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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.

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• RaspberryPi HLS streaming with nginx and ffmpeg ; v4l2 error : ioctl(VIDIOC_STREAMON) : Protocol error

  22 janvier 2021, par Mirco Weber

  I'm trying to realize a baby monitoring with a Raspberry Pi (Model 4B, 4GB RAM) and an ordinary Webcam (with integrated Mic).
I followed this Tutorial : https://github.com/DeTeam/webcam-stream/blob/master/Tutorial.md

  


  Shortly described :

  


  

  1. I installed and configured an nginx server with rtmp module enabled.

  


  2. I installed ffmpeg with this configuration —enable-gpl —enable-nonfree —enable-mmal —enable-omx-rpi

  


  3. I tried to stream ;)

  


  


  The configuration of nginx seems to be working (sometimes streaming works, the server starts without any complication and when the server is up and running, the webpage is displayed).
The configuration of ffmpeg seems to be fine as well, since streaming sometimes works...

  


  I was trying a couple of different ffmpeg-commands ; all of them are sometimes working and sometimes resulting in an error.
The command looks like following :

  


  ffmpeg -re
-f v4l2
-i /dev/video0
-f alsa
-ac 1
-thread_queue_size 4096
-i hw:CARD=Camera,DEV=0
-profile:v high
-level:v 4.1
-vcodec h264_omx
-r 10
-b:v 512k
-s 640x360
-acodec aac
-strict
-2
-ac 2
-ab 32k
-ar 44100
-f flv
rtmp://localhost/show/stream;


  


  Note : I rearranged the code to make it easier to read. In the terminal, it is all in one line.
Note : There is no difference when using -f video4linux2 instead of -f v4l2

  


  The camera is recognized by the system :

  


  pi@raspberrypi:~ $ v4l2-ctl --list-devices
bcm2835-codec-decode (platform:bcm2835-codec):
    /dev/video10
    /dev/video11
    /dev/video12

bcm2835-isp (platform:bcm2835-isp):
    /dev/video13
    /dev/video14
    /dev/video15
    /dev/video16

HD Web Camera: HD Web Camera (usb-0000:01:00.0-1.2):
    /dev/video0
    /dev/video1


  


  When only using -i /dev/video0, audio transmission never worked.
The output of arecord -L was :

  


  pi@raspberrypi:~ $ arecord -L
default
    Playback/recording through the PulseAudio sound server
null
    Discard all samples (playback) or generate zero samples (capture)
jack
    JACK Audio Connection Kit
pulse
    PulseAudio Sound Server
usbstream:CARD=Headphones
    bcm2835 Headphones
    USB Stream Output
sysdefault:CARD=Camera
    HD Web Camera, USB Audio
    Default Audio Device
front:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    Front speakers
surround21:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    2.1 Surround output to Front and Subwoofer speakers
surround40:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    4.0 Surround output to Front and Rear speakers
surround41:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    4.1 Surround output to Front, Rear and Subwoofer speakers
surround50:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    5.0 Surround output to Front, Center and Rear speakers
surround51:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    5.1 Surround output to Front, Center, Rear and Subwoofer speakers
surround71:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    7.1 Surround output to Front, Center, Side, Rear and Woofer speakers
iec958:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    IEC958 (S/PDIF) Digital Audio Output
dmix:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    Direct sample mixing device
dsnoop:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    Direct sample snooping device
hw:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    Direct hardware device without any conversions
plughw:CARD=Camera,DEV=0
    HD Web Camera, USB Audio
    Hardware device with all software conversions
usbstream:CARD=Camera
    HD Web Camera
    USB Stream Output


  


  that's why i added -i hw:CARD=Camera,DEV=0.

  


  As mentioned above, it worked very well a couple of times with this configuration and commands.
But very often, i get the following error message when starting to stream :

  


  pi@raspberrypi:~ $ ffmpeg -re -f video4linux2 -i /dev/video0 -f alsa -ac 1 -thread_queue_size 4096 -i hw:CARD=Camera,DEV=0 -profile:v high -level:v 4.1 -vcodec h264_omx -r 10 -b:v 512k -s 640x360 -acodec aac -strict -2 -ac 2 -ab 32k -ar 44100 -f flv rtmp://localhost/show/stream
ffmpeg version N-100673-g553eb07737 Copyright (c) 2000-2021 the FFmpeg developers
  built with gcc 8 (Raspbian 8.3.0-6+rpi1)
  configuration: --enable-gpl --enable-nonfree --enable-mmal --enable-omx-rpi --extra-ldflags=-latomic
  libavutil      56. 63.101 / 56. 63.101
  libavcodec     58.117.101 / 58.117.101
  libavformat    58. 65.101 / 58. 65.101
  libavdevice    58. 11.103 / 58. 11.103
  libavfilter     7. 96.100 /  7. 96.100
  libswscale      5.  8.100 /  5.  8.100
  libswresample   3.  8.100 /  3.  8.100
  libpostproc    55.  8.100 / 55.  8.100
[video4linux2,v4l2 @ 0x2ea4600] ioctl(VIDIOC_STREAMON): Protocol error
/dev/video0: Protocol error


  


  And when I'm swithing to /dev/video1 (since this was also an output for v4l2-ctl --list-devices), I get the following error message :

  


  pi@raspberrypi:~ $ ffmpeg -re -f v4l2 -i /dev/video1 -f alsa -ac 1 -thread_queue_size 4096 -i hw:CARD=Camera,DEV=0 -profile:v high -level:v 4.1 -vcodec h264_omx -r 10 -b:v 512k -s 640x360 -acodec aac -strict -2 -ac 2 -ab 32k -ar 44100 -f flv rtmp://localhost/show/stream
ffmpeg version N-100673-g553eb07737 Copyright (c) 2000-2021 the FFmpeg developers
  built with gcc 8 (Raspbian 8.3.0-6+rpi1)
  configuration: --enable-gpl --enable-nonfree --enable-mmal --enable-omx-rpi --extra-ldflags=-latomic
  libavutil      56. 63.101 / 56. 63.101
  libavcodec     58.117.101 / 58.117.101
  libavformat    58. 65.101 / 58. 65.101
  libavdevice    58. 11.103 / 58. 11.103
  libavfilter     7. 96.100 /  7. 96.100
  libswscale      5.  8.100 /  5.  8.100
  libswresample   3.  8.100 /  3.  8.100
  libpostproc    55.  8.100 / 55.  8.100
[video4linux2,v4l2 @ 0x1aa4610] ioctl(VIDIOC_G_INPUT): Inappropriate ioctl for device
/dev/video1: Inappropriate ioctl for device


  


  When using the video0 input, the webcam's LED that recognizes an access is constantly on. When using video1not.

  


  After hours and days of googling and tears and whiskey, for the sake of my liver, my marriage and my physical and mental health, I'm very sincerly asking for your help...
What the f**k is happening and what can I do to make it work ???

  


  Thanks everybody :)

  


  UPDATE 1 :

  


  

  1. using the full path to ffmpeg does not change anything...

  


  2. /dev/video0 and /dev/video1 have access rights for everybody

  


  3. sudo ffmpeg ... does not change anything as well

  


  4. the problem seems to be at an "early stage". Stripping the command down to ffmpeg -i /dev/video0 results in the same problem

  


  


  UPDATE 2 :
  
It seems that everything is working when I first start another Application that needs access to the webcam and then ffmpeg...
Might be some driver issue, but when I'm looking for loaded modules with lsmod, there is absolutely no change before and after I started the application...
Any help still appreciated...

  


  UPDATE 3 :
  
I was checking the output of dmesg.
  
When I started the first application I received this message :
  
uvcvideo: Failed to query (GET_DEF) UVC control 12 on unit 2: -32 (exp. 4).

  And when I started ffmpeg, nothing happend but everything worked...

  


• How do I stream audio from a mic in a raspberry pi with FFmpeg ?

  23 mars 2024, par Ignacio

  I'm trying to follow this to stream audio from a mic in my raspberry pi.

  


  ignacio@pi-satellite-bigbedroom:~ $ ffmpeg -re -f pulse -ac 1 -i plughw:CARD=seeed2micvoicec,DEV=0 -f rtsp -rtsp_transport tcp rtsp://192.168.86.151:8554/live.stream
ffmpeg version 4.3.6-0+deb11u1+rpt5 Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 10 (Debian 10.2.1-6)
  configuration: --prefix=/usr --extra-version=0+deb11u1+rpt5 --toolchain=hardened --incdir=/usr/include/aarch64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --disable-mmal --enable-neon --enable-v4l2-request --enable-libudev --enable-epoxy --enable-sand --libdir=/usr/lib/aarch64-linux-gnu --arch=arm64 --enable-pocketsphinx --enable-libdc1394 --enable-libdrm --enable-vout-drm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
  libpostproc    55.  7.100 / 55.  7.100
plughw:CARD=seeed2micvoicec,DEV=0: No such process


  


  I believe this shows the cards I have :

  


  ignacio@pi-satellite-bigbedroom:~ $ pactl list sources
Source #0
    State: SUSPENDED
    Name: alsa_output.platform-bcm2835_audio.analog-stereo.monitor
    Description: Monitor of Built-in Audio Analog Stereo
    Driver: module-alsa-card.c
    Sample Specification: s16le 2ch 44100Hz
    Channel Map: front-left,front-right
    Owner Module: 4
    Mute: no
    Volume: front-left: 65536 / 100% / 0.00 dB,   front-right: 65536 / 100% / 0.00 dB
            balance 0.00
    Base Volume: 65536 / 100% / 0.00 dB
    Monitor of Sink: alsa_output.platform-bcm2835_audio.analog-stereo
    Latency: 0 usec, configured 0 usec
    Flags: DECIBEL_VOLUME LATENCY 
    Properties:
        device.description = "Monitor of Built-in Audio Analog Stereo"
        device.class = "monitor"
        alsa.card = "0"
        alsa.card_name = "bcm2835 Headphones"
        alsa.long_card_name = "bcm2835 Headphones"
        alsa.driver_name = "snd_bcm2835"
        device.bus_path = "platform-bcm2835_audio"
        sysfs.path = "/devices/platform/soc/3f00b840.mailbox/bcm2835_audio/sound/card0"
        device.form_factor = "internal"
        device.string = "0"
        module-udev-detect.discovered = "1"
        device.icon_name = "audio-card"
    Formats:
        pcm

Source #1
    State: IDLE
    Name: alsa_output.platform-soc_sound.stereo-fallback.monitor
    Description: Monitor of Built-in Audio Stereo
    Driver: module-alsa-card.c
    Sample Specification: s16le 2ch 44100Hz
    Channel Map: front-left,front-right
    Owner Module: 12
    Mute: no
    Volume: front-left: 65536 / 100% / 0.00 dB,   front-right: 65536 / 100% / 0.00 dB
            balance 0.00
    Base Volume: 65536 / 100% / 0.00 dB
    Monitor of Sink: alsa_output.platform-soc_sound.stereo-fallback
    Latency: 0 usec, configured 2000000 usec
    Flags: DECIBEL_VOLUME LATENCY 
    Properties:
        device.description = "Monitor of Built-in Audio Stereo"
        device.class = "monitor"
        alsa.card = "2"
        alsa.card_name = "seeed-2mic-voicecard"
        alsa.long_card_name = "seeed-2mic-voicecard"
        alsa.driver_name = "snd_soc_simple_card"
        device.bus_path = "platform-soc:sound"
        sysfs.path = "/devices/platform/soc/soc:sound/sound/card2"
        device.form_factor = "internal"
        device.string = "2"
        module-udev-detect.discovered = "1"
        device.icon_name = "audio-card"
    Formats:
        pcm

Source #2
    State: RUNNING
    Name: alsa_input.platform-soc_sound.stereo-fallback
    Description: Built-in Audio Stereo
    Driver: module-alsa-card.c
    Sample Specification: s16le 2ch 44100Hz
    Channel Map: front-left,front-right
    Owner Module: 12
    Mute: no
    Volume: front-left: 32845 /  50% / -18.00 dB,   front-right: 32845 /  50% / -18.00 dB
            balance 0.00
    Base Volume: 20724 /  32% / -30.00 dB
    Monitor of Sink: n/a
    Latency: 688 usec, configured 10000 usec
    Flags: HARDWARE HW_MUTE_CTRL HW_VOLUME_CTRL DECIBEL_VOLUME LATENCY 
    Properties:
        alsa.resolution_bits = "16"
        device.api = "alsa"
        device.class = "sound"
        alsa.class = "generic"
        alsa.subclass = "generic-mix"
        alsa.name = "bcm2835-i2s-wm8960-hifi wm8960-hifi-0"
        alsa.id = "bcm2835-i2s-wm8960-hifi wm8960-hifi-0"
        alsa.subdevice = "0"
        alsa.subdevice_name = "subdevice #0"
        alsa.device = "0"
        alsa.card = "2"
        alsa.card_name = "seeed-2mic-voicecard"
        alsa.long_card_name = "seeed-2mic-voicecard"
        alsa.driver_name = "snd_soc_simple_card"
        device.bus_path = "platform-soc:sound"
        sysfs.path = "/devices/platform/soc/soc:sound/sound/card2"
        device.form_factor = "internal"
        device.string = "hw:2"
        device.buffering.buffer_size = "352800"
        device.buffering.fragment_size = "176400"
        device.access_mode = "mmap+timer"
        device.profile.name = "stereo-fallback"
        device.profile.description = "Stereo"
        device.description = "Built-in Audio Stereo"
        module-udev-detect.discovered = "1"
        device.icon_name = "audio-card"
    Ports:
        analog-input: Analog Input (type: Analog, priority: 10000, availability unknown)
    Active Port: analog-input
    Formats:
        pcm


  


  I want to use the mic from the seeed-2mic-voicecard.

  


  Thanks for the help