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• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now. 

• Android FFmpegPlayer Streaming Service onClick notification

  8 octobre 2013, par agony

  I have a MainActivity class that displays the list of streams available for my project and the StreamingActivity class where the streaming is done.

  If the user selected an item from the list it will start the StreamingActivity and start playing the stream.
  I'm having trouble to continue streaming music when the user pressed the notification and returning it to the StreamingActivity class if the user pressed or clicked the home menu or when the app goes to onDestroy().

  I'm using FFmpegPlayer for my project 'coz it requires to play mms :// live streams for local FM station.

  Here's my code :

  public class StreamingActivity extends BaseActivity  implements ActionBar.TabListener,
  
  PlayerControlListener, IMediaPlayerServiceClient {
  
  
  
  
  
  private StatefulMediaPlayer mMediaPlayer;
  
  private FFmpegService mService;
  
  private boolean mBound;
  
  
  
  public static final String TAG = "StationActivity";
  
  
  
  private static Bundle mSavedInstanceState;
  
  
  
  private static PlayerFragment mPlayerFragment;
  
  private static DJListFragment mDjListFragment;
  
  
  
  private SectionsPagerAdapter mSectionsPagerAdapter;
  
  private ViewPager mViewPager;
  
  
  
  private String stream = "";
  
  private String fhz = "";
  
  private String page = "0";
  
  
  
  private Dialog shareDialog;
  
      private ProgressDialog dialog;
  
  
  
  private boolean isStreaming;
  
  
  
  
  
  /*************************************************************************************************************/
  
  
  
  @Override
  
  public void onCreate(Bundle savedInstanceState){
  
      super.onCreate(savedInstanceState);
  
      setContentView(R.layout.activity_station);
  
  
  
      Bundle bundle = getIntent().getExtras();
  
      if(bundle !=null){
  
          fhz = bundle.getString("fhz");
  
          stream = bundle.getString("stream");
  
      } 
  
  
  
      Log.d(TAG, "page: " + page + " fhz: " + fhz + " stream: " + stream + " isStreaming: " + isStreaming);
  
  
  
      getSupportActionBar().setTitle("Radio \n" + fhz);
  
      getSupportActionBar().setDisplayHomeAsUpEnabled(true);
  
      getSupportActionBar().setNavigationMode(ActionBar.NAVIGATION_MODE_TABS);
  
  
  
      mPlayerFragment = (PlayerFragment) Fragment.instantiate(this, PlayerFragment.class.getName(), null);
  
      mDjListFragment = (DJListFragment) Fragment.instantiate(this, DJListFragment.class.getName(), null);
  
  
  
      mSectionsPagerAdapter = new SectionsPagerAdapter(getSupportFragmentManager());
  
  
  
      mViewPager = (ViewPager) findViewById(R.id.pager);
  
      mViewPager.setAdapter(mSectionsPagerAdapter);
  
      mViewPager.setCurrentItem(Integer.parseInt(page));
  
  
  
      mSavedInstanceState = savedInstanceState;
  
  
  
      Tab playingTab = getSupportActionBar().newTab();
  
      playingTab.setText(getString(R.string.playing_label));
  
      playingTab.setTabListener(this);
  
  
  
      Tab djTab = getSupportActionBar().newTab();
  
      djTab.setText(getString(R.string.dj_label));
  
      djTab.setTabListener(this);
  
  
  
      getSupportActionBar().addTab(playingTab);
  
      getSupportActionBar().addTab(djTab);
  
  
  
      // When swiping between different sections, select the corresponding
  
      // tab. We can also use ActionBar.Tab#select() to do this if we have
  
      // a reference to the Tab.
  
      mViewPager.setOnPageChangeListener(new ViewPager.SimpleOnPageChangeListener() {
  
          @Override
  
          public void onPageSelected(int position) {
  
              StationActivity.this.getSupportActionBar().setSelectedNavigationItem(position);
  
          }
  
      });
  
  
  
      if (mSavedInstanceState != null) {
  
          getSupportActionBar().setSelectedNavigationItem(mSavedInstanceState.getInt("tab", 0));
  
      }
  
  
  
      dialog = new ProgressDialog(this);
  
  
  
      bindToService();
  
  
  
      UriBean.getInstance().setStream(stream);
  
      Log.d(TAG ,"stream: " + UriBean.getInstance().getStream());
  
  
  
  }
  
  
  
  /********************************************************************************************************/
  
  
  
  public class SectionsPagerAdapter extends FragmentPagerAdapter {
  
      public SectionsPagerAdapter(FragmentManager fm) {
  
          super(fm);
  
      }
  
  
  
      @Override
  
      public Fragment getItem(int position) {
  
          if (position == 0) {
  
              return mPlayerFragment;
  
          } else {
  
              return mDjListFragment;
  
          }
  
      }
  
  
  
      @Override
  
      public int getCount() {
  
          return 2;
  
      }
  
  }
  
  
  
  @Override
  
  public void onTabSelected(Tab tab, FragmentTransaction ft) {
  
      // When the given tab is selected, switch to the corresponding page in the ViewPager.
  
      mViewPager.setCurrentItem(tab.getPosition());
  
  }
  
  
  
  @Override
  
  public void onTabUnselected(Tab tab, FragmentTransaction ft) { }
  
  
  
  @Override
  
  public void onTabReselected(Tab tab, FragmentTransaction ft) { }
  
  
  
  /********************************************************************************************************/
  
  
  
  public void showLoadingDialog() {
  
      dialog.setMessage("Buffering...");
  
      dialog.show();
  
  }
  
  
  
  public void dismissLoadingDialog() {
  
      dialog.dismiss();
  
  }
  
  
  
  /********************************************************************************************************/
  
  
  
  /**
  
   * Binds to the instance of MediaPlayerService. If no instance of MediaPlayerService exists, it first starts
  
   * a new instance of the service.
  
   */
  
  public void bindToService() {
  
      Intent intent = new Intent(this, FFmpegService.class);
  
  
  
      if (Util.isFFmpegServiceRunning(getApplicationContext())){
  
          // Bind to Service
  
          Log.i(TAG, "bindService");
  
          bindService(intent, mConnection, Context.BIND_AUTO_CREATE);
  
      } else {
  
          //start service and bind to it
  
          Log.i(TAG, "startService & bindService");
  
          startService(intent);
  
          bindService(intent, mConnection, Context.BIND_AUTO_CREATE);
  
      }
  
  }
  
  
  
  
  
  /** 
  
   * Defines callbacks for service binding, passed to bindService()
  
   */
  
  private ServiceConnection mConnection = new ServiceConnection() {
  
      @Override
  
      public void onServiceConnected(ComponentName className, IBinder serviceBinder) {
  
          Log.d(TAG,"service connected");
  
  
  
          //bound with Service. get Service instance
  
          MediaPlayerBinder binder = (FFmpegService.MediaPlayerBinder) serviceBinder;
  
          mService = binder.getService();
  
  
  
          //send this instance to the service, so it can make callbacks on this instance as a client
  
          mService.setClient(StationActivity.this);
  
          mBound = true;
  
  
  
          Log.d(TAG, "isPlaying === SERVICE: " + mService.isPlaying());
  
  
  
          //if
  
  
  
          startStreaming();
  
      }
  
  
  
      @Override
  
      public void onServiceDisconnected(ComponentName arg0) {
  
          mBound = false;
  
          mService = null;
  
      }
  
  };
  
  
  
  /********************************************************************************************************/
  
  
  
  @Override
  
  public void onPlayerPlayStop() {
  
      Log.d(TAG, "onPlayerPlayStop");
  
  
  
      Log.v(TAG, "isStreaming: " + isStreaming);
  
      Log.v(TAG, "mBound:  " + mBound);
  
  
  
      if (mBound) {
  
          Log.d(TAG, "bound.............");
  
          mMediaPlayer = mService.getMediaPlayer();
  
          //pressed pause ->pause
  
          if (!PlayerFragment.play.isChecked()) {
  
              if (mMediaPlayer.isStarted()) {
  
                  Log.d(TAG, "pause");
  
                  mService.pauseMediaPlayer();
  
              }
  
          } else { //pressed play
  
              // STOPPED, CREATED, EMPTY, -> initialize
  
              if (mMediaPlayer.isStopped() || mMediaPlayer.isCreated() || mMediaPlayer.isEmpty()) {
  
                  startStreaming();
  
              } else if (mMediaPlayer.isPrepared() || mMediaPlayer.isPaused()) { //prepared, paused -> resume play
  
                  Log.d(TAG, "start");
  
                  mService.startMediaPlayer();
  
              }
  
          }
  
  
  
          Log.d(TAG, "isPlaying === SERVICE: " + mService.isPlaying());
  
      } 
  
  }
  
  
  
  /********************************************************************************************************/
  
  
  
  @Override
  
  public void onDownload() {
  
      Toast.makeText(this, "Not yet available...", Toast.LENGTH_SHORT).show();
  
  }
  
  
  
  @Override
  
  public void onComment() {
  
      FragmentManager fm = getSupportFragmentManager();
  
      DialogFragment newFragment = MyAlertDialogFragment.newInstance();
  
      newFragment.show(fm, "comment_dialog");
  
  }
  
  
  
  @Override
  
  public void onShare() {
  
      showShareDialog();
  
  }
  
  
  
  /********************************************************************************************************/
  
  
  
  private void startStreaming() {
  
      Log.d(TAG, "@startLoading");
  
      boolean isNetworkFound = Util.checkConnectivity(getApplicationContext());
  
      if(isNetworkFound) {
  
          Log.d(TAG, "network found");
  
          mService.initializePlayer(stream);
  
          isStreaming = true;
  
      } else {
  
          Toast.makeText(getApplicationContext(), "No internet connection found...", Toast.LENGTH_SHORT).show();
  
      }
  
  
  
      Log.d(TAG, "isStreaming: " + isStreaming);
  
      Log.d(TAG, "isPlaying === SERVICE: " + mService.isPlaying());
  
  }
  
  
  
  @Override
  
  public void onInitializePlayerStart() {
  
      showLoadingDialog();
  
  }
  
  
  
  @Override
  
  public void onInitializePlayerSuccess() {
  
      dismissLoadingDialog();
  
      PlayerFragment.play.setChecked(true);
  
  
  
  
  
      Log.d(TAG, "isPlaying === SERVICE: " + mService.isPlaying());
  
  }
  
  
  
  @Override
  
  public void onError() {
  
      Toast.makeText(getApplicationContext(), "Not connected to the server...", Toast.LENGTH_SHORT).show();
  
  }
  
  
  
      @Override
  
  public void onDestroy() {
  
      Log.d(TAG, "onDestroy");
  
      super.onDestroy();
  
      uiHelper.onDestroy();
  
  
  
      Log.d(TAG, "isPlaying === SERVICE: " + mService.isPlaying());
  
      if (mBound) {
  
          mService.unRegister();
  
          unbindService(mConnection);
  
          mBound = false;
  
      }
  
  
  
      Log.d(TAG, "service: " + Util.isFFmpegServiceRunning(getApplicationContext()));
  
  }
  
  
  
  @Override
  
  public void onStop(){
  
      Log.d(TAG, "onStop");
  
      super.onStop();
  
  }
  
  
  
  /*******************************************************************************************************/
  
  
  
  @Override
  
  public boolean onOptionsItemSelected(MenuItem item) {
  
      int itemId = item.getItemId();
  
      switch (itemId){
  
      case android.R.id.home:
  
          onBackPressed();
  
          break;
  
      default:
  
          break;
  
      }    
  
      return true;
  
  }
  
  
  
  @Override
  
  public boolean onKeyDown(int keyCode, KeyEvent event) {
  
      Log.d(TAG, "@onKeyDown");
  
      if (keyCode == KeyEvent.KEYCODE_BACK && event.getRepeatCount() == 0){
  
          //this.moveTaskToBack(true);
  
          onBackPressed();
  
          return true;
  
      }
  
      return super.onKeyDown(keyCode, event);
  
  }
  
  }
  
  
  
  
  
  
  
  
  
  
  
  
  
  public class FFmpegService  extends Service implements IMediaPlayerThreadClient {
  
  
  
  private FFmpegPlayerThread mMediaPlayerThread       = new FFmpegPlayerThread(this);
  
  private final Binder mBinder                        = new MediaPlayerBinder();
  
  private IMediaPlayerServiceClient mClient;
  
  //private StreamStation mCurrentStation;
  
  
  
  private boolean mIsSupposedToBePlaying = false;
  
  
  
  private boolean isPausedInCall = false;
  
  private PhoneStateListener phoneStateListener;
  
  private TelephonyManager telephonyManager;
  
  
  
  @Override
  
  public void onCreate(){
  
      mMediaPlayerThread.start();
  
  }
  
  
  
  /**
  
   * A class for clients binding to this service. The client will be passed an object of this class
  
   * via its onServiceConnected(ComponentName, IBinder) callback.
  
   */
  
  public class MediaPlayerBinder extends Binder {
  
      /**
  
       * Returns the instance of this service for a client to make method calls on it.
  
       * @return the instance of this service.
  
       */
  
      public FFmpegService getService() {
  
          return FFmpegService.this;
  
      }
  
  }
  
  
  
  /**
  
   * Returns the contained StatefulMediaPlayer
  
   * @return
  
   */
  
  public StatefulMediaPlayer getMediaPlayer() {
  
      return mMediaPlayerThread.getMediaPlayer();
  
  }
  
  
  
  public boolean isPlaying() {
  
      return mIsSupposedToBePlaying;
  
  }
  
  
  
  @Override
  
  public IBinder onBind(Intent arg0) {
  
      return mBinder;
  
  }
  
  
  
  @Override
  
  public int onStartCommand(Intent intent, int flags, int startId) {
  
  
  
      telephonyManager = (TelephonyManager) getSystemService(Context.TELEPHONY_SERVICE);
  
      phoneStateListener = new PhoneStateListener() {
  
          @Override
  
          public void onCallStateChanged(int state, String incomingNumber) {
  
              // String stateString = "N/A";
  
              Log.v("FFmpegService", "Starting CallStateChange");
  
              switch (state) {
  
              case TelephonyManager.CALL_STATE_OFFHOOK:
  
              case TelephonyManager.CALL_STATE_RINGING:
  
                  if (mMediaPlayerThread != null) {
  
                      pauseMediaPlayer();
  
                      isPausedInCall = true;
  
                  }
  
                  break;
  
              case TelephonyManager.CALL_STATE_IDLE:
  
                  // Phone idle. Start playing.
  
                  if (mMediaPlayerThread != null) {
  
                      if (isPausedInCall) {
  
                          isPausedInCall = false;
  
                          startMediaPlayer();
  
                      }
  
                  }
  
                  break;
  
              }
  
          }
  
      };
  
  
  
      // Register the listener with the telephony manager
  
      telephonyManager.listen(phoneStateListener, PhoneStateListener.LISTEN_CALL_STATE);
  
  
  
      return START_STICKY;
  
  }
  
  
  
  /**
  
   * Sets the client using this service.
  
   * @param client The client of this service, which implements the IMediaPlayerServiceClient interface
  
   */
  
  public void setClient(IMediaPlayerServiceClient client) {
  
      this.mClient = client;
  
  }
  
  
  
  
  
  public void initializePlayer(final String station) {
  
      //mCurrentStation = station;
  
      mMediaPlayerThread.initializePlayer(station);
  
  }
  
  
  
  public void startMediaPlayer() {
  
  
  
      Intent notificationIntent = new Intent(getApplicationContext(), StreamingActivity.class);
  
      //notificationIntent.putExtra("page", "0");
  
      //notificationIntent.putExtra("isPlaying", isPlaying());
  
      notificationIntent.addFlags(Intent.FLAG_ACTIVITY_SINGLE_TOP | Intent.FLAG_ACTIVITY_CLEAR_TOP);
  
      PendingIntent contentIntent = PendingIntent.getActivity(getApplicationContext(), 0 , notificationIntent , PendingIntent.FLAG_UPDATE_CURRENT);
  
  
  
      NotificationCompat.Builder mBuilder = new NotificationCompat.Builder(this)
  
              .setSmallIcon(R.drawable.ic_launcher)
  
              .setContentTitle("You are listening to Radio...")
  
              .setContentText("test!!!")
  
              .setContentIntent(contentIntent);
  
  
  
      startForeground(1, mBuilder.build());
  
  
  
      NotificationManager notificationManager = (NotificationManager) getSystemService(Context.NOTIFICATION_SERVICE);
  
      notificationManager.notify(1, mBuilder.build());
  
  
  
      mIsSupposedToBePlaying = true;
  
      mMediaPlayerThread.startMediaPlayer();
  
  }
  
  
  
  public void dismissNotification(Context context) {
  
      String ns = Context.NOTIFICATION_SERVICE;
  
      NotificationManager mNotificationManager = (NotificationManager) getSystemService(ns);
  
      mNotificationManager.cancel(1);
  
  }
  
  
  
  /**
  
   * Pauses playback
  
   */
  
  public void pauseMediaPlayer() {
  
      Log.d("MediaPlayerService","pauseMediaPlayer() called");
  
      mMediaPlayerThread.pauseMediaPlayer();
  
      stopForeground(true);
  
      mIsSupposedToBePlaying = false;
  
      dismissNotification(this);
  
  }
  
  /**
  
   * Stops playback
  
   */
  
  public void stopMediaPlayer() { 
  
      stopForeground(true);
  
      mMediaPlayerThread.stopMediaPlayer();
  
  
  
      mIsSupposedToBePlaying = false;
  
      dismissNotification(this);
  
  }
  
  
  
  public void resetMediaPlayer() {
  
      mIsSupposedToBePlaying = false;
  
      stopForeground(true);
  
      mMediaPlayerThread.resetMediaPlayer();
  
      dismissNotification(this);
  
  }
  
  
  
  @Override
  
  public void onError() {
  
      mIsSupposedToBePlaying = false;
  
      mClient.onError();
  
      dismissNotification(this);
  
  }
  
  
  
  @Override
  
  public void onInitializePlayerStart() {
  
      mClient.onInitializePlayerStart();
  
  }
  
  
  
  @Override
  
  public void onInitializePlayerSuccess() {
  
      startMediaPlayer();
  
      mClient.onInitializePlayerSuccess();
  
      mIsSupposedToBePlaying = true;
  
  }
  
  
  
  public void unRegister() {
  
      this.mClient = null;
  
      mIsSupposedToBePlaying = false;
  
      dismissNotification(this);
  
  }
  
  
  
  }

  Hoping someone can help me here...

• Google Analytics 4 (GA4) vs Matomo

  7 avril 2022, par Erin

  Google announced that Universal Analytics’ days are numbered. Universal Analytics will be replaced by Google Analytics 4 (or GA4) on the 1st of July 2023. 

  If Google Analytics users want to compare year-on-year data, they have until July 2022 to get set up and start collecting data before the sun sets on Universal Analytics (or UA).

  But is upgrading to Google Analytics 4 the right move ? There’s a lot to consider, and many organisations are looking for an alternative to Google Analytics. So in this blog, we’ll compare GA4 to Matomo – the leading Google Analytics alternative. 

  In this blog, we’ll look at :

  What is Matomo ?

  Matomo is a powerful privacy-first web analytics platform that gives you 100% data ownership. First launched in 2007, Matomo is now the world’s leading open-source web analytics platform and is used by more than 1 million websites. 

  Matomo’s core values are based on ethical data collection and processing. Consistently more businesses and organisations from around the globe are adopting data-privacy-compliant web analytics solutions like Matomo. 

  Matomo offers both Cloud and On-Premise solutions (and a five-star rated WordPress plugin), making for an adaptable and flexible solution. 

  What is Google Analytics 4 ?

  Google Analytics 4 is the latest version of Google Analytics and represents a completely new approach to data-modelling than its predecessor, Universal Analytics. For an in-depth look at how GA4 and UA compare, check out this Google Analytics 4 vs Universal Analytics comparison. 

  Google Analytics 4 will soon be the only available version of analytics software from Google. So what’s the issue ? Surely, in 2022, Google makes it easy to migrate to their newest (and only) analytics platform ? Not quite.

  Google Analytics 4 vs Matomo

  Whilst the core purpose of GA4 and Matomo is similar (providing web analytics that help to optimise your website and grow your business), there are several key differences that organisations should consider before making the switch.

  Importing Historical Data from Universal Analytics

  Google Analytics 4

  Users assuming that historical data from Universal Analytics could be imported into Google Analytics 4 were faced with swift disappointment. Unfortunately, Google Analytics 4 does not have an option to import data from its predecessor, Universal Analytics. This means that businesses won’t be able to import and compare data from previous years.

  Matomo

  If you don’t want to start from scratch with your web analytics data, then Matomo is an ideal solution for data continuity. Matomo offers users the ability to import their historical Universal Analytics data. So you can keep all that valuable historical data you’ve collected over the years.

  

  User Interface

  Google Analytics 4

  GA4’s new user interface has been met with mixed reviews. Many claim that it’s overly complex and difficult to navigate. Some have even suggested that the tool has been designed specifically for enterprises with specialised analytics teams. 

  

  Matomo

  Matomo, on the other hand, is recognised for an easy to use interface, with a rating of 4.5 out of 5 stars for ease of use on Capterra. Matomo perfectly balances powerful features with a user-friendly interface so valuable insights are only a click away. There’s a reason why over 1 million websites are using Matomo. 

  

  Advanced Behavioural Analytics Features 

  Google Analytics 4

  While Google Analytics is undoubtedly robust in some areas (machine learning, for instance), what it really lacks is advanced behavioural analytics. Heatmaps, session recordings and other advanced tools can give you valuable insights into how users are engaging with your site. Well beyond pageviews and other metrics.

  Unfortunately, with this new generation of GA, Google still hasn’t introduced these features. So users have to manage subscriptions and tracking in third-party behavioural analytics tools like Hotjar or Lucky Orange, for example. This is inefficient, costly and time-consuming to manage. 

  

  Matomo 

  Meanwhile, Matomo is a one-stop shop for all of your web analytics needs. Not only do you get access to the metrics you’ve grown accustomed to with Universal Analytics, but you also get built-in behavioural analytics features like Heatmaps, Scroll Depth, Session Recordings and more. 

  Want to know if visitors are reaching your call to action at the bottom of the page ? Scroll Depth will answer that.

  Want to know why visitors aren’t clicking through to the next page ? Heatmaps will give you the insights you need.

  You get the picture – the full picture, that is. 

  

  Data Accuracy

  Google Analytics 4

  GA4 aims to make web and app analytics more privacy-centric by reducing the reliance on cookies to record certain events across platforms and devices. 

  However, when site and application visitors opt-out of cookie tracking, GA4 instead relies on machine learning to fill in the gaps. Data sampling could mean that your business is making business decisions based on inaccurate reports. 

  Matomo

  Data is the backbone of web analytics, so why make critical business decisions on sampled data ? With Matomo, you’re guaranteed 100% unsampled accurate data. So you can rest assured that any decisions you make are based on actual facts. 

  Compliance with Privacy Laws (GDPR, CCPA, etc.) 

  Google Analytics 4

  Google is making changes in an attempt to become compliant with privacy laws. However, even with GA4, users are still transferring data to the US. For this reason, both Austrian and French governments have ruled Google Analytics illegal under GDPR.

  The only possible workaround is “Privacy Shield 2.0”, but GDPR experts are still sceptical of this one. 

  Matomo

  If compliance with global privacy laws is a concern (and it should be), then Matomo is the clear winner here. 

  As an EU hosted web analytics tool, your data is stored in Europe, and no data is transferred to the US. On the other hand, if you choose to self-host, the data is stored in your country of choice.

  In addition, with cookieless tracking enabled, you can say goodbye to those pesky cookie consent screens. 

  Also, remember that under GDPR, and many other data privacy laws like CCPA and LGPD, end users have a legal right to access, amend and/or erase the personal data collected about them. 

  With Matomo you get 100% ownership of your web analytics data. This means that we don’t on-sell to third parties ; can’t claim ownership of the data ; and you can export your data at any time.

  

  Wrap up

  At the end of the day, the worst thing an organisation can do is nothing. Waiting until July 2023 to migrate to GA4 or another web analytics platform would be very disruptive and costly. Organisations need to consider their options now and start migrating in the next few months. 

  With all that said, moving to Google Analytics 4 could prove to be a costly and time-consuming operation. The global trend towards increased data privacy is a threat to platforms like Google Analytics which uses data for advertising and transfers data across borders.

  With Matomo, you get an easy to use all-in-one web analytics platform and keep your historical Universal Analytics data. Plus, you can future-proof your business by being compliant with global privacy laws and get access to advanced behavioural analytics features. 

  There’s a lot to weigh up here but fortunately, getting started with Matomo is easy. Try it free for 21-days (no credit card required) and see for yourself why over 1 million websites choose Matomo. 

  While this is the end of the road for Universal Analytics, it’s also an opportune time for organisations to find a better fit web analytics tool. 

  21 day free trial. No credit card required.

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