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• How to cheat on video encoder comparisons

  21 juin 2010, par Dark Shikari — benchmark, H.264, stupidity, test sequences

  Over the past few years, practically everyone and their dog has published some sort of encoder comparison. Sometimes they’re actually intended to be something for the world to rely on, like the old Doom9 comparisons and the MSU comparisons. Other times, they’re just to scratch an itch — someone wants to decide for themselves what is better. And sometimes they’re just there to outright lie in favor of whatever encoder the author likes best. The latter is practically an expected feature on the websites of commercial encoder vendors.

  One thing almost all these comparisons have in common — particularly (but not limited to !) the ones done without consulting experts — is that they are horribly done. They’re usually easy to spot : for example, two videos at totally different bitrates are being compared, or the author complains about one of the videos being “washed out” (i.e. he screwed up his colorspace conversion). Or the results are simply nonsensical. Many of these problems result from the person running the test not “sanity checking” the results to catch mistakes that he made in his test. Others are just outright intentional.

  The result of all these mistakes, both intentional and accidental, is that the results of encoder comparisons tend to be all over the map, to the point of absurdity. For any pair of encoders, it’s practically a given that a comparison exists somewhere that will “prove” any result you want to claim, even if the result would be beyond impossible in any sane situation. This often results in the appearance of a “controversy” even if there isn’t any.

  Keep in mind that every single mistake I mention in this article has actually been done, usually in more than one comparison. And before I offend anyone, keep in mind that when I say “cheating”, I don’t mean to imply that everyone that makes the mistake is doing it intentionally. Especially among amateur comparisons, most of the mistakes are probably honest.

  So, without further ado, we will investigate a wide variety of ways, from the blatant to the subtle, with which you too can cheat on your encoder comparisons.

  Blatant cheating

  1. Screw up your colorspace conversions. A common misconception is that converting from YUV to RGB and back is a simple process where nothing can go wrong. This is quite untrue. There are two primary attributes of YUV : PC range (0-255) vs TV range (16-235) and BT.709 vs BT.601 conversion coefficients. That sums up to a total of 4 possible different types of YUV. When people compare encoders, they often use different frontends, some of which make incorrect assumptions about these attributes.

  Incorrect assumptions are so common that it’s often a matter of luck whether the tool gets it right or not. It doesn’t help that most videos don’t even properly signal which they are to begin with ! Often even the tool that the person running the comparison is using to view the source material gets the conversion wrong.

  Subsampling YUV (aka what everyone uses) adds yet another dimension to the problem : the locations which the chroma data represents (“chroma siting”) isn’t constant. For example, JPEG and MPEG-2 define different positions. This is even worse because almost nobody actually handles this correctly — the best approach is to simply make sure none of your software is doing any conversion. A mistake in chroma siting is what created that infamous PSNR graph showing Theora beating x264, which has been cited for ages since despite the developers themselves retracting it after realizing their mistake.

  Keep in mind that the video encoder is not responsible for colorspace conversion — almost all video encoders operate in the YUV domain (usually subsampled 4:2:0 YUV, aka YV12). Thus any problem in colorspace conversion is usually the fault of the tools used, not the actual encoder.

  How to spot it : “The color is a bit off” or “the contrast of the video is a bit duller”. There were a staggering number of “H.264 vs Theora” encoder comparisons which came out in favor of one or the other solely based on “how well the encoder kept the color” — making the results entirely bogus.

  2. Don’t compare at the same (or nearly the same) bitrate. I saw a VP8 vs x264 comparison the other day that gave VP8 30% more bitrate and then proceeded to demonstrate that it got better PSNR. You would think this is blindingly obvious, but people still make this mistake ! The most common cause of this is assuming that encoders will successfully reach the target bitrate you ask of them — particularly with very broken encoders that don’t. Always check the output filesizes of your encodes.

  How to spot it : The comparison lists perfectly round bitrates for every single test, as opposed to the actual bitrates achieved by the encoders, which will never be exactly matching in any real test.

  3. Use unfair encoding settings. This is a bit of a wide topic : there are many ways to do this. We’ll cover the more blatant ones in this part. Here’s some common ones :

  a. Simply cheat. Intentionally pick awful settings for the encoder you don’t like.

  b. Don’t consider performance. Pick encoding settings without any regard for some particular performance goal. For example, it’s perfectly reasonable to say “use the best settings possible, regardless of speed”. It’s also reasonable to look for a particular encoding speed target. But what isn’t reasonable is to pick extremely fast settings for one encoder and extremely slow settings for another encoder.

  c. Don’t attempt match compatibility options when it’s reasonable to do so. Keyframe interval is a classic one of these : shorter values reduce compression but improve seeking. An easy way to cheat is to simply not set them to the same value, biasing towards whatever encoder has the longer interval. This is most common as an accidental mistake with comparisons involving ffmpeg, where the default keyframe interval is an insanely low 12 frames.

  How to spot it : The comparison doesn’t document its approach regarding choice of encoding settings.

  4. Use ratecontrol methods unfairly. Constant bitrate is not the same as average bitrate — using one instead of the other is a great way to completely ruin a comparison. Another method is to use 1-pass bitrate mode for one encoder and 2-pass or constant quality for another. A good general approach is that, for any given encoder, one should use 2-pass if available and constant quality if not (it may take a few runs to get the bitrate you want, of course).

  Of course, it’s also fine to run a comparison with a particular mode in mind — for example, a comparison targeted at streaming applications might want to test using 1-pass CBR. Of course, in such a case, if CBR is not available in an encoder, you can’t compare to that encoder.

  How to spot it : It’s usually pretty obvious if the encoding settings are given.

  5. Use incredibly old versions of encoders. As it happens, Debian stable is not the best source for the most recent encoding software. Equally, using recent versions known to be buggy.

  6. Don’t distinguish between video formats and the software that encodes them. This is incredibly common : I’ve seen tests that claim to compare “H.264″ against something else while in fact actually comparing “Quicktime” against something else. It’s impossible to compare all H.264 encoders at once, so don’t even try — just call the comparison “Quicktime versus X” instead of “H.264 versus X”. Or better yet, use a good H.264 encoder, like x264 and don’t bother testing awful encoders to begin with.

  Less-obvious cheating

  1. Pick a bitrate that’s way too low. Low bitrate testing is very effective at making differences between encoders obvious, particularly if doing a visual comparison. But past a certain point, it becomes impossible for some encoders to keep up. This is usually an artifact of the video format itself — a scalability limitation. Practically all DCT-based formats have this kind of limitation (wavelets are mostly immune).

  In reality, this is rarely a problem, because one could merely downscale the video to resolve the problem — lower resolutions need fewer bits. But people rarely do this in comparisons (it’s hard to do it fairly), so the best approach is to simply not use absurdly low bitrates. What is “absurdly low” ? That’s a hard question — it ends up being a matter of using one’s best judgement.

  This tends to be less of a problem in larger-scale tests that use many different bitrates.

  How to spot it : At least one of the encoders being compared falls apart completely and utterly in the screenshots.

  Biases towards, a lot : Video formats with completely scalable coding methods (Dirac, Snow, JPEG-2000, SVC).

  Biases towards, a little : Video formats with coding methods that improve scalability, such as arithmetic coding, B-frames, and run-length coding. For example, H.264 and Theora tend to be more scalable than MPEG-4.

  2. Pick a bitrate that’s way too high. This is staggeringly common mistake : pick a bitrate so high that all of the resulting encodes look absolutely perfect. The claim is then made that “there’s no significant difference” between any of the encoders tested. This is surprisingly easy to do inadvertently on sources like Big Buck Bunny, which looks transparent at relatively low bitrates. An equally common but similar mistake is to test at a bitrate that isn’t so high that the videos look perfect, but high enough that they all look very good. The claim is then made that “the difference between these encoders is small”. Well, of course, if you give everything tons of bitrate, the difference between encoders is small.

  How to spot it : You can’t tell which image is the source and which is the encode.

  3. Making invalid comparisons using objective metrics. I explained this earlier in the linked blog post, but in short, if you’re going to measure PSNR, make sure all the encoders are optimized for PSNR. Equally, if you’re going to leave the encoder optimized for visual quality, don’t measure PSNR — post screenshots instead. Same with SSIM or any other objective metric. Furthermore, don’t blindly do metric comparisons — always at least look at the output as a sanity test. Finally, do not claim that PSNR is particularly representative of visual quality, because it isn’t.

  How to spot it : Encoders with psy optimizations, such as x264 or Theora 1.2, do considerably worse than expected in PSNR tests, but look much better in visual comparisons.

  4. Lying with graphs. Using misleading scales on graphs is a great way to make the differences between encoders seem larger or smaller than they actually are. A common mistake is to scale SSIM linearly : in fact, 0.99 is about twice as good as 0.98, not 1% better. One solution for this is to use db to compare SSIM values.

  5. Using lossy screenshots. Posting screenshots as JPEG is a silly, pointless way to worsen an encoder comparison.

  Subtle cheating

  1. Unfairly pick screenshots for comparison. Comparing based on stills is not ideal, but it’s often vastly easier than comparing videos in motion. But it also opens up the door to unfairness. One of the most common mistakes is to pick a frame immediately after (or on) a keyframe for one encoder, but which isn’t for the other encoder. Particularly in the case of encoders that massively boost keyframe quality, this will unfairly bias in favor of the one with the recent keyframe.

  How to spot it : It’s very difficult to tell, if not impossible, unless they provide the video files to inspect.

  2. Cherry-pick source videos. Good source videos are incredibly hard to come by — almost everything is already compressed and what’s left is usually a very poor example of real content. Here’s some common ways to bias unfairly using cherry-picking :

  a. Pick source videos that are already heavily compressed. Pre-compressed source isn’t much of an issue if your target quality level for testing is much lower than that of the source, since any compression artifacts in the source will be a lot smaller than those created by the encoders. But if the source is already very compressed, or you’re testing at a relatively high quality level, this becomes a significant issue.

  Biases towards : Anything that uses a similar transform to the source content. For MPEG-2 source material, this biases towards formats that use the 8x8dct or a very close approximation : MPEG-1/2/4, H.263, and Theora. For H.264 source material, this biases towards formats that use a 4×4 transform : H.264 and VP8.

  b. Pick standard test clips that were not intended for this purpose. There are a wide variety of uncompressed “standard test clips“. Some of these are not intended for general-purpose use, but rather exist to test specific encoder capabilities. For example, Mobile Calendar (“mobcal”) is extremely sharp and low motion, serving to test interpolation capabilities. It will bias incredibly heavily towards whatever encoder uses more B-frames and/or has higher-precision motion compensation. Other test clips are almost completely static, such as the classic “akiyo”. These are also not particularly representative of real content.

  c. Pick very noisy content. Noise is — by definition — not particularly compressible. Both in terms of PSNR and visual quality, a very noisy test clip will tend to reduce the differences between encoders dramatically.

  d. Pick a test clip to exercise a specific encoder feature. I’ve often used short clips from Touhou games to demonstrate the effectiveness of x264′s macroblock-tree algorithm. I’ve sometimes even used it to compare to other encoders as part of such a demonstration. I’ve also used the standard test clip “parkrun” as a demonstration of adaptive quantization. But claiming that either is representative of most real content — and thus can be used as a general determinant of how good encoders are — is of course insane.

  e. Simply encode a bunch of videos and pick the one your favorite encoder does best on.

  3. Preprocessing the source. A encoder test is a test of encoders, not preprocessing. Some encoding apps may add preprocessors to the source, such as noise reduction. This may make the video look better — possibly even better than the source — but it’s not a fair part of comparing the actual encoders.

  4. Screw up decoding. People often forget that in addition to encoding, a test also involves decoding — a step which is equally possible to do wrong. One common error caused by this is in tests of Theora on content whose resolution isn’t divisible by 16. Decoding is often done with ffmpeg — which doesn’t crop the edges properly in some cases. This isn’t really a big deal visually, but in a PSNR comparison, misaligning the entire frame by 4 or 8 pixels is a great way of completely invalidating the results.

  The greatest mistake of all

  Above all, the biggest and most common mistake — and the one that leads to many of the problems mentioned here – is the mistaken belief that one, or even a few tests can really represent all usage fairly. Any comparison has to have some specific goal — to compare something in some particular case, whether it be “maximum offline compression ignoring encoding speed” or “real-time high-speed video streaming” or whatnot. And even then, no comparison can represent all use-cases in that category alone. An encoder comparison can only be honest if it’s aware of its limitations.

• Ode to the Gravis Ultrasound

  1er août 2011, par Multimedia Mike — General

  WARNING : This post is a bunch of nostalgia. Feel free to follow along if you recall the DOS days of the early-mid 1990s.

  I finally let go of my Gravis Ultrasound MAX sound card a little while ago. It felt like the end of an era for me, even though I had scarcely used the card in recent memory.

  
  
  

  The Beginning
  What is the Gravis Ultrasound ? Only the finest PC sound card from the classic DOS days. Back in the day (very early 1990s), most consumer PC sound cards were Yamaha OPL FM synthesizers paired with a basic digital to analog converter (DAC). Gravis, a company known for game controllers, dared to break with the dominant paradigm of Sound Blaster clones and create a sound card that had 32 digital channels.
  
  I heard about the GUS sometime in 1992 through one of the dominant online services at the time, Prodigy. Through the message boards, I learned of a promotion with Electronic Arts in which customers could pre-order a GUS at a certain discount along with 2 EA games from a selected catalog (with progressive discounts when ordering more games from the list). I know I got the DOS version of PowerMonger ; I think the other was Night Shift, though that doesn’t seem to be an EA title.

  Anyway, 1992 saw many maddening delays of the GUS hardware. Finally, reports of GUS shipments began to trickle into the Prodigy message forums. Then one day in November, 1992, mine arrived. Into the 286 machine it went and a valiant attempt at software installation was made. A friend and I fought with the software late into the evening, trying to make this thing work reasonably. I remember grabbing a pair of old headphones sitting near the computer that were used for an ancient (even for the time) portable radio. That was the only means of sound reproduction we had available at that moment. And it still sounded incredible.

  After graduating to progressively superior headphones, I would later return to that original pair only to feel my ears were being physically assaulted. Strange, they sounded fine that first night I was trying to make the GUS work. I guess this was my first understanding that the degree to which one is a snobby audiophile is all a matter of hard-earned experience.

  Technology
  The GUS was powered by something called a GF1 which was supposed to use a technology called wavetable synthesis. In the early days, I thought (and I wasn’t alone in this) that this meant that the GF1 chip had a bunch of digitized instrument samples stored in the ASIC. That wasn’t it.

  However, it did feature 32 digital channels at a time when most PC audio cards had 2 (plus that Yamaha FM synthesizer). There was some hemming and hawing about how the original GUS couldn’t drive all 32 channels at a full 44.1 kHz ("CD quality") playback rate. It’s true— if 14 channels were enabled, all could be played at 44.1 kHz. Enabling more channels started progressive degradation and with all 32 channels, each was only playing at around 19 kHz. Still, from my emerging game programmer perspective, that allowed for 8-channel tracker music and 6 channels of sound effects, all at the vaunted CD level of quality.

  Games and Compatibility
  The primary reason to have a discrete sound card was for entertainment applications — ahem, games. GUS support was pretty sketchy out of the gate (ostensibly a major reason for the card’s delay). While many sound cards offered Sound Blaster emulation by basically having the same hardware as Sound Blaster cards, the GUS took a software route towards emulating the SB. To do this required a program called the Sound Blaster Operating System, or SBOS.

  Oh, how awesome it was to hear the program exclaim "SBOS installed !" And how harshly it grated on your nerves after the 200th time hearing it due to so many reboots and fiddling with options to make your games work. Also, I’ve always wondered if there’s something special about sampling an ’s’ sound — does it strain the sampling frequency range ? Perhaps the phrase was sampled at too low a bitrate because the ’s’ sounds didn’t come through very clearly, which is something you notice after hundreds of iterations when there are 3 ’s’ sounds in the phrase.

  Fortunately, SBOS became less relevant with the advent of Mega-Em, a separate emulator which intercepted calls to Roland MIDI systems and routed them to the very capable GUS. Roland-supporting games sounded beautiful.

  Eventually, more and more DOS games were released with native Gravis support, sometimes with the help of The Miles Sound System (from our friends at Rad Game Tools — you know, the people behind Smacker and Bink). The library changelog is quite the trip down PC memory lane.

  An important area where the GUS shined brightly was that of demos and music trackers. The emerging PC demo scene embraced the powerful GUS (aided, no doubt, by Gravis’ sponsorship of the community) and the coolest computer art and music of the time natively supported the card.

  Programming
  At this point in my life, I was a budding programmer in high school and was fairly intent on programming video games. So far, I had figured out how to make a few blips using a borrowed Sound Blaster card. I went to great lengths to learn how to program the Gravis Ultrasound.

  Oh you kids today, with your easy access to information at the tips of your fingers thanks to Google and the broader internet. I had to track down whatever information I could find through a combination of Prodigy message boards and local dialup BBSes and FidoNet message bases. Gravis was initially tight-lipped about programming information for its powerful card, as was de rigueur of hardware companies (something that largely persists to this day). But Gravis eventually saw an opportunity to one-up encumbent Creative Labs and released a full SDK for the Ultrasound. I wanted the SDK badly.

  So it was early-mid 1993. Gravis released an SDK. I heard that it was available on their support BBS. Their BBS with a long distance phone number. If memory serves, the SDK was only in the neighborhood of 1.5 Mbytes. That takes a long time to transfer via a 2400 baud modem at a time when long distance phone charges were still a thing and not insubstantial.

  Luckily, they also put the SDK on something called an ’FTP site’. Fortunately, about this time, I had the opportunity to get some internet access via the local university.

  Indeed, my entire motivation for initially wanting to get on the internet was to obtain special programming information. Is that nerdy enough for you ?

  I see that the GUS SDK is still available via the Gravis FTP site. The file GUSDK222.ZIP is dated 1998 and is less than a megabyte.

  Next Generation : CD Support
  So I had my original GUS by the end of 1992. That was just the first iteration of the Gravis Ultrasound. The next generation was the GUS MAX. When I was ready to get into the CD-ROM era, this was what I wanted in my computer. This is because the GUS MAX had CD-ROM support. This is odd to think about now when all optical drives have SATA interfaces and (P)ATA interfaces before that— what did CD-ROM compatibility mean back then ? I wasn’t quite sure. But in early 1995, I headed over to Computer City (R.I.P.) and bought a new GUS MAX and Sony double-speed CD-ROM drive to install in the family’s PC.

  
  
  

  About the "CD-ROM compatibility" : It seems that there were numerous competing interfaces in the early days of CD-ROM technology. The GUS MAX simply integrated 3 different CD-ROM controllers onto the audio card. This was superfluous to me since the Sony drive came with an appropriate controller card anyway, though I didn’t figure out that the extra controller card was unnecessary until after I installed it. No matter ; computers of the day were rife with expansion ports.

  
  
  The 3 different CD-ROM controllers on the GUS MAX
  

  Explaining The Difference
  It was difficult to explain the difference in quality to those who didn’t really care. Sometime during 1995, I picked up a quasi-promotional CD-ROM called "The Gravis Ultrasound Experience" from Babbage’s computer store (remember when that was a thing ?). As most PC software had been distributed on floppy discs up until this point, this CD-ROM was an embarrassment of riches. Tons of game demos, scene demos, tracker music, and all the latest GUS drivers and support software.

  Further, the CD-ROM had a number of red book CD audio tracks that illustrated the difference between Sound Blaster cards and the GUS. I remember loaning this to a tech-savvy coworker who disbelieved how awesome the GUS was. The coworker took it home, listened to it, and wholly agreed that the GUS audio sounded better than the SB audio in the comparison — and was thoroughly confused because she was hearing this audio emanating from her Sound Blaster. It was the difference between real-time and pre-rendered audio, I suppose, but I failed to convey that message. I imagine the same issue comes up even today regarding real-time video rendering vs., e.g., a pre-rendered HD cinematic posted on YouTube.

  Regrettably, I can’t find that CD-ROM anymore which leads me to believe that the coworker never gave it back. Too bad, because it was quite the treasure trove.

  Aftermath
  According to folklore I’ve heard, Gravis couldn’t keep up as the world changed to Windows and failed to deliver decent drivers. Indeed, I remember trying to keep my GUS in service under Windows 95 well into 1998 but eventually relented and installed some kind of more appropriate sound card that was better supported under Windows.

  Of course, audio output capability has been standard issue for any PC for at least 10 years and many people aren’t even aware that discrete sound cards still exist. Real-time audio rendering has become less essential as full musical tracks can be composed and compressed into PCM format and delivered with the near limitless space afforded by optical storage.

  A few years ago, it was easy to pick up old GUS cards on eBay for cheap. As of this writing, there are only a few and they’re pricy (but perhaps not selling). Maybe I was just viewing during the trough of no value a few years ago.

  Nowadays, of course, anyone interested in studying the old GUS or getting a nostalgia fix need only boot up the always-excellent DOSBox emulator which provides remarkable GUS emulation support.

• B2B Marketing Attribution Guide : How to Master It in 2024

  21 mai 2024, par Erin

  The last thing you want is to invest your advertising dollars in channels, campaigns and ads that don’t work. But B2B marketing attribution — figuring out which marketing efforts drive revenue — is far from easy.

  With longer sales funnels and multiple people from the same company involved in the same sales process, B2B (business-to-business) is a different ballgame from B2C (business-to-consumer) marketing.

  In this guide, we break down what B2B marketing attribution is, how it’s different, which tools you can use to set it up and the best practices.

  What is B2B marketing attribution ?

  Marketing attribution in B2B companies is about figuring out where your high-value leads come from — nailing down long customer journeys across many different touchpoints.

  

  The goal is to determine which campaigns and content contributed to various parts of the customer journey. It’s a complex process that needs a reliable, privacy-focused web analytics tool and a CRM that integrates with it.

  This process significantly differs from traditional marketing attribution, where you focus more on short sales cycles from individual customers. With multiple contributing decision makers, B2B attribution requires more robust systems.

  What makes marketing attribution different for B2B ?

  The key differences between B2B and B2C marketing attribution are a longer sales funnel and more people involved in the sales process.

  The B2B sales funnel is significantly longer and more complex

  The typical B2C sales funnel is often broken down into four simple stages :

  1. Awareness : when a prospect first finds out about your product or brand
  2. Interest : where a prospect starts to learn about the benefits of your product
  3. Desire : when a prospect understands that they need your product
  4. Action : the actual process of closing the sale

  Even the most simplified B2B sales funnel includes several key stages.

  

  Here’s a brief overview of each :

  1. Awareness : Buyers recognise they have a problem and start looking for solutions. Stand out with blog posts, social media updates, ebooks and whitepapers.
  2. Consideration : Buyers are aware of your company and are comparing options. Provide product demos, webinars and case studies to address their concerns and build trust.
  3. Conversion : Buyers have chosen your product or company. Offer live demos, customer service, case studies and testimonials to finalise the purchase.
  4. Loyalty : Buyers have made a purchase and are now customers. Nurture relationships with thank you emails, follow-ups, how-tos, reward programs and surveys to encourage repeat business.
  5. Advocacy : Loyal customers become advocates, promoting your brand to others. Encourage this with surveys, testimonial requests and a referral program.

  A longer sales cycle typically involves not only more touchpoints but also extended decision-making processes.

  More teams are involved in the marketing and sales process

  The last differentiation in B2B attribution is the number of people involved. Instead of clear-cut sales and marketing teams, revenue teams are becoming more common.

  They include all go-to-market teams like sales, marketing, customer success and customer support. In B2B sales, long-term customer relationships can be incredibly valuable. As such, the focus shifts away from new customer acquisition alone.

  For example, you can also track and optimise your onboarding process. Marketing gets involved in post-sale efforts to boost loyalty. Sales reps follow up with customer success to get new sales angles and insights. Customer support insights drive future product development.

  Everyone works together to meet high-level company goals.

  The next section will explore how to set up an attribution system.

  How to find the right mix of B2B marketing attribution tools

  For most B2B marketing teams, the main struggle with attribution is not with the strategy but with creating a reliable system that gives them the data points they need to implement that strategy.

  We’ll outline one approach you can take to achieve this without a million-dollar budget or internal data science team.

  Use website analytics to track touchpoints

  The first thing you want to do is install a reliable website analytics solution on your website. 

  Once you’ve got your analytics in place, use campaign tracking parameters to track touchpoints from external campaigns like email newsletters, social media ads, review sites (like Capterra) and third-party partner campaigns.

  This way, you get a clear picture of which sources are driving traffic and conversions, helping you improve your marketing strategies.

  With analytics installed, you can track the referring sources of visits, engagement and conversion events. A robust solution like Matomo tracks everything from traffic sources, marketing attribution and visitor counts to behavioural analytics, like clicks, scrolling patterns and form interactions on your site.

  Marketing attribution will give you a cohesive view of which traffic sources and campaigns drive conversions and revenue over long periods. With Matomo’s marketing attribution feature, you can even use different marketing attribution models to compare results :

  

  For example, in a single report, you can compare the last interaction, first interaction and linear (three common marketing attribution models). 

  In total, Matomo has 6 available attribution models to choose from :

  1. First interaction
  2. Last interaction
  3. Last non-direct 
  4. Linear
  5. Position based
  6. Time decay 

  These additional attribution models are crucial for B2B sites. While other web analytics solutions often limit to last-click attribution, this model isn’t optimal for B2B with extended sales cycles.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Use a CRM to integrate customer data from multiple sources

  Use your CRM software to integrate customer data from multiple sources. This will give you the ability to get meaningful B2B marketing insights. For example, you can get company-level insights so you can view conversion information by company, not just by person.

  Done effectively, you can close the loop back to analytics data by integrating data from multiple teams and platforms. 

  Implement self-reported attribution

  To further enhance the data, add qualifying questions in the lead signup process to create a hybrid attribution model. This is also known as self-reported attribution.

  

  Your web analytics platform won’t always be able to track the source of certain visits — for instance, “dark social” or peer-to-peer sharing, where links are shared privately and are not easily traceable by analytics tools.

  Doing self-reported attribution is crucial for getting a holistic image of your customer journey. 

  However, self-reported attribution isn’t foolproof ; users may click randomly or inaccurately recall where they first heard about you. So it’s essential to blend this data with your analytics to gain a more accurate understanding.

  Best practices for handling B2B prospect data in a privacy-sensitive world 

  Lastly, it’s important to respect your prospects’ privacy and comply with privacy regulations when conducting B2B marketing attribution.

  Privacy regulations and their enforcement are rapidly gaining momentum around the globe. Meta recently received a record GDPR fine of €1.2 billion for insufficient privacy measures when handling user data by the Irish Data Protection Agency.

  If you don’t want to risk major fines (or customers feeling betrayed), you shouldn’t follow in the same footsteps.

  Switch to a privacy-friendly web analytics

  Instead of using a controversial solution like Google Analytics, use a privacy-friendly web analytics solution like Matomo, Fathom or Plausible. 

  These alternatives not only ensure compliance with regulations like GDPR but also provide peace of mind amid the uncertain relationship between Google and GDPR. Google Analytics has faced bans in recent years, raising concerns about the future of the solution.

  While organisations governed by GDPR can currently use Google Analytics, there’s no guarantee of its continued availability.

  Make the switch to privacy-friendly web analytics to avoid potential fines and disruptive rulings that could force you to change platforms urgently. Such disruptions can be catastrophic for marketing teams heavily reliant on web analytics for tracking campaigns, business goals and marketing efforts.

  Improve your B2B marketing attribution with Matomo

  Matomo’s privacy-by-design architecture makes it the perfect analytics platform for the modern B2B marketer. Matomo enables you to meet even the strictest privacy regulations.

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