first comes to your website from an emai campaignl, and then, a week later, directly visits and buys a product, the email campaign gets all the credit for the sale.

  This attribution model tells a bit more about the whole sales process, shedding some more light on what other channels may have influenced the purchase decision.

  4. Linear

  Another common attribution model is linear.

  This model distributes completely equal credit across every single touchpoint (that’s tracked). 

  Imagine someone comes to your website in different ways : first, they find it through a Google search, then they click a link in an email from your campaign the next day, followed by visiting from a Facebook post a few days later, and finally, a week later, they come from a TikTok ad. 

  Here’s how the attribution is divided among these sources :

  • 25% Organic
  • 25% Email
  • 25% Facebook
  • 25% TikTok ad

  This attirubtion model provides a balanced perspective on the contribution of various sources to a user’s journey on your website.

  5. Position-based

  Position-based attribution is when you give 40% credit to both the first and last touchpoints and 20% credit is spread between the touchpoints in between.

  This model is preferred if you want to identify the initial touchpoint that kickstarted a conversion journey and the final touchpoint that sealed the deal.

  The downside is that you don’t gain much insight into the middle of the customer journey, which can make it hard to make effective decisions.

  For example, someone may have been interacting with your email newsletter for seven weeks, which allowed them to be nurtured and build a relationship with you.

  But that relationship and trust-building effort will be overlooked by the blog post that brought them in and the social media ad that eventually converted them.

  6. Time decay

  The final attribution model is called time decay attribution.

  This is all about giving credit based on the timing of the interactions someone had with your brand.

  For example, the touchpoints that just preceded the sale get the highest score, while the first touchpoints get the lowest score.

  For example, let’s use that scenario from above with the linear model :

  • 25% SEO
  • 25% Email
  • 25% Facebook ad
  • 25% Organic TikTok

  But, instead of splitting credit by 25% to each channel, you weigh the ones closer to the sale with more credit.

  Instead, time decay may look at these same channels like this :

  • 5% SEO (6 weeks ago)
  • 20% Email (3 weeks ago)
  • 30% Facebook ad (1 week ago)
  • 45% Organic TikTok (2 days ago)

  One downside is that it underestimates brand awareness campaigns. And, if you have longer sales cycles, it also isn’t the most accurate, as mid-stage nurturing and relationship building are underlooked. 

  Leverage Matomo : A marketing attribution tool

  Attribution tracking is a crucial part of leading an effective marketing strategy.

  But it’s impossible to do this without the right tools.

  A marketing attribution tool can give you insights into your best-performing channels automatically. 

  

  One of the best marketing attribution tools available is Matomo, a web analytics tool that helps you understand what’s going on with your website and different channels in one easy-to-use dashboard.

  With Matomo, you get marketing attribution as a plug-in or within Matomo On-Premise or for free in Matomo Cloud.

  The best part is it’s all done with crystal-clear data. Matomo gives you 100% accurate data since it doesn’t use data sampling on any plans like Google Analytics.

  To start tracking attribution today, try Matomo’s 21-day free trial. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

  Start improving your websites now