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• 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.

• The screen recorder utility has failed to store the actual screen recording iOS

  31 mai 2023, par manoj

  I am getting the below issue when I run my code to record the screen on failure.

  


  An unknown server-side error occurred while processing the command. Original error : The screen recorder utility has failed to store the actual screen recording at '/var/folders/js/h2_7h9bj1fj8cn19tqcm8hnw0000gn/T/202341-30610-87cfav.bsg2u/appium_3f7703.mp4'

  


  Note : This issue is occurring during the tearDown.

  


  the code is showing stop recording is failing and this issue is occurring for only one test class

  


  public static File screenRecording(String prefix) throws IOException {
        File classpathRoot = new File(System.getProperty("user.dir"));
        String screenRec = ((CanRecordScreen) driver).stopRecordingScreen();
        byte[] screenRecord = Base64.decodeBase64(screenRec);
        String destinationPath = classpathRoot.getAbsolutePath() + "/screenRecordings/" + driver.getPlatformName()
                + " Video " + prefix + " " + new Date() + ".mp4";
        Path filePath = Paths.get(destinationPath);
        Files.write(filePath, screenRecord);
        File recordedFile = FileUtils.getFile(String.valueOf(filePath));
        return recordedFile;
    }


  


  The same thing when I run it successfully is passing in other test

  


  This is the thread that I was reffering

  


  Appium stack

  


  [ INFO ] 2023-05-26 13:38:51.636 [TestHelpers.PropertiesHelper.getRestartDeviceProperty(PropertiesHelper.java:181)] - Loading the 'RestartDevice' Property from the 'appiumTests.properties' file
[ INFO ] 2023-05-26 13:38:51.641 [TestFixtures.BaseTestFixture.globalSetup(BaseTestFixture.java:81)] - Building Appium Server...
[ INFO ] 2023-05-26 13:38:51.733 [TestFixtures.BaseTestFixture.globalSetup(BaseTestFixture.java:88)] - Appium server is built.
[ INFO ] 2023-05-26 13:38:51.733 [TestFixtures.BaseTestFixture.globalSetup(BaseTestFixture.java:89)] - Starting appium server...
[Appium] Welcome to Appium v1.22.2
[Appium] Non-default server args:
[Appium]   port: 3022
[Appium]   logFile: /Users/subh/IdeaProjects/tile_mobile_automation/logs/appium.log
[Appium]   loglevel: info
[Appium]   relaxedSecurityEnabled: true
[Appium] Appium REST http interface listener started on 0.0.0.0:3022
[HTTP] --> GET /wd/hub/status
[HTTP] {}
[HTTP] <-- GET /wd/hub/status 200 5 ms - 68
[HTTP] 
[ INFO ] 2023-05-26 13:38:53.069 [TestFixtures.BaseTestFixture.globalSetup(BaseTestFixture.java:91)] - Appium server started.

[ffmpeg] Output #0, mp4, to '/var/folders/js/h2_7h9bj1fj8cn19tqcm8hnw0000gn/T/2023426-9341-y22m7g.0jfd/appium_eca508.mp4':
[ffmpeg]   Metadata:
[ffmpeg]     encoder         : Lavf59.27.100
[ffmpeg]   Stream #0:0: Video: h264 (avc1 / 0x31637661), yuvj420p(pc, bt470bg/unknown/unknown, progressive), 720x1280 [SAR 520:633 DAR 195:422], q=2-31, 25 fps, 12800 tbn
[ffmpeg]     Metadata:
[ffmpeg]       encoder         : Lavc59.37.100 libx264
[ffmpeg] 
[ffmpeg]     Side data:
[ffmpeg]       cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[ffmpeg] 
[XCUITest] Starting screen capture on the device 'xxxxxxxx-xxxxxxxxxxxxxxxx' with command: 'ffmpeg -f mjpeg -i http://127.0.0.1:9100 -vf scale=720:1280 -vcodec h264 -y /var/folders/js/h2_7h9bj1fj8cn19tqcm8hnw0000gn/T/2023426-9341-y22m7g.0jfd/appium_eca508.mp4'. Will timeout in 1800000ms
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/start_recording_screen 200 621 ms - 12
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/device/terminate_app
[HTTP] {"bundleId":"com.apple.Preferences"}
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/device/terminate_app 200 6 ms - 15
[HTTP] 
[ INFO ] 2023-05-26 13:39:15.082 [TestFixtures.BaseTestFixture.setUp(BaseTestFixture.java:200)] - App Version is 2.115.0(7936)
<-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element 200 208 ms - 137
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/CC010000-0000-0000-1709-000000000000/click
[HTTP] {"id":"CC010000-0000-0000-1709-000000000000"}
[W3C (9a4d93a9)] Driver proxy active, passing request on via HTTP proxy
[WD Proxy] Replacing sessionId 643ECEF8-D0E3-47D0-AE3D-3FC884C69235 with 9a4d93a9-b723-4e2e-abad-db859e5efeed
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/CC010000-0000-0000-1709-000000000000/click 200 805 ms - 65
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context
[HTTP] {}
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context 200 1 ms - 22
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element
[HTTP] {"using":"id","value":"OK"}
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element 200 309 ms - 137
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/39020000-0000-0000-1709-000000000000/displayed
[HTTP] {}
[W3C (9a4d93a9)] Driver proxy active, passing request on via HTTP proxy
[WD Proxy] Replacing sessionId 643ECEF8-D0E3-47D0-AE3D-3FC884C69235 with 9a4d93a9-b723-4e2e-abad-db859e5efeed
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/39020000-0000-0000-1709-000000000000/displayed 200 134 ms - 65
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context
[HTTP] {}
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context 200 1 ms - 22
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element
[HTTP] {"using":"id","value":"OK"}
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element 200 253 ms - 137
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/39020000-0000-0000-1709-000000000000/text
[HTTP] {}
[W3C (9a4d93a9)] Driver proxy active, passing request on via HTTP proxy
[WD Proxy] Replacing sessionId 643ECEF8-D0E3-47D0-AE3D-3FC884C69235 with 9a4d93a9-b723-4e2e-abad-db859e5efeed
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/39020000-0000-0000-1709-000000000000/text 200 117 ms - 65
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context
[HTTP] {}
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context 200 1 ms - 22
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element
[HTTP] {"using":"id","value":"OK"}
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element 200 247 ms - 137
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/39020000-0000-0000-1709-000000000000/click
[HTTP] {"id":"39020000-0000-0000-1709-000000000000"}
[W3C (9a4d93a9)] Driver proxy active, passing request on via HTTP proxy
[WD Proxy] Replacing sessionId 643ECEF8-D0E3-47D0-AE3D-3FC884C69235 with 9a4d93a9-b723-4e2e-abad-db859e5efeed
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/39020000-0000-0000-1709-000000000000/click 200 778 ms - 65
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context
[HTTP] {}
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/context 200 1 ms - 22
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element
[HTTP] {"using":"id","value":"btn_add_tile"}
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element 200 285 ms - 137
[HTTP] 
[HTTP] --> GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/06010000-0000-0000-1709-000000000000/displayed
[HTTP] {}
[W3C (9a4d93a9)] Driver proxy active, passing request on via HTTP proxy
[WD Proxy] Replacing sessionId 643ECEF8-D0E3-47D0-AE3D-3FC884C69235 with 9a4d93a9-b723-4e2e-abad-db859e5efeed
[HTTP] <-- GET /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/element/06010000-0000-0000-1709-000000000000/displayed 200 133 ms - 65
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/app/reset
[HTTP] {}
[DevCon Factory] Releasing connections for xxxxxxxx-xxxxxxxxxxxxxxxx device on 9100 port number
[DevCon Factory] No cached connections have been found
[DevCon Factory] Releasing connections for xxxxxxxx-xxxxxxxxxxxxxxxx device on any port number
[DevCon Factory] Found cached connections to release: ["00008110-0004150236E8401E:8100"]
[DevCon Factory] Releasing the listener for 'xxxxxxxx-xxxxxxxxxxxxxxxx:8100'
[BaseDriver] The following capabilities are not standard capabilities and should have an extension prefix:
[BaseDriver]   appPushTimeout
[BaseDriver]   app
[BaseDriver]   automationName
[BaseDriver]   deviceName
[BaseDriver]   fullReset
[BaseDriver]   newCommandTimeout
[BaseDriver]   noReset
[BaseDriver]   platformVersion
[BaseDriver]   processArguments
[BaseDriver]   showXcodeLog
[BaseDriver]   udid
[BaseDriver]   xcodeOrgId
[BaseDriver]   xcodeSigningId
[BaseDriver] Session created with session id: 7950b9ef-eac2-4169-a035-2d183deacf68
[XCUITest] Determining device to run tests on: udid: 'xxxxxxxx-xxxxxxxxxxxxxxxx', real device: true
[XCUITest] Normalized platformVersion capability value '16.4.1' to '16.4'
[BaseDriver] Using local app '/Users/subh/IdeaProjects/tile_mobile_automation/apps/tile_appstore_adhoc.ipa'
[BaseDriver] Will reuse previously cached application at '/var/folders/js/h2_7h9bj1fj8cn19tqcm8hnw0000gn/T/2023426-9341-af6qqi.jfr2v/tile.app'
[WebDriverAgent] Using WDA path: '/usr/local/lib/node_modules/appium/node_modules/appium-webdriveragent'
[WebDriverAgent] Using WDA agent: '/usr/local/lib/node_modules/appium/node_modules/appium-webdriveragent/WebDriverAgent.xcodeproj'
[XCUITest] Setting up real device
[XCUITest] App installation succeeded after 14402ms
[DevCon Factory] Requesting connection for device xxxxxxxx-xxxxxxxxxxxxxxxx on local port 8100, device port 8100
[DevCon Factory] Successfully requested the connection for xxxxxxxx-xxxxxxxxxxxxxxxx:8100
[WebDriverAgent] Will reuse previously cached WDA instance at 'http://127.0.0.1:8100/' with 'com.facebook.WebDriverAgentRunner'. Set the wdaLocalPort capability to a value different from 8100 if this is an undesired behavior.
[WebDriverAgent] Using provided WebdriverAgent at 'http://127.0.0.1:8100/'
[WD Proxy] Determined the downstream protocol as 'W3C'
[XCUITest] Skipping setting of the initial display orientation. Set the "orientation" capability to either "LANDSCAPE" or "PORTRAIT", if this is an undesired behavior.
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/app/reset 200 18735 ms - 14
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/device/terminate_app
[HTTP] {"bundleId":"com.apple.Preferences"}
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/device/terminate_app 200 1046 ms - 14
INFO: Loading the 'SaveVideoOnSuccess' Property from the 'appiumTests.properties' file
[HTTP] 
[HTTP] --> POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/stop_recording_screen
[HTTP] {}
[DevCon Factory] Releasing connections for xxxxxxxx-xxxxxxxxxxxxxxxx device on 9100 port number
[DevCon Factory] No cached connections have been found
[XCUITest] The screen recorder utility has failed to store the actual screen recording at '/var/folders/js/h2_7h9bj1fj8cn19tqcm8hnw0000gn/T/2023426-9341-y22m7g.0jfd/appium_eca508.mp4'
[DevCon Factory] Releasing connections for xxxxxxxx-xxxxxxxxxxxxxxxx device on 9100 port number
[DevCon Factory] No cached connections have been found
[HTTP] <-- POST /wd/hub/session/9a4d93a9-b723-4e2e-abad-db859e5efeed/appium/stop_recording_screen 500 14 ms - 841
[HTTP] 

org.openqa.selenium.WebDriverException: An unknown server-side error occurred while processing the command. Original error: The screen recorder utility has failed to store the actual screen recording at '/var/folders/js/h2_7h9bj1fj8cn19tqcm8hnw0000gn/T/2023426-9341-y22m7g.0jfd/appium_eca508.mp4'
Build info: version: '3.141.59', revision: 'e82be7d358', time: '2018-11-14T08:17:03'
System info: host: 'localhost', ip: 'fe80:0:0:0:8c8:c2c0:abb2:3b1a%en0', os.name: 'Mac OS X', os.arch: 'x86_64', os.version: '11.2', java.version: '11.0.11'
Driver info: io.appium.java_client.ios.IOSDriver
Capabilities {app: /Users/subh/IdeaProjects/ti..., appPushTimeout: 50000, automationName: XCUITest, browserName: , databaseEnabled: false, deviceName: Tile DEV QA?s iPhone, fullReset: true, javascriptEnabled: true, locationContextEnabled: false, networkConnectionEnabled: false, newCommandTimeout: 30, noReset: false, platform: MAC, platformName: ios, platformVersion: 16.4.1, processArguments: {arguments: -com.apple.CoreData.Concurr...}, showXcodeLog: true, takesScreenshot: true, udid: xxxxxxxx-xxxxxxxxxxxxxxxx, webStorageEnabled: false, xcodeOrgId: XK64B7G5HB, xcodeSigningId: iPhone Developer}
Session ID: 9a4d93a9-b723-4e2e-abad-db859e5efeed

  at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
  at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
  at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
  at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)
  at org.openqa.selenium.remote.http.W3CHttpResponseCodec.createException(W3CHttpResponseCodec.java:187)
  at org.openqa.selenium.remote.http.W3CHttpResponseCodec.decode(W3CHttpResponseCodec.java:122)
  at org.openqa.selenium.remote.http.W3CHttpResponseCodec.decode(W3CHttpResponseCodec.java:49)
  at org.openqa.selenium.remote.HttpCommandExecutor.execute(HttpCommandExecutor.java:158)
  at io.appium.java_client.remote.AppiumCommandExecutor.execute(AppiumCommandExecutor.java:250)
  at org.openqa.selenium.remote.RemoteWebDriver.execute(RemoteWebDriver.java:552)
  at io.appium.java_client.DefaultGenericMobileDriver.execute(DefaultGenericMobileDriver.java:45)
  at io.appium.java_client.AppiumDriver.execute(AppiumDriver.java:1)
  at io.appium.java_client.ios.IOSDriver.execute(IOSDriver.java:1)
  at io.appium.java_client.screenrecording.CanRecordScreen.stopRecordingScreen(CanRecordScreen.java:72)
  at TestFixtures.BaseTestFixture.tearDown(BaseTestFixture.java:253)
  at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
  at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
  at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
  at java.base/java.lang.reflect.Method.invoke(Method.java:566)
  at org.testng.internal.invokers.MethodInvocationHelper.invokeMethod(MethodInvocationHelper.java:135)
  at org.testng.internal.invokers.MethodInvocationHelper.invokeMethodConsideringTimeout(MethodInvocationHelper.java:65)
  at org.testng.internal.invokers.ConfigInvoker.invokeConfigurationMethod(ConfigInvoker.java:381)
  at org.testng.internal.invokers.ConfigInvoker.invokeConfigurations(ConfigInvoker.java:319)
  at org.testng.internal.invokers.TestInvoker.runConfigMethods(TestInvoker.java:803)
  at org.testng.internal.invokers.TestInvoker.runAfterConfigurations(TestInvoker.java:772)
  at org.testng.internal.invokers.TestInvoker.invokeMethod(TestInvoker.java:748)
  at org.testng.internal.invokers.TestInvoker.invokeTestMethod(TestInvoker.java:220)
  at org.testng.internal.invokers.MethodRunner.runInSequence(MethodRunner.java:50)
  at org.testng.internal.invokers.TestInvoker$MethodInvocationAgent.invoke(TestInvoker.java:945)
  at org.testng.internal.invokers.TestInvoker.invokeTestMethods(TestInvoker.java:193)
  at org.testng.internal.invokers.TestMethodWorker.invokeTestMethods(TestMethodWorker.java:146)
  at org.testng.internal.invokers.TestMethodWorker.run(TestMethodWorker.java:128)
  at java.base/java.util.ArrayList.forEach(ArrayList.java:1541)
  at org.testng.TestRunner.privateRun(TestRunner.java:808)
  at org.testng.TestRunner.run(TestRunner.java:603)
  at org.testng.SuiteRunner.runTest(SuiteRunner.java:429)
  at org.testng.SuiteRunner.runSequentially(SuiteRunner.java:423)
  at org.testng.SuiteRunner.privateRun(SuiteRunner.java:383)
  at org.testng.SuiteRunner.run(SuiteRunner.java:326)
  at org.testng.SuiteRunnerWorker.runSuite(SuiteRunnerWorker.java:52)
  at org.testng.SuiteRunnerWorker.run(SuiteRunnerWorker.java:95)
  at org.testng.TestNG.runSuitesSequentially(TestNG.java:1249)
  at org.testng.TestNG.runSuitesLocally(TestNG.java:1169)
  at org.testng.TestNG.runSuites(TestNG.java:1092)
  at org.testng.TestNG.run(TestNG.java:1060)
  at com.intellij.rt.testng.IDEARemoteTestNG.run(IDEARemoteTestNG.java:66)
  at com.intellij.rt.testng.RemoteTestNGStarter.main(RemoteTestNGStarter.java:109)




  


• Meta Receives a Record GDPR Fine from The Irish Data Protection Commission

  29 mai 2023, par Erin — GDPR

  The Irish Data Protection Commission (the DPC) issued a €1.2 billion fine to Meta on May, 22nd 2023 for violating the General Data Protection Regulation (GDPR). 

  The regulator ruled that Meta was unlawfully transferring European users’ data to its US-based servers and taking no sufficient measures for ensuring users’ privacy. 

  Meta must now suspend data transfer within five months and delete EU/EEA users’ personal data that was illegally transferred across the border. Or they risk facing another round of repercussions. 

  Meta continued to transfer personal user data to the USA following an earlier ruling of The Court of Justice of the European Union (CJEU), which already address problematic EU-U.S. data flows. Meta continued those transfers on the basis of the updated Standard Contractual Clauses (“SCCs”), adopted by the European Commission in 2021. 

  The Irish regulator successfully proved that these arrangements had not sufficiently addressed the “fundamental rights and freedoms” of the European data subjects, outlined in the CJEU ruling. Meta was not doing enough to protect EU users’ data against possible surveillance and unconsented usage by US authorities or other authorised entities.

  Why European Regulators Are After The US Big Tech Firms ? 

  GDPR regulations have been a sore area of compliance for US-based big tech companies. 

  Effectively, they had to adopt a host of new measures for collecting user consent, ensuring compliant data storage and the right to request data removal for a substantial part of their user bases. 

  The wrinkle, however, is that companies like Google and Meta among others, don’t have separate data processing infrastructure for different markets. Instead, all the user data gets commingled on the companies’ servers, which are located in the US. 

  Data storage facilities’ location is an issue. In 2020, the CJEU made a historical ruling, called the invalidation of the Privacy Shield. Originally, international companies were allowed to transfer data between the EU and the US if they adhered to seven data protection principles. This arrangement was called the Privacy Shield. 

  However, the continuous investigation found that the Privacy Shield scheme was not GDPR compliant and therefore companies could no longer use it to justify cross-border data transfers.

  The invalidation of the Privacy Shield gave ground for further investigations of the big tech companies’ compliance statuses. 

  In March 2022, the Irish DPC issued the first €17 million fine to Meta for “insufficient technical and organisational measures to ensure information security of European users”. In September 2022, Meta was again hit with a €405 million fine for Instagram breaching GDPR principles. 

  2023 began with another series of rulings, with the DPC concluding that Meta had breaches of the GDPR relating to its Facebook service (€210 million fine) and breaches related to Instagram (€180 million fine). 

  Clearly, Meta already knew they weren’t doing enough for GDPR compliance and yet they refused to take privacy-focused action. 

  Is Google GDPR Compliant ?

  Google has a similar “track record” as Meta when it comes to ensuring full compliance with the GDPR. Although Google has said to provide users with more controls for managing their data privacy, the proposed solutions are just scratching the surface. 

  In the background, Google continues to leverage its ample reserves of user browsing, behavioural and device data in product development and advertising. 

  In 2022, the Irish Council for Civil Liberties (ICCL) found that Google used web users’ information in its real-time bidding ad system without their knowledge or consent. The French data regulator (CNIL), in turn, fined Google for €150 million because of poor cookie consent banners the same year. 

  Google Analytics GDPR compliance status is, however, the bigger concern.

  Neither Google Univeral Analytics (UA) nor Google Analytics 4 are GDPR compliant, following the Privacy Shield framework invalidation in 2020. 

  Fines from individual regulators in Sweden, France, Austria, Italy, Denmark, Finland and Norway ruled that Google Analytics is non-GDPR compliant and is therefore illegal to use. 

  The regulatory rulings not just affect Google, but also GA users. Because the product is in breach of European privacy laws, people using it are complacent. Privacy groups like noyb, for example, are exercising their right to sue individual websites, using Google Analytics.

  How to Stay GDPR Compliant With Website Analytics 

  To avoid any potential risk exposure, selectively investigate each website analytics provider’s data storage and management practices. 

  Inquire about the company’s data storage locations among the first things. For example, Matomo Cloud keeps all the data in the EU, while Matomo On-Premise edition gives you the option to store data in any country of your choice. 

  Secondly, ask about their process for consent tracking and subsequent data analysis. Our website analytics product is fully GDPR compliant as we have first-party cookies enabled by default, offer a convenient option of tracking out-outs, provide a data removal mechanism and practice safe data storage. In fact, Matomo was approved by the French Data Protection Authority (CNIL) as one of the few web analytics apps that can be used to collect data without tracking consent. 

  Using an in-built GDPR Manager, Matomo users can implement the right set of controls for their market and their industry. For example, you can implement extra data or IP anonymization ; disable visitor logs and profiles. 

  Thanks to our privacy-by-design architecture and native controls, users can make their Matomo analytics compliant even with the strictest privacy laws like HIPAA, CCPA, LGPD and PECR. 

  Learn more about GDPR-friendly website analytics.

  Final Thoughts

  Since the GDPR came into effect in 2018, over 1,400 fines have been given to various companies in breach of the regulations. Meta and Google have been initially lax in response to European regulatory demands. But as new fines follow and the consumer pressure mounts, Big Tech companies are forced to take more proactive measures : add opt-outs for personalised ads and introduce an alternative mechanism to third-party cookies. 

  Companies, using non-GDPR-compliant tools risk finding themselves in the crossfire of consumer angst and regulatory criticism. To operate an ethical, compliant business consider privacy-focused alternatives to Google products, especially in the area of website analytics.