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• How to increase conversions to meet your business goals

  8 septembre 2020, par Joselyn Khor — Analytics Tips, Marketing
   Through optimizing your messaging, content, or your page layouts, you can increase conversions by getting your visitors through a clear pathway to achieve your business goals.

  Conversion Rate Optimization

  When we talk about optimizing websites to improve and increase conversions, we’re really talking about conversion rate optimization (CRO).

  CRO is the process of learning what the most valuable content/aspect of your website is and how to best optimize this for your visitors to increase its chance to convert. It typically involves generating ideas for elements on your site or app that can be improved, learning which pathways visitors are most likely going to take to conversion and then validating those assumptions through A/B testing and multivariate testing to transform learning into actionable insights.

  Conversion Rate

  The conversion rate is expressed as a % and the goal for any business should be to increase the % of conversions for any given goal e.g. in February a website had 200 newsletter sign-ups from 1,000 visitors on its sign-up page, a conversion rate of 20%. CRO should be used to increase the sign-up rate from 20% to 25%, and then eventually from 25% to 30% and so on.

  CRO cheat sheet

  You need to consider your website or business’ objectives (bigger picture) as well as your website goals (smaller achievements). Whatever the aim of your website, it’s crucial for this to be your starting point. Figure out what you want your website to do and what you want visitors to get from it. When you do that, you’ll know what conversions to focus on.
  

  • Define your business/website’s objectives. Do you want the website to drive sales ? Is the website a hub to raise awareness for a charity ? Do you want to increase readership for your news site ?
  • Define what your conversion goals are. This helps you narrow your focus so you follow a path to meet your overall objectives. By defining these, you clarify for yourself the next actions you should take, such as wanting to funnel users through to a sign up landing page. Then you’ll need to optimize and test your sign up landing page. If conversions are low, then tweak it and measure the results until you find you’ve increased conversion rates.
  • Conversion goals can include :
    • Purchases in your ecommerce store
    • eBook downloads
    • Sign ups to your mailing list
    • Visitors successfully filling in a contact form
  • Figure out what your Key Performance Indicators (KPIs) are and the metrics you need to focus on to achieve them.

  How to increase conversions with Matomo

  1. Set goals
  2. Heatmaps
  3. Session Recordings
  4. A/B test
  5. Form Analytics
  6. Funnels
  7. Behaviour

  1. Set goals

  “Make Sure Goals Are Clearly Understood. To prove the value of an analytics-focused company, any project you take on needs to have clear goals. If you don’t have a goal in mind you’ll fail. Everyone involved in the project needs to be aligned around the goals.”

  A goal is the measure of a successful action that you want your visitors to take. The more goals you track, the more you can learn about behavioural changes as you implement and modify paths that lead to conversions over time.

  

  You’ll understand which channels and campaigns (SEO, PPC, newsletter, blogging etc.) are converting the best for your business, which cities/countries are most popular, what devices are working and how engaged your visitors are before converting. Learn more

  2. Set Heatmaps

  This is vital to show how your visitors are engaging with your website, blog pages, signup and sales pages. If you want to learn how your visitors really engage with your website to increase conversions, Heatmaps lets you see the results visually without any guesswork.

  

  By showing where your visitors try to click, move the mouse or how far down they’re scrolling on each page, you can effortlessly discover how your visitors truly engage with your most important web pages. Rather than guessing, rely on facts to prove if the changes you make actually improve your website or not. Learn more

  How to improve conversion rates with Heatmaps :

  • If you’ve got important information that will sell your service/product or bring you loyal followers, make sure it’s in the hot zones as shown in your heatmaps.
  • Try to rearrange parts of your pages to see if that increases engagement.
  • Make it easy for people to take important actions by having the CTA above-the-fold where 100% of visitors see it. Make sure you don’t clutter this section with too many messages or actions.
  • You can also identify areas to add links as heatmaps shows where people want to click.
  • Find what content is most popular on the page

  3. Session Recordings

  This is a conversion research technique where you learn what your users are trying to do and make sure your website is optimized to give them what they want. With Session Recordings you can playback all the interactions your visitors took on your website, such as clicks, mouse movements, scrolls, resizes, form interactions and page changes in a video. Truly understand how real visitors are using your website and what experiences they’re having.

  Also, by understanding what’s working you’re increasing the usability of your website, Session Recordings allow you to identify problem areas as well as where users are getting stuck. Learn more

  

  How to improve conversion rates with Session Recordings : For example, on a product landing page, you see your visitor highlighting specific words and putting it into search. With this you can observe what they’re trying to find and what they’re actually interested in. As you tweak the page to ensure what the visitor wants can be easily found, you’re taking steps to increase the chance for more conversions.

  4. A/B Testing

  Test anything and test anywhere to increase your conversions. Grow your website by comparing different versions of your landing pages to determine what works best for your users. Subtle tweaks across different versions of your landing pages can have a significant impact on converting incoming traffic.

  

  The changes for each landing page could be :

  • A different headline
  • Less copy vs more copy
  • Different calls-to-action
  • Colour schemes, forms, fonts, links, testimonials,
  • Or, it could be an entirely different page layout altogether.

  The idea is to see if either page A or page B (or C or D) was most successful in getting your visitors to the next step in the conversion funnel. Learn more

  How Matomo used A/B Testing : For our sign up page we tested three different CTAs and found how phrasing words differently could help improve conversion rates. Both “Start improving your websites” and “Start converting more users now” were stronger CTAs and converted 7% more than, “Start my free 30-day trial”.

  5. Form Analytics

  Form Analytics gives you powerful insights into how your visitors interact with your forms (like cart, sign-up and checkout forms).

  

  Online forms can come in thousands of different variations. It’s an area on your website that if not done right, could lead to you missing out on converting a large portion of your visitors. Rely on facts when you change your forms. Learn more

  How to improve conversion rates with Form Analytics : By proving whether your form is doing better when you change it and by how much. This lets you consistently increase form submission rates (conversions) on your website which is crucial to the success of your business.

  6. Funnels

  At a glance you will learn the steps (actions, events and pages) your users go through to the desired outcomes you want them to achieve whether it’s a sale, sign-up or any other particular goal you have defined.

  

  Looking at the entire conversion funnel and focusing on usability, you’ll be able to identify where your visitors are having problems, where they aren’t understanding the flow of your webpages and identify obstacles that get in the way of your users reaching that end goal. Learn more

  How to improve conversion rates with Funnels : Learn what makes your visitors take action (or what stops them) in progressing to the next step in the conversion funnel. At each step, you’ll discover what content/layout resonates with your visitors and you can optimize your website to have the greatest impact on your business.

  TRY IT FOR FREE

  7. Behaviour

  This is one of the most important features to help you optimize your website for conversions. Learning visitor behaviour is a driving force to increase conversions. How ? It lets you identify where you could be taking action to increase conversions. You get to learn first-hand what content or feature on your site is or isn’t working for your visitors. 

  

  Engagement is essential to help increase conversion rates. If your visitors aren’t interested in the content on your site, then there’s very little chance they’ll be interested in what you have to offer. Learn more

  How to improve conversion rates with Behaviour : Get started by reducing bounce rates on important pages, testing messaging on your most popular entry pages, testing on the highest exit pages to reduce visitors leaving the site, learning pathways through Users Flow and Transitions to see if users are taking pathways that lead them to conversions or are the journeys currently long or go in odd directions. Discover how your visitors are responding to your content. The happier your visitors are to stay on your site, the more likely they’ll be able to move through the journey to help you achieve the goals you’ve set for your site.

  TRY IT FOR FREE

  Do privacy-focused industries need conversion optimization ?

  For industries that place extra emphasis on privacy and security, Matomo is a complete analytics tool that can cater for all your needs. You get the full benefits of a web analytics and conversion optimization platform as well as peace of mind knowing Matomo places emphasis on security/privacy and adheres strictly to GDPR.

  If you operate in a data sensitive industry like in government, healthcare, finance, education etc. you can rest assured knowing your user’s privacy is respected and that you will have 100% data ownership.

  Other conversion optimization metrics in Matomo to look at :

  Get a good indication that your conversion optimization efforts are working by knowing where to look and this starts by going through the metrics in your analytics. Below we list how you can make a start.

  “Best” metrics are hard to determine so you’ll need to ask yourself what you want your site to do. How do you want your users to behave or what kind of customer journey do you want them to have ?

  You can start with :

  • Decreasing abandonment rate
  • Decreasing bounce rate
  • Increasing interactions per visit
  • Reducing exit rates on pages that significantly impact your visitors to leave your site
  • Constantly test and learn what content resonates with your visitors
  • Look to advance more users through each stage of the conversion funnel
  • Improve your forms to increase submission rates
  • Always improve the conversion rate % for your goals e.g. if you currently have a 5% conversion rate for selling a product, aim for 10% ; if 30% of your visitors are downloading your e-book, then aim for 40%, then 50% and so on.

  Through optimizing your messaging, content or your page layouts, you will increase conversions by getting your visitors through a clear pathway to meet your website’s goal.

  TRY IT FOR FREE

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required. 

• Stream ffmpeg transcoding result to S3

  7 juin 2019, par mabead

  I want to transcode a large file using FFMPEG and store the result directly on AWS S3. This will be done inside of an AWS Lambda that has limited tmp space so I can’t store the transcoding result locally and then upload it to S3 in a second step. I won’t have enough tmp space. I therefore want to store the FFMPEG output directly on S3.

  I therefore created a S3 pre-signed url that allows ’PUT’ :

  var outputPath = s3Client.GetPreSignedURL(new Amazon.S3.Model.GetPreSignedUrlRequest
  
  {
  
      BucketName = "my-bucket",
  
      Expires = DateTime.UtcNow.AddMinutes(5),
  
      Key = "output.mp3",
  
      Verb = HttpVerb.PUT,
  
  });

  I then called ffmpeg with the resulting pre-signed url :

  ffmpeg -i C:\input.wav -y -vn -ar 44100 -ac 2 -ab 192k -f mp3 https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D

  FFMPEG returns an exit code of 1 with the following output :

  ffmpeg version N-93120-ga84af760b8 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 8.2.1 (GCC) 20190212
  
    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. 26.100 / 56. 26.100
  
    libavcodec     58. 47.100 / 58. 47.100
  
    libavformat    58. 26.101 / 58. 26.101
  
    libavdevice    58.  6.101 / 58.  6.101
  
    libavfilter     7. 48.100 /  7. 48.100
  
    libswscale      5.  4.100 /  5.  4.100
  
    libswresample   3.  4.100 /  3.  4.100
  
    libpostproc    55.  4.100 / 55.  4.100
  
  Guessed Channel Layout for Input Stream #0.0 : stereo
  
  Input #0, wav, from 'C:\input.wav':
  
    Duration: 00:04:16.72, bitrate: 3072 kb/s
  
      Stream #0:0: Audio: pcm_s32le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s32, 3072 kb/s
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (pcm_s32le (native) -> mp3 (libmp3lame))
  
  Press [q] to stop, [?] for help
  
  Output #0, mp3, to 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D':
  
    Metadata:
  
      TSSE            : Lavf58.26.101
  
      Stream #0:0: Audio: mp3 (libmp3lame), 44100 Hz, stereo, s32p, 192 kb/s
  
      Metadata:
  
        encoder         : Lavc58.47.100 libmp3lame
  
  size=     577kB time=00:00:24.58 bitrate= 192.2kbits/s speed=49.1x    
  
  size=    1109kB time=00:00:47.28 bitrate= 192.1kbits/s speed=47.2x    
  
  [tls @ 000001d73d786b00] Error in the push function.
  
  av_interleaved_write_frame(): I/O error
  
  Error writing trailer of https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D: I/O error
  
  size=    1143kB time=00:00:48.77 bitrate= 192.0kbits/s speed=  47x    
  
  video:0kB audio:1144kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
  
  [tls @ 000001d73d786b00] The specified session has been invalidated for some reason.
  
  [tls @ 000001d73d786b00] Error in the pull function.
  
  [https @ 000001d73d784fc0] URL read error:  -5
  
  Conversion failed!

  As you can see, I have a URL read error. This is a little surprising to me since I want to output to this url and not read it.

  Anybody know how I can store directly my FFMPEG output directly to S3 without having to store it locally first ?

  Edit 1
  I then tried to use the -method PUT parameter and use http instead of https to remove TLS from the equation. Here’s the output that I got when running ffmpeg with the -v trace option.

  ffmpeg version N-93120-ga84af760b8 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 8.2.1 (GCC) 20190212
  
    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. 26.100 / 56. 26.100
  
    libavcodec     58. 47.100 / 58. 47.100
  
    libavformat    58. 26.101 / 58. 26.101
  
    libavdevice    58.  6.101 / 58.  6.101
  
    libavfilter     7. 48.100 /  7. 48.100
  
    libswscale      5.  4.100 /  5.  4.100
  
    libswresample   3.  4.100 /  3.  4.100
  
    libpostproc    55.  4.100 / 55.  4.100
  
  Splitting the commandline.
  
  Reading option '-i' ... matched as input url with argument 'C:\input.wav'.
  
  Reading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.
  
  Reading option '-vn' ... matched as option 'vn' (disable video) with argument '1'.
  
  Reading option '-ar' ... matched as option 'ar' (set audio sampling rate (in Hz)) with argument '44100'.
  
  Reading option '-ac' ... matched as option 'ac' (set number of audio channels) with argument '2'.
  
  Reading option '-ab' ... matched as option 'ab' (audio bitrate (please use -b:a)) with argument '192k'.
  
  Reading option '-f' ... matched as option 'f' (force format) with argument 'mp3'.
  
  Reading option '-method' ... matched as AVOption 'method' with argument 'PUT'.
  
  Reading option '-v' ... matched as option 'v' (set logging level) with argument 'trace'.
  
  Reading option 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D' ... matched as output url.
  
  Finished splitting the commandline.
  
  Parsing a group of options: global .
  
  Applying option y (overwrite output files) with argument 1.
  
  Applying option v (set logging level) with argument trace.
  
  Successfully parsed a group of options.
  
  Parsing a group of options: input url C:\input.wav.
  
  Successfully parsed a group of options.
  
  Opening an input file: C:\input.wav.
  
  [NULL @ 000001fb37abb180] Opening 'C:\input.wav' for reading
  
  [file @ 000001fb37abc180] Setting default whitelist 'file,crypto'
  
  Probing wav score:99 size:2048
  
  [wav @ 000001fb37abb180] Format wav probed with size=2048 and score=99
  
  [wav @ 000001fb37abb180] Before avformat_find_stream_info() pos: 54 bytes read:65590 seeks:1 nb_streams:1
  
  [wav @ 000001fb37abb180] parser not found for codec pcm_s32le, packets or times may be invalid.
  
      Last message repeated 1 times
  
  [wav @ 000001fb37abb180] All info found
  
  [wav @ 000001fb37abb180] stream 0: start_time: -192153584101141.156 duration: 256.716
  
  [wav @ 000001fb37abb180] format: start_time: -9223372036854.775 duration: 256.716 bitrate=3072 kb/s
  
  [wav @ 000001fb37abb180] After avformat_find_stream_info() pos: 204854 bytes read:294966 seeks:1 frames:50
  
  Guessed Channel Layout for Input Stream #0.0 : stereo
  
  Input #0, wav, from 'C:\input.wav':
  
    Duration: 00:04:16.72, bitrate: 3072 kb/s
  
      Stream #0:0, 50, 1/48000: Audio: pcm_s32le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s32, 3072 kb/s
  
  Successfully opened the file.
  
  Parsing a group of options: output url https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D.
  
  Applying option vn (disable video) with argument 1.
  
  Applying option ar (set audio sampling rate (in Hz)) with argument 44100.
  
  Applying option ac (set number of audio channels) with argument 2.
  
  Applying option ab (audio bitrate (please use -b:a)) with argument 192k.
  
  Applying option f (force format) with argument mp3.
  
  Successfully parsed a group of options.
  
  Opening an output file: https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D.
  
  [http @ 000001fb37b15140] Setting default whitelist 'http,https,tls,rtp,tcp,udp,crypto,httpproxy'
  
  [tcp @ 000001fb37b16c80] Original list of addresses:
  
  [tcp @ 000001fb37b16c80] Address 52.216.8.203 port 80
  
  [tcp @ 000001fb37b16c80] Interleaved list of addresses:
  
  [tcp @ 000001fb37b16c80] Address 52.216.8.203 port 80
  
  [tcp @ 000001fb37b16c80] Starting connection attempt to 52.216.8.203 port 80
  
  [tcp @ 000001fb37b16c80] Successfully connected to 52.216.8.203 port 80
  
  [http @ 000001fb37b15140] request: PUT /output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D HTTP/1.1
  
  Transfer-Encoding: chunked
  
  User-Agent: Lavf/58.26.101
  
  Accept: */*
  
  Connection: close
  
  Host: landr-distribution-reportsdev-mb.s3.amazonaws.com
  
  Icy-MetaData: 1
  
  Successfully opened the file.
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (pcm_s32le (native) -> mp3 (libmp3lame))
  
  Press [q] to stop, [?] for help
  
  cur_dts is invalid (this is harmless if it occurs once at the start per stream)
  
  detected 8 logical cores
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'time_base' to value '1/48000'
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'sample_rate' to value '48000'
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'sample_fmt' to value 's32'
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'channel_layout' to value '0x3'
  
  [graph_0_in_0_0 @ 000001fb37b21080] tb:1/48000 samplefmt:s32 samplerate:48000 chlayout:0x3
  
  [format_out_0_0 @ 000001fb37b22cc0] Setting 'sample_fmts' to value 's32p|fltp|s16p'
  
  [format_out_0_0 @ 000001fb37b22cc0] Setting 'sample_rates' to value '44100'
  
  [format_out_0_0 @ 000001fb37b22cc0] Setting 'channel_layouts' to value '0x3'
  
  [format_out_0_0 @ 000001fb37b22cc0] auto-inserting filter 'auto_resampler_0' between the filter 'Parsed_anull_0' and the filter 'format_out_0_0'
  
  [AVFilterGraph @ 000001fb37b0d940] query_formats: 4 queried, 6 merged, 3 already done, 0 delayed
  
  [auto_resampler_0 @ 000001fb37b251c0] picking s32p out of 3 ref:s32
  
  [auto_resampler_0 @ 000001fb37b251c0] [SWR @ 000001fb37b252c0] Using fltp internally between filters
  
  [auto_resampler_0 @ 000001fb37b251c0] ch:2 chl:stereo fmt:s32 r:48000Hz -> ch:2 chl:stereo fmt:s32p r:44100Hz
  
  Output #0, mp3, to 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D':
  
    Metadata:
  
      TSSE            : Lavf58.26.101
  
      Stream #0:0, 0, 1/44100: Audio: mp3 (libmp3lame), 44100 Hz, stereo, s32p, delay 1105, 192 kb/s
  
      Metadata:
  
        encoder         : Lavc58.47.100 libmp3lame
  
  cur_dts is invalid (this is harmless if it occurs once at the start per stream)
  
      Last message repeated 6 times
  
  size=     649kB time=00:00:27.66 bitrate= 192.2kbits/s speed=55.3x    
  
  size=    1207kB time=00:00:51.48 bitrate= 192.1kbits/s speed=51.5x    
  
  av_interleaved_write_frame(): Unknown error
  
  No more output streams to write to, finishing.
  
  [libmp3lame @ 000001fb37b147c0] Trying to remove 47 more samples than there are in the queue
  
  Error writing trailer of https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D: Error number -10054 occurred
  
  size=    1251kB time=00:00:53.39 bitrate= 192.0kbits/s speed=51.5x    
  
  video:0kB audio:1252kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
  
  Input file #0 (C:\input.wav):
  
    Input stream #0:0 (audio): 5014 packets read (20537344 bytes); 5014 frames decoded (2567168 samples); 
  
    Total: 5014 packets (20537344 bytes) demuxed
  
  Output file #0 (https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D):
  
    Output stream #0:0 (audio): 2047 frames encoded (2358144 samples); 2045 packets muxed (1282089 bytes); 
  
    Total: 2045 packets (1282089 bytes) muxed
  
  5014 frames successfully decoded, 0 decoding errors
  
  [AVIOContext @ 000001fb37b1f440] Statistics: 0 seeks, 2046 writeouts
  
  [http @ 000001fb37b15140] URL read error:  -10054
  
  [AVIOContext @ 000001fb37ac4400] Statistics: 20611126 bytes read, 1 seeks
  
  Conversion failed!

  So it looks like it is able to connect to my S3 pre-signed url but I still have the Error writing trailer error coupled with a URL read error.