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• Linear Attribution Model : What Is It and How Does It Work ?

  16 février 2024, par Erin

  Want a more in-depth way to understand the effectiveness of your marketing campaigns ? Then, the linear attribution model could be the answer.

  Although you can choose from several different attribution models, a linear model is ideal for giving value to every touchpoint along the customer journey. It can help you identify your most effective marketing channels and optimise your campaigns. 

  So, without further ado, let’s explore what a linear attribution model is, when you should use it and how you can get started. 

  What is a linear attribution model ?

  A linear attribution model is a multi-touch method of marketing attribution where equal credit is given to each touchpoint. Every marketing channel used across the entire customer journey gets credit, and each is considered equally important. 

  So, if a potential customer has four interactions before converting, each channel gets 25% of the credit.

  

  Let’s look at how linear attribution works in practice using a hypothetical example of a marketing manager, Sally, who is looking for an alternative to Google Analytics. 

  Sally starts her conversion path by reading a Matomo article comparing Matomo to Google Analytics she finds when searching on Google. A few days later she signs up for a webinar she saw on Matomo’s LinkedIn page. Two weeks later, Sally gets a sign-off from her boss and decides to go ahead with Matomo. She visits the website and starts a free trial by clicking on one of the paid Google Ads. 

  Using a linear attribution model, we credit each of the channels Sally uses (organic traffic, organic social, and paid ads), ensuring no channel is overlooked in our marketing analysis. 

  Are there other types of attribution models ?

  Absolutely. There are several common types of attribution models marketing managers can use to measure the impact of channels in different ways. 

  

  • First interaction : Also called a first-touch attribution model, this method gives all the credit to the first channel in the customer journey. This model is great for optimising the top of your sales funnel.
  • Last interaction : Also called a last-touch attribution model, this approach gives all the credit to the last channel the customer interacts with. It’s a great model for optimising the bottom of your marketing funnel. 
  • Last non-direct interaction : This attribution model excludes direct traffic and credits the previous touchpoint. This is a fantastic alternative to a last-touch attribution model, especially if most customers visit your website before converting. 
  • Time decay attribution model : This model adjusts credit according to the order of the touchpoints. Those nearest the conversion get weighted the highest. 
  • Position-based attribution model : This model allocates 40% of the credit to the first and last touchpoints and splits the remaining 20% evenly between every other interaction.

  Why use a linear attribution model ?

  Marketing attribution is vital if you want to understand which parts of your marketing strategy are working. All of the attribution models described above can help you achieve this to some degree, but there are several reasons to choose a linear attribution model in particular. 

  It uses multi-touch attribution

  Unlike single-touch attribution models like first and last interaction, linear attribution is a multi-touch attribution model that considers every touchpoint. This is vital to get a complete picture of the modern customer journey, where customers interact with companies between 20 and 500 times. 

  Single-touch attribution models can be misleading by giving conversion credit to a single channel, especially if it was the customer’s last use. In our example above, Sally’s last interaction with our brand was through a paid ad, but it was hardly the most important. 

  It’s easy to understand

  Attribution models can be complicated, but linear attribution is easy to understand. Every touchpoint gets the same credit, allowing you to see how your entire marketing function works. This simplicity also makes it easy for marketers to take action. 

  It’s great for identifying effective marketing channels

  Because linear attribution is one of the few models that provides a complete view of the customer journey, it’s easy to identify your most common and influential touchpoints. 

  It accounts for the top and bottom of your funnel, so you can also categorise your marketing channels more effectively and make more informed decisions. For example, PPC ads may be a more common bottom-of-the-full touchpoint and should, therefore, not be used to target broad, top-of-funnel search terms.

  Are there any reasons not to use linear attribution ?

  Linear attribution isn’t perfect. Like all attribution models, it has its weaknesses. Specifically, linear attribution can be too simple, dilute conversion credit and unsuitable for long sales cycles.

  

  It can be too simple

  Linear attribution lacks nuance. It only considers touchpoints while ignoring other factors like brand image and your competitors. This is true for most attribution models, but it’s still important to point it out. 

  It can dilute conversion credit

  In reality, not every touchpoint impacts conversions to the same extent. In the example above, the social media post promoting the webinar may have been the most effective touchpoint, but we have no way of measuring this. 

  The risk with using a linear model is that credit can be underestimated and overestimated — especially if you have a long sales cycle. 

  It’s unsuitable for very long sales cycles

  Speaking of long sales cycles, linear attribution models won’t add much value if your customer journey contains dozens of different touchpoints. Credit will get diluted to the point where analysis becomes impossible, and the model will also struggle to measure the precise ways certain touchpoints impact conversions. 

  Should you use a linear attribution model ?

  A linear attribution model is a great choice for any company with shorter sales cycles or a reasonably straightforward customer journey that uses multiple marketing channels. In these cases, it helps you understand the contribution of each touchpoint and find your best channels. 

  It’s also a practical choice for small businesses and startups that don’t have a team of data scientists on staff or the budget to hire outside help. Because it’s so easy to set up and understand, anyone can start generating insights using this model. 

  How to set up a linear attribution model

  Are you sold on the idea of using a linear attribution model ? Then follow the steps below to get started :

  

  Choose a marketing attribution tool

  Given the market is worth $3.1 billion, you won’t be surprised to learn there are plenty of tools to choose from. But choose carefully. The tool you pick can significantly impact your success with attribution modelling. 

  Take Google Analytics, for instance. While GA4 offers several marketing attribution models for free, including linear attribution, it lacks accuracy due to cookie consent rejection and data sampling. 

  Accurate marketing attribution is included as a feature in Matomo Cloud and is available as a plugin for Matomo On-Premise users. We support a full range of attribution models that use 100% accurate data because we don’t use data sampling, and cookie consent isn’t an issue (with the exception of Germany and the UK). That means you can trust our insights.

  Matomo’s marketing attribution is available out of the box, and we also provide access to raw data, allowing you to develop your custom attribution model. 

  Collect data

  The quality of your marketing attribution also depends on the quality and quantity of your data. It’s why you need to avoid a platform that uses data sampling. 

  This should include :

  • General data from your analytics platform, like pages visited and forms filled
  • Goals and conversions, which we’ll discuss in more detail in the next step
  • Campaign tracking data so you can monitor the behaviour of traffic from different referral channels
  • Behavioural data from features like Heatmaps or Session Recordings

  Set up goals and conversions

  You can’t assign conversion values to customer journey touchpoints if you don’t have conversion goals in place. That’s why the next step of the process is to set up conversion tracking in your web analytics platform. 

  Depending on your type of business and the product you sell, conversions could take one of the following forms :

  • A product purchase
  • Signing up for a webinar
  • Downloading an ebook
  • Filling in a form
  • Starting a free trial

  Setting up these kinds of goals is easy if you use Matomo. 

  Just head to the Goals section of the dashboard, click Manage Goals and then click the green Add A New Goal button. 

  Fill in the screen below, and add a Goal Revenue at the bottom of the page. Doing so will mean Matomo can automatically calculate the value of each touchpoint when using your attribution model. 

  

  If your analytics platform allows it, make sure you also set up Event Tracking, which will allow you to analyse how many users start to take a desired action (like filling in a form) but never complete the task. 

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  Test and validate

  As we’ve explained, linear attribution is a great model in some scenarios, but it can fall short if you have a long or complex sales funnel. Even if you’re sure it’s the right model for your company, testing and validating is important. 

  Ideally, your chosen attribution tool should make this process pretty straightforward. For example, Matomo’s Marketing Attribution feature makes comparing and contrasting three different attribution models easy. 

  Here we compare the performance of three attribution models—linear, first-touch, and last-non-direct—in Matomo’s Marketing Attribution dashboard, providing straightforward analysis.

  

  If you think linear attribution accurately reflects the value of your channels, you can start to analyse the insights it generates. If not, then consider using another attribution model.

  Don’t forget to take action from your marketing efforts, either. Linear attribution helps you spot the channels that contribute most to conversions, so allocate more resources to those channels and see if you can improve your conversion rate or boost your ROI. 

  Make the most of marketing attribution with Matomo

  A linear attribution model lets you measure every touchpoint in your customer journey. It’s an easy attribution model to start with and lets you identify and optimise your most effective marketing channels. 

  However, accurate data is essential if you want to benefit the most from marketing attribution data. If your web analytics solution doesn’t play nicely with cookies or uses sampled data, then your linear model isn’t going to tell you the whole story. 

  That’s why over 1 million sites trust Matomo’s privacy-focused web analytics, ensuring accurate data for a comprehensive understanding of customer journeys.

  Now you know what linear attribution modelling is, start employing the model today by signing up for a free 21-day trial, no credit card required. 

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• Moviepy is unable to load video

  20 octobre 2024, par Alex

  Using python 3.11.10 and moviepy 1.0.3 on ubuntu 24.04.1 (in a VirtualBox 7.1.3 on windows 10) I have problems to load a video clip. The test code is just

  


  from moviepy.editor import VideoFileClip
clip = VideoFileClip("testvideo.ts")


  


  but the error is

  


  Traceback (most recent call last):&#xA;  File "/home/alex/.cache/pypoetry/virtualenvs/pypdzug-WqasAXAr-py3.11/lib/python3.11/site-packages/moviepy/video/io/ffmpeg_reader.py", line 285, in ffmpeg_parse_infos&#xA;    line = [l for l in lines if keyword in l][index]&#xA;           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^&#xA;IndexError: list index out of range&#xA;&#xA;During handling of the above exception, another exception occurred:&#xA;&#xA;Traceback (most recent call last):&#xA;  File "/home/alex/Repos/pypdzug/tester.py", line 5, in <module>&#xA;    clip = VideoFileClip("testvideo.ts")&#xA;           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^&#xA;  File "/home/alex/.cache/pypoetry/virtualenvs/pypdzug-WqasAXAr-py3.11/lib/python3.11/site-packages/moviepy/video/io/VideoFileClip.py", line 88, in __init__&#xA;    self.reader = FFMPEG_VideoReader(filename, pix_fmt=pix_fmt,&#xA;                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^&#xA;  File "/home/alex/.cache/pypoetry/virtualenvs/pypdzug-WqasAXAr-py3.11/lib/python3.11/site-packages/moviepy/video/io/ffmpeg_reader.py", line 35, in __init__&#xA;    infos = ffmpeg_parse_infos(filename, print_infos, check_duration,&#xA;            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^&#xA;  File "/home/alex/.cache/pypoetry/virtualenvs/pypdzug-WqasAXAr-py3.11/lib/python3.11/site-packages/moviepy/video/io/ffmpeg_reader.py", line 289, in ffmpeg_parse_infos&#xA;    raise IOError(("MoviePy error: failed to read the duration of file %s.\n"&#xA;OSError: MoviePy error: failed to read the duration of file testvideo.ts.&#xA;Here are the file infos returned by ffmpeg:&#xA;&#xA;ffmpeg version 4.2.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2019 the FFmpeg developers&#xA;  built with gcc 8 (Debian 8.3.0-6)&#xA;  configuration: --enable-gpl --enable-version3 --enable-static --disable-debug --disable-ffplay --disable-indev=sndio --disable-outdev=sndio --cc=gcc --enable-fontconfig --enable-frei0r --enable-gnutls --enable-gmp --enable-libgme --enable-gray --enable-libaom --enable-libfribidi --enable-libass --enable-libvmaf --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librubberband --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libvorbis --enable-libopus --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libdav1d --enable-libxvid --enable-libzvbi --enable-libzimg&#xA;  libavutil      56. 31.100 / 56. 31.100&#xA;  libavcodec     58. 54.100 / 58. 54.100&#xA;  libavformat    58. 29.100 / 58. 29.100&#xA;  libavdevice    58.  8.100 / 58.  8.100&#xA;  libavfilter     7. 57.100 /  7. 57.100&#xA;  libswscale      5.  5.100 /  5.  5.100&#xA;  libswresample   3.  5.100 /  3.  5.100&#xA;  libpostproc    55.  5.100 / 55.  5.100&#xA;</module>

  


  It says it failed to read the duration of the file, but the file plays properly (with mplayer) and ffmpeg -i testvideo.ts returns

  


  ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)
  configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared
  libavutil      58. 29.100 / 58. 29.100
  libavcodec     60. 31.102 / 60. 31.102
  libavformat    60. 16.100 / 60. 16.100
  libavdevice    60.  3.100 / 60.  3.100
  libavfilter     9. 12.100 /  9. 12.100
  libswscale      7.  5.100 /  7.  5.100
  libswresample   4. 12.100 /  4. 12.100
  libpostproc    57.  3.100 / 57.  3.100
Input #0, mpegts, from 'testvideo.ts':
  Duration: 00:10:10.13, start: 0.133333, bitrate: 3256 kb/s
  Program 1 
    Metadata:
      service_name    : 2024-10-04 11:49:49.917
      service_provider: gvos-6.0
  Stream #0:0[0x100]: Video: h264 (High) ([27][0][0][0] / 0x001B), yuv420p(progressive), 1920x1080, 15 fps, 15 tbr, 90k tbn


  


  Here the duration is clearly given to be 10 minutes and 10.13 seconds. So what could be the cause of this error/issue ?

  


• 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.