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• Announcing our latest open source project : DeviceDetector

  30 juillet 2014, par Stefan Giehl — Community, Development, Meta, DeviceDetector

  This blog post is an announcement for our latest open source project release : DeviceDetector ! The Universal Device Detection library will parse any User Agent and detect the browser, operating system, device used (desktop, tablet, mobile, tv, cars, console, etc.), brand and model.

  Read on to learn more about this exciting release.

  Why did we create DeviceDetector ?

  Our previous library UserAgentParser only had the possibility to detect operating systems and browsers. But as more and more traffic is coming from mobile devices like smartphones and tablets it is getting more and more important to know which devices are used by the websites visitors.

  To ensure that the device detection within Piwik will gain the required attention, so it will be as accurate as possible, we decided to move that part of Piwik into a separate project, that we will maintain separately. As an own project we hope the DeviceDetector will gain a better visibility as well as a better support by and for the community !

  DeviceDetector is hosted on GitHub at piwik/device-detector. It is also available as composer package through Packagist.

  How DeviceDetector works

  Every client requesting data from a webserver identifies itself by sending a so-called User-Agent within the request to the server. Those User Agents might contain several information such as :

  • client name and version (clients can be browsers or other software like feed readers, media players, apps,…)
  • operating system name and version
  • device identifier, which can be used to detect the brand and model.

  For Example :

  Mozilla/5.0 (Linux; Android 4.4.2; Nexus 5 Build/KOT49H) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.99 Mobile Safari/537.36

  This User Agent contains following information :

  Operating system is Android 4.4.2, client uses the browser Chrome Mobile 32.0.1700.99 and the device is a Google Nexus 5 smartphone.

  What DeviceDetector currently detects

  DeviceDetector is able to detect bots, like search engines, feed fetchers, site monitors and so on, five different client types, including around 100 browsers, 15 feed readers, some media players, personal information managers (like mail clients) and mobile apps using the AFNetworking framework, around 80 operating systems and nine different device types (smartphones, tablets, feature phones, consoles, tvs, car browsers, cameras, smart displays and desktop devices) from over 180 brands.

  Note : Piwik itself currently does not use the full feature set of DeviceDetector. Client detection is currently not implemented in Piwik (only detected browsers are reported, other clients are marked as Unknown). Client detection will be implemented into Piwik in the future, follow #5413 to stay updated.

  Performance of DeviceDetector

  Our detections are currently handled by an enormous number of regexes, that are defined in several .YML Files. As parsing these .YML files is a bit slow, DeviceDetector is able to cache the parsed .YML Files. By default DeviceDetector uses a static cache, which means that everything is cached in static variables. As that only improves speed for many detections within one process, there are also adapters to cache in files or memcache for speeding up detections across requests.

  How can users help contribute to DeviceDetector ?

  Submit your devices that are not detected yet

  If you own a device, that is currently not correctly detected by the DeviceDetector, please create a issue on GitHub
  In order to check if your device is detected correctly by the DeviceDetector go to your Piwik server, click on ‘Settings’ link, then click on ‘Device Detection’ under the Diagnostic menu. If the data does not match, please copy the displayed User Agent and use that and your device data to create a ticket.

  Submit a list of your User Agents

  In order to create new detections or improve the existing ones, it is necessary for us to have lists of User Agents. If you have a website used by mostly non desktop devices it would be useful if you send a list of the User Agents that visited your website. To do so you need access to your access logs. The following command will extract the User Agents :

  zcat ~/path/to/access/logs* | awk -F'"' '{print $6}' | sort | uniq -c | sort -rn | head -n20000 > /home/piwik/top-user-agents.txt

  If you want to help us with those data, please get in touch at devicedetector@piwik.org

  Submit improvements on GitHub

  As DeviceDetector is free/libre library, we invite you to help us improving the detections as well as the code. Please feel free to create tickets and pull requests on Github.

  What’s the next big thing for DeviceDetector ?

  Please check out the list of issues in device-detector issue tracker.

  We hope the community will answer our call for help. Together, we can build DeviceDetector as the most powerful device detection library !

  Happy Device Detection,

• Announcing our latest open source project : DeviceDetector

  30 juillet 2014, par Stefan Giehl — Community, Development, Meta, DeviceDetector

  This blog post is an announcement for our latest open source project release : DeviceDetector ! The Universal Device Detection library will parse any User Agent and detect the browser, operating system, device used (desktop, tablet, mobile, tv, cars, console, etc.), brand and model.

  Read on to learn more about this exciting release.

  Why did we create DeviceDetector ?

  Our previous library UserAgentParser only had the possibility to detect operating systems and browsers. But as more and more traffic is coming from mobile devices like smartphones and tablets it is getting more and more important to know which devices are used by the websites visitors.

  To ensure that the device detection within Piwik will gain the required attention, so it will be as accurate as possible, we decided to move that part of Piwik into a separate project, that we will maintain separately. As an own project we hope the DeviceDetector will gain a better visibility as well as a better support by and for the community !

  DeviceDetector is hosted on GitHub at piwik/device-detector. It is also available as composer package through Packagist.

  How DeviceDetector works

  Every client requesting data from a webserver identifies itself by sending a so-called User-Agent within the request to the server. Those User Agents might contain several information such as :

  • client name and version (clients can be browsers or other software like feed readers, media players, apps,…)
  • operating system name and version
  • device identifier, which can be used to detect the brand and model.

  For Example :

  Mozilla/5.0 (Linux; Android 4.4.2; Nexus 5 Build/KOT49H) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.99 Mobile Safari/537.36

  This User Agent contains following information :

  Operating system is Android 4.4.2, client uses the browser Chrome Mobile 32.0.1700.99 and the device is a Google Nexus 5 smartphone.

  What DeviceDetector currently detects

  DeviceDetector is able to detect bots, like search engines, feed fetchers, site monitors and so on, five different client types, including around 100 browsers, 15 feed readers, some media players, personal information managers (like mail clients) and mobile apps using the AFNetworking framework, around 80 operating systems and nine different device types (smartphones, tablets, feature phones, consoles, tvs, car browsers, cameras, smart displays and desktop devices) from over 180 brands.

  Note : Piwik itself currently does not use the full feature set of DeviceDetector. Client detection is currently not implemented in Piwik (only detected browsers are reported, other clients are marked as Unknown). Client detection will be implemented into Piwik in the future, follow #5413 to stay updated.

  Performance of DeviceDetector

  Our detections are currently handled by an enormous number of regexes, that are defined in several .YML Files. As parsing these .YML files is a bit slow, DeviceDetector is able to cache the parsed .YML Files. By default DeviceDetector uses a static cache, which means that everything is cached in static variables. As that only improves speed for many detections within one process, there are also adapters to cache in files or memcache for speeding up detections across requests.

  How can users help contribute to DeviceDetector ?

  Submit your devices that are not detected yet

  If you own a device, that is currently not correctly detected by the DeviceDetector, please create a issue on GitHub
  In order to check if your device is detected correctly by the DeviceDetector go to your Piwik server, click on ‘Settings’ link, then click on ‘Device Detection’ under the Diagnostic menu. If the data does not match, please copy the displayed User Agent and use that and your device data to create a ticket.

  Submit a list of your User Agents

  In order to create new detections or improve the existing ones, it is necessary for us to have lists of User Agents. If you have a website used by mostly non desktop devices it would be useful if you send a list of the User Agents that visited your website. To do so you need access to your access logs. The following command will extract the User Agents :

  zcat ~/path/to/access/logs* | awk -F'"' '{print $6}' | sort | uniq -c | sort -rn | head -n20000 > /home/piwik/top-user-agents.txt

  If you want to help us with those data, please get in touch at devicedetector@piwik.org

  Submit improvements on GitHub

  As DeviceDetector is free/libre library, we invite you to help us improving the detections as well as the code. Please feel free to create tickets and pull requests on Github.

  What’s the next big thing for DeviceDetector ?

  Please check out the list of issues in device-detector issue tracker.

  We hope the community will answer our call for help. Together, we can build DeviceDetector as the most powerful device detection library !

  Happy Device Detection,

• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.