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• 7 Benefits Segmentation Examples + How to Get Started

  26 mars 2024, par Erin

  Every copywriter knows the importance of selling a product’s benefits, not its features. So why should your marketing efforts be different ?

  Answer : they shouldn’t.

  It’s time to stop using demographic or behavioural traits to group customers and start using benefits segmentation instead.

  Benefits segmentation groups your customers based on the value they get from your product or service. In this article, we’ll cover seven real-life examples of benefits segmentation, explain why it’s so powerful and show how to get started today.

  What is benefits segmentation ?

  Benefits segmentation is a way for marketers to group their target market based on the value they get from their products or services. It is a form of customer segment marketing. Other types of market segmentation include :

  • Geographic segmentation
  • Demographic segmentation
  • Psychographic segmentation
  • Behavioural segmentation
  • Firmographic segmentation

  Customers could be the same age, from the same industry and live in the same location but want drastically different things from the same product. Some may like the design of your products, others the function, and still more the price. 

  Whatever the benefits, you can make your marketing more effective by building advertising campaigns around them.

  Why use benefits segmentation ?

  Appealing to the perceived benefits of your product is a powerful marketing strategy. Here are the advantages of you benefit segmentation can expect :

  

  More effective marketing campaigns

  Identifying different benefits segments lets you create much more targeted marketing campaigns. Rather than appeal to a broad customer base, you can create specific ads and campaigns that speak to a small part of your target audience. 

  These campaigns tend to be much more powerful. Benefits-focused messaging better resonates with your audience, making potential customers more likely to convert.

  Better customer experience 

  Customers use your products for a reason. By showing you understand their needs through benefits segmentation, you deliver a much better customer experience — in terms of messaging and how you develop new products. 

  In today’s world, experience matters. 80% of customers say a company’s experience is as important as its products and services.

  Stronger customer loyalty

  When products or services are highly targeted at potential customers, they are more likely to return. More than one-third (36%) of customers would return to a brand if they had a positive experience, even if cheaper or more convenient alternatives exist.

  Using benefits segmentation will also help you attract the right kind of people in the first place — people who will become long-term customers because your benefits align with their needs. 

  Improved products and services

  Benefits segmentation makes it easier to tailor products or services to your audiences’ wants and needs. 

  Rather than creating a product meant to appeal to everyone but doesn’t fulfil a real need, your team can create different ranges of the same product that target different benefits segments. 

  Higher conversion rates

  Personalising your pitch to individual customers is powerful. It drives performance and creates better outcomes for your target customer. Companies that grow faster drive 40 per cent more revenue from personalisation than their slower-growing counterparts.

  When sales reps understand your product’s benefits, talking to customers about them and demonstrating how the product solves particular pain points is much easier. 

  In short, benefits segmentation can lead to higher conversion rates and a better return on investment. 

  7 examples of benefits segmentation

  Let’s take a look at seven examples of real-life benefits segmentation to improve your understanding :

  Nectar

  Mattress manufacturer Nectar does a great job segmenting their product range by customer benefits. That’s a good thing, given how many different things people want from their mattress. 

  It’s not just a case of targeting back sleepers vs. side sleepers ; they focus on more specific benefits like support and cooling. 

  

  Take a look at the screenshot above. Nectar mentions the benefits of each mattress in multiple places, making it easy for customers to find the perfect mattress. If you care about value, for example, you might choose “The Nectar.” If pressure relief and cooling are important to you, you might pick the “Nectar Premier.”

  24 Hour Fitness

  A gym is a gym is a gym, right ? Not when people use it to achieve different goals, it’s not. And that’s what 24 Hour Fitness exploits when they sell memberships to their audience. 

  As you can see from its sales page, 24 Hour Fitness targets the benefits that different customers get from their products :

  

  Customers who just care about getting access to weights and treadmills for as cheap as possible can buy the Silver Membership. 

  But getting fit isn’t the only reason people go to the gym. That’s why 24 Hour Fitness targets its Gold Membership to those who want the “camaraderie” of studio classes led by “expert instructors.”

  Finally, some people value being able to access any club, anywhere in the country. Consumers value flexibility greatly, so 24 Hour Fitness limits this perk to its top-tier membership. 

  Notion

  Notion is an all-in-one productivity and note-taking app that aims to be the only productivity tool people and teams need. Trying to be everything to all people rarely works, however, which is why Notion cleverly tweaks its offering to appeal to the desires of different customer segments :

  

  For price-conscious individuals, it provides a pared solution that doesn’t bloat the user experience with features or benefits these consumers don’t care about.

  The Plus tier is the standard offering for teams who need a way to collaborate online. Still, there are two additional tiers for businesses that target specific benefits only certain teams need. 

  For teams that benefit from a longer history or additional functionality like a bulk export, Notion offers the Business tier at almost double the price of the standard Plus tier. Finally, the Enterprise tier for businesses requires much more advanced security features. 

  Apple

  Apple is another example of a brand that designs and markets products to customers based on specific benefits.

  

  Why doesn’t Apple just make one really good laptop ? Because customers want different things from them. Some want the lightest or smallest laptop possible. Others need ones with higher processing power or larger screens.

  One product can’t possibly deliver all those benefits. So, by understanding the precise reasons people need a laptop, Apple can create and market products around the benefits that are most likely to be sold. 

  Tesla

  In the same way Apple understands that consumers need different things from their laptops, Tesla understands that consumers derive different benefits from their cars. 

  It’s why the company sells four cars (and now a truck) that cover various sizes, top speeds, price points and more. 

  

  Tesla even asks customers about the benefits they want from their car when helping them to choose a vehicle. By asking customers to pick how they will use their new vehicle, Tesla can ensure the car’s benefits match up to the consumers’ goals. 

  Dynamite Brands

  Dynamite Brands is a multi-brand, community-based business that targets remote entrepreneurs around the globe. But even this heavily niched-down business still needs to create benefit segments to serve its audience better. 

  It’s why the company has built several different brands instead of trying to serve every customer under a single banner :

  

  If you just want to meet other like-minded entrepreneurs, you can join the Dynamite Circle, for example. But DC Black might be a better choice if you care more about networking and growing your business.

  It’s the same with the two recruiting brands. Dynamite Jobs targets companies that just want access to a large talent pool. Remote First Recruiting targets businesses that benefit from a more hands-on approach to hiring where a partner does the bulk of the work.

  Garmin

  Do you want your watch to tell the time or do you want it to do more ? If you fall into the latter category, Garmin has designed dozens of watches that target various benefits.

  

  Do you want a watch that tracks your fitness without looking ugly ? Buy the Venu. 

  Want a watch designed for runners ? Buy the Forerunner. 

  Do you need a watch that can keep pace with your outdoor lifestyle ? Buy the Instinct. 

  Just like Apple, Garmin can’t possibly design a single watch that delivers all these benefits. Instead, each watch is carefully built for the target customer’s needs. Yes, it makes the target market smaller, but it makes the product more appealing to those who care about those benefits.

  How to get started with benefits segmentation

  According to Gartner, 63% of digital marketing leaders struggle with personalisation. Don’t be one of them. Here’s how you can improve your personalisation efforts using benefits segmentation. 

  Research and define benefits

  The first step to getting started with benefit segmentation is understanding all the benefits customers get from your products. 

  You probably already know some of the benefits, but don’t underestimate the importance of customer research. Hold focus groups, survey customers and read customer reviews to discover what customers love about your products. 

  Create benefit-focused customer personas

  Now you understand the benefits, it’s time to create customer personas that reflect them. Group consumers who like similar benefits and see if they have any other similarities. 

  Price-conscious consumers may be younger. Maybe people who care about performance have a certain type of job. The more you can do to flesh out what the average benefits-focused consumer looks like, the easier it will be to create campaigns. 

  Create campaigns focused on each benefit

  Now, we get to the fun part. Make the benefit-focused customer personas you created in the last step the focus of your marketing campaigns going forward. 

  Don’t try to appeal to everyone. Just make your campaigns appeal to these people.

  Go deeper with segmentation analytics

  The quality of your benefit segmentation strategy hinges on the quality of your data. That’s why using a an accurate web analytics solution like Matomo to track how each segment behaves online using segmentation analytics is important.

  

  This data can make your marketing campaigns more targeted and effective.

  Benefits segmentation in practice

  Let’s say you have an e-commerce website selling a wide range of household items, and you want to create a benefit segment for “Tech Enthusiasts” who are interested in the latest gadgets and cutting-edge technology. You want to track and analyse their behaviour separately to tailor marketing campaigns or website content specifically for this group.

  1. Identify characteristics : Determine key characteristics or behaviours that define the “Tech Enthusiasts” segment. 

  This might include frequent visits to product pages of the latest tech products, site searches that contain different tech product names, engaging with tech-specific content in emails or spending more time on technology-related blog posts.

  One quick and surefire way to identify characteristics of a segment is to look historically at specific tech product purchases in your Matomo and work your way backwards to find out what steps a “Tech Enthusiast” takes before making a purchase. For instance, you might look at User Flows to discover this.

  

  2. Create segments in Matomo : Using Matomo’s segmentation features, you can create a segment that includes users exhibiting these characteristics. For instance :

  • • Segment by page visits : Create a segment that includes users who visited tech product pages or spent time on tech blogs.

  

  • • Segment by event tracking : If you’ve set up event tracking for specific actions (like clicking on “New Tech” category buttons), create a segment based on these events.
    • Combine conditions : Combine various conditions (e.g., pages visited, time spent, specific actions taken) to create a comprehensive segment that accurately represents “Tech Enthusiasts.”

  3. Track and analyse : Apply this segment to your analytics data in Matomo to track and analyse the behaviour of this group separately. Monitor metrics like their conversion rates, time spent on site or specific products they engage with.
  4. Tailor marketing : Use the insights from analysing this segment to tailor marketing strategies. This could involve creating targeted campaigns or customising website content to cater specifically to these users.

  Remember, the key is to define criteria that accurately represent the segment you want to target, use Matomo’s segmentation tools to isolate this group, and effectively derive actionable insights to cater to their preferences or needs.

  Try Matomo for Free

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  Track your segmentation efforts 

  Benefits segmentation is a fantastic way to improve your marketing. It can help you deliver a better customer experience, improve your product offering and help your sales reps close more deals. 

  Segmenting your audience with an analytics platform lets you go even deeper. But doing so in a privacy-sensitive way can be difficult. 

  That’s why over 1 million websites choose Matomo as their web analytics solution. Matomo provides exceptional segmentation capabilities while remaining 100% accurate and compliant with global privacy laws.

  Find out how Matomo’s insights can level up your marketing efforts with our 21-day free trial, no credit card required.

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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. 

• Capturing audio and video from different sources, how to sync ?

  16 février 2017, par aerodavo

  Here is my code :

  Lapaki:~ Lapaki$ /Users/Lapaki/Desktop/ffmpeg -f avfoundation -video_size 960x540 -pixel_format uyvy422 -framerate ntsc -thread_queue_size 8B -i "XI:none" -f avfoundation -thread_queue_size 8B -i "none:XI" -vf 'crop=iw-240:ih:120:0' -af 'asetpts=PTS+.58735/TB' -pix_fmt yuv420p -aspect 4:3 -s 720x480 -q:v 3 -maxrate 5000k -bufsize 2000k -acodec ac3 -ac 2 -ab 256k -ar 48000 -f dvd /Users/Lapaki/Desktop/FF\ Test/`date +%F`\ `date +%H_%M_%S`.mpg
  
  ffmpeg version 3.2.3-tessus Copyright (c) 2000-2017 the FFmpeg developers
  
    built with Apple LLVM version 8.0.0 (clang-800.0.42.1)
  
    configuration: --cc=/usr/bin/clang --prefix=/opt/ffmpeg --extra-version=tessus --enable-avisynth --enable-fontconfig --enable-gpl --enable-libass --enable-libbluray --enable-libfreetype --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopus --enable-libschroedinger --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzmq --enable-version3 --disable-ffplay --disable-indev=qtkit --disable-indev=x11grab_xcb
  
    libavutil      55. 34.101 / 55. 34.101
  
    libavcodec     57. 64.101 / 57. 64.101
  
    libavformat    57. 56.101 / 57. 56.101
  
    libavdevice    57.  1.100 / 57.  1.100
  
    libavfilter     6. 65.100 /  6. 65.100
  
    libswscale      4.  2.100 /  4.  2.100
  
    libswresample   2.  3.100 /  2.  3.100
  
    libpostproc    54.  1.100 / 54.  1.100
  
  Input #0, avfoundation, from 'XI:none':
  
    Duration: N/A, start: 648413.295900, bitrate: N/A
  
      Stream #0:0: Video: rawvideo (UYVY / 0x59565955), uyvy422, 960x540, 29.97 fps, 29.97 tbr, 1000k tbn, 1000k tbc
  
  Input #1, avfoundation, from 'none:XI':
  
    Duration: N/A, start: 648413.884042, bitrate: 3072 kb/s
  
      Stream #1:0: Audio: pcm_f32le, 48000 Hz, stereo, flt, 3072 kb/s
  
  Output #0, dvd, to '/Users/Lapaki/Desktop/FF Test/2017-02-16 04_16_33.mpg':
  
    Metadata:
  
      encoder         : Lavf57.56.101
  
      Stream #0:0: Video: mpeg2video (Main), yuv420p, 720x480 [SAR 8:9 DAR 4:3], q=2-31, 200 kb/s, 29.97 fps, 90k tbn, 29.97 tbc
  
      Metadata:
  
        encoder         : Lavc57.64.101 mpeg2video
  
      Side data:
  
        cpb: bitrate max/min/avg: 5000000/0/200000 buffer size: 2000000 vbv_delay: -1
  
      Stream #0:1: Audio: ac3, 48000 Hz, stereo, fltp, 256 kb/s
  
      Metadata:
  
        encoder         : Lavc57.64.101 ac3
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (rawvideo (native) -> mpeg2video (native))
  
    Stream #1:0 -> #0:1 (pcm_f32le (native) -> ac3 (native))
  
  Press [q] to stop, [?] for help
  
  [swscaler @ 0x7f8e0c8ab400] Warning: data is not aligned! This can lead to a speedloss
  
  frame=   33 fps=0.0 q=3.0 size=     266kB time=00:00:01.06 bitrate=2051.9kbits/sframe=   49 fps= 48 q=3.0 size=     444kB time=00:00:01.54 bitrate=2358.8kbits/sframe=   64 fps= 42 q=3.0 size=     652kB time=00:00:02.08 bitrate=2560.5kbits/sframe=   79 fps= 39 q=3.0 size=     838kB time=00:00:02.59 bitrate=2642.4kbits/sframe=   94 fps= 37 q=3.0 size=    1022kB time=00:00:03.07 bitrate=2720.0kbits/sframe=  109 fps= 36 q=3.0 size=    1208kB time=00:00:03.59 bitrate=2756.5kbits/sframe=  124 fps= 35 q=3.0 size=    1406kB time=00:00:04.07 bitrate=2830.0kbits/sframe=  127 fps= 35 q=3.0 Lsize=    1474kB time=00:00:04.19 bitrate=2876.4kbits/s dup=12 drop=0 speed=1.15x    
  
  video:1310kB audio:113kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 3.604597%

  The start of Input #0 is 648413.295900, and the start of Input #1 is 648413.884042.

  I’ve been able to keep the audio and video in very close sync by subtracting the two values (which I assume are wallclock values), and using the asetpts audio filter to delay the audio stream of the recorded mpeg-2 file by that amount.

  I’d like to be able to do this exactly though, and that value changes slightly every time I start a new capture. Not to mention, I’d like to be able to do this reliably on different machines, where I assume the value will most likely be different, thus using a calculation as opposed to a fixed number is obviously the best option, if it’s possible.

  Is there a way to subtract the wallclock start time of input #0 from the wallclock start time of input #1 ? I’d like to do this inside the asetpts filter, instead of manually finding the difference from a previous run, which again is slightly different every time...

  I was thinking something like -af asetpts=PTS-([1:0]RTCSTART-[0:0]RTCSTART)/TB might work, but I have no idea how to format it.

  Thanks in advance !