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• 7 Fintech Marketing Strategies to Maximise Profits in 2024

  24 juillet 2024, par Erin

  Fintech investment skyrocketed in 2021, but funding tanked in the following two years. A -63% decline in fintech investment in 2023 saw the worst year in funding since 2017. Luckily, the correction quickly floored, and the fintech industry will recover in 2024, but companies will have to work much harder to secure funds.

  F-Prime’s The 2024 State of Fintech Report called 2023 the year of “regulation on, risk off” amid market pressures and regulatory scrutiny. Funding is rising again, but investors want regulatory compliance and stronger growth performance from fintech ventures.

  Here are seven fintech marketing strategies to generate the growth investors seek in 2024.

  Top fintech marketing challenges in 2024

  Following the worst global investment run since 2017 in 2023, fintech marketers need to readjust their goals to adapt to the current market challenges. The fintech honeymoon is over for Wall Street with regulator scrutiny, closures, and a distinct lack of profitability giving investors cold feet.

  Here are the biggest challenges fintech marketers face in 2024 :

  • Market correction : With fewer rounds and longer times between them, securing funds is a major challenge for fintech businesses. F-Prime’s The 2024 State of Fintech Report warns of “a high probability of significant shutdowns in 2024 and 2025,” highlighting the importance of allocating resources and budgets effectively.
  • Contraction : Aside from VC funding decreasing by 64% in 2023, the payments category now attracts a large majority of fintech investment, meaning there’s a smaller share from a smaller pot to go around for everyone else.
  • Competition : The biggest names in finance have navigated heavy disruption from startups and, for the most part, emerged stronger than ever. Meanwhile, fintech is no longer Wall Street’s hottest commodity as investors turn their attention to AI.
  • Regulations : Regulatory scrutiny of fintech intensified in 2023 – particularly in the US – contributing to the “regulation on, risk off” summary of F-Prime’s report.
  • Investor scrutiny : With market and industry challenges intensifying, investors are putting their money behind “safer” ventures that demonstrate real, sustainable profitability, not short-term growth.
  • Customer loyalty : Even in traditional baking and finance, switching is surging as customers seek providers who better meet their needs. To achieve the sustainable growth investors are looking for, fintech startups need to know their ideal customer profile (ICP), tailor their products/services and fintech marketing campaigns to them, and retain them throughout the customer lifecycle.

  

  The good news for fintech marketers is that the market correction is leveling out in 2024. In The 2024 State of Fintech Report, F-Prime says that “heading into 2024, we see the fintech market amid a rebound,” while McKinsey expects fintech revenue to grow “almost three times faster than those in the traditional banking sector between 2023 and 2028.”

  Winning back investor confidence won’t be easy, though. F-Prime acknowledges that investors are prioritising high-performance fintech ventures, particularly those with high gross margins. Fintech marketers need to abandon the growth-at-all-costs mindset and switch to a data-driven optimisation, growth and revenue system.

  7 fintech marketing strategies

  Given the current state of the fintech industry and relatively low levels of investor confidence, fintech marketers’ priority is building a new culture of sustainable profit. This starts with rethinking priorities and switching up the marketing goals to reflect longer-term ambitions.

  So, here are the fintech marketing strategies that matter most in 2024.

  1. Optimise for profitability over growth at all costs

  To progress from the growth-at-all-cost mindset, fintech marketers need to optimise for different KPIs. Instead of flexing metrics like customer growth rate, fintech companies need to take a more balanced approach to measuring sustainable profitability.

  This means holding on to existing customers – and maximising their value – while they acquire new customers. It also means that, instead of trying to make everyone a target customer, you concentrate on targeting the most valuable prospects, even if it results in a smaller overall user base.

  Optimising for profitability starts with putting vanity metrics in their place and pinpointing the KPIs that represent valuable business growth :

  • Gross profit margin
  • Revenue growth rate
  • Cash flow
  • Monthly active user growth (qualify “active” as completing a transaction)
  • Customer acquisition cost
  • Customer retention rate
  • Customer lifetime value
  • Avg. revenue per user
  • Avg. transactions per month
  • Avg. transaction value

  With a more focused acquisition strategy, you can feed these insights into every company level. For example, you can prioritise customer engagement, revenue, retention, and customer service in product development and customer experience (CX).

  To ensure all marketing efforts are pulling towards these KPIs, you need an attribution system that accurately measures the contribution of each channel.

  Marketing attribution (aka multi-touch attribution) should be used to measure every touchpoint in the customer journey and accurately credit them for driving revenue. This helps you allocate the correct budget to the channels and campaigns, adding real value to the business (e.g., social media marketing vs content marketing).

  Example : Mastercard helps a digital bank acquire 10 million high-value customers

  For example, Mastercard helped a digital bank in Latin America achieve sustainable growth beyond customer acquisition. The fintech company wanted to increase revenue through targeted acquisition and profitable engagement metrics.

  Strategies included :

  • A more targeted acquisition strategy for high-value customers
  • Increasing avg. spend per customer
  • Reducing acquisition cost
  • Customer retention

  As a result, Mastercard’s advisors helped this fintech company acquire 10 million new customers in two years. More importantly, they increased customer spending by 28% while reducing acquisition costs by 13%, creating a more sustainable and profitable growth model.

  2. Use web and app analytics to remotivate users before they disengage

  Engagement is the key to customer retention and lifetime value. To prevent valuable customers from disengaging, you need to intervene when they show early signs of losing interest, but they’re still receptive to your incentivisation tactics (promotions, rewards, milestones, etc.).

  By integrating web and app analytics, you can identify churn patterns and pinpoint the sequences of actions that lead to disengaging. For example, you might determine that customers who only log in once a month, engage with one dashboard, or drop below a certain transaction rate are at high risk for churn.

  Using a tool like Matomo for web and app analytics, you can detect these early signs of disengagement. Once you identify your churn risks, you can create triggers to automatically fire re-engagement campaigns. You can also use CRM and session data to personalize campaigns to directly address the cause of disengagement, e.g., valuable content or incentives to increase transaction rates.

  Example : Dynamic Yield fintech re-engagement case study

  In this Dynamic Yield case study, one leading fintech company uses customer spending patterns to identify those most likely to disengage. The company set up automated campaigns with personalised in-app messaging, offering time-bound incentives to increase transaction rates.

  

  With fully automated re-engagement campaigns, this fintech company increased customer retention through valuable engagement and revenue-driving actions.

  3. Identify the path your most valuable customers take

  Why optimise web experiences for everyone when you can tailor the online journey for your most valuable customers ? Use customer segmentation to identify the shared interests and habits of your most valuable customers. You can learn a lot about customers based on where the pages they visit and the content they engage with before taking action.

  Use these insights to optimise funnels that motivate prospects displaying the same customer behaviours as your most valuable customers.

  Get 20-40% more data with Matomo

  One of the biggest issues with Google Analytics and many similar tools is that they produce inaccurate data due to data sampling. Once you collect a certain amount of data, Google reports estimates instead of giving you complete, accurate insights.

  This means you could be basing important business decisions on inaccurate data. Furthermore, when investors are nervous about the uncertainty surrounding fintech, the last thing they want is inaccurate data.

  Matomo is the reliable, accurate alternative to Google Analytics that uses no data sampling whatsoever. You get 100% access to your web analytics data, so you can base every decision on reliable insights. With Matomo, you can access between 20% and 40% more data compared to Google Analytics.

  

  With Matomo, you can confidently unlock the full picture of your marketing efforts and give potential investors insights they can trust.

  Try Matomo for Free

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

  Start for free

  No credit card required

  4. Reduce onboarding dropouts with marketing automation

  Onboarding dropouts kill your chance of getting any return on your customer acquisition cost. You also miss out on developing a long-term relationship with users who fail to complete the onboarding process – a hit on immediate ROI and, potentially, long-term profits.

  The onboarding process also defines the first impression for customers and sets a precedent for their ongoing experience.

  An engaging onboarding experience converts more potential customers into active users and sets them up for repeat engagement and valuable actions.

  Example : Maxio reduces onboarding time by 30% with GUIDEcx

  Onboarding optimisation specialists, GUIDEcx helped Maxio cut six weeks off their onboarding times – a 30% reduction.

  

  With a shorter onboarding schedule, more customers are committing to close the deal during kick-off calls. Meanwhile, by increasing automated tasks by 20%, the company has unlocked a 40% increase in capacity, allowing it to handle more customers at any given time and multiplying its capacity to generate revenue.

  5. Increase the value in TTFV with personalisation

  Time to first value (TTFV) is a key metric for onboarding optimisation, but some actions are more valuable than others. By personalising the experience for new users, you can increase the value of their first action, increasing motivation to continue using your fintech product/service.

  The onboarding process is an opportunity to learn more about new customers and deliver the most rewarding user experience for their particular needs.

  Example : Betterment helps users put their money to work right away

  Betterment has implemented a quick, personalised onboarding system instead of the typical email signup process. The app wants to help new customers put their money to work right away, optimising for the first transaction during onboarding itself.

  

  It personalises the experience by prompting new users to choose their goals, set up the right account for them, and select the best portfolio to achieve their goals. They can complete their first investment within a matter of minutes and professional financial advice is only ever a click away.

  Optimise account signups with Matomo

  If you want to create and optimise a signup process like Betterment, you need an analytics system with a complete conversion rate optimisation (CRO) toolkit. 

  

  Matomo includes all the CRO features you need to optimise user experience and increase signups. With heatmaps, session recordings, form analytics, and A/B testing, you can make data-driven decisions with confidence.

  Try Matomo for Free

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

  Start for free

  No credit card required

  6. Use gamification to drive product engagement

  Gamification can create a more engaging experience and increase motivation for customers to continue using a product. The key is to reward valuable actions, engagement time, goal completions, and the small objectives that build up to bigger achievements.

  Gamification is most effective when used to help individuals achieve goals they’ve set for themselves, rather than the goals of others (e.g., an employer). This helps explain why it’s so valuable to fintech experience and how to implement effective gamification into products and services.

  Example : Credit Karma gamifies personal finance

  Credit Karma helps users improve their credit and build their net worth, subtly gamifying the entire experience.

  

  Users can set their financial goals and link all of their accounts to keep track of their assets in one place. The app helps users “see your wealth grow” with assets, debts, and investments all contributing to their next wealth as one easy-to-track figure.

  

  7. Personalise loyalty programs for retention and CLV

  Loyalty programs tap into similar psychology as gamification to motivate and reward engagement. Typically, the key difference is that – rather than earning rewards for themselves – you directly reward customers for their long-term loyalty.

  That being said, you can implement elements of gamification and personalisation into loyalty programs, too. 

  Example : Bank of America’s Preferred Rewards

  Bank of America’s Preferred Rewards program implements a tiered rewards system that rewards customers for their combined spending, saving, and borrowing activity.

  

  The program incentivises all customer activity with the bank and amplifies the rewards for its most active customers. Customers can also set personal finance goals (e.g., saving for retirement) to see which rewards benefit them the most.

  Conclusion

  Fintech marketing needs to catch up with the new priorities of investors in 2024. The pre-pandemic buzz is over, and investors remain cautious as regulatory scrutiny intensifies, security breaches mount up, and the market limps back into recovery.

  To win investor and consumer trust, fintech companies need to drop the growth-at-all-costs mindset and switch to a marketing philosophy of long-term profitability. This is what investors want in an unstable market, and it’s certainly what customers want from a company that handles their money.

  Unlock the full picture of your marketing efforts with Matomo’s robust features and accurate reporting. Trusted by over 1 million websites, Matomo is chosen for its compliance, accuracy, and powerful features that drive actionable insights and improve decision-making.

   Start your free 21-day trial now. No credit card required.

  Try Matomo for Free

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• opencv does not find ffmpeg functions during compilation (make)

  17 avril 2022, par titicplusplus

  I am currently trying to compile OpenCV with CUDA.
So I downloaded opencv 4.5.5 and opencv_contrib and followed this tutorial : https://gist.github.com/raulqf/f42c718a658cddc16f9df07ecc627be7

  


  cd opencv-4.5.5/
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_TBB=ON \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1 \
-D WITH_CUDA=ON \
-D BUILD_opencv_cudacodec=OFF \
-D WITH_CUDNN=OFF \
-D OPENCV_DNN_CUDA=OFF \
-D CUDA_ARCH_BIN=7.5 \
-D WITH_V4L=ON \
-D WITH_QT=ON \
-D WITH_OPENGL=ON \
-D WITH_GSTREAMER=ON \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_PC_FILE_NAME=opencv.pc \
-D OPENCV_ENABLE_NONFREE=ON \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D BUILD_EXAMPLES=OFF \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.5.5/modules ../


  


  The cmake command generated these lines :

  


  -- General configuration for OpenCV 4.5.5 =====================================
--   Version control:               unknown
-- 
--   Extra modules:
--     Location (extra):            /mnt/704E048C4E044D72/build/opencv/opencv_contrib-4.5.5/modules
--     Version control (extra):     unknown
-- 
--   Platform:
--     Timestamp:                   2022-04-17T16:01:44Z
--     Host:                        Linux 5.4.0-107-lowlatency x86_64
--     CMake:                       3.16.3
--     CMake generator:             Unix Makefiles
--     CMake build tool:            /usr/bin/make
--     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 (5 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
--       AVX2 (33 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?:      YES
--     C++ standard:                11
--     C++ Compiler:                /usr/bin/c++  (ver 8.4.0)
--     C++ flags (Release):         -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-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 -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-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  
--     Linker flags (Debug):        -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed  
--     ccache:                      NO
--     Precompiled headers:         NO
--     Extra dependencies:          m pthread cudart_static dl rt nppc nppial nppicc nppicom nppidei nppif nppig nppim nppist nppisu nppitc npps cublas cufft -L/usr/lib/x86_64-linux-gnu
--     3rdparty dependencies:
-- 
--   OpenCV modules:
--     To be built:                 alphamat aruco barcode bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cvv datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hdf hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python2 python3 quality rapid reg rgbd saliency sfm 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:                 java julia matlab ovis viz
--     Applications:                tests perf_tests apps
--     Documentation:               NO
--     Non-free algorithms:         YES
-- 
--   GUI:                           QT5
--     QT:                          YES (ver 5.12.8 )
--       QT OpenGL support:         YES (Qt5::OpenGL 5.12.8)
--     GTK+:                        YES (ver 3.24.20)
--       GThread :                  YES (ver 2.64.6)
--       GtkGlExt:                  NO
--     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGL.so /usr/lib/x86_64-linux-gnu/libGLU.so)
--     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 / 4.1.0)
--     JPEG 2000:                   build (ver 2.4.0)
--     OpenEXR:                     /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so (ver 2_3)
--     HDR:                         YES
--     SUNRASTER:                   YES
--     PXM:                         YES
--     PFM:                         YES
-- 
--   Video I/O:
--     DC1394:                      YES (2.2.5)
--     FFMPEG:                      YES
--       avcodec:                   YES (58.54.100)
--       avformat:                  YES (58.29.100)
--       avutil:                    YES (56.31.100)
--       swscale:                   YES (5.5.100)
--       avresample:                YES (4.0.0)
--     GStreamer:                   YES (1.16.2)
--     v4l/v4l2:                    YES (linux/videodev2.h)
-- 
--   Parallel framework:            TBB (ver 2020.1 interface 11101)
-- 
--   Trace:                         YES (with Intel ITT)
-- 
--   Other third-party libraries:
--     Intel IPP:                   2020.0.0 Gold [2020.0.0]
--            at:                   /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/icv
--     Intel IPP IW:                sources (2020.0.0)
--               at:                /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/iw
--     VA:                          NO
--     Lapack:                      NO
--     Eigen:                       YES (ver 3.3.7)
--     Custom HAL:                  NO
--     Protobuf:                    build (3.19.1)
-- 
--   NVIDIA CUDA:                   YES (ver 10.1, CUFFT CUBLAS FAST_MATH)
--     NVIDIA GPU arch:             75
--     NVIDIA PTX archs:
-- 
--   OpenCL:                        YES (no extra features)
--     Include path:                /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/3rdparty/include/opencl/1.2
--     Link libraries:              Dynamic load
-- 
--   Python 2:
--     Interpreter:                 /usr/bin/python2.7 (ver 2.7.18)
--     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.18)
--     numpy:                       /usr/lib/python2.7/dist-packages/numpy/core/include (ver 1.16.5)
--     install path:                lib/python2.7/dist-packages/cv2/python-2.7
-- 
--   Python 3:
--     Interpreter:                 /usr/bin/python3 (ver 3.8.10)
--     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.8.so (ver 3.8.10)
--     numpy:                       /home/famillevincent/.local/lib/python3.8/site-packages/numpy/core/include (ver 1.22.3)
--     install path:                lib/python3.8/site-packages/cv2/python-3.8
-- 
--   Python (for build):            /usr/bin/python2.7
-- 
--   Java:                          
--     ant:                         NO
--     JNI:                         /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
--     Java wrappers:               NO
--     Java tests:                  NO
-- 
--   Install to:                    /usr/local
-- -----------------------------------------------------------------
-- 
-- Configuring done
-- Generating done


  


  CMake have detected ffmpeg libraries, but when I run make -j8. I have this error :

  


  [ 39%] Building CXX object apps/interactive-calibration/CMakeFiles/opencv_interactive-calibration.dir/calibPipeline.cpp.o
Scanning dependencies of target opencv_cudafilters
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_transfer_data »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_get_hw_config »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_get_hwframe_constraints »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_send_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_get_buffer »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_ctx_create_derived »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_ctx_create »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_alloc »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_receive_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_send_frame »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_parameters_copy »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_packet_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_find_type_by_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_init »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_alloc »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_receive_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_codec_iterate »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_init »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_get_type_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_constraints_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_receive_frame »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_get_by_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_get_hw_frames_parameters »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_send_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_create_derived »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_packet_alloc »
collect2: error: ld returned 1 exit status
make[2]: *** [apps/visualisation/CMakeFiles/opencv_visualisation.dir/build.make:92 : bin/opencv_visualisation] Erreur 1
make[1]: *** [CMakeFiles/Makefile2:11412 : apps/visualisation/CMakeFiles/opencv_visualisation.dir/all] Erreur 2
make[1]: *** Attente des tâches non terminées....
[ 39%] Building CXX object apps/interactive-calibration/CMakeFiles/opencv_interactive-calibration.dir/frameProcessor.cpp.o


  


  So what can I do to compile opencv with cuda and ffmpeg ?
  
Thank you in advance for your answers.

  


  I use Ubuntu 20.04 with g++8

  


• The Ultimate Guide to HeatMap Software

  20 septembre 2021, par Ben Erskine — Analytics Tips, Plugins, Heatmaps

  One of the most effective ways to improve the user experience on your website is to use heatmap software. As well as in-depth insight on how to improve your website and funnels, user behaviour analytics complement traditional web metrics with insights from your customers’ point of view. 

  Heatmap software shows actual user behaviour. That means that you have a visual representation of why a customer might not be converting instead of guessing. 

  By tracking clicks, mouse movement, and page scrolling as well as analysing above the fold content engagement and overall session recordings, heatmap software helps improve user experience and therefore customer retention and conversions.  

  

  What is heatmap software ?

  Heatmap software is a data visualisation tool that uses colour to show what actions a user is taking on a website. 

  If there is a design element on a page that many users engage with, it will show as red/hot. For elements that are less engaging, it will show on the analysis as blue/cold. 

   

  Heatmap software like Matomo helps businesses to improve user experience and increase conversions by tracking elements such as :

  • Flow through sales funnels 
  • Individual session recordings for user experience 
  • A/B page comparisons 
  • Form analytics for sign ups and conversions 
  • Media interaction such as instruction or sales videos
  • Clicks on important links and information 

  Using data visualisation software like a heatmap provides more in-depth data when combined with standard website metrics. 

  What is heatmap software used for ?

  Heatmap software tracks website user behaviour to improve website performance and increase conversions. 

  Heatmaps can show you a detailed analysis of : 

  • Where visitors are clicking (or not clicking) 
  • Where visitors are hovering with their mouse
  • How far users are scrolling or stopping 
  • Where the focus is above the fold 
  • What roadblocks or frictions customers are facing in the sales funnel

  Analysing activity on your website and across channels from your customers point of view is critical in developing a customer-centric business model. 

  This is because heatmaps not only show you what customers are doing but why they are doing it. 

  Heatmap software is ideal for businesses updating and redesigning websites. It also helps to answer important growth questions such as “how can we improve our user experience ?” and “why is our sales funnel not converting better ?”. 

  The benefits of using data visualisation like heatmaps for your website

  Heatmaps are critical for improving websites because they drastically improve customer experience. 

  Customer experience is one of the most important factors in modern business success. A Walker study found that customer experience is one of the biggest differentiators between brands, overtaking other factors such as price. 

  Where straightforward website metrics show customers left a page without action, data visualisation and session recordings show what happens in between them arriving and leaving. This gives web developers and marketers invaluable insights to improve website design and ultimately increase conversions. 

  How heatmap software improves your website and conversions

  There are a few key ways that heatmap software boosts website performance and conversions. All of them focus on both creating a seamless buyer journey and using data to improve results over time. 

  How heatmap software improves conversions ; 

  • By improving UX and usability– 70% of online businesses fail due to bad usability. Heatmaps identify user frustrations and optimise accordingly 
  • By improving content structure – Heatmaps take the guesswork out of design layout and content structure by showing real visitor experiences on your website 
  • By comparing A/B landing pages – Using heatmaps on alternate landing pages can show you why conversions are working or not working based on user activity on the page
  • By optimising across devices – See how your visitors are interacting with your content to learn how well optimised your website is for various devices and remove roadblocks 

  Heatmap analytics you need to improve website user experience

  Click heatmap

  Click heatmaps are useful for two key reasons.

  Firstly, it shows where website users are clicking. 

  Heatmaps that show clicks give you a visual representation of whether copy and CTA links are clear from the customers’ point of view. It can also show whether a customer is clicking on a design feature that doesn’t link anywhere. 

  Secondly, it shows where website users are not clicking. This is just as important when developing funnels and improving user experiences.

  For example, you may have a CTA button for a free trial or purchase. A click heatmap analysis would show if this isn’t clicked on mobile devices and informs developers that it needs to be more mobile-friendly.

  

  Mouse move or hover heatmap

  Like a click heatmap, a mouse hover heatmap shows how you can improve the overall user experience.

  For example, hover heatmaps identify where your visitors engage on a particular webpage. Ideally, of course, you want them to engage with CTAs. Analysing their mouse movements or where they are hovering for more information gives you an indication of any page elements that are distracting them or not working.

  

  Scroll heatmap

  A scroll heatmap uses colours to visualise how far down in a page your visitors scroll. For most web pages, the top will have the most impressions and will naturally get less views (i.e. get “colder” on the heatmap) further down the page. 

  This lets you find out if there is important content positioned too far down the page or if the page is designed to encourage users to keep scrolling.

  No matter how good your product or service is, it won’t convert if potential customers aren’t engaged and scrolling far enough to see it.

  

  Above the fold analysis 

  Above the fold is the content that a visitor sees without scrolling. 

  In a heatmap, the “Average Above the Fold” line will show you how much content your visitors see on average when they open your page. It also shows whether the page design is engaging, whether it encourages visitors to keep scrolling, and whether important information is too far down the page and therefore being missed. 

  Above the fold analysis is arguably the most important as this is the section that the highest number of traffic will see. Using this information ensures that the right content for conversion is seen by the highest number of visitors. 

  

  Session recording

  Session Recording lets you record a real visitor session, so you can see clicks, mouse movements, scrolls, window resizes, page changes, and form interactions all in one. 

  They allow you to understand the experience from the point of view of your visitor and then optimise your website to maximise your success.

  Heatmap software like Matomo takes this one step further and allows you to gather session recordings for individual segments. By analysing sessions based on segments, you can further personalise and optimise based on customer history and patterns.

  

  Final thoughts on heatmap software 

  Heatmap software improves your user experience by easily spotting critical issues that you can then address. 

  As well as that, heatmap analytics like clicks, mouse movement, scroll, above the fold analysis and session recordings increase your marketing ROI by making the most of your existing traffic. 

  It’s a win-win ! 

  Now that you know what heatmap software is, the benefits of using heatmaps on your website and how it can improve your user experience, check out more handy resources.

  10 Proven Ways Heatmaps Improve Website Conversions

  How to use Behavioural Analytics to Improve Website Performance

  Heatmap Overview Video

  Session Recording Overview Video