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• "undefined reference to av···@···"ffmpeg error，when i cross compile opencv4.5.3 which include ffmpeg lib

  11 mai 2024, par caiping Peng

  everyone,It is sorry to bother you,but i need some help.
I'm working on an embedded deployment project,doing object detection work to real-time video stream. So I have to port my c++ inference prog to RKNN1808 platform. I compile this program with CMake tool,but I cant finish my work because opencv lib cant be compiled rightly.
To FFmpeg,my configure commend is following :

  


  ./configure --enable-cross-compile --cross-prefix=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu- --target-os=linux --arch=aarch64 --prefix=/usr/local/ffmpeg  --enable-shared


  


  then I am gonna show you the ffmpeg version :

  


  libavutil      56. 70.100
libavcodec     58.134.100
libavformat    58. 76.100
libavdevice    58. 13.100
libavfilter     7.110.100
libswscale      5.  9.100
libswresample   3.  9.100
libpostproc    55.  9.100


  


  next ,I use following commend to build cmake project :

  


  cmake -D CMAKE_BUILD_TYPE=RELEASE  -D CMAKE_C_COMPILER=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-gcc -D CMAKE_CXX_COMPILER=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-g++ -D BUILD_SHARED_LIBS=ON -D CMAKE_CXX_FLAGS=-fPIC -D CMAKE_C_FLAGS=-fPIC -D CMAKE_EXE_LINKER_FLAGS=-lpthread -ldl -D ENABLE_PIC=ON -D WITH_1394=OFF -D WITH_ARAVIS=OFF -D WITH_ARITH_DEC=ON -D WITH_ARITH_ENC=ON -D WITH_CLP=OFF -D WITH_CUBLAS=OFF -D WITH_CUDA=OFF -D WITH_CUFFT=OFF -D WITH_FFMPEG=ON -D WITH_GSTREAMER=ON -D WITH_GSTREAMER_0_10=OFF -D WITH_HALIDE=OFF -D WITH_HPX=OFF -D WITH_IMGCODEC_HDR=ON -D WITH_IMGCODEC_PXM=ON -D WITH_IMGCODEC_SUNRASTER=ON -D WITH_INF_ENGINE=OFF -D WITH_IPP=OFF -D WITH_ITT=OFF -D WITH_JASPER=ON -D WITH_JPEG=ON -D WITH_LAPACK=ON -D WITH_LIBREALSENSE=OFF -D WITH_NVCUVID=OFF -D WITH_OPENCL=OFF -D WITH_OPENCLAMDBLAS=OFF -D WITH_OPENCLAMDFFT=OFF -D WITH_OPENCL_SVM=OFF -D WITH_OPENEXR=OFF -D WITH_OPENGL=OFF -D WITH_OPENMP=OFF -D WITH_OPENNNI=OFF -D WITH_OPENNNI2=OFF -D WITH_OPENVX=OFF -D WITH_PNG=OFF -D WITH_PROTOBUF=OFF -D WITH_PTHREADS_PF=ON -D WITH_PVAPI=OFF -D WITH_QT=OFF -D WITH_QUIRC=OFF  -D WITH_TBB=OFF -D WITH_TIFF=ON -D WITH_VULKAN=OFF -D WITH_WEBP=ON -D WITH_XIMEA=OFF -D CMAKE_INSTALL_PREFIX=../CrossCompileResult  -D WITH_GTK=OFF  -D BUILD_opencv_dnn=OFF ..


  


  following is the outpt about FFmpeg :

  


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



  


  After building the cmake project,I compiled this project with comment 【make -j16】.After not so long time,I got the Error :

  


  [ 49%] Linking CXX executable ../../bin/opencv_annotation
[ 49%] Building CXX object modules/ts/CMakeFiles/opencv_ts.dir/src/ts_tags.cpp.o
[ 49%] Built target opencv_annotation
[ 49%] Linking CXX executable ../../bin/opencv_visualisation
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavcodec.so.58, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavformat.so.58, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavutil.so.56, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libswscale.so.5, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_init_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_get_riff_video_tags@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_send_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_receive_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_get_mov_video_tags@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_decoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_decoder_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_alloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_transfer_data@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_malloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avio_open@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_alloc_context@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_sub_q@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_network_init@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_free@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_flush_buffers@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_encoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_getContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_receive_frame@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_write_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_close_input@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_seek_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_freeContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_set@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_descriptor_get_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_scale@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_unref@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_parse_string@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_get_buffer@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_freep@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_find_stream_info@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_read_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_free_context@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_default_get_format@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_init@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_register_all@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_get_buffer@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_guess_sample_aspect_ratio@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_new_stream@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_constraints_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_ctx_create_derived@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_unref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_buffer_unref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_write_trailer@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_rescale_ts@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_get_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_send_frame@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_hw_config@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_buffer_ref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_get@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_free@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_is_decoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_open_input@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_lockmgr_register@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_alloc@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_create_derived@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_send_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_alloc@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_log_set_level@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_image_get_buffer_size@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_open2@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_is_encoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_guess_format@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_image_fill_arrays@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_receive_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_getCachedContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_get_tag@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_get_hwframe_constraints@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_ctx_create@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_iterate@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_log_set_callback@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_opt_set@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_get_id@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_write_header@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_parameters_copy@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_pix_fmt_to_codec_tag@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_alloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_mallocz@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_find_input_format@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_hw_frames_parameters@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_get_type_name@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avio_close@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_init@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_close@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_find_type_by_name@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_context_defaults3@LIBAVCODEC_58'
collect2: error: ld returned 1 exit status
make[2]: *** [apps/visualisation/CMakeFiles/opencv_visualisation.dir/build.make:89: bin/opencv_visualisation] Error 1
make[1]: *** [CMakeFiles/Makefile2:3357: apps/visualisation/CMakeFiles/opencv_visualisation.dir/all] Error 2
make[1]: *** Waiting for unfinished jobs....
[ 49%] Linking CXX shared library ../../lib/libopencv_calib3d.so
[ 49%] Built target opencv_calib3d
[ 50%] Linking CXX static library ../../lib/libopencv_ts.a
[ 50%] Built target opencv_ts
make: *** [Makefile:163: all] Error 2



  


  I dont know what's wrong with it,It has confused me for a few days,I real hope someone can help me solve the prob.
I promise the the ffmpeg version match the version of opencv strictly,promising the PKG_CONFIG_PATH is right.

  


  I have tried many method like changing opencv version or ffmpeg version,recompiling the ffmpeg,changing PKG_CONFIG_PATH,coping ffmpeg pc file from /usr/local/ffmpeg/lib/pkgconfig to /usr/local/lib/pkgconfig.
I hope somebody can give some idea about how to solve this problem.

  


• CRO Program : Best Practices and KPIs to Track [2024]

  8 mai 2024, par Erin

  Driving traffic to your website is only one part of the equation ; the second part is getting those visitors to convert by completing a desired action — creating an account, signing up for a newsletter or completing a purchase. 

  But if you fail to optimise your website for conversions, you’ll have a hard time guiding visitors further down the funnel and turning them into customers.

  That’s where a CRO program (or conversion rate optimisation) can help. 

  This article will cover conversion rate optimisation best practices and outline key metrics and KPIs to start tracking to see an improvement in your conversion rates.

  What is a CRO program ? 

  In the simplest terms, a CRO program — also called a CRO plan — is a digital marketing strategy. It focuses on implementing different tactics that can lead to an increase in conversion rate and maximising revenue. 

  

  One thing to remember is that the definition of “conversion” varies from business to business. The most obvious type of conversion would be a financial transaction or a completed form — but it comes down to what you consider a valuable action. 

  Many different actions can count as conversions, depending on your marketing goals. 

  Besides making a purchase, other common examples of key conversion moments include creating a new account, signing up for a free trial, booking a demo and subscribing to an email newsletter. 

  Another thing worth noting is that while the average conversion rate on e-commerce websites is 3.76%, it might fluctuate across different industries and device types. Case in point — desktop devices have higher conversion rates than mobile devices, clocking in at 4.79% and 3.32%, respectively. 

  So, in addition to defining your key conversion moments, you should also go over conversion insights relevant to your specific industry. 

  

  The importance of conversion rate optimisation 

  You’d be right to assume that the ultimate goal of a conversion rate optimisation process is to drive revenue through higher conversion rates — but don’t focus solely on the numbers. The core principle of a CRO program is improving the customer experience. Once you’ve achieved that, the increase in conversion rate will follow. 

  

  According to a recent report, global conversion rate optimisation (CRO) software sales are expected to reach $3.7 billion by 2032 — up from $1.1 billion in 2021. 

  This growth indicates the increasing interest in strategies and tools that can help optimise the conversion funnel. Businesses are looking for ways to keep potential customers engaged and improve the average conversion rate — without necessarily increasing their spending. 

  Here are a few reasons why a CRO program deserves a spot in your broader digital marketing strategies : 

  • It can lower your cost per acquisition (CPA) : A CRO program is about optimising your conversion funnel by leveraging existing assets and website traffic rather than increasing your spending — which lowers the costs of acquiring new customers and, in turn, drives ROI. 
  • It can maximise customer lifetime value (CLV) : If you can turn one-time buyers into repeat customers, you’ll be one step closer to building a loyal user base and increasing your CLV. 
  • It can lead to increased sales and boost your revenue : Higher conversion rates typically mean higher revenue ; that’s arguably the most obvious benefit of implementing a CRO program. 
  • It improves the overall user experience : The goal is to make your site more accessible, easier to navigate and more engaging. Delivering the experience people want — and expect — when navigating your website is one of the core principles of a CRO program.
  • It helps you to get to know your customers better : You can’t meet your customers’ needs without taking the time to know them, create user personas and understand their preferences, pain points and conversion barriers they may be facing. 

  Conversion optimisation gives you a competitive edge in revenue and brand reputation. 

  5 CRO best practices 

  

  Here are five conversion rate optimisation strategies and best practices that can make a real difference in the customer experience — and drive potential conversions. 

  Create a CRO roadmap in advance 

  First and foremost, you’ll need a well-defined “game plan” that aligns with and reflects your conversion goals. 

  A CRO roadmap is a detailed manual that outlines how to implement different elements of your CRO-related efforts. Marketing teams can refer to this step-by-step framework for test planning, prioritisation and resource allocation while optimising their marketing strategy. 

  While conversion rate optimisation can be a complex process — especially when you don’t know what to tackle first — we’ve found that there are three things you need to consider when setting the foundations of a successful CRO program : 

  • The “why” behind your website traffic : You’re likely using different online marketing strategies — from SEO to pay-per-click (PPC). So, it’s best to start by gathering channel-specific conversion insights through marketing attribution. Then identify which of these efforts have the biggest impact on your target audience. 
  • The so-called “conversion blockers” that tell you where and why visitors tend to leave without completing a desired action : Funnel analysis might reveal problematic pages — drop-off points where you tend to lose most of your visitors. 
  • Your “hooks” : User feedback can be of great help here ; you can learn a lot by simply asking your customers to fill out a quick online survey and tell you what motivated them to take action.

  Before working on that “game plan,” perform a pre-test analysis. 

  Matomo combines web analytics and user behaviour analytics with features like Heatmaps, Session Recordings, Form Analytics, Funnel Analytics, A/B Testing and User Flow. It can give you those initial benchmarks for measuring progress and a potential increase in conversion rate. 

  Validate your ideas with A/B and multivariate testing 

  Conversion rate optimisation is an iterative process. So, it shouldn’t come as a surprise that A/B testing variants of page layouts, CTAs, headlines, copy and other elements is a big part of it.

  Multivariate and A/B testing allows you to test a wide range of elements across your site and identify what works — and, more importantly, what doesn’t — in terms of driving conversions.

  On that note, Matomo’s A/B Testing feature can support your conversion rate optimisation process by identifying variants that perform better based on statistical significance. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Get to know your website visitors 

  Driving conversions comes down to understanding potential customer’s pain points and needs — and delivering an experience that positions you as the solution and gets them to take action. 

  Here are a few things that can help you understand your website visitors better : 

  • Collecting customer feedback through surveys and using it to identify main areas for improvement 
  • Creating detailed customer personas and optimising your website design and messaging based on your target audience’s pain points, needs and wants 
  • Using heatmaps — colour-coded data visualisation tools that illustrate user interactions — and scroll maps to get a comprehensive overview of online sessions and identify the most engaging elements and those that stand out as potential conversion barriers 

  Matomo’s Heatmaps can help you identify the most-clicked elements on the page and show how far users scroll — providing powerful user insights you can use to optimise these pages.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Remove friction points 

  As we previously discussed, identifying friction points and barriers to conversion — issues that prevent visitors from converting — is one of the crucial aspects of developing a CRO plan. 

  Many different “conversion blockers” are worth looking into, including : 

  • Lengthy or otherwise complex checkout processes 
  • No guest checkout feature 
  • Device type, browser and OS compatibility issues 
  • Slow site speed and other technical issues
  • Lack of free shipping and limited payment methods 
  • Absence of social proof (customer reviews and testimonials) and trust badges

  Once you’ve identified what’s slowing down or completely discouraging users from reaching key conversion moments, take the time to address it. 

  Switch to text-based CTAs 

  Calls-to-action (CTAs) play a crucial role in guiding customers from interest to action. However, sometimes they fail to do their job — encouraging website visitors to proceed to the next step — effectively. 

  The most obvious reason is that your CTAs aren’t visually engaging or clear enough. In that case, you can try using action-oriented language and stronger visual elements and aligning the CTA copy with the context of the page. 

  But more often than not, the issue comes down to a phenomenon called “banner blindness” — the tendency of website visitors to ignore (either intentionally or unintentionally) elements on a page that resemble banner ads. 

  And if that’s what’s preventing visitors from converting, consider switching to text-based CTAs. 

  Conversion rate optimisation metrics and KPIs 

  At this point, you should know the outcomes you hope to achieve. Your next step should be to figure out how you’re going to measure and analyse results — and identify the changes that made the most impact on your conversion funnel. 

  After all, your CRO action plan should be based on data — assumptions and “gut feelings” will rarely lead to a notable increase in conversion rates. 

  

  That brings us to key performance indicators (KPIs) : 

  Tracking CRO metrics and website KPIs can help you understand the customer’s journey and path to purchase, identify opportunities for improving the user experience (UX) and determine how to optimise conversions.

  That said, you shouldn’t try to track every metric in the book ; think about your ultimate goal and identify the metrics and KPIs most relevant to your business. 

  We’ll assume that you’re already tracking macro- and micro-conversions. However, we’ve outlined a few additional key conversion rate optimisation metrics you should keep an eye on to make sure that your CRO program is performing as intended : 

  • Cost-per-conversion : By measuring how much you spend on each successful conversion — again, completed forms, sign-ups and sales all count as key conversion moments — you’ll be in a better position to assess the cost-effectiveness of your online marketing strategies.
  • Starter rate : This metric tells you the number of people who start filling out the form, after seeing it. This metric is particularly important for companies that rely on getting leads from forms. 
  • Average order value (AOV) : This metric is important for e-commerce sites to understand the value of their transactions. AOV calculates the average monetary value of each order.

  That’s not all ; you can also use a web analytics tool like Matomo to gain granular insights into visitors : 

  • Unique, new and returning visitors : Tracking the number of new and returning visitors your website gets within a given timeframe will help you understand your user base and determine if your content resonates with them. While you want a constant stream of new traffic, don’t overlook the importance of returning visitors ; they’re the foundation of a loyal customer base.
  • User flows : By analysing the user flows, you’ll have a visual representation of how visitors use your website, which will help you understand their journey and the specific path they take. 
  • Bounce rate : This metric tells you how many users viewed a single page on your site and ended up leaving before they took any kind of action. As such, it’s a clear indicator of how good your content, CTAs and website layout are at keeping users engaged.
  • Exit rate : Another key metric to track is the exit rate — the percentage of users who drop off at a specific page. High-exit pages usually lack important information and CTAs, cause frustration or otherwise fail to meet users’ expectations. Keep in mind that there’s a difference between bounce rate and exit rate — the latter involves users who viewed at least one other page. 

  There are many other user engagement metrics you should keep an eye on in addition to the ones mentioned above — including time on-page, actions per visit, scroll depth and traffic source. You’ll find all this information — and more — in Matomo’s Page Analytics Report. 

  Conclusion 

  Implementing a CRO program can be a time-consuming and iterative process. However, it’s vital for guiding your marketing efforts and making data-driven decisions that’ll ultimately help you drive growth and reach your business goals. 

  It’s best to start by identifying where your website visitors come from and what contributes to — or prevents them from — taking further action. But that’s easier said than done. You’ll need to leverage web analytics tools like Matomo to gather powerful user insights and monitor your website’s performance. 

  As an all-in-one, privacy-friendly web analytics solution, Matomo combines traditional web analytics and advanced behavioural analytics — delivering a consistent experience based on 100% accurate, unsampled data.

  Join the 1 million websites that have chosen Matomo as their web analytics platform. Start your 21-day free trial today — and see how Matomo can help you improve your website’s conversion rates. No credit card required.

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• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

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  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

  Try Matomo free for 21 days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

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