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• Benefits and Shortcomings of Multi-Touch Attribution

  13 mars 2023, par Erin — Analytics Tips

  Few sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer. 

  Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales. 

  Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates. 

  The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process. 

  If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it. 

  What Are the Benefits of Multi-Touch Attribution ?

  Remember an old parable of blind men and an elephant ?

  Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.

  Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too. 

  Better Understanding of Customer Journeys 

  On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages : 

  • Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel). 
  • Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel). 
  • Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel). 

  You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel. 

  For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion. 

  This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that. 

  Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.

  

  Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion. 

  For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion. 

  A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines. 

  The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.

  Improved Budget Allocation 

  Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.

  First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions. 

  For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.

  

  By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types). 

  Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :

  “Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.

  More Accurate Measurements 

  The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance. 

  In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking. 

  Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :

  • How many touchpoints are involved in the conversions ? 
  • How long does it take for a lead to convert on average ? 
  • When and where do different audience groups convert ? 
  • What is your average win rate for different types of campaigns ?

  Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect. 

  At the highest level, you need to collect two data points :

  • Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals. 
  • Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events. 

  Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them. 

  The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used. 

  Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo). 

  Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.

  Learn more about selecting the optimal multi-channel attribution model for your business.

  What Are the Limitations of Multi-Touch Attribution ?

  Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry. 

  Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email. 

  In addition, you should keep in mind several other limitations of multi-touch attribution software.

  Limited Marketing Mix Analysis 

  Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.

  Multi-touch attribution tools cannot evaluate the impact of :

  • Dark social channels 
  • Word-of-mouth 
  • Offline promotional events
  • TV or out-of-home ad campaigns 

  If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.

  Time-Based Constraints 

  Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles. 

  

  Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel. 

  At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc. 

  Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ? 

  The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time. 

  

  Limited Access to Raw Data 

  In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied. 

  Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues. 

  In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making. 

  With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data. 

  AI Application 

  On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies. 

  To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.

  Difficult Technical Implementation 

  Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.

  

  Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc. 

  Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams. 

  Conclusion 

  Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations. 

  That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool. 

  Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool ! 

  Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried. 

• What is White Label Analytics ? Everything You Need to Know

  6 février 2024, par Erin

  Reports are a core part of a marketing agency’s offering. It’s how you build trust with clients by highlighting your efforts and demonstrating your results. 

  But all too often, those reports deliver a jarring and incohesive experience. The culprit ? The logos, colours and names of third-party brands your agency uses to deliver work and create the reports. 

  Luckily, there’s a way to make sure your reports elevate your agency’s stature ; not undermine it. 

  By white labelling your tools, you can deliver a clear and cohesive brand experience — one that strengthens the client relationship rather than diminishing it. 

  In this article, we explain what white label analytics tools are, why it’s important to white label your analytics solution and how you can do it using Matomo. 

  What is white label analytics ?

  White labelling is the process of redesigning a product or service using your company’s brand. The term comes from the act of putting a white label on a product that covers the original branding and allows the reseller to personalise the product.

  White label analytics, then, is a way to customise your analytics software with your agency’s logo and colours. When you white label your analytics, you ensure your reports, dashboards and interface provide a consistent and familiar user experience.

  

  The alternative is to provide your clients with an analytics report containing the logo and branding of your analytics software provider — whether that’s Google Analytics, Matomo, or another tool. 

  For some clients, it can create a confusing experience that takes attention away from your agency’s results.

  Why white label analytics is important

  There are plenty of reasons to white label your analytics tool, from improving your client’s experience to generating additional revenue. Here are four of the most important benefits to know :

  

  Improve the client experience

  You want your clients to have a seamless user experience with your agency’s brand, whether they visit your website, log into their client portal, or read one of your reports. 

  By white labelling your analytics platform, you can give your clients a visually appealing experience that stays in line with the rest of your branding and doesn’t leave them confused about who they are interacting with or which company is providing the service they pay for. 

  This is especially important if your agency uses other third-party tools like a client portal or productivity platform that also allows for custom branding. 

  Strengthen client relationships

  When you use white labelling to remove solution providers’ logos, you ensure your brand gets all of the credit for the hard work you’ve been doing. This can strengthen the agency-client relationship and reaffirm the importance of your agency. 

  But, white labelling allows you to tell a better story through your reports and increases the perceived value you offer. There are no other brands, logos, or names to confuse the narrative or detract from your key points — or to stop the client from understanding just how much value you provide. 

  Save time and increase productivity 

  White labelling your analytics platform can save your team a significant amount of time when creating client reports. 

  There’s no need to carefully screenshot graphs to add them to your own branded report. You can simply email clients a report using your white labelled analytics platform, assuring them of a seamlessly branded experience.

  The upshot is that your team can spend more time on billable work, improving the value they deliver to existing clients or opening up capacity to take on even more work. 

  Increase monetisation opportunities

  Whether you are an agency or consultant, white labelling an analytics solution gives you the opportunity to package and sell analytics as part of your own services. This can open up new revenue streams, help you to diversify your income, and reach a wider audience.

  The beauty of a white label offering is that there is no allusion to the company providing the underlying service.

  The most important elements of an analytics platform to white label 

  A white label analytics solution should offer a broad range of customisation options that range from surface-level branding to functional elements like tracking codes. 

  Below we take a look at the top components you should be able to customise with your chosen platform. 

  Logo and Favicon

  The logo is the first thing clients will see when they open up their analytics platform or look at your reports. It should make your services instantly recognisable, which is why it’s so jarring when clients read a report with another company’s brand slapped on every chart. 

  This should be the very first thing you change since it will be on almost every page and report your client views. Don’t stop there, however. If you send clients web-based reports, you’ll also want to change the platform’s favicon — the small logo you see next to your website in a browser. 

  Customising both your logo and favicon is easy with Matomo. 

  Just head to Administration, then General Settings and click Use a custom Logo under Brand settings.

  

  Upload your brand, click Save, and it will automatically populate your brand in place of the Matomo logo across the platform, just like in the image above.

  Brand name

  Most analytics platforms will mention their brand names repeatedly across the site, so it’s important to change these, too.

  Otherwise, you risk clients reading your analytics reports in detail or playing around with your platform’s settings and getting confused when another seemingly unrelated name keeps popping up. 

  Again, this is easily done with Matomo’s White Label plugin. 

  Head to Administration, then General Settings. Scroll to the bottom of the page to find WhiteLabel settings.

  Enter your brand or product name in the first box and click Save. 

  

  Just like your logo, this will replace every instance of Matomo’s brand name with your own.

  Brand colours

  Changing your analytics platform’s colours to match your own is almost as important as swapping out the logo. 

  Failure to do so could mean the charts and graphs you add to your client reports could cause confusion. 

  You can also use Matomo’s WhiteLabel settings to change the platform’s background and font colours. 

  Just enter a new header background and font colour using hexadecimal values.

  

  This change will also apply to automated email reports. 

  Custom tracking

  Tracking requests and links are an overlooked element of analytics when it comes to white labelling. Most people wouldn’t think twice about them, but they are an easy way for someone in the know to identify which platform you are using. 

  With Matomo’s White Label plugin, it’s possible to customise every request Matomo makes to your clients’ websites. 

  If left unbranded, tracking requests contain the following references : matomo.js and matomo.php. 

  By clicking the Whitelabel tracking endpoint box on the WhiteLabel settings page, those references will be replaced with js/tracker.js and js/tracker.php

  You’ll need to update your tracking code to reflect these changes, otherwise, requests will still contain Matomo branding. 

  Try Matomo for Free

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

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  Links

  Finally, you’ll want to remove any links to any additional content offered by the analytics company. These are usually included to improve the user experience, but they are best removed if you are letting clients access your platform. 

  With Matomo, you can remove all links by clicking the relevant box in WhiteLabel settings. 

  You can also use the Show Marketplace only to Super Users checkbox to limit the visibility of Matomo’s Marketplace to everyone bar Super Users.

  Can you white label Google Analytics ?

  In a word : no. 

  Google Analytics might be the most popular analytics platform, but it comes up short if you want to customise its appearance. 

  This can be a particular problem for agencies that need to stand out from competitors offering the same generic reports. You can add more context, detail and graphs to your analytics reports, of course. But you’ll never be able to create completely custom, brand-cohesive reports using Google Analytics. 

  3 analytics platforms you can white label

  While you can’t white label Google Analytics, there are several web analytics providers that do offer a white labelling service. Here are three of the best :

  Matomo

  As you’ve already seen, Matomo is the ideal web analytics platform if you want to let your own brand shine through. Matomo lets you personalise the entire dashboard and all of your reports. That includes :

  • Adding your brand logo and favicon
  • Changing the font and background colours 
  • Removing third-party links
  • Tracking using custom URLs 
  • Develop your own custom theme

  Matomo offers a 21-day free trial (no credit card required). If you want to get remove the Matomo branding, you need the White Label plugin, which starts at just $179 per year after a free trial.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Clicky

  Clicky is a simple, privacy-focused web analytics platform with a white label offering. Like Matomo, you can add your logo and change the platform’s colours. 

  Clicky offers a seven-day free trial and charges a $99 setup fee, with prices starting from $49 and rising to $399. 

  Plausible 

  Plausible is another privacy-focused Google Analytics alternative that offers white labelling. The difference here is that it’s pretty complex to set up. 

  Rather than customising Plausible’s platform, for instance, you need to embed its dashboard into your own user interface. If you want to create your own custom dashboard, you’ll need to use an API. 

  Plausible offers a 30-day free trial.

  Leverage white label analytics today with Matomo

  Don’t put up with confusing unbranded clients a moment longer. White label your analytics platform so the next time you sit down to share insights with your clients, they’ll only see one brand : yours.

  Matomo makes it quick and easy to customise the look of your analytics platform and all of the reports you generate. If you already use Matomo, try the White Label plugin free for 30 days.

  If not, try Matomo with a free 21-day trial. No credit card required. 

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• ffmpeg concat video length is not the sum of durations

  9 novembre 2019, par Mason

  I’m simply trying to put some images together into a video with specific durations. I’m using the command

  ffmpeg -f concat -i concat.txt -r 30 video.mp4

  # concat.txt
  
  
  
  file capture12.png
  
  duration 1
  
  file capture57.png
  
  duration 3
  
  file capture156.png
  
  duration 7
  
  file capture234.png
  
  duration 5
  
  file capture272.png
  
  duration 3

  The output of my console is

  ffmpeg version 4.2.1 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 9.1.1 (GCC) 20190807
  
    configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
  
    libavutil      56. 31.100 / 56. 31.100
  
    libavcodec     58. 54.100 / 58. 54.100
  
    libavformat    58. 29.100 / 58. 29.100
  
    libavdevice    58.  8.100 / 58.  8.100
  
    libavfilter     7. 57.100 /  7. 57.100
  
    libswscale      5.  5.100 /  5.  5.100
  
    libswresample   3.  5.100 /  3.  5.100
  
    libpostproc    55.  5.100 / 55.  5.100
  
  Input #0, concat, from 'concat.txt':
  
    Duration: 00:00:19.00, start: 0.000000, bitrate: 0 kb/s
  
      Stream #0:0: Video: png, rgba(pc), 983x553, 25 tbr, 25 tbn, 25 tbc
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
  
  Press [q] to stop, [?] for help
  
  [libx264 @ 000001b3e55c5c80] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
  
  [libx264 @ 000001b3e55c5c80] profile High 4:4:4 Predictive, level 3.1, 4:4:4, 8-bit
  
  [libx264 @ 000001b3e55c5c80] 264 - core 158 r2984 3759fcb - H.264/MPEG-4 AVC codec - Copyleft 2003-2019 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 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 'video.mp4':
  
    Metadata:
  
      encoder         : Lavf58.29.100
  
      Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv444p, 983x553, q=-1--1, 30 fps, 15360 tbn, 30 tbc
  
      Metadata:
  
        encoder         : Lavc58.54.100 libx264
  
      Side data:
  
        cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
  
  frame=  629 fps=400 q=-1.0 Lsize=     124kB time=00:00:20.86 bitrate=  48.8kbits/s dup=624 drop=0 speed=13.3x
  
  video:116kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 7.050883%
  
  [libx264 @ 000001b3e55c5c80] frame I:3     Avg QP:13.09  size: 20222
  
  [libx264 @ 000001b3e55c5c80] frame P:158   Avg QP:10.80  size:   207
  
  [libx264 @ 000001b3e55c5c80] frame B:468   Avg QP:13.75  size:    53
  
  [libx264 @ 000001b3e55c5c80] consecutive B-frames:  0.8%  0.0%  0.0% 99.2%
  
  [libx264 @ 000001b3e55c5c80] mb I  I16..4: 23.0% 58.6% 18.4%
  
  [libx264 @ 000001b3e55c5c80] mb P  I16..4:  0.0%  0.0%  0.2%  P16..4:  0.1%  0.0%  0.0%  0.0%  0.0%    skip:99.7%
  
  [libx264 @ 000001b3e55c5c80] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  1.3%  0.0%  0.0%  direct: 0.0%  skip:98.7%  L0:66.5% L1:33.5% BI: 0.0%
  
  [libx264 @ 000001b3e55c5c80] 8x8 transform intra:54.0% inter:7.4%
  
  [libx264 @ 000001b3e55c5c80] coded y,u,v intra: 15.3% 0.7% 0.7% inter: 0.0% 0.0% 0.0%
  
  [libx264 @ 000001b3e55c5c80] i16 v,h,dc,p: 86% 14%  0%  0%
  
  [libx264 @ 000001b3e55c5c80] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 65%  7% 28%  0%  0%  0%  0%  0%  0%
  
  [libx264 @ 000001b3e55c5c80] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 50% 14% 14%  3%  4%  5%  4%  4%  3%
  
  [libx264 @ 000001b3e55c5c80] Weighted P-Frames: Y:0.0% UV:0.0%
  
  [libx264 @ 000001b3e55c5c80] ref P L0: 87.3%  6.3%  5.6%  0.8%
  
  [libx264 @ 000001b3e55c5c80] ref B L0: 45.3% 54.3%  0.4%
  
  [libx264 @ 000001b3e55c5c80] ref B L1: 99.6%  0.4%
  
  [libx264 @ 000001b3e55c5c80] kb/s:45.07

  I can see it clearly saying Duration 00:00:19.00 (as it should be) and then later saying that the final video is 00:00:20.86. How do I get these numbers to match ?