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• CCPA vs GDPR : Understanding Their Impact on Data Analytics

  19 mars, par Alex Carmona

  With over 400 million internet users in Europe and 331 million in the US (11% of which reside in California alone), understanding the nuances of privacy laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is crucial for compliant and ethical consumer data collection.

  Navigating this compliance landscape can be challenging for businesses serving European and Californian markets.

  This guide explores the key differences between CCPA and GDPR, their impact on data analytics, and how to ensure your business meets these essential privacy requirements.

  What is the California Consumer Privacy Act (CCPA) ?

  The California Consumer Privacy Act (CCPA) is a data privacy law that gives California consumers control over their personal information. It applies to for-profit businesses operating in California that meet specific criteria related to revenue, data collection and sales.

  Origins and purpose

  The CCPA addresses growing concerns about data privacy and how businesses use personal information in California. The act passed in 2018 and went into effect on 1 January 2020.

  Key features

  • Grants consumers the right to know what personal information is collected
  • Provides the right to delete personal information
  • Allows consumers to opt out of the sale of their personal information
  • Prohibits discrimination against consumers who exercise their CCPA rights

  Key definitions under the CCPA framework

  • Business : A for-profit entity doing business in California and meeting one or more of these conditions :
    • Has annual gross revenues over $25 million ;
    • Buys, receives, sells or shares 50,000 or more consumers’ personal information ; or
    • Derives 50% or more of its annual revenues from selling consumers’ personal information
  • Consumer : A natural person who is a California resident
  • Personal Information : Information that could be linked to, related to or used to identify a consumer or household, such as online identifiers, IP addresses, email addresses, social security numbers, cookie identifiers and more

  What is the General Data Protection Regulation (GDPR) ?

  The General Data Protection Regulation (GDPR) is a data privacy and protection law passed by the European Union (EU). It’s one of the strongest and most influential data privacy laws worldwide and applies to all organisations that process the personal data of individuals in the EU.

  Origins and purpose

  The GDPR was passed in 2016 and went into effect on 25 May 2018. It aims to harmonise data privacy laws in Europe and give people in the European Economic Area (EEA) privacy rights and control over their data.

  Key features

  • Applies to all organisations that process the personal data of individuals in the EEA
  • Grants individuals a wide range of privacy rights over their data
  • Requires organisations to obtain explicit and informed consent for most data processing
  • Mandates appropriate security measures to protect personal data
  • Imposes significant fines and penalties for non-compliance

  Key definitions under the GDPR framework

  • Data Subject : An identified or identifiable person
  • Personal Data : Any information relating to a data subject
  • Data Controller : The entity or organisation that determines how personal data is processed and what for
  • Data Processor : The entity or organisation that processes the data on behalf of the controller

  CCPA vs. GDPR : Key similarities

  The CCPA and GDPR enhance consumer privacy rights and give individuals greater control over their data.

  Dimension	CCPA	GDPR
  Purpose	Protect consumer privacy	Protect individual data rights
  Key Rights	Right to access, delete and opt out of sale	Right to access, rectify, erase and restrict processing
  Transparency	Requires transparency around data collection and use	Requires transparency about data collection, processing and use

  CCPA vs. GDPR : Key differences

  While they have similar purposes, the CCPA and GDPR differ significantly in their scope, approach and specific requirements.

  Dimension	CCPA	GDPR
  Scope	For-profit businesses only	All organisations processing EU consumer data
  Territorial Reach	California-based natural persons	All data subjects within the EEA
  Consent	Opt-out system	Opt-in system
  Penalties	Per violation based on its intentional or negligent nature	Case-by-case based on comprehensive assessment
  Individual Rights	Narrower (relative to GDPR)	Broader (relative to CCPA)

  CCPA vs. GDPR : A multi-dimensional comparison

  The previous sections gave a broad overview of the similarities and differences between CCPA and GDPR. Let’s now examine nine key dimensions where these regulations converge or diverge and discuss their impact on data analytics.

  

  #1. Scope and territorial reach

  The GDPR has a much broader scope than the CCPA. It applies to all organisations that process the personal data of individuals in the EEA, regardless of their business model, purpose or physical location.

  The CCPA applies to medium and large for-profit businesses that derive a substantial portion of their earnings from selling Californian consumers’ personal information. It doesn’t apply to non-profits, government agencies or smaller for-profit companies.

  Impact on data analytics

  The difference in scope significantly impacts data analytics practices. Smaller businesses may not need to comply with either regulation, some may only need to follow the CCPA, while most global businesses must comply with both. This often requires different methods for collecting and processing data in California, Europe, and elsewhere.

  #2. Penalties and fines for non-compliance

  Both the CCPA and GDPR impose penalties for non-compliance, but the severity of fines differs significantly :

  CCPA	Maximum penalty
  	$2,500 per unintentional violation $7,500 per intentional violation

  “Per violation” means per violation per impacted consumer. For example, three intentional CCPA violations affecting 1,000 consumers would result in 3,000 total violations and a $22.5 million maximum penalty (3,000 × $7,500).

  

  The largest CCPA fine to date was Zoom’s $85 million settlement in 2021.

  

  In contrast, the GDPR has resulted in 2,248 fines totalling almost €6.6 billion since 2018 — €2.4 billion of which were for non-compliance.

  GDPR	Maximum penalty
  	€20 million or 4% of all revenue earned the previous year

  So far, the biggest fine imposed under the GDPR was Meta’s €1.2 billion fine in May 2023 — 15 times more than Zoom had to pay California.

  Impact on data analytics

  The significant difference in potential fines demonstrates the importance of regulatory compliance for data analytics professionals. Non-compliance can have severe financial consequences, directly affecting budget allocation and business operations.

  Businesses must ensure their data collection, storage and processing practices comply with regulations in both Europe and California.

  Choosing privacy-first, compliance-ready analytics platforms like Matomo is instrumental for mitigating non-compliance risks.

  #3. Data subject rights and consumer rights

  The CCPA and GDPR give people similar rights over their data, but their limitations and details differ.

  Rights common to the CCPA and GDPR

  • Right to Access/Know : People can access their personal information and learn what data is collected, its source, its purpose and how it’s shared
  • Right to Delete/Erasure : People can request the deletion of their personal information, with some exceptions
  • Right to Non-Discrimination : Businesses can’t discriminate against people who exercise their privacy rights

  Consumer rights unique to the CCPA

  • Right to Opt Out of Sale : Consumers can prohibit the sale of their personal information
  • Right to Notice : Businesses must inform consumers about data collection practices
  • Right to Disclosure : Consumers can request specific information collected about them

  Data subject rights unique to the GDPR

  • Right to be Informed : Broader transparency requirements encompass data retention, automated decision-making and international transfers
  • Right to Rectification : Data subjects may request the correction of inaccurate data
  • Right to Restrict Processing : Consumers may limit data use in certain situations
  • Right to Data Portability : Businesses must provide individual consumer data in a secure, portable format when requested
  • Right to Withdraw Consent : Consumers may withdraw previously granted consent to data processing

  	CCPA	GDPR
  Right to Access or Know	✓	✓
  Right to Delete or Erase	✓	✓
  Right to Non-Discrimination	✓	✓
  Right to Opt-Out	✓
  Right to Notice	✓
  Right to Disclosure	✓
  Right to be Informed		✓
  Right to Rectification		✓
  Right to Restrict Processing		✓
  Right to Data Portability		✓
  Right to Withdraw Consent		✓

  Impact on data analytics

  Data analysts must understand these rights and ensure compliance with both regulations, which could potentially require separate data handling processes for EU and California consumers.

  #4. Opt-out vs. opt-in

  The CCPA generally follows an opt-out model, while the GDPR requires explicit consent from individuals before processing their data.

  Impact on data analytics

  For CCPA compliance, businesses can collect data by default if they provide opt-out mechanisms. Failing to process opt-out requests can result in severe penalties, like Sephora’s $1.2 million fine.

  Under GDPR, organisations must obtain explicit consent before collecting any data, which can limit the amount of data available for analysis.

  #5. Parental consent

  The CCPA and GDPR have provisions regarding parental consent for processing children’s data. The CCPA requires parental consent for children under 13, while the GDPR sets the age at 16, though member states can lower it to 13.

  Impact on data analytics

  This requirement significantly impacts businesses targeting younger audiences. In Europe and the US, companies must implement different methods to verify users’ ages and obtain parental consent when necessary.

  The California Attorney General’s Office recently fined Tilting Point Media LLC $500,000 for sharing children’s data without parental consent.

  #6. Data security requirements

  Both regulations require businesses to implement adequate security measures to protect personal data. However, the GDPR has more prescriptive requirements, outlining specific security measures and emphasising a risk-based approach.

  Impact on data analytics

  Data analytics professionals must ensure that data is processed and stored securely to avoid breaches and potential fines.

  #7. International data transfers

  Both the CCPA and GDPR address international data transfers. Under the CCPA, businesses must only inform consumers about international transfers. The GDPR has stricter requirements, including ensuring adequate data protection safeguards for transfers outside the EEA.

  

  Other rules, like the Payment Services Directive 2 (PSD2), also affect international data transfers, especially in the financial industry.

  PSD2 requires strong customer authentication and secure communication channels for payment services. This adds complexity to cross-border data flows.

  Impact on data analytics

  The primary impact is on businesses serving European residents from outside Europe. Processing data within the European Union is typically advisable. Meta’s record-breaking €1.2 billion fine was specifically for transferring data from the EEA to the US without sufficient safeguards.

  Choosing the right analytics platform helps avoid these issues.

  For example, Matomo offers a free, open-source, self-hosted analytics platform you can deploy anywhere. You can also choose a managed, GDPR-compliant cloud analytics solution with all data storage and processing servers within the EU (in Germany), ensuring your data never leaves the EEA.

  #8. Enforcement mechanisms

  The California Attorney General is responsible for enforcing CCPA requirements, while in Europe, the Data Protection Authority (DPA) in each EU member state enforces GDPR requirements.

  Impact on data analytics

  Data analytics professionals should be familiar with their respective enforcement bodies and their powers to support compliance efforts and minimise the risk of fines and penalties.

  #9. Legal basis for personal data processing

  The GDPR outlines six legal grounds for processing personal data :

  • Consent
  • Contract
  • Legal obligation
  • Vital interests
  • Public task
  • Legitimate interests

  The CCPA doesn’t explicitly define lawful bases but focuses on consumer rights and transparency in general.

  Impact on data analytics

  Businesses subject to the GDPR must identify and document a valid lawful basis for each processing activity.

  Compliance rules under CCPA and GDPR

  Complying with the CCPA and GDPR requires a comprehensive approach to data privacy. Here’s a summary of the essential compliance rules for each framework :

  

  CCPA compliance rules

  • Create clear and concise privacy policies outlining data collection and use practices
  • Give consumers the right to opt-out
  • Respond to consumer requests to access, delete and correct their personal information
  • Implement reasonable security measures for consumers’ personal data protection
  • Never discriminate against consumers who exercise their CCPA rights

  GDPR compliance rules

  • Obtain explicit and informed consent for data processing activities
  • Implement technical and organisational controls to safeguard personal data
  • Designate a Data Protection Officer (DPO) if necessary
  • Perform data protection impact assessments (DPIAs) for high-risk processing activities
  • Maintain records of processing activities
  • Promptly report data breaches to supervisory authorities

  Navigating the CCPA and GDPR with confidence

  Understanding the nuances of the CCPA and GDPR is crucial for businesses operating in the US and Europe. These regulations significantly impact data collection and analytics practices.

  Implementing robust data security practices and prioritising privacy and compliance are essential to avoid severe penalties and build trust with today’s privacy-conscious consumers.

  Privacy-centric analytics platforms like Matomo enable businesses to collect, analyse and use data responsibly and transparently, extracting valuable insights while maintaining compliance with both CCPA and GDPR requirements.

  Start your 21-day free trial today !

  no credit card required

• Can't set seeker in GSTREAMER cv2, python

  29 avril, par Alperen Ölçer

  I want to skip n seconds forward and backward in gstreamer cv2 capture for recorded videos. But when I use cap_gstreamer.set(cv2.CAP_PROP_POS_FRAMES, fps*skip_second) it resets seeker to beginning of video. How can I solve it ? I wrote an example, used recorded clock video.

  


  import cv2

video_p = '/home/alperenlcr/Videos/clock.mp4'

cap_gstreamer = cv2.VideoCapture(video_p, cv2.CAP_GSTREAMER)
cap_ffmpeg = cv2.VideoCapture(video_p, cv2.CAP_FFMPEG)

fps = cap_gstreamer.get(cv2.CAP_PROP_FPS)
skip_second = 100

im1 = cv2.resize(cap_gstreamer.read()[1], (960, 540))
im1_ffmpeg = cv2.resize(cap_ffmpeg.read()[1], (960, 540))

cap_gstreamer.set(cv2.CAP_PROP_POS_FRAMES, fps*skip_second)
cap_ffmpeg.set(cv2.CAP_PROP_POS_FRAMES, fps*skip_second)

im2 = cv2.resize(cap_gstreamer.read()[1], (960, 540))
im2_ffmpeg = cv2.resize(cap_ffmpeg.read()[1], (960, 540))

merge_gstreamer = cv2.hconcat([im1, im2])
merge_ffmpeg = cv2.hconcat([im1_ffmpeg, im2_ffmpeg])

cv2.imshow(str(skip_second) + ' gstreamer', merge_gstreamer)
cv2.imshow(str(skip_second) + ' ffmpeg', merge_ffmpeg)
cv2.waitKey(0)
cv2.destroyAllWindows()

cap_gstreamer.release()
cap_ffmpeg.release()



  


  Result :


  


  My cv2 build is like :

  


  >>> print(cv2.getBuildInformation())

General configuration for OpenCV 4.8.1 =====================================
  Version control:               4.8.1-dirty

  Extra modules:
    Location (extra):            /home/alperenlcr/SourceInstalls/opencv_contrib/modules
    Version control (extra):     4.8.1

  Platform:
    Timestamp:                   2024-12-02T13:44:58Z
    Host:                        Linux 6.8.0-49-generic x86_64
    CMake:                       3.22.1
    CMake generator:             Unix Makefiles
    CMake build tool:            /usr/bin/gmake
    Configuration:               RELEASE

  CPU/HW features:
    Baseline:                    SSE SSE2 SSE3
      requested:                 SSE3
    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX
      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
      SSE4_1 (18 files):         + SSSE3 SSE4_1
      SSE4_2 (2 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
      FP16 (1 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
      AVX (8 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
      AVX2 (37 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2
      AVX512_SKX (8 files):      + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX

  C/C++:
    Built as dynamic libs?:      NO
    C++ standard:                11
    C++ Compiler:                /usr/bin/c++  (ver 10.5.0)
    C++ flags (Release):         -fsigned-char -ffast-math -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
    C++ flags (Debug):           -fsigned-char -ffast-math -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
    C Compiler:                  /usr/bin/cc
    C flags (Release):           -fsigned-char -ffast-math -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
    C flags (Debug):             -fsigned-char -ffast-math -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
    Linker flags (Release):      -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
    Linker flags (Debug):        -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
    ccache:                      NO
    Precompiled headers:         NO
    Extra dependencies:          /usr/lib/x86_64-linux-gnu/libjpeg.so /usr/lib/x86_64-linux-gnu/libpng.so /usr/lib/x86_64-linux-gnu/libtiff.so /usr/lib/x86_64-linux-gnu/libz.so /usr/lib/x86_64-linux-gnu/libfreetype.so /usr/lib/x86_64-linux-gnu/libharfbuzz.so Iconv::Iconv m pthread cudart_static dl rt nppc nppial nppicc nppidei nppif nppig nppim nppist nppisu nppitc npps cublas cudnn cufft -L/usr/lib/x86_64-linux-gnu -L/usr/lib/cuda/lib64
    3rdparty dependencies:       libprotobuf ade ittnotify libwebp libopenjp2 IlmImf quirc ippiw ippicv

  OpenCV modules:
    To be built:                 aruco bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python3 quality rapid reg rgbd saliency shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
    Disabled:                    cudacodec world
    Disabled by dependency:      -
    Unavailable:                 alphamat cvv hdf java julia matlab ovis python2 sfm viz
    Applications:                tests perf_tests examples apps
    Documentation:               NO
    Non-free algorithms:         NO

  GUI:                           GTK2
    QT:                          NO
    GTK+:                        YES (ver 2.24.33)
      GThread :                  YES (ver 2.72.4)
      GtkGlExt:                  NO
    OpenGL support:              NO
    VTK support:                 NO

  Media I/O: 
    ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
    JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver 80)
    WEBP:                        build (ver encoder: 0x020f)
    PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
    TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 / 4.3.0)
    JPEG 2000:                   build (ver 2.5.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES

  Video I/O:
    DC1394:                      NO
    FFMPEG:                      YES
      avcodec:                   YES (58.134.100)
      avformat:                  YES (58.76.100)
      avutil:                    YES (56.70.100)
      swscale:                   YES (5.9.100)
      swresample:                YES (3.9.100)
    GStreamer:                   YES (1.20.3)
    v4l/v4l2:                    YES (linux/videodev2.h)

  Parallel framework:            TBB (ver 2021.5 interface 12050)

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2021.8 [2021.8.0]
           at:                   /home/alperenlcr/SourceInstalls/opencv/build/3rdparty/ippicv/ippicv_lnx/icv
    Intel IPP IW:                sources (2021.8.0)
              at:                /home/alperenlcr/SourceInstalls/opencv/build/3rdparty/ippicv/ippicv_lnx/iw
    VA:                          NO
    Lapack:                      NO
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)
    Flatbuffers:                 builtin/3rdparty (23.5.9)

  NVIDIA CUDA:                   YES (ver 11.5, CUFFT CUBLAS NVCUVID NVCUVENC FAST_MATH)
    NVIDIA GPU arch:             86
    NVIDIA PTX archs:

  cuDNN:                         YES (ver 8.6.0)

  OpenCL:                        YES (no extra features)
    Include path:                /home/alperenlcr/SourceInstalls/opencv/3rdparty/include/opencl/1.2
    Link libraries:              Dynamic load

  ONNX:                          NO

  Python 3:
    Interpreter:                 /usr/bin/python3 (ver 3.10.12)
    Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.10.so (ver 3.10.12)
    numpy:                       /usr/lib/python3/dist-packages/numpy/core/include (ver 1.21.5)
    install path:                lib/python3.10/dist-packages/cv2/python-3.10

  Python (for build):            /usr/bin/python3

  Java:                          
    ant:                         NO
    Java:                        NO
    JNI:                         NO
    Java wrappers:               NO
    Java tests:                  NO

  Install to:                    /usr/local
-----------------------------------------------------------------



  


• 4 ways to create more effective funnels

  24 février 2020, par Jake Thornton — Uncategorized

  Accurately measuring the success of your customer’s journey on your website is vital to increasing conversions and having the best outcome for your business. When it comes to website analytics, the Funnels feature is the best place to start measuring each touch point in the customer journey. From here you’ll find out where you lose your visitors so you can make changes to your website and convert more in the future.

  The funnels feature lets you measure the steps (actions, events and pages) your users go through to reach the desired outcomes you want them to achieve. This gives you valuable insights into the desired journey for your customers. 

  When creating a funnel with the funnels feature, you anticipate the customer journey that you want to measure, for example : 

  Step 1 – Visitor lands on your homepage and sees the promotion you’re offering. 
  Step 2 – They click the call-to-action (CTA) button which leads them to information on the product
  Step 3 – They add the product to their cart
  Step 4 – They fill in their personal information and credit card details
  Step 5 – They click the “pay now” button

  From here you can see exactly how many visitors you lose between each step. Then you can implement new techniques to decrease these drop-offs and evaluate the success of your changes over time.

  But what about the non-conventional routes to conversion ?

  That’s right, visitors can end up in all different directions on your website. It’s important to use other features in Matomo to discover these popular pathways your visitors may be taking before the point of conversion.

  Here are 4 Matomo features for discovering important alternative funnels on your website :

  Transitions

  The transitions feature lets you visualise mini funnels on selected pages. You can see how visitors landed on a specific page, and then where they moved on to from this specific page.

  First you need to identify the page(s) that sells your product or service the most. 

  Whether it’s your homepage, a product page or an information page on your services. The transitions feature will then show you the before and after pathways visitors are already taking to get from page to page

  

  The transitions feature is located under Behaviour – Pages. Find the important page you would like to analyse and click on the Transitions icon.

  

  In the example above, you’ll see 18% of visitors who entered from internal pages came from the homepage, which you may have already suspected as the first step in your conversion funnel.

  However, the exact same % of visitors are also entering through a blog post article called /best-of-the-best/

  In this case, it highlights the importance of creating funnels with popular blog posts as the first step in the funnel. Your visitors may have found this post through social media, a search engine etc. Whatever the case, your blog posts could be your biggest influencer for conversions on your website.

  >> Learn more about Transitions

  Page Overlay

  The overlay feature lets you see exactly where visitors are clicking on your landing pages which moves them either in the right or wrong direction in the conversion funnel. 

  If you see a high percentage of clicks to a page that’s off the beaten track from your desired conversion funnel, use the Funnels feature to follow this pathway and analyse how they get back to the pathway you initially intended them to take.

  

  The best thing about the page overlay feature is the visualisation showing the results on the landing page itself. This gives you an idea of where they may be getting distracted by the wrong content.

  You can locate the page overlay feature beside the transitions feature, shown in the screenshot below.

  

  The page overlay feature also gives you a summary of the pageviews, clicks, bounce rates, exit rates and average time spent on page, so you can measure the overall success of each page in the display menu.

  >> Learn more about Page Overlay

  Users Flow

  If you’re looking to see many of the most popular pathways your visitors are taking all at once, then Users Flow is a powerful feature which shows this visualisation.

  Note : For Matomo On-Premise users, Users Flow is a premium feature. More information here.

  

  The thicker the blue line between interactions means the more popular the pathway is. 

  Here you can see how visitors are navigating their way through your website before converting, this presenting clear steps in the conversion funnel that require monitoring and improving on to ensure your efforts are going into the right areas on your website.

  >> Learn more about Users Flow

  Row Evolution

  Another important feature to use which is integrated within the funnels feature, is row evolution which shows you important changes in your user’s behaviour over time.

  

  Having row evolution integrated within the funnels feature gives you a big advantage as it lets you measure the specific metrics and landing pages within your conversion funnel.

  

  You’ll be able to see the increases and decreases in entries and exits to your landing page, as well as increases and decreases in the number of visitors who proceed to the next step in the funnel, and the conversion rate %.

  You’ll also be able to add annotations so you can note all the changes you make to your landing pages over time and quickly identify how these changes impacted your conversion funnels.

  >>Learn more about Row Evolution

  Continually create more and more funnels !

  Measuring the success of the desired pathway you want your customers to take is crucial to ensure you are presenting the best possible user experience for your visitors.

  However, creating funnels for the less desired pathways is equally important. This way you’ll discover popular journeys your visitors are taking within your website you weren’t previously aware of, and can monitor them to make sure they still work in the future. You’ll be able to fix pain points easier and find faster ways to get visitors back on the right track to converting.