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• Overcoming Fintech and Finserv’s Biggest Data Analytics Challenges

  13 septembre 2024, par Daniel Crough — Banking and Financial Services, Marketing, Security

  Data powers innovation in financial technology (fintech), from personalized banking services to advanced fraud detection systems. Industry leaders recognize the value of strong security measures and customer privacy. A recent survey highlights this focus, with 72% of finance Chief Risk Officers identifying cybersecurity as their primary concern.

  Beyond cybersecurity, fintech and financial services (finserv) companies are bogged down with massive amounts of data spread throughout disconnected systems. Between this, a complex regulatory landscape and an increasingly tech-savvy and sceptical consumer base, fintech and finserv companies have a lot on their plates.

  How can marketing teams get the information they need while staying focused on compliance and providing customer value ? 

  This article will examine strategies to address common challenges in the finserv and fintech industries. We’ll focus on using appropriate tools, following effective data management practices, and learning from traditional banks’ approaches to similar issues.

  What are the biggest fintech data analytics challenges, and how do they intersect with traditional banking ?

  Recent years have been tough for the fintech industry, especially after the pandemic. This period has brought new hurdles in data analysis and made existing ones more complex. As the market stabilises, both fintech and finserve companies must tackle these evolving data issues.

  Let’s examine some of the most significant data analytics challenges facing the fintech industry, starting with an issue that’s prevalent across the financial sector :

  1. Battling data silos

  In a recent survey by InterSystems, 54% of financial institution leaders said data silos are their biggest barrier to innovation, while 62% said removing silos is their priority data strategy for the next year.

  

  Data silos segregate data repositories across departments, products and other divisions. This is a major issue in traditional banking and something fintech companies should avoid inheriting at all costs.

  Siloed data makes it harder for decision-makers to view business performance with 360-degree clarity. It’s also expensive to maintain and operationalise and can evolve into privacy and data compliance issues if left unchecked.

  To avoid or remove data silos, develop a data governance framework and centralise your data repositories. Next, simplify your analytics stack into as few integrated tools as possible because complex tech stacks are one of the leading causes of data silos.

  Use an analytics system like Matomo that incorporates web analytics, marketing attribution and CRO testing into one toolkit.

  

  Matomo’s support plans help you implement a data system to meet the unique needs of your business and avoid issues like data silos. We also offer data warehouse exporting as a feature to bring all of your web analytics, customer data, support data, etc., into one centralised location.

  Try Matomo for free today, or contact our sales team to discuss support plans.

  2. Compliance with laws and regulations

  A survey by Alloy reveals that 93% of fintech companies find it difficult to meet compliance regulations. The cost of staying compliant tops their list of worries (23%), outranking even the financial hit from fraud (21%) – and this in a year marked by cyber threats.

  

  Data privacy laws are constantly changing, and the landscape varies across global regions, making adherence even more challenging for fintechs and traditional banks operating in multiple markets. 

  In the US market, companies grapple with regulations at both federal and state levels. Here are some of the state-level legislation coming into effect for 2024-2026 :

  • Oregon – Oregon Consumer Privacy Act (July 1, 2024)
  • Florida – Florida Digital Bill of Rights (July 1, 2024)
  • Texas – Texas Data Privacy and Security Act (July 1, 2024)
  • Montana – Montana Consumer Data Privacy Act (October 1, 2024)
  • Delaware – Delaware Personal Privacy Act (January 1, 2025)
  • Iowa – Iowa Consumer Data Protection Act (January 1, 2025)
  • New Jersey – SB 332 (January 15, 2025)
  • Tennessee – Tennessee Information Protection Act (July 1, 2025)
  • Indiana – Indiana Consumer Data Protection Act ( January 1, 2026)

  Other countries are also ramping up regional regulations. For instance, Canada has Quebec’s Act Respecting the Protection of Personal Information in the Private Sector and British Columbia’s Personal Information Protection Act (BC PIPA).

  Ignorance of country- or region-specific laws will not stop companies from suffering the consequences of violating them.

  The only answer is to invest in adherence and manage business growth accordingly. Ultimately, compliance is more affordable than non-compliance – not only in terms of the potential fines but also the potential risks to reputation, consumer trust and customer loyalty.

  This is an expensive lesson that fintech and traditional financial companies have had to learn together. GDPR regulators hit CaixaBank S.A, one of Spain’s largest banks, with multiple multi-million Euro fines, and Klarna Bank AB, a popular Swedish fintech company, for €720,000.

  To avoid similar fates, companies should :

  1. Build solid data systems
  2. Hire compliance experts
  3. Train their teams thoroughly
  4. Choose data analytics tools carefully

  Remember, even popular tools like Google Analytics aren’t automatically safe. Find out how Matomo helps you gather useful insights while sticking to rules like GDPR.

  3. Protecting against data security threats

  Cyber threats are increasing in volume and sophistication, with the financial sector becoming the most breached in 2023.

  

  The cybersecurity risks will only worsen, with WEF estimating annual cybercrime expenses of up to USD $10.5 trillion globally by 2025, up from USD $3 trillion in 2015.

  While technology brings new security solutions, it also amplifies existing risks and creates new ones. A 2024 McKinsey report warns that the risk of data breaches will continue to increase as the financial industry increasingly relies on third-party data tools and cloud computing services unless they simultaneously improve their security posture.

  The reality is that adopting a third-party data system without taking the proper precautions means adopting its security vulnerabilities.

  In 2023, the MOVEit data breach affected companies worldwide, including financial institutions using its file transfer system. One hack created a global data crisis, potentially affecting the customer data of every company using this one software product.

  The McKinsey report emphasises choosing tools wisely. Why ? Because when customer data is compromised, it’s your company that takes the heat, not the tool provider. As the report states :

  “Companies need reliable, insightful metrics and reporting (such as security compliance, risk metrics and vulnerability tracking) to prove to regulators the health of their security capabilities and to manage those capabilities.”

  Don’t put user or customer data in the hands of companies you can’t trust. Work with providers that care about security as much as you do. With Matomo, you own all of your data, ensuring it’s never used for unknown purposes.

  

  4. Protecting users’ privacy

  With security threats increasing, fintech companies and traditional banks must prioritise user privacy protection. Users are also increasingly aware of privacy threats and ready to walk away from companies that lose their trust.

  Cisco’s 2023 Data Privacy Benchmark Study reveals some eye-opening statistics :

  • 94% of companies said their customers wouldn’t buy from them if their data wasn’t protected, and 
  • 95% see privacy as a business necessity, not just a legal requirement.

  Modern financial companies must balance data collection and management with increasing privacy demands. This may sound contradictory for companies reliant on dated practices like third-party cookies, but they need to learn to thrive in a cookieless web as customers move to banks and service providers that have strong data ethics.

  This privacy protection journey starts with implementing web analytics ethically from the very first session.

  

  The most important elements of ethically-sound web analytics in fintech are :

  1. 100% data ownership : Make sure your data isn’t used in other ways by the tools that collect it.
  2. Respecting user privacy : Only collect the data you absolutely need to do your job and avoid personally identifiable information.
  3. Regulatory compliance : Stick with solutions built for compliance to stay out of legal trouble.
  4. Data transparency : Know how your tools use your data and let your customers know how you use it.

  Read our guide to ethical web analytics for more information.

  5. Comparing customer trust across industries 

  While fintech companies are making waves in the financial world, they’re still playing catch-up when it comes to earning customer trust. According to RFI Global, fintech has a consumer trust score of 5.8/10 in 2024, while traditional banking scores 7.6/10.

  

  This trust gap isn’t just about perception – it’s rooted in real issues :

  • Security breaches are making headlines more often.
  • Privacy regulations like GDPR are making consumers more aware of their rights.
  • Some fintech companies are struggling to handle fraud effectively.

  According to the UK’s Payment Systems Regulator, digital banking brands Monzo and Starling had some of the highest fraudulent activity rates in 2022. Yet, Monzo only reimbursed 6% of customers who reported suspicious transactions, compared to 70% for NatWest and 91% for Nationwide.

  So, what can fintech firms do to close this trust gap ?

  • Start with privacy-centric analytics from day one. This shows customers you value their privacy from the get-go.
  • Build and maintain a long-term reputation free of data leaks and privacy issues. One major breach can undo years of trust-building.
  • Learn from traditional banks when it comes to handling issues like fraudulent transactions, identity theft, and data breaches. Prompt, customer-friendly resolutions go a long way.
  • Remember : cutting-edge financial technology doesn’t make up for poor customer care. If your digital bank won’t refund customers who’ve fallen victim to credit card fraud, they’ll likely switch to a traditional bank that will.

  The fintech sector has made strides in innovation, but there’s still work to do in establishing trustworthiness. By focusing on robust security, transparent practices, and excellent customer service, fintech companies can bridge the trust gap and compete more effectively with traditional banks.

  6. Collecting quality data

  Adhering to data privacy regulations, protecting user data and implementing ethical analytics raises another challenge. How can companies do all of these things and still collect reliable, quality data ?

  Google’s answer is using predictive models, but this replaces real data with calculations and guesswork. The worst part is that Google Analytics doesn’t even let you use all of the data you collect in the first place. Instead, it uses something called data sampling once you pass certain thresholds.

  In practice, this means that Google Analytics uses a limited set of your data to calculate reports. We’ve discussed GA4 data sampling at length before, but there are two key problems for companies here :

  1. A sample size that’s too small won’t give you a full representation of your data.
  2. The more visitors that come to your site, the less accurate your reports will become.

  For high-growth companies, data sampling simply can’t keep up. Financial marketers widely recognise the shortcomings of big tech analytics providers. In fact, 80% of them say they’re concerned about data bias from major providers like Google and Meta affecting valuable insights.

  This is precisely why CRO:NYX Digital approached us after discovering Google Analytics wasn’t providing accurate campaign data. We set up an analytics system to suit the company’s needs and tested it alongside Google Analytics for multiple campaigns. In one instance, Google Analytics failed to register 6,837 users in a single day, approximately 9.8% of the total tracked by Matomo.

  In another instance, Google Analytics only tracked 600 visitors over 24 hours, while Matomo recorded nearly 71,000 visitors – an 11,700% discrepancy.

  

  Financial companies need a more reliable, privacy-centric alternative to Google Analytics that captures quality data without putting users at potential risk. This is why we built Matomo and why our customers love having total control and visibility of their data.

  Unlock the full power of fintech data analytics with Matomo

  Fintech companies face many data-related challenges, so compliant web analytics shouldn’t be one of them. 

  With Matomo, you get :

  • An all-in-one solution that handles traditional web analytics, behavioural analytics and more with strong integrations to minimise the likelihood of data siloing
  • Full compliance with GDPR, CCPA, PIPL and more
  • Complete ownership of your data to minimise cybersecurity risks caused by negligent third parties
  • An abundance of ways to protect customer privacy, like IP address anonymisation and respect for DoNotTrack settings
  • The ability to import data from Google Analytics and distance yourself from big tech
  • High-quality data that doesn’t rely on sampling
  • A tool built with financial analytics in mind

  Don’t let big tech companies limit the power of your data with sketchy privacy policies and counterintuitive systems like data sampling. 

  Start your Matomo free trial or request a demo to unlock the full power of fintech data analytics without putting your customers’ personal information at unnecessary risk.

• Six Best Amplitude Alternatives

  10 décembre 2024, par Daniel Crough

  Product analytics is big business. Gone are the days when we could only guess what customers were doing with our products or services. Now, we can track, visualise, and analyse how they interact with them and, with that, constantly improve and optimise. 

  The problem is that many product analytics tools are expensive and complicated — especially for smaller businesses. They’re also packed with functionality more attuned to the needs of massive companies. 

  Amplitude is such a tool. It’s brilliant and it has all the bells and whistles that you’ll probably never need. Fortunately, there are alternatives. In this guide, we’ll explore the best of those alternatives and, along the way, provide the insight you’ll need to select the best analytics tool for your organisation. 

  Amplitude : a brief overview

  To set the stage, it makes sense to understand exactly what Amplitude offers. It’s a real-time data analytics tool for tracking user actions and gaining insight into engagement, retention, and revenue drivers. It helps you analyse that data and find answers to questions about what happened, why it happened, and what to do next.

  However, as good as Amplitude is, it has some significant disadvantages. While it does offer data export functionality, that seems deliberately restricted. It allows data exports for specific events, but it’s not possible to export complete data sets to manipulate or format in another tool. Even pulling it into a CSV file has a 10,000-row limit. There is an API, but not many third-party integration options.

  Getting data in can also be a problem. Amplitude requires manual tags on events that must be tracked for analysis, which can leave holes in the data if every possible subsequent action isn’t tagged. That’s a time-consuming exercise, and it’s made worse because those tags will have to be updated every time the website or app is updated. 

  As good as it is, it can also be overwhelming because it’s stacked with features that can create confusion for novice or inexperienced analysts. It’s also expensive. There is a freemium plan that limits functionality and events. Still, when an organisation wants to upgrade for additional functionality or to analyse more events, the step up to the paid plan is massive.

  Lastly, Amplitude has made some strides towards being a web analytics option, but it lacks some basic functionality that may frustrate people who are trying to see the full picture from web to app.

  Snapshot of Amplitude alternatives

  So, in place of Amplitude, what product analytics tools are available that won’t break the bank and still provide the functionality needed to improve your product ? The good news is that there are literally hundreds of alternatives, and we’ve picked out six of the best.

  1. Matomo – Best privacy-focused web and mobile analytics
  2. Mixpanel – Best for product analytics
  3. Google Analytics – Best free option
  4. Adobe Analytics – Best for predictive analytics
  5. Umami – Best lightweight tool for product analytics
  6. Heap – Best for automatic user data capture

  A more detailed analysis of the Amplitude alternatives

  Now, let’s dive deeper into each of the six Amplitude alternatives. We’ll cover standout features, integrations, pricing, use cases and community critiques. By the end, you’ll know which analytics tool can help optimise website and app performance to grow your business.

  1. Matomo – Best privacy-friendly web and app analytics

  Privacy is a big concern these days, especially for organisations with a presence in the European Union (EU). Unlike other analytics tools, Matomo ensures you comply with privacy laws and regulations, like the General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA).

  Matomo helps businesses get the insights they need without compromising user privacy. It’s also one of the few self-hosted tools, ensuring data never has to leave your site.

  Matomo is open-source, which is also rare in this class of tools. That means it’s available for anyone to adapt and customise as they wish. Everything you need to build custom APIs is there.

  

  Its most useful capabilities include visitor logs and session recordings to trace the entire customer journey, spot drop-off points, and fine-tune sales funnels. The platform also comes with heatmaps and A/B testing tools. Heatmaps provide a useful visual representation of your data, while A/B testing allows for more informed, data-driven decisions.

  Despite its range of features, many reviewers laud Matomo’s user interface for its simplicity and user-friendliness. 

  Why Matomo : Matomo is an excellent alternative because it fills in the gaps where Amplitude comes up short, like with cookieless tracking. Also, while Amplitude focuses mainly on behavioural analytics, Matomo offers both behavioural and traditional analytics, which allows more profound insight into your data. Furthermore, Matomo fully complies with the strictest privacy regulations worldwide, including GDPR, LGPD, and HIPAA.

  Standout features include multi-touch attribution, visits log, content engagement, ecommerce, customer segments, event tracking, goal tracking, custom dimensions, custom reports, automated email reports, tag manager, sessions recordings, roll-up reporting that can pull data from multiple websites or mobile apps, Google Analytics importer, Matomo tag manager, comprehensive visitor tracking, heatmaps, and more.

  Integrations with 100+ technologies, including Cloudflare, WordPress, Magento, Google Ads, Drupal, WooCommerce, Vue, SharePoint and Wix.

  Pricing is free for Matomo On-Premise and $23 per month for Matomo Cloud, which comes with a 21-day free trial (no credit card required).

  Strengths

  • Privacy focused
  • Cookieless consent banners
  • 100% accurate, unsampled data
  • Open-source code 
  • Complete data ownership (no sharing with third parties)
  • Self-hosting and cloud-based options
  • Built-in GDPR Manager
  • Custom alerts, white labelling, dashboards and reports

  Community critiques 

  • Premium features are expensive and proprietary
  • Learning curve for non-technical users

  2. Mixpanel – Best for product analytics

  Mixpanel is a dedicated product analytics tool. It tracks and analyses customer interactions with a product across different platforms and helps optimise digital products to improve the user experience. It works with real-time data and can provide answers from customer and revenue data in seconds.

  It also presents data visualisations to show how customers interact with products.

  

  Mixpanel allows you to play around filters and views to reveal and chart some useful customer trends. (Image source)

  Why Mixpanel : One of the strengths of this platform is the ability to test hypotheses. Need to test an ambitious idea ? Mixpanel data can do it with real user analytics. That allows you to make data-driven decisions to find the best path forward.

  Standout features include automatic funnel segment analysis, behavioural segmentation, cohort segmentation, collaboration support, customisable dashboards, data pipelines, filtered data views, SQL queries, warehouse connectors and a wide range of pre-built integrations.

  Integrations available include Appcues, AppsFlyer, AWS, Databox, Figma, Google Cloud, Hotjar, HubSpot, Intercom, Integromat, MailChimp, Microsoft Azure, Segment, Slack, Statsig, VWO, Userpilot, WebEngage, Zapier, ZOH) and dozens of others.

  Pricing starts with a freemium plan valid for up to 20 million events per month. The growth plan is affordable at $25 per month and adds features like no-code data transformations and data pipeline add-ons. The enterprise version runs at a monthly cost of $833 and provides the full suite of features and services and premium support.

  There’s a caveat. Those prices only allow up to 1,000 Monthly Tracked Users (MTUs), calculated based on the number of visitors that perform a qualifying event each month. Beyond that, MTU plans start at $20,000 per year.

  Strengths

  • User behaviour and interaction tracking
  • Unlimited cohort segmentation capabilities
  • Drop-off analysis showing where users get stuck
  • A/B testing capabilities

  Community critiques 

  • Expensive enterprise features
  • Extensive setup and configuration requirements

  3. Google Analytics 4 – Best free web analytics tool

  The first thing to know about Google Analytics 4 is that it’s a web analytics tool. In other words, it tracks sessions, not user behaviours in app environments. It can provide details on how people found your website and how they go there, but it doesn’t offer much detail on how people use your product. 

  There is also an enterprise version, Google Analytics 360, which is not free. We’ve broken down the differences between the two versions elsewhere.

  

  GA4’s audience overview shows visitors, sessions, session lengths, bounce rates, and user engagement data. (Image source)

   

  Why Google Analytics : It’s great for gauging the effectiveness of marketing campaigns, tracking goal completions (purchases, cart additions, etc.) and spotting trends and patterns in user engagement.

  Standout features include built-in automation, customisable conversion goals, data drill-down functionality, detailed web acquisition metrics, media spend ROI calculations and out-of-the-box web analytics reporting.

  Integrations include all major CRM platforms, CallRail, DoubleClick DCM, Facebook, Hootsuite, Marketo, Shopify, VWO, WordPress, Zapier and Zendesk, among many others.

  Pricing is free for the basic version (Google Analytics 4) and scales based on features and data volume. The advanced features (in Google Analytics 360) are pitched at enterprises, and pricing is custom.

  Strengths

  • Free to start
  • Multiple website management
  • Traffic source details
  • Up-to-date traffic data

  Community critiques 

  • Steep learning curve 
  • Data sampling

  4. Adobe Analytics – Best for predictive analytics

  A fully configured Adobe Analytics implementation is the Swiss army knife of analytics tools. It begins with web analytics, adds product analytics, and then wraps it up nicely with predictive analytics.

  Unlike all the Amplitude alternatives here, there’s no free version. Adobe Analytics has a complicated pricing matrix with options like website analytics, marketing analytics, attribution, and predictive analytics. It also has a wide range of customisation options that will appeal to large businesses. But for smaller organisations, it may all be a bit too much.

  Mixpanel allows you to play around filters and views to reveal and chart some useful customer trends. (Image source)

  

  Adobe Analytics’ cross-channel attribution ties actions from different channels into a single customer journey. (Image source)

   

  Why Adobe Analytics : For current Adobe customers, this is a logical next step. Either way, Adobe Analytics can combine, evaluate, and analyse data from any part of the customer journey. It analyses that data with predictive intelligence to provide insights to enhance customer experiences.

   

  Standout features include AI-powered prediction analysis, attribution analysis, multi-channel data collection, segmentation and detailed customer journey analytics, product analytics and web analytics.

   

  Integrations are available through the Adobe Experience Cloud Exchange. Adobe Analytics also supports data exchange with brands such as BrightEdge, Branch.io, Google Ads, Hootsuite, Invoca, Salesforce and over 200 other integrations.

   

  Pricing starts at $500 monthly, but prospective customers are encouraged to contact the company for a needs-based quotation.

   

  Strengths

  • Drag-and-drop interface
  • Flexible segmentation 
  • Easy-to-create conversion funnels
  • Threshold-based alerts and notifications

  Community critiques 

  • No free version
  • Lack of technical support
  • Steep learning curve

  5. Umami – Best lightweight tool for web analytics

  The second of our open-source analytics solutions is Umami, a favourite in the software development community. Like Matomo, it’s a powerful and privacy-focused alternative that offers complete data control and respects user privacy. It’s also available as a cloud-based freemium plan or as a self-hosted solution.

   

  

  Umami’s dashboard reveals the busiest times of day and which pages are visited when.(Image source)

   

  Why Umami : Unami has a clear and simple user interface (UI) that lets you measure important metrics such as page visits, referrers, and user agents. It also features event tracking, although some reviewers complain that it’s quite limited.

  Standout features can be summed up in five words : privacy, simplicity, lightweight, real-time, and open-source. Unami’s UI is clean, intuitive and modern, and it doesn’t slow down your website. 

  Integrations include plugins for VuePress, Gatsby, Craft CMS, Docusaurus, WordPress and Publii, and a module for Nuxt. Unami’s API communicates with Javascript, PHP Laravel and Python.

  Pricing is free for up to 100k monthly events and three websites, but with limited support and data retention restrictions. The Pro plan costs $20 a month and gives you unlimited websites and team members, a million events (plus $0.00002 for each event over that), five years of data and email support. Their Enterprise plan is priced custom.

  Strengths

  • Freemium plan
  • Open-source
  • Lightweight 

  Community critiques 

  • Limited support options
  • Data retention restrictions
  • No funnel functionality

  6. Heap – Best for automatic data capture

  Product analytics with a twist is a good description of Heap. It features event auto-capture to track user interactions across all touchpoints in the user journey. This lets you fully understand how and why customers engage with your product and website. 

  Using a single Javascript snippet, Heap automatically collects data on everything users do, including how they got to your website. It also helps identify how different cohorts engage with your product, providing the critical insights teams need to boost conversion rates.

  

  Heap’s journeys feature combines funnel and path analysis. (Image source)

   

  Why Heap : The auto-capture functionality solves a major shortcoming of many product analytics tools — manual tracking. Instead of having to set up manual tags on events, Heap automatically captures all data on user activity from the start. 

  Standout features include event auto-capture, session replay, heatmaps, segments (or cohorts) and journeys, the last of which combines the functions of funnel and path analysis tools into a single feature.

  Integrations include AWS, Google, Microsoft Azure, major CRM platforms, Snowflake and many other data manipulation platforms.

  Pricing is quote-based across all payment tiers. There is also a free plan and a 14-day free trial.

  Strengths

  • Session replay
  • Heatmaps 
  • User segmentation
  • Simple setup 
  • Event auto-capture 

  Community critiques 

  • No A/B testing functionality
  • No GDPR compliance support

  Choosing the best solution for your team

  When selecting a tool, it’s crucial to understand how product analytics and web analytics solutions differ. 

  Product analytics tools track users or accounts and record the features they use, the funnels they move through, and the cohorts they’re part of. Web analytics tools focus more on sessions than users because they’re interested in data that can help improve website usage. 

  Some tools combine product and web analytics to do both of these jobs.

  Area of focus

  Product analytics tools track user behaviour within SaaS- or app-based products. They’re helpful for analysing features, user journeys, engagement metrics, product development and iteration. 

  Web analytics tools analyse web traffic, user demographics, and traffic sources. They’re most often used for marketing and SEO insights.

  Level of detail

  Product analytics tools provide in-depth tracking and analysis of user interactions, feature usage, and cohort analysis.

  Web analytics tools provide broader data on page views, bounce rates, and conversion tracking to analyse overall site performance.

  Whatever tools you try, your first step should be to search for reviews online to see what people who’ve used them think about them. There are some great review sites you can try. See what people are saying on Capterra, G2, Gartner Peer Insights, or TrustRadius. 

  Use Matomo to power your web and app analytics

  Web and product analytics is a competitive field, and there are many other tools worth considering. This list is a small cross-section of what’s available.

  That said, if you have concerns about privacy and costs, consider choosing Matomo. Start your 21-day free trial today.

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• FFmpeg and reserved color primaries [closed]

  21 janvier, par Yoz

  I am trying to get thumbnails from a hevc video downloaded from https://github.com/stashapp/stash/issues/4124#issuecomment-1720057183 and it works with most recent ffmpeg 7.1 (installed via homebrew on mac) printing :

  


  ffmpeg -i input.mp4 -frames:v 1 out.jpg


  


  ffmpeg version 7.1 Copyright (c) 2000-2024 the FFmpeg developers
  built with Apple clang version 16.0.0 (clang-1600.0.26.4)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/7.1_4 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags='-Wl,-ld_classic' --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libaribb24 --enable-libbluray --enable-libdav1d --enable-libharfbuzz --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-audiotoolbox --enable-neon
  libavutil      59. 39.100 / 59. 39.100
  libavcodec     61. 19.100 / 61. 19.100
  libavformat    61.  7.100 / 61.  7.100
  libavdevice    61.  3.100 / 61.  3.100
  libavfilter    10.  4.100 / 10.  4.100
  libswscale      8.  3.100 /  8.  3.100
  libswresample   5.  3.100 /  5.  3.100
  libpostproc    58.  3.100 / 58.  3.100
[hevc @ 0x134f07530] VPS 0 does not exist
[hevc @ 0x134f07530] SPS 0 does not exist.
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'input.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2mp41
    creation_time   : 2023-09-14T19:46:05.000000Z
    encoder         : HandBrake 1.5.1 2022011000
  Duration: 00:01:26.05, start: 0.000000, bitrate: 231 kb/s
  Stream #0:0[0x1](und): Video: hevc (Main) (hvc1 / 0x31637668), yuv420p(tv, bt709/reserved/bt709), 648x648 [SAR 1:1 DAR 1:1], 188 kb/s, 30 fps, 30 tbr, 90k tbn (default)
      Metadata:
        creation_time   : 2023-09-14T19:46:05.000000Z
        handler_name    : VideoHandler
        vendor_id       : [0][0][0][0]
  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 36 kb/s (default)
      Metadata:
        creation_time   : 2023-09-14T19:46:05.000000Z
        handler_name    : Mono
        vendor_id       : [0][0][0][0]
[hevc @ 0x1358065c0] VPS 0 does not exist
[hevc @ 0x1358065c0] SPS 0 does not exist.
Stream mapping:
  Stream #0:0 -> #0:0 (hevc (native) -> mjpeg (native))
Press [q] to stop, [?] for help
Output #0, image2, to 'out.jpg':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2mp41
    encoder         : Lavf61.7.100
  Stream #0:0(und): Video: mjpeg, yuv420p(pc, bt709/reserved/bt709, progressive), 648x648 [SAR 1:1 DAR 1:1], q=2-31, 200 kb/s, 30 fps, 30 tbn (default)
      Metadata:
        creation_time   : 2023-09-14T19:46:05.000000Z
        handler_name    : VideoHandler
        vendor_id       : [0][0][0][0]
        encoder         : Lavc61.19.100 mjpeg
      Side data:
        cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: N/A
[image2 @ 0x134f16080] The specified filename 'out.jpg' does not contain an image sequence pattern or a pattern is invalid.
[image2 @ 0x134f16080] Use a pattern such as %03d for an image sequence or use the -update option (with -frames:v 1 if needed) to write a single image.
[out#0/image2 @ 0x134f10480] video:5KiB audio:0KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown
frame=    1 fps=0.0 q=5.1 Lsize=N/A time=00:00:00.03 bitrate=N/A speed=4.07x   


  


  however, when I use custom compiled ffmpeg.wasm it fails with :

  


  ffmpeg version N-118050-ga518b5540d Copyright (c) 2000-2024 the FFmpeg developers
  built with emcc (Emscripten gcc/clang-like replacement + linker emulating GNU ld) 3.1.73 (ac676d5e437525d15df5fd46bc2c208ec6d376a3)
  configuration: --target-os=none --arch=x86_32 --enable-cross-compile --enable-version3 --enable-zlib --enable-libaom --disable-encoder=libaom_av1 --enable-libopenh264 --enable-libkvazaar --enable-libvpx --enable-libmp3lame --enable-libtheora --enable-libvorbis --enable-libopus --enable-libwebp --enable-libsvtav1 --enable-librubberband --disable-x86asm --disable-inline-asm --disable-stripping --disable-programs --disable-doc --disable-debug --disable-runtime-cpudetect --disable-autodetect --extra-cflags='-O3 -flto -I/ffmpeg-wasm/build/include -pthread -msimd128' --extra-cxxflags='-O3 -flto -I/ffmpeg-wasm/build/include -pthread -msimd128' --extra-ldflags='-O3 -flto -I/ffmpeg-wasm/build/include -pthread -msimd128 -L/ffmpeg-wasm/build/lib' --pkg-config-flags=--static --nm=emnm --ar=emar --ranlib=emranlib --cc=emcc --cxx=em++ --objcc=emcc --dep-cc=emcc --enable-gpl --enable-libx264 --enable-libx265
  libavutil      59. 49.100 / 59. 49.100
  libavcodec     61. 26.100 / 61. 26.100
  libavformat    61.  9.100 / 61.  9.100
  libavdevice    61.  4.100 / 61.  4.100
  libavfilter    10.  6.101 / 10.  6.101
  libswscale      8. 12.100 /  8. 12.100
  libswresample   5.  4.100 /  5.  4.100
  libpostproc    58.  4.100 / 58.  4.100
[hevc @ 0x38d0000] VPS 0 does not exist
[hevc @ 0x38d0000] SPS 0 does not exist.
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'input.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2mp41
    creation_time   : 2023-09-14T19:46:05.000000Z
    encoder         : HandBrake 1.5.1 2022011000
  Duration: 00:01:26.05, start: 0.000000, bitrate: 231 kb/s
  Stream #0:0[0x1](und): Video: hevc (Main) (hvc1 / 0x31637668), yuv420p(tv, bt709/reserved/bt709), 648x648 [SAR 1:1 DAR 1:1], 188 kb/s, 30 fps, 30 tbr, 90k tbn (default)
    Metadata:
      creation_time   : 2023-09-14T19:46:05.000000Z
      handler_name    : VideoHandler
      vendor_id       : [0][0][0][0]
  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 36 kb/s (default)
    Metadata:
      creation_time   : 2023-09-14T19:46:05.000000Z
      handler_name    : Mono
      vendor_id       : [0][0][0][0]
[hevc @ 0x38d0300] VPS 0 does not exist
[hevc @ 0x38d0300] SPS 0 does not exist.
Stream mapping:
  Stream #0:0 -> #0:0 (hevc (native) -> mjpeg (native))
Press [q] to stop, [?] for help
[swscaler @ 0x8ca0000] Unsupported input (Not supported): fmt:yuv420p csp:bt709 prim:reserved trc:bt709 -> fmt:yuv420p csp:bt709 prim:reserved trc:bt709
[vf#0:0 @ 0x3830900] Error while filtering: Not supported
[vf#0:0 @ 0x3830900] Task finished with error code: -138 (Not supported)
[vost#0:0/mjpeg @ 0x385ae40] [enc:mjpeg @ 0x3878b80] Could not open encoder before EOF
[vf#0:0 @ 0x3830900] Terminating thread with return code -138 (Not supported)
[vost#0:0/mjpeg @ 0x385ae40] Task finished with error code: -28 (Invalid argument)
[vost#0:0/mjpeg @ 0x385ae40] Terminating thread with return code -28 (Invalid argument)
[out#0/image2 @ 0x3851580] Nothing was written into output file, because at least one of its streams received no packets.
frame=    0 fps=0.0 q=0.0 Lsize=       0KiB time=N/A bitrate=N/A speed=N/A    
Conversion failed!
Process finished with exit code -138.


  


  I figured out the issue is color primaries prim:reserved, and the command can be updated to a working one by re-writing input primaries as following :

  


  ffmpeg -i input.mp4 -vf "colorspace=all=bt709:iprimaries=bt709" -frames:v 1 out.jpg


  


  However, I would like to compile ffmpeg.wasm so that it handles reserved primaries just like the one from homebrew.

  


  Any idea what the compiled ffmpeg.wasm is missing ?