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• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.

• How to Read DJI H264 FPV Feed as OpenCV Mat Object ?

  29 mai 2019, par Walter Morawa

  TDLR : All DJI developers would benefit from decoding raw H264 video stream byte arrays to a format compatible with OpenCV.

  I’ve spent a lot of time looking for a solution to reading DJI’s FPV feed as an OpenCV Mat object. I am probably overlooking something fundamental, since I am not too familiar with Image Encoding/Decoding.

  Future developers who come across it will likely run into a bunch of the same issues I had. It would be great if DJI developers could use opencv directly without needing a 3rd party library.

  I’m willing to use ffmpeg or JavaCV if necessary, but that’s quite the hurdle for most Android developers as we’re going to have to use cpp, ndk, terminal for testing, etc. That seems like overkill. Both options seem quite time consuming. This JavaCV H264 conversion seems unnecessarily complex. I found it from this relevant question.

  I believe the issue lies in the fact that we need to decode both the byte array of length 6 (info array) and the byte array with current frame info simultaneously.

  Basically, DJI’s FPV feed comes in a number of formats.

  1. Raw H264 (MPEG4) in VideoFeeder.VideoDataListener

      // The callback for receiving the raw H264 video data for camera live view
  
      mReceivedVideoDataListener = new VideoFeeder.VideoDataListener() {
  
          @Override
  
          public void onReceive(byte[] videoBuffer, int size) {
  
              //Log.d("BytesReceived", Integer.toString(videoStreamFrameNumber));
  
              if (videoStreamFrameNumber++%30 == 0){
  
                  //convert video buffer to opencv array
  
                  OpenCvAndModelAsync openCvAndModelAsync = new OpenCvAndModelAsync();
  
                  openCvAndModelAsync.execute(videoBuffer);
  
              }
  
              if (mCodecManager != null) {
  
                  mCodecManager.sendDataToDecoder(videoBuffer, size);
  
              }
  
          }
  
      };

  2. DJI also has it’s own Android decoder sample with FFMPEG to convert to YUV format.

      @Override
  
      public void onYuvDataReceived(final ByteBuffer yuvFrame, int dataSize, final int width, final int height) {
  
          //In this demo, we test the YUV data by saving it into JPG files.
  
          //DJILog.d(TAG, "onYuvDataReceived " + dataSize);
  
          if (count++ % 30 == 0 && yuvFrame != null) {
  
              final byte[] bytes = new byte[dataSize];
  
              yuvFrame.get(bytes);
  
              AsyncTask.execute(new Runnable() {
  
                  @Override
  
                  public void run() {
  
                      if (bytes.length >= width * height) {
  
                          Log.d("MatWidth", "Made it");
  
                          YuvImage yuvImage = saveYuvDataToJPEG(bytes, width, height);
  
                          Bitmap rgbYuvConvert = convertYuvImageToRgb(yuvImage, width, height);
  
  
  
                          Mat yuvMat = new Mat(height, width, CvType.CV_8UC1);
  
                          yuvMat.put(0, 0, bytes);
  
                          //OpenCv Stuff
  
                      }
  
                  }
  
              });
  
          }
  
      }

  Edit : For those who want to see DJI’s YUV to JPEG function, here it is from the sample application :

  private YuvImage saveYuvDataToJPEG(byte[] yuvFrame, int width, int height){
  
          byte[] y = new byte[width * height];
  
          byte[] u = new byte[width * height / 4];
  
          byte[] v = new byte[width * height / 4];
  
          byte[] nu = new byte[width * height / 4]; //
  
          byte[] nv = new byte[width * height / 4];
  
  
  
          System.arraycopy(yuvFrame, 0, y, 0, y.length);
  
          Log.d("MatY", y.toString());
  
          for (int i = 0; i < u.length; i++) {
  
              v[i] = yuvFrame[y.length + 2 * i];
  
              u[i] = yuvFrame[y.length + 2 * i + 1];
  
          }
  
          int uvWidth = width / 2;
  
          int uvHeight = height / 2;
  
          for (int j = 0; j < uvWidth / 2; j++) {
  
              for (int i = 0; i < uvHeight / 2; i++) {
  
                  byte uSample1 = u[i * uvWidth + j];
  
                  byte uSample2 = u[i * uvWidth + j + uvWidth / 2];
  
                  byte vSample1 = v[(i + uvHeight / 2) * uvWidth + j];
  
                  byte vSample2 = v[(i + uvHeight / 2) * uvWidth + j + uvWidth / 2];
  
                  nu[2 * (i * uvWidth + j)] = uSample1;
  
                  nu[2 * (i * uvWidth + j) + 1] = uSample1;
  
                  nu[2 * (i * uvWidth + j) + uvWidth] = uSample2;
  
                  nu[2 * (i * uvWidth + j) + 1 + uvWidth] = uSample2;
  
                  nv[2 * (i * uvWidth + j)] = vSample1;
  
                  nv[2 * (i * uvWidth + j) + 1] = vSample1;
  
                  nv[2 * (i * uvWidth + j) + uvWidth] = vSample2;
  
                  nv[2 * (i * uvWidth + j) + 1 + uvWidth] = vSample2;
  
              }
  
          }
  
          //nv21test
  
          byte[] bytes = new byte[yuvFrame.length];
  
          System.arraycopy(y, 0, bytes, 0, y.length);
  
          for (int i = 0; i < u.length; i++) {
  
              bytes[y.length + (i * 2)] = nv[i];
  
              bytes[y.length + (i * 2) + 1] = nu[i];
  
          }
  
          Log.d(TAG,
  
                "onYuvDataReceived: frame index: "
  
                    + DJIVideoStreamDecoder.getInstance().frameIndex
  
                    + ",array length: "
  
                    + bytes.length);
  
          YuvImage yuver = screenShot(bytes,Environment.getExternalStorageDirectory() + "/DJI_ScreenShot", width, height);
  
          return yuver;
  
      }
  
  
  
      /**
  
       * Save the buffered data into a JPG image file
  
       */
  
      private YuvImage screenShot(byte[] buf, String shotDir, int width, int height) {
  
          File dir = new File(shotDir);
  
          if (!dir.exists() || !dir.isDirectory()) {
  
              dir.mkdirs();
  
          }
  
          YuvImage yuvImage = new YuvImage(buf,
  
                  ImageFormat.NV21,
  
                  width,
  
                  height,
  
                  null);
  
  
  
          OutputStream outputFile = null;
  
  
  
          final String path = dir + "/ScreenShot_" + System.currentTimeMillis() + ".jpg";
  
  
  
          try {
  
              outputFile = new FileOutputStream(new File(path));
  
          } catch (FileNotFoundException e) {
  
              Log.e(TAG, "test screenShot: new bitmap output file error: " + e);
  
              //return;
  
          }
  
          if (outputFile != null) {
  
              yuvImage.compressToJpeg(new Rect(0,
  
                      0,
  
                      width,
  
                      height), 100, outputFile);
  
          }
  
          try {
  
              outputFile.close();
  
          } catch (IOException e) {
  
              Log.e(TAG, "test screenShot: compress yuv image error: " + e);
  
              e.printStackTrace();
  
          }
  
  
  
          runOnUiThread(new Runnable() {
  
              @Override
  
              public void run() {
  
                  displayPath(path);
  
              }
  
          });
  
          return yuvImage;
  
      }

  3. DJI also appears to have a "getRgbaData" function, but there is literally not a single example online or by DJI. Go ahead and Google "DJI getRgbaData"... There’s only the reference to the api documentation that explains the self explanatory parameters and return values but nothing else. I couldn’t figure out where to call this and there doesn’t appear to be a callback function as there is with YUV. You can’t call it from the h264b byte array directly, but perhaps you can get it from the yuv data.

  Option 1 is much more preferable to option 2, since YUV format has quality issues. Option 3 would also likely involve a decoder.

  Here’s a screenshot that DJI’s own YUV conversion produces.

  I’ve looked at a bunch of things about how to improve the YUV, remove green and yellow colors and whatnot, but at this point if DJI can’t do it right, I don’t want to invest resources there.

  Regarding Option 1, I know there’s FFMPEG and JavaCV that seem like good options if I have to go the video decoding route.

  Moreover, from what I understand, OpenCV can’t handle reading and writing video files without FFMPEG, but I’m not trying to read a video file, I am trying to read an H264/MPEG4 byte[] array. The following code seems to get positive results.

      /* Async OpenCV Code */
  
      private class OpenCvAndModelAsync extends AsyncTask {
  
          @Override
  
          protected double[] doInBackground(byte[]... params) {//Background Code Executing. Don't touch any UI components
  
              //get fpv feed and convert bytes to mat array
  
              Mat videoBufMat = new Mat(4, params[0].length, CvType.CV_8UC4);
  
              videoBufMat.put(0,0, params[0]);
  
              //if I add this in it says the bytes are empty.
  
              //Mat videoBufMat = Imgcodecs.imdecode(encodeVideoBuf, Imgcodecs.IMREAD_ANYCOLOR);
  
              //encodeVideoBuf.release();
  
              Log.d("MatRgba", videoBufMat.toString());
  
              for (int i = 0; i< videoBufMat.rows(); i++){
  
                  for (int j=0; j< videoBufMat.cols(); j++){
  
                      double[] rgb = videoBufMat.get(i, j);
  
                      Log.i("Matrix", "red: "+rgb[0]+" green: "+rgb[1]+" blue: "+rgb[2]+" alpha: "
  
                              + rgb[3] + " Length: " + rgb.length + " Rows: "
  
                              + videoBufMat.rows() + " Columns: " + videoBufMat.cols());
  
                  }
  
              }
  
              double[] center = openCVThingy(videoBufMat);
  
              return center;
  
          }
  
          protected void onPostExecute(double[] center) {
  
              //handle ui or another async task if necessary
  
          }
  
      }

  Rows = 4, Columns > 30k. I get lots of RGB values that seem valid, such as red = 113, green=75, blue=90, alpha=220 as a made up example ; however, I get a ton of 0,0,0,0 values. That should be somewhat okay, since Black is 0,0,0 (although I would have thought the alpha would be higher) and I have a black object in my image. I also don’t seem to get any white values 255, 255, 255, even though there is also plenty of white area. I’m not logging the entire byte so it could be there, but I have yet to see it.

  However, when I try to compute the contours from this image, I almost always get that the moments (center x, y) are exactly in the center of the image. This error has nothing to do with my color filter or contours algorithm, as I wrote a script in python and tested that I implemented it correctly in Android by reading a still image and getting the exact same number of contours, position, etc in both Python and Android.

  I noticed it has something to do with the videoBuffer byte size (bonus points if you can explain why every other length is 6)

  2019-05-23 21:14:29.601 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 2425
  
  2019-05-23 21:14:29.802 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 2659
  
  2019-05-23 21:14:30.004 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:30.263 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6015
  
  2019-05-23 21:14:30.507 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:30.766 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4682
  
  2019-05-23 21:14:31.005 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:31.234 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 2840
  
  2019-05-23 21:14:31.433 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4482
  
  2019-05-23 21:14:31.664 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:31.927 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4768
  
  2019-05-23 21:14:32.174 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:32.433 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4700
  
  2019-05-23 21:14:32.668 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:32.864 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4740
  
  2019-05-23 21:14:33.102 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 6
  
  2019-05-23 21:14:33.365 21431-22086/com.dji.simulatorDemo D/VideoBufferSize: 4640

  My questions :

  I. Is this the correct format to read an h264 byte as mat ?
  Assuming the format is RGBA, that means row = 4 and columns = byte[].length, and CvType.CV_8UC4. Do I have height and width correct ? Something tells me YUV height and width is off. I was getting some meaningful results, but the contours were exactly in the center, just like with the H264.

  II. Does OpenCV handle MP4 in android like this ? If not, do we need to use FFMPEG or JavaCV ?

  III. Does the int size have something to do with it ? Why is the int size occassionally 6, and other times 2400 to 6000 ? I’ve heard about the difference between this frames information and information about the next frame, but I’m simply not knowledgeable enough to know how to apply that here.

  I’m starting to think this is where the issue lies. Since I need to get the 6 byte array for info about next frame, perhaps my modulo 30 is incorrect. So should I pass the 29th or 31st frame as a format byte for each frame ? How is that done in opencv or are we doomed to use the complicated ffmpeg ? How would I go about joining the neighboring frames/ byte arrays ?

  IV. Can I fix this using Imcodecs ? I was hoping opencv would natively handle whether a frame was color from this frame or info about next frame. I added the below code, but I am getting an empty array :

  Mat videoBufMat = Imgcodecs.imdecode(new MatOfByte(params[0]), Imgcodecs.IMREAD_UNCHANGED);

  This also is empty :

  Mat encodeVideoBuf = new Mat(4, params[0].length, CvType.CV_8UC4);
  
  encodeVideoBuf.put(0,0, params[0]);
  
  Mat videoBufMat = Imgcodecs.imdecode(encodeVideoBuf, Imgcodecs.IMREAD_UNCHANGED);

  V. Should I try converting the bytes into Android jpeg and then import it ? Why is djis yuv decoder so complicated looking ? It makes me cautious from wanting to try ffmpeg or Javacv and just stick to Android decoder or opencv decoder.

  VI. At what stage should I resize the frames to speed up calculations ?

  Edit : DJI support got back to me and confirmed they don’t have any samples for doing what I’ve described. This is a time for we the community to make this available for everyone !

  Upon further research, I don’t think opencv will be able to handle this as opencv’s android sdk has no functionality for video files/url’s (apart from a homegrown MJPEG codec).

  So is there a way in Android to convert to mjpeg or similar in order to read ? In my application, I only need 1 or 2 frames per second, so perhaps I can save the image as jpeg.

  But for real time applications we will likely need to write our own decoder. Please help so that we can make this available to everyone ! This question seems promising :

• How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation

  13 mars 2023, par Erin — Analytics Tips

  If you struggle to connect the dots on your customer journeys, you are researching the correct solution. 

  Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.

  That said, each attribution model has inherent limitations, which make the selection process even harder.

  This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation. 

  Pros and Cons of Different Attribution Models 

  

  First Interaction 

  First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.

  Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU). 

  Pros 

  • Reflects the start of the customer journey
  • Shows channels that bring in the best-qualified leads 
  • Helps track brand awareness campaigns

  Cons 

  • Ignores the impact of later interactions at the middle and bottom of the funnel 
  • Doesn’t provide a full picture of users’ decision-making process 

  Last Interaction 

  Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels. 

  If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect. 

  Pros 

  • Reports bottom-of-the-funnel events
  • Requires minimal data and configurations 
  • Helps estimate cost-per-lead or cost-per-acquisition

  Cons 

  • No visibility into assisted conversions and prior visitor interactions 
  • Overemphasise the importance of the last channel (which can often be direct traffic) 

  Last Non-Direct Interaction 

  Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product. 

  Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion. 

  Pros 

  • Improved channel visibility, compared to Last-Touch 
  • Avoids over-valuing direct visits
  • Reports on lead-generation efforts

  Cons 

  • Doesn’t work for account-based marketing (ABM) 
  • Devalues the quality over quantity of leads 

  Linear Model

  Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.

  It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.

  Pros 

  • Focuses on all touch points associated with a conversion 
  • Reflects more steps in the customer journey 
  • Helps analyse longer sales cycles

  Cons 

  • Doesn’t accurately reflect the varying roles of each touchpoint 
  • Can dilute the credit if too many touchpoints are involved 

  Time Decay Model 

  Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).

  This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns. 

  Pros 

  • Helps track longer sales cycles and reports on each touchpoint involved 
  • Allows customising the half-life of decay to improve reporting 
  • Promotes conversion optimization at BoFu stages

  Cons 

  • Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
  • Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)

  Position-Based Model 

  Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches. 

  For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels. 

  Pros 

  • Helps establish the main channels for lead generation and conversion
  • Adds extra layers of visibility, compared to first- and last-touch attribution models 
  • Promotes budget allocation toward the most strategic touchpoints

  Cons 

  • Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
  • Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints

  How to Choose the Right Multi-Touch Attribution Model For Your Business 

  If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.

  To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability. 

  Marketing Objectives 

  Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities. 

  In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase. 

  When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales. 

  Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases. 

  Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….

  Sales Cycle Length 

  As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry. 

  Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months. 

  That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution. 

  Data Availability 

  Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data. 

  Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK. 

  Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting. 

  Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature. 

  When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts. 

  How to Implement Multi-Touch Attribution

  Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking). 

  Here’s a step-by-step walkthrough to help you get started. 

  Select a Multi-Touch Attribution Tool 

  The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.

  To make the right call prioritise five factors :

  • Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models. 
  • Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options. 
  • Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users. 
  • Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software. 
  • Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis. 

  Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations. 

  Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price). 

  Set Up Proper Data Collection 

  Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up : 

  • Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page. 
  • Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints. 
  • Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc. 

  Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.

  Configure Goals and Events 

  Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours. 

  For example : If your goal is lead generation, you can track :

  • Newsletter sign ups 
  • Product demo requests 
  • Gated content downloads 
  • Free trial account registration 
  • Contact form submission 
  • On-site call bookings 

  In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action. 

  To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc). 

  Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy. 

  Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner). 

  Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.

  Test and Validated the Selected Model 

  A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases. 

  For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that. 

  That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis. 

  Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group. 

  In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great. 

  The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results. 

  Conclusion

  A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI. 

  Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types. 

  As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.

  Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.