[{"data":1,"prerenderedAt":1828},["ShallowReactive",2],{"blog-en-google-analytics-4-brief-introduction-and-vision":3,"related-en-google-analytics-4-brief-introduction-and-vision":628},{"id":4,"title":5,"author":6,"body":7,"category":605,"description":13,"extension":606,"image":607,"isToc":608,"langAlt":6,"meta":609,"metaDescription":6,"navigation":608,"path":617,"published":608,"publishedAt":618,"readingTimeMinutes":619,"readingTimeText":620,"relatedArticles":621,"seo":624,"stem":625,"teaser":626,"updatedAtCustom":6,"__hash__":627},"blog_en\u002Fen\u002Fblog\u002Fgoogle-analytics-4-brief-introduction-and-vision.md","Google Analytics 4 – Brief Introduction And Vision",null,{"type":8,"value":9,"toc":582},"minimark",[10,14,26,31,79,82,90,94,97,101,104,109,112,116,141,145,155,162,168,248,254,257,264,268,300,303,309,315,322,325,329,336,339,379,383,386,394,405,408,411,465,475,479,482,491,496,499,505,510,513,519,524,527,533,538,541,547,552,555,561,566,569,576],[11,12,13],"p",{},"This is not an easy task and it is certainly not achieved by just replacing the measurement script. The new event-driven concept, data streaming functionalities, and the possibility to integrate Google Analytics with the Google Cloud Platform will ensure a far better understanding of customer behavior and provide actionable insights. This flexibility, however, is a trade-off with complexity so LOOK BEFORE YOU LEAP, or maybe just read our thoughts on what Google Analytics 4 (GA4) is going to bring.",[11,15,16,17,21,22,25],{},"Google Analytics 4 brings a totally different concept of User-centric event-based analytics, many new functions, and exciting features of stream data processing. As of today, many of the existing Google Analytics (Universal Analytics) features are in GA4 not available, and it is uncertain whether all of them will be ported, or replaced by something else. Although it is very difficult to make a prediction with such ",[18,19,20],"strong",{},"uncertainty",", it does not mean you have to sit and wait. This is certainly a less risky strategy, but you will not learn anything. Many specialists (including us) are still hesitant to throw out Universal Analytics entirely and only use GA4 for web projects, on the other side we certainly see the benefit of GA4 for some projects, especially where Google Cloud specialists are available. So which path should one choose? This article is for those who would like to understand the benefits of early GA4 adoption and the limitations it still has. To ease the effort, we have ",[18,23,24],{},"compiled an important set of information we consider most relevant",".",[27,28,30],"h2",{"id":29},"quick-insight-for-those-who-have-landed-here","Quick insight for those who have landed here",[32,33,34,41,48,51,54,57,64,67,70,73,76],"ul",{},[35,36,37,40],"li",{},[18,38,39],{},"Google Analytics 4 (GA4)"," is a new version of Google Analytics.",[35,42,43,44,47],{},"The current version called ",[18,45,46],{},"Universal Analytics (UA)"," will still be available for some time",[35,49,50],{},"Google is not yet forcing anybody to migrate to the new GA4 version.",[35,52,53],{},"It is uncertain how historical data will be migrated; we believe that there will be an option to do it. Even Google uses the data and needs some continuity.",[35,55,56],{},"Turning on GA4 without a plan that assures it will meet all of the business needs would be very unwise.",[35,58,59,60,63],{},"Google Analytics 4 is finally leaving the legacy concept of sessions and pageview and switching to ",[18,61,62],{},"User-centric context-aware event analytics."," What this means will be further explained later in this article.",[35,65,66],{},"GA4 is still under development and is not a finished product. New releases and new features are released very often - and are quickly removed once Google realizes they were wrong. This is actually a good sign as it indicates that Google is investing a lot.",[35,68,69],{},"Some features that are part of UA will not be migrated over. It is unclear now which ones will be, but you can already get some sense and indications.",[35,71,72],{},"GA4 offers much wider flexibility to collect and process data, but better quality checks and practices will need to be followed.",[35,74,75],{},"The GA4 data model is incompatible with the UA data model. Turning on the new tracking pixel will not solve the migration problem.",[35,77,78],{},"As UA and GA4 are built with a different philosophy, it will not be possible to match the results. Each system will describe the same situation differently.",[11,80,81],{},"We would like to stress this again. No one is going to turn off UA next month. DO NOT WORRY, BUT GET PREPARED! The new tool is not fully fledged, and it will still require several months of development to make it match the functionality of its predecessor. On the other hand, no one knows when UA is going to be deprecated, so it is a good time to start learning the new approach.",[83,84,85,89],"note",{},[86,87,88],"em",{},"Google suggests implementing GA4 along with the existing Universal Analytics for learning and testing purposes to better understand the measurement differences and to explore implementation procedures.","\n This is however not to be taken as the replacement installation. So be prepared to throw out any data collected in this experimentation phase.\n",[27,91,93],{"id":92},"who-should-read-this-article","Who should read this article?",[11,95,96],{},"As was already mentioned earlier, this article is suitable for anyone who would like to build an opinion on the new GA4 and is familiar with the current version. We often compare both versions, to describe what changed, what we believe will not be migrated over, or what awaits users. This is not, however, a full tutorial on GA4. We are only offering a set of practical opinions we believe are worthy of considering before you make any decision, in hopes that it will help you prepare a better migration plan. It is aimed at people who are using or implementing Universal Analytics and have already heard about the new version (GA4).",[27,98,100],{"id":99},"brief-history","Brief history",[11,102,103],{},"The first version of Google Analytics under the Google hood was called Classic Analytics and it was released in 2008 (it was actually the 6th version of Urchin). It does not exist anymore and was replaced in 2013 by a newer version called Universal Analytics, which most of you are using. The backend data model did not evolve a lot and practically remained the same for both versions. The client-side tracking did change a lot though.",[105,106],"image-with-caption",{"source":107,"caption":108},"\u002Fupload\u002Fgoogle-analytics-timeline.webp","Picture 1 - Google Analytics timeline",[11,110,111],{},"As mobile applications started to grow in importance, there was a need to have a similar solution to track activities and build reports. Similar to Urchin, Google acquired another company that was developing a product called Firebase. In 2016, Google came out with a new analytics solution for integrated mobile applications. Anybody who had to work with user analytics was surely annoyed with the fact that website data were sitting in a different database than the mobile app data, and were not easily merged. This was a great disadvantage because both major competitors, Adobe Analytics and Piano Analytics, were able to unify them. A decision to align website data and mobile data into one platform in 2019 was the reason behind something weirdly named Google Analytics: Web + App or GAv2.",[105,113],{"source":114,"caption":115},"\u002Fupload\u002Fgoogle-analytics-evolution.webp","Picture 2 - Google Analytics evolution",[11,117,118,119,122,123,126,127,130,131,122,134,137,138],{},"The advantage of combining both data streams has a caveat. At least some collected events must have the same meaning to plot them together, otherwise, it would make no sense to do it. That is where the problem begins. A traditional ",[86,120,121],{},"pageview"," or ",[86,124,125],{},"session"," does not make sense for a mobile application, as analogically an ",[86,128,129],{},"activity"," is hardly understandable for a web application. The beauty of a unified platform lies in sharing the same context model - simply naming the identical events on both platforms the same way. This means that one tool will need to adopt the naming strategy of the other, and because Firebase was more flexible in matching its events to reality, the old GA model has to be thrown out. Unfortunately, Google did not decide to throw out the mobile measurement model too and the “same” things like ",[86,132,133],{},"page_view",[86,135,136],{},"screen_view"," are named differently. The requirement to align data from two diverse yet similar streams, while keeping the backward compatibility, resulted in the new Google Analytics 4 platform in 2020. The possibility to create custom events is an amazing feature as on one side you do not need to stick to something that does not make any sense to you. On the other hand, it might end up in a situation where each GA4 implementation is different. We are big proponents of a Command Query Responsibility Segregation (CQRS) architecture and believe this was the right move, especially in a situation when ",[18,139,140],{},"GA4 events could be consumed in real-time by the Google Cloud.",[27,142,144],{"id":143},"conceptual-differences-between-universal-analytics-and-ga4","Conceptual differences between Universal Analytics and GA4",[146,147,149],"h3",{"id":148},"data-model",[150,151,154],"icon-heading",{"icon":152,"tooltip":153},"up","Positive change","Data model",[11,156,157,158,161],{},"Google Analytics is, in the GA4 version, shifting from a strictly defined data model to a ",[18,159,160],{},"more flexible structure",". It also simplifies the way data are sent. Hits will just become events. So, for instance, a page view hit will be an event, and e-commerce hits are events. Besides the predefined set of events, you can create your own, but keep in mind that only 500 distinctly named events are allowed.",[11,163,164,165],{},"Each event can have up to 25 parameters, while some parameters are already set for predefined events, and for others it is up to you. ",[18,166,167],{},"Deciding what parameters to use and how to name them is considered the biggest challenge.",[169,170,171],"figure",{},[172,173,176,177,176,182,176,199,176],"table",{"className":174},[175],"two-columns"," ",[178,179,181],"caption",{"style":180},"caption-side:bottom","Table 1 - Comparing various hit types to events",[183,184,176,185],"thead",{},[186,187,188,194],"tr",{},[189,190,191],"th",{},[18,192,193],{},"Universal Analytics hit  types",[189,195,196],{},[18,197,198],{},"GA 4 event names",[200,201,202,215,227,238],"tbody",{},[186,203,204,210],{},[205,206,207],"td",{},[86,208,209],{},"hit:pageview",[205,211,212],{},[86,213,214],{},"event:page_view",[186,216,217,222],{},[205,218,219],{},[86,220,221],{},"hit:event",[205,223,224],{},[86,225,226],{},"event:{custom name}",[186,228,229,234],{},[205,230,231],{},[86,232,233],{},"hit:social",[205,235,236],{},[86,237,226],{},[186,239,240,245],{},[205,241,242],{},[86,243,244],{},"….",[205,246,247],{},"....",[146,249,251],{"id":250},"cross-platform-tracking",[150,252,253],{"icon":152,"tooltip":153},"Cross-platform tracking",[11,255,256],{},"GA4 was built on the principle of FireBase measurement, and those of you who have implemented mobile application measurement via FireBase have already encountered the GA4 property before. Google has not allowed the creation of mobile application properties for over a year and all of the new measurements must be in GA4.",[11,258,259,260,263],{},"You still have a choice for new website measurement and we hope that while GA4 lacks UA functionalities this will remain so. Just be careful when creating a new property. ",[18,261,262],{},"By default, GA4 is pre-checked",", and you have to change it manually.",[105,265],{"source":266,"caption":267},"\u002Fupload\u002Fgoogle-analytics-web-and-app.webp","Picture 3 - Google Analytics Web and App tracking options",[169,269,270],{},[172,271,273,276,176,290,176],{"className":272},[175],[178,274,275],{"style":180},"Table 2 - Comparison Web\u002FApp tracking",[183,277,176,278],{},[186,279,280,285],{},[189,281,282],{},[18,283,284],{},"Universal  Analytics",[189,286,287],{},[18,288,289],{},"GA4",[200,291,292],{},[186,293,294,297],{},[205,295,296],{},"Web and mobile apps are measured  separately into disconnected properties.",[205,298,299],{},"Web and mobile are measured into one  property.",[11,301,302],{},"Now having both Mobile App and Web data in one property is beneficial for User Cross-Device analysis. If your users are able to log in, you can track them with their User ID. This must be configured in the Reporting Identity.",[146,304,306],{"id":305},"user-analytics-instead-of-session-analytics",[150,307,308],{"icon":152,"tooltip":153},"User analytics instead of session analytics",[11,310,311,312],{},"The lack of User reporting functionalities in Universal Analytics forced us to do it with other tools. For instance, using BigQuery with visualizations in Data Studio or Power Bi. As GA4 is changing the approach, ",[18,313,314],{},"more user analytics insights might be accomplished directly in its UI.",[11,316,317,318,321],{},"Why is it so important? Consider for instance the following scenario, where the fallacy of the session approach is quite visible. All of you know a Conversion ratio, which is calculated as ",[86,319,320],{},"#conversions\u002F#sessions"," in UA. The higher it is the better. You now decide to improve it by increasing the brand and product awareness. What is happening? You attract more sessions and as more prospects come to find out about what you have to offer, your conversion ratio is declining. Oh, no! Well, building awareness has an impact on conversion, but it takes more time, and users need to come more often. So a better metric to evaluate such activity would be calculated as #conversions\u002F#users.",[11,323,324],{},"Another similar metric fallacy is the Bounce rate, which, if not linked to the content that the user is interacting with, has little practical benefit.",[105,326],{"source":327,"caption":328},"\u002Fupload\u002Fcustomer-centric-approach.webp","Picture 4 - Customer Centric Approach",[11,330,331,332,335],{},"Therefore, your analytical approach should change to some extent. Forget KPIs built on sessions. Your main KPIs should be based on User metrics. Moreover, you should pay more attention to the ",[18,333,334],{},"details of the customer journey"," and analyze the sequence of individual events within the user's life cycle.",[11,337,338],{},"Above, we mentioned changes in the conceptual approach that you will have to adapt to, but your business goals and processes must be adjusted as well. However, this approach is not directly related to GA4 only. A user-centric and event-based approach has been around for years, and GA4 is just adopting it now. You can choose to be user-centric even with Universal Analytics, but GA4 will lead you to it.",[169,340,341],{},[172,342,176,344,176,347,176,359,176],{"className":343},[175],[178,345,346],{"style":180},"Table 3 - Comparison of approaches",[183,348,176,349],{},[186,350,351,355],{},[189,352,353],{},[18,354,284],{},[189,356,357],{},[18,358,289],{},[200,360,361],{},[186,362,363,366],{},[205,364,365],{},"In Universal Analytics, everything revolves around sessions or page views. These are the primary entities in most of the default reports. In Universal Analytics it is possible to use a user scoped dimension user ID, but even with creating a duplicate and special user view, the benefit is cumbersome. To calculate any user metric from session or hit level metrics is not possible in UA itself.",[205,367,368,369,372,373,176,376,25],{},"User metrics become the primary metrics. The menu in the interface of the new GA4 itself is ",[18,370,371],{},"strongly user-oriented",". The main reports are located under the “Life cycle” tab, and the individual items in this menu refer to ",[18,374,375],{},"the individual steps in the user's",[18,377,378],{},"life cycle",[105,380],{"source":381,"caption":382},"\u002Fupload\u002Fga4-life-cycle.webp","Picture 5 - Life cycle menu in Google Analytics 4",[11,384,385],{},"In GA4, you will not find a custom dimension on a session-level scope at this moment. Although the session concept is still available, and even used in some default reports, it is no longer dominant.",[146,387,389],{"id":388},"reporting-and-data-explorations",[150,390,393],{"icon":391,"tooltip":392},"indif","Neutral change","Reporting and data explorations",[11,395,396,397,400,401,404],{},"GA4 is not yet suitable for someone who is used to doing exploratory analysis in the reporting interface. On the other hand, its ",[18,398,399],{},"reporting functionality is more simple for common users"," who got the system configured by someone else. If you use the Web Analytics tools to continuously check a single KPI, you might be ok. If you need a bit more information about what is happening, meaning you go into Analytics to understand specific changes or want to analyze some trends, it will take you much longer to do it with GA4. You will need to build the analysis in the Analysis hub first, and then you can play with the data. However, if you are a veteran data analyst, you might enjoy the Analysis Hub functionality. This tool ",[18,402,403],{},"provides a querying and reporting interface"," that allows analyses that are impossible to do in Universal Analytics without first exporting data to an external database.",[11,406,407],{},"Feedback from other early adopters is that you either love or hate the new GA4 reporting interface. If you plan to use Analysis Hub or BigQuery and nothing else, you will be happy. If you are used to the default reports in Universal Analytics, you will miss a lot of features.",[11,409,410],{},"We are uncertain whether GA4 will ever have as rich of reporting possibilities as Universal Analytics. Without knowledge of Data Studio or BigQuery, or the option to make default reports from Analysis Hub, it is now not as useful.",[169,412,413],{},[172,414,176,416,176,419,176,431,176],{"className":415},[175],[178,417,418],{"style":180},"Table 4 - Comparison of reporting and analysis capabilities",[183,420,176,421],{},[186,422,423,427],{},[189,424,425],{},[18,426,284],{},[189,428,429],{},[18,430,289],{},[200,432,433,441,449,457],{},[186,434,435,438],{},[205,436,437],{},"Default reports are usable to start with. Default reports allow for various sets of data visualization.",[205,439,440],{},"Default reports are not as usable without configuration. It has a static set of charts with limited interactivity.",[186,442,443,446],{},[205,444,445],{},"The custom reporting functionality allows for building quick reports and sharing them.",[205,447,448],{},"The custom reporting functionality is not available.",[186,450,451,454],{},[205,452,453],{},"It has basic exploratory analysis in default or custom reports.",[205,455,456],{},"Basic exploratory analysis is not possible, and you have to use the complex Analysis Hub.",[186,458,459,462],{},[205,460,461],{},"Deeper Exploratory analysis, however, is not possible.",[205,463,464],{},"Deeper Exploratory analysis is possible in Analysis Hub or in BigQuery.",[466,467,471,472],"external-link",{"title":468,"link":469,"button":470},"Functionalities comparison","https:\u002F\u002Fcrossmasters.com\u002Fen\u002Fblog\u002Fgoogle-analytics-4-ultimate-testing-and-comparison-report\u002F","Read more","\nIf you would like to know more details and the difference between various the features and modules of both versions of Google Analytics, read the article \n",[86,473,474],{},"Google Analytics 4 - The Ultimate Testing and Comparison Report.",[27,476,478],{"id":477},"summary","Summary",[11,480,481],{},"Although there are some missing functionalities, GA4 is ready for light usage and if you still do not know how to start adopting it, below are some quick recommendation steps.",[146,483,485,176,488],{"id":484},"step-1-ga4-as-a-playground",[18,486,487],{},"Step 1)",[18,489,490],{},"GA4 as a playground",[11,492,493],{},[86,494,495],{},"The duration between 2 and 5 months",[11,497,498],{},"Start collecting data into the GA4 property as soon as possible. This will give you the touch and feel of the difference between UA and GA4 and you will know which features you are still missing for production adoption. Use the default measurement (page views and automatic enhanced measurement). Do not spend time configuring events. This will come later and will require much more effort and preparation. Focus on data quality and verification where the default measurement differs from your measurement configuration. Look for sessions and traffic differences especially.",[146,500,502],{"id":501},"step-2-ga4-as-a-playground-for-advanced-features",[18,503,504],{},"Step 2) GA4 as a playground for advanced features",[11,506,507],{},[86,508,509],{},"The duration between 3 and 6 months",[11,511,512],{},"Start thinking about conversion events. These might be purchases, time spent on pages, etc. It should be anything essential for the KPI evaluation you currently use in UA. This step will require some implementation effort. At this stage, you must consider the user-centric analytical philosophy. Some legacy KPIs will not be reproducible in the new environment. You will need to adapt and learn this new approach. Prepare others for philosophical differences between UA and GA4.",[146,514,516],{"id":515},"step-3-ga4-production-ready-for-basic-functionalities",[18,517,518],{},"Step 3) GA4 production-ready for basic functionalities",[11,520,521],{},[86,522,523],{},"The duration between 1 week and 1 month",[11,525,526],{},"GA4 is now at the stage where you might start using it for production purposes. You will most likely still need to use UA, but GA4 should be ready to take over some of the requirements. Before you make GA4 production-ready, decide if the old data collected have some value or if they were polluted, and not worthy of keeping. Most of the analysts or companies we interviewed start with a fresh new property and delete the low-quality garbage later. After this stage GA4 is now in production mode for your usage.",[146,528,530],{"id":529},"step-4-ga4-becoming-the-primary-web-analytics-tool",[18,531,532],{},"Step 4) GA4 becoming the primary Web Analytics tool",[11,534,535],{},[86,536,537],{},"The duration between 4 months and 1 year",[11,539,540],{},"Start by porting over other remaining measurements that could be moved into GA4. As of today, depending on the usage maturity, GA4 covers between 30% and 80% of UA functionalities. It is critical to understand what others are using in UA and what they need before sunsetting UA. While getting more data and configuration on GA4, pay attention to the data quality. This is not the playing phase (Step 1 and Step 2 are) you might quickly ruin both your and the GA4’s reputation in your company, or at the minimum, unnecessarily prolong this stage. Train others on the philosophical differences and advocate for the new approach. This is the hardest part that requires some time.",[146,542,544],{"id":543},"step-5-waiting-for-google-to-enhance-ga4",[18,545,546],{},"Step 5) Waiting for Google to enhance GA4",[11,548,549],{},[86,550,551],{},"The duration is unknown, and we expect Google to get the tool there by mid-2023",[11,553,554],{},"At this stage, GA4 should provide functionalities that will match its predecessor. If you do not use UA to its maximum potential, you will likely reach saturation sooner. At this moment, from a technical perspective, the new version is equivalent to its predecessor. You should start forcing the remaining UA users to make the switch. Maybe removing credentials in the old UA would be a gentle push to start the move.",[146,556,558],{"id":557},"step-6-sunset-ua",[18,559,560],{},"Step 6) Sunset UA",[11,562,563],{},[86,564,565],{},"The duration from 0 days up to two years",[11,567,568],{},"We recommend having at least 2 years of good quality data covering all your needs in GA4 before it is safe to sunset UA.",[11,570,571,572,575],{},"If you start with ",[18,573,574],{},"Step 1",", the minimal time required to fully switch to GA4 is at least one year with fast adoption. The more realistic scenario for bigger companies and more users is about two years. We do not know any heavy UA users that have reached Step 4 (March 2021). Adding to that, it looks like the realistic estimate that companies could be ready to sunset UA with the required amount of data you have to collect is within a 2.5 to 3-year time frame, so potentially after Christmas 2023. We expect that Google will provide information about when they plan to sunset UA, but we do not know that at the moment.",[577,578,581],"action",{"link":579,"button":580},"\u002Fen\u002Fget-in-touch\u002F","Contact Us","\n We have successfully implemented digital measurement and Google Analytics for many of our clients. Contact us and we can ensure the smooth transition from Universal  Analytics to Google Analytics 4.\n",{"title":583,"searchDepth":584,"depth":584,"links":585},"",2,[586,587,588,589,596],{"id":29,"depth":584,"text":30},{"id":92,"depth":584,"text":93},{"id":99,"depth":584,"text":100},{"id":143,"depth":584,"text":144,"children":590},[591,593,594,595],{"id":148,"depth":592,"text":154},3,{"id":250,"depth":592,"text":253},{"id":305,"depth":592,"text":308},{"id":388,"depth":592,"text":393},{"id":477,"depth":584,"text":478,"children":597},[598,600,601,602,603,604],{"id":484,"depth":592,"text":599},"Step 1) GA4 as a playground",{"id":501,"depth":592,"text":504},{"id":515,"depth":592,"text":518},{"id":529,"depth":592,"text":532},{"id":543,"depth":592,"text":546},{"id":557,"depth":592,"text":560},"Guides","md","\u002Fupload\u002Fga-4-intro-and-vision.webp",true,{"externalLinks":610},[611,614],{"url":612,"name":613},"https:\u002F\u002Fcrossmasters.com\u002Fen\u002Fblog\u002Fnew-data-api-for-google-analytics-4","New Data API for Google Analytics 4",{"url":615,"name":616},"https:\u002F\u002Fcrossmasters.com\u002Fen\u002Fblog\u002Fnotes-on-new-features-of-google-analytics-reporting-api-v4\u002F","Notes on new features of Google Analytics Reporting API V4","\u002Fen\u002Fblog\u002Fgoogle-analytics-4-brief-introduction-and-vision","2021-06-29T08:05:00+00:00",16.745,"17 min read",[622,623],"content\u002Fen\u002Fblog\u002Fzoom-in-on-measurement-hub.md","content\u002Fen\u002Fblog\u002Fdata-layer-validation-what-why-and-how.md",{"title":5,"description":13},"en\u002Fblog\u002Fgoogle-analytics-4-brief-introduction-and-vision","There is a lot of buzz about the new Google Analytics 4. You might even get the perception that not migrating now will cause you a lot of trouble in the future. DO NOT PANIC! GA4 is indeed going to change the future of “free” web analytics, however, this change will only come after you throw out the old measurement paradigm.","5w2c3ZhS1lV6pcjEVpL6CQ1Yhqm6MKcKohdCDkOONwc",[629,830],{"id":630,"title":631,"author":6,"body":632,"category":816,"description":636,"extension":606,"image":817,"isToc":818,"langAlt":6,"meta":819,"metaDescription":6,"navigation":608,"path":820,"published":608,"publishedAt":821,"readingTimeMinutes":822,"readingTimeText":823,"relatedArticles":824,"seo":826,"stem":827,"teaser":828,"updatedAtCustom":6,"__hash__":829},"blog_en\u002Fen\u002Fblog\u002Fdata-layer-validation-what-why-and-how.md","Data layer Validation – what, why, and how",{"type":8,"value":633,"toc":803},[634,637,641,648,651,661,664,667,673,677,680,683,687,690,694,697,701,704,708,711,721,725,728,733,744,748,765,768,772,780,789,796],[11,635,636],{},"In the world of information, relevant and accurate data make the difference, especially in saturated markets. Understanding your customers and delivering the best digital experience helps to build lasting relationships and increasing customer lifetime value (CLV). In order to extract the required information on the customers from the website\u002F e-shop, and build new strategies of more effective communication, web tracking is indispensable. To set up well-working web tracking, you need to implement a data layer.",[27,638,640],{"id":639},"what-is-a-data-layer","What is a data layer?",[11,642,643,644,647],{},"In case the term ",[86,645,646],{},"data layer"," is new to you or just not too familiar, explaining it as a JavaScript Object will not tell you much. However, do not be discouraged. Yes, you do need to go an extra mile to implement it, and some coding is needed (you may team up with developers or hire an agency), the long-term benefits are worth it all.",[11,649,650],{},"Here is a simple data layer in a raw view",[652,653,658],"pre",{"className":654,"code":656,"language":657},[655],"language-text","{\n    \"page\": {\n        \"type\": \"list\",\n        \"trail\": \"marketing\u002Farticles\",\n        \"list\": {\n            \"pageNumber\": 2,\n            \"filters\": {\n                \"years\": [\n                    \"2020\",\n                    \"2019\"\n                ],\n                \"keywords\": [\n                    \"affilates\",\n                    \"seo\"\n                ]\n            }\n        },\n        \"locale\": \"cs-CZ\",\n        \"currencyCode\": \"CZK\",\n        \"countryCode\": \"CZ\"\n    },\n    \"session\": {\n        \"machine\": \"external\",\n        \"deviceType\": \"mobile\",\n        \"env\": \"prod\"\n    },\n    \"user\": {\n        \"username\": \"tester123\",\n        \"id\": \"66oc39119520732e1s1f23ead6c57\",\n        \"segment\": \"customer.premium\",\n        \"transactionCount\": 2,\n        \"transactionValue\": 799.99\n    },\n    \"event\": \"page\"\n}\n","text",[659,660,656],"code",{"__ignoreMap":583},[11,662,663],{},"Simply put, a data layer is a method of collecting and distributing data from your website. On the deeper and more technical level, a data layer is a complex structure behind the websites or mobile apps to extract timely and consistent visitor\u002Fuser information. It holds the data you need and sends it to other applications, preferably firstly to tag management system (TMS) and from there to other analytical and marketing platforms. This way, customer actions are translated into variables and dimensions. The type of data that is contained in the data layer depends on the business requirements, such as transaction, behavioral, demographic, device, and more. The more information and varieties you need, the more complex the data layer gets.",[11,665,666],{},"Dividing the process into layers:",[11,668,669],{},[670,671],"img",{"alt":583,"src":672},"\u002Fupload\u002Fdatalayerillustration.webp",[27,674,676],{"id":675},"why-is-a-data-layer-a-must","Why is a data layer a must?",[11,678,679],{},"To maximize the potential of your website, get to know your audience, and provide more personalized content, you need relevancy, consistency, and accuracy of your data in all platforms. Starting from your web via a data layer. The benefits go way beyond just knowing how much. The quality of the data is what counts.",[11,681,682],{},"From the perspective of practically on the background, a data layer standardizes data across technologies (analytical and marketing) and the collection maintains consistency despite changes on the web. You may know that changes on any website can drastically throw off your tracking, and if you ever experienced a measurement problem you know that the impact is even more disastrous. The data layer helps to reduce development time and the number of iterations between the development and marketers when implementing new technologies.",[146,684,686],{"id":685},"sounds-great-but","Sounds great, but …",[11,688,689],{},"As previously said, the data layer reduces time. However, as the website is not a static but a very dynamic environment, and even a small change can cause many mistakes. To prevent mistakes, you need to check for mistakes, which can seem too complicated and time-consuming. Manual control is one way, yet not very effective.",[27,691,693],{"id":692},"data-layer-validation","Data layer validation",[11,695,696],{},"Data layer validation should come in regularly to prevent errors and sustain web measurement the way you want it. But forget the traditional method. Some tools can help you validate easier, or at least look into your data layer, row by row.",[146,698,700],{"id":699},"experience-comes-in","Experience comes in",[11,702,703],{},"Validating one data layer of a smaller website takes time but it is manageable. Imagine validating 10 or 100 very complex e-shops. Then you start thinking of a better solution. First, research of available tools comes in. After some time, you realize it got you nowhere, or the options are just not sufficient. We went through all the steps and more deeply to figure out how to tackle this case.",[27,705,707],{"id":706},"meet-waaila-tracking-validator","Meet Waaila Tracking Validator",[11,709,710],{},"After trials and failures, we decided to develop our own tool for data layer validation. We put our experience with writing data layer specifications, our clients’ needs, and user experience, and released a Chrome extension that can inspect and validate your data layer through particular events and pages, just like a customer would progress on the website, which makes it easier to spot errors and, not less important, easier for the developers to understand the data layer as well.",[11,712,713,720],{},[714,715,719],"a",{"href":716,"rel":717},"https:\u002F\u002Fwaaila.com\u002Fen\u002Ftracking-validator",[718],"nofollow","Waaila Tracking Validator"," extension is using JSON Schema standard, checks if your data layer on the website corresponds with the structure of the data layer defined in your custom JSON schema, and looks for inconsistencies.",[146,722,724],{"id":723},"waaila-tracking-validator-in-action","Waaila Tracking Validator in action",[11,726,727],{},"The tool allows you to validate the data layer against custom JSON schema and it is pretty simple to use. You insert the JSON schema into the tool, confirm, and start validating.",[729,730,732],"h4",{"id":731},"benefits","Benefits",[32,734,735,738,741],{},[35,736,737],{},"Developed for analysts who create data layer specifications",[35,739,740],{},"Benefits the developers who often get lost in the data layer",[35,742,743],{},"Lowers the number of iterations",[729,745,747],{"id":746},"features","Features",[32,749,750,753,756,759,762],{},[35,751,752],{},"Automatically validate the syntax of your data layer to easily detect various typos, such as lower\u002Fupper key, spaces, etc. that are very easily overlooked.",[35,754,755],{},"Semi-automatic validation of semantic. You need to manually choose the context of the web page, however, the validator automatically checks the data layer against the schema.",[35,757,758],{},"The exact location of the error in the data layer. Being able to see where exactly the error occurs and what problems it causes helps to understand general issues of the data layer, when not implemented correctly, and speed the process of retrieval.",[35,760,761],{},"Error highlighting proved to be very effective in the process of implementing changes into the data layer, especially when the developers do not understand the requirements, consequently decreasing the number of discussions among teams.",[35,763,764],{},"The tool is quick and responsive, the validation takes only a few seconds compared to long manual crawling.",[577,766,767],{"link":579,"button":580},"\nWe can help you specify your data layer and implement digital measurement.\n",[146,769,771],{"id":770},"got-you-hooked","Got you hooked?",[11,773,774,775,25],{},"Try the tool for free on ",[714,776,779],{"href":777,"rel":778},"https:\u002F\u002Fchrome.google.com\u002Fwebstore\u002Fdetail\u002Fwaaila-tracking-validator\u002Fjkmohgcefflkfjoemjnpigiokpjeohcl",[718],"Google Chrome store",[11,781,782,783,788],{},"Build your own ",[714,784,787],{"href":785,"rel":786},"https:\u002F\u002Fwaaila.com\u002Fen\u002Fdocs\u002Ftracking-validator\u002F",[718],"Validation schema",", and start validating the data layer instantly!",[11,790,791,792,25],{},"Find out more in-depth descriptions and the process in ",[714,793,795],{"href":785,"rel":794},[718],"the extensive documentation",[11,797,798,802],{},[714,799,470],{"href":800,"rel":801},"https:\u002F\u002Fcrossmasters.com\u002Fen\u002Fblog\u002Fzoom-in-on-measurement-hub\u002F",[718]," about the data layer implementation.",{"title":583,"searchDepth":584,"depth":584,"links":804},[805,806,809,812],{"id":639,"depth":584,"text":640},{"id":675,"depth":584,"text":676,"children":807},[808],{"id":685,"depth":592,"text":686},{"id":692,"depth":584,"text":693,"children":810},[811],{"id":699,"depth":592,"text":700},{"id":706,"depth":584,"text":707,"children":813},[814,815],{"id":723,"depth":592,"text":724},{"id":770,"depth":592,"text":771},"Products","\u002Fupload\u002Fdata-strategy-article-cover.webp",false,{},"\u002Fen\u002Fblog\u002Fdata-layer-validation-what-why-and-how","2020-10-07T01:26:03.000+00:00",5.535,"6 min read",[622,825],"content\u002Fen\u002Fblog\u002Fstarting-with-waaila.md",{"title":631,"description":636},"en\u002Fblog\u002Fdata-layer-validation-what-why-and-how","Many websites, especially e-shops underestimate the power of well-implemented data layer. Here is why and how you should make sure it is done right.","7nW396Sw7ENACMxQjmoUxQrG9x3N1nAbt07oOefXhPM",{"id":831,"title":832,"author":6,"body":833,"category":816,"description":583,"extension":606,"image":1815,"isToc":608,"langAlt":6,"meta":1816,"metaDescription":6,"navigation":608,"path":1817,"published":608,"publishedAt":1818,"readingTimeMinutes":1819,"readingTimeText":1820,"relatedArticles":1821,"seo":1824,"stem":1825,"teaser":1826,"updatedAtCustom":6,"__hash__":1827},"blog_en\u002Fen\u002Fblog\u002Fzoom-in-on-measurement-hub.md","Zoom in on Measurement Hub",{"type":8,"value":834,"toc":1786},[835,839,842,845,848,852,855,858,862,866,880,884,887,891,894,898,901,905,908,911,914,917,920,924,927,931,934,938,961,965,968,972,975,979,982,986,989,993,996,1000,1003,1007,1010,1014,1017,1037,1041,1044,1047,1050,1054,1057,1061,1064,1072,1075,1078,1113,1605,1625,1629,1632,1635,1638,1725,1729,1732,1736,1740,1743,1747,1750,1754,1757,1761,1764,1768,1771,1775,1778,1782],[27,836,838],{"id":837},"introduction","Introduction",[11,840,841],{},"The process for better customer experience and personalization through improved segmentation starts with data collection of customers' journeys and interactions. In order to achieve the best possible outcomes, it is necessary to collect the data uniformly and set meaningful measurements across various platforms.",[11,843,844],{},"When all marketing and analytical platforms are dependent on the website data, the accuracy of data and its right integration are of the utmost importance. Leveraging the collected data is a competitive advantage that generates exponentially higher marketing investment returns.",[11,846,847],{},"In a perfect scenario, the company has reliable, responsive, and reasonably-priced development resources, the development team is highly skilled, continually cooperates with the SEO and marketing team, or there is an affordable third-party partner. But very frequently that is not the case. The task of high-quality data collected, analyzed, and measured can be vastly complex.",[27,849,851],{"id":850},"what-is-measurement-hub","What is Measurement Hub",[11,853,854],{},"Measurement Hub is not just a box solution, rather a group of complex scripts, yet elevated, more powerful, and more robust deployed via Tag Management System (TMS), commonly used one is Google Tag Manager (GTM). The codes are verified and are standardly pre-defined, can be slightly adjusted, or in case of a complicated case, they can be deeply overhauled. In addition to the scripts, it includes definitions of events and entities for data layer specification. The specification of the data layer is customizable to fit every specific need of every company in order to reach desirable goals. Fully implemented with an omnichannel context, Measurement Hub is dynamic, happening on the user's browser. The data is flowing through; however, it can be stored with integrated data storage. Therefore, Measurement Hub's value is not embedded in the codes only, but also in the expertise of consultancy.",[11,856,857],{},"Simply, Measurement Hub consists of a data layer on the front continuing to tag management system reading, consuming, and distributing the data, including establishing accurate measuring into marketing and analytical platforms. Similar to TMS implementation but much more extended.",[105,859],{"source":860,"caption":861},"\u002Fupload\u002Fmhub-schema.webp","Visualization of data flow",[27,863,865],{"id":864},"what-measurement-hub-covers","What Measurement Hub covers?",[32,867,868,871,874,877],{},[35,869,870],{},"Data layer specification (specification of entities, parameters of entities, events)",[35,872,873],{},"Measurement specification (specification of mapping Data Layer into marketing and analytics platforms)",[35,875,876],{},"Code for all commonly used marketing and analytics platforms (codes which will be implemented into TMS)",[35,878,879],{},"Settings of an analytic platform",[146,881,883],{"id":882},"maintenance-and-support","Maintenance and support",[11,885,886],{},"After the implementation, standardly you are fully independent, the code is under your ownership in your TMS. We provide an introductory explanation of the configuration and the specification is handed over in an editable document as well. Upon request, we can provide further consultations. We can also manage the Measurement hub and TMS for you.",[27,888,890],{"id":889},"what-measurement-hub-does-not-cover","What Measurement Hub does not cover?",[11,892,893],{},"Measurement Hub doesn’t focus on settings in marketing platforms. After the implementation of Measurement Hub, management of these marketing platforms is done on the company's side or their agencies, but we can help with that. At least there will be measurement specifications for the platform administrator to see what exactly is sent there. In case of additional questions or advice, we provide consultations for the standard hourly rate.",[27,895,897],{"id":896},"why-is-its-implementation-essential","Why is its implementation essential",[11,899,900],{},"Just naming a few of the many advantages of Measurement Hub, the emphasis lays on data layer specification creating the foundation for the rest of the measurement.",[146,902,904],{"id":903},"data-layer-specification","Data layer specification",[11,906,907],{},"Fulfillment of Measurement Hub provides numerous advantages; however, the most valuable one is data layer specification, which imposes data consistency throughout the entire data flow to\u002Ffrom\u002Fin all the platforms, marketing, and analytical.",[11,909,910],{},"A well-constructed data layer (DL) can portray a roadmap to customer communication since thinking beforehand about customer interaction data supports its definition in order to connect all applications.",[11,912,913],{},"Making the data clean and consistent fastens the process and makes it more precise. Measurement Hub offers its own standard, which can fit any case. The specification of DL is customized for the individual needs ensuring the pre-defined requirements will be measured accurately everywhere.",[11,915,916],{},"Data layer specification is crucial for setting up website independence. Often, during website implementation, the data layer gets lost or broken and the measurement with it. The specification helps to discover it is broken and guides the development to fix it correctly. Additionally, the specification within Measurement Hub brings a conceptual solution and when done right from the beginning, it prevents the unnecessary failures of gradual implementation. It serves as a general overview and clear explanation of data layer events and what the data represents for the analysts.",[11,918,919],{},"Without the specification, the quality of the data cannot be validated with such accuracy and newcomers have nothing to understand of your data layer. To elaborate, the specification serves as a detailed manual not only for the development team but also helps your new team members to understand your data layer and measurement. It is better to show the actual documentation and explain the process in practice, than trying to describe it abstractly. From the experience, we observed it is a very handful in case you are expanding your team or there is a sudden change in staff.",[146,921,923],{"id":922},"measurement-specification","Measurement specification",[11,925,926],{},"Mapping of entire data flow within the data infrastructure, from the data layer to each platform is important for analysts, performance teams, online marketing teams, marketing agencies, etc. Knowing what transfers where contributes not only to a better overview and a general understanding of the process, but it can set a foundation for better decisions in, for instance, marketing or business strategy, and unlocks new opportunities.",[146,928,930],{"id":929},"validation","Validation",[11,932,933],{},"Part of the Measurement Hub implementation is a well-coordinated multilevel validation process which is done by our team of specialists who are well trained for this purpose. But there are also several tools, which are helping to reveal some errors in implementation automatically or just leverage effectiveness. Most of these tools were created by the Measurement Hub team itself for these purposes.",[146,935,937],{"id":936},"among-other-advantages-are","Among other advantages are:",[32,939,940,943,946,949,952,955,958],{},[35,941,942],{},"Utilizing the full potential of marketing platforms",[35,944,945],{},"Easier implementation of custom tracking",[35,947,948],{},"Easy control & well-manageable code of measuring",[35,950,951],{},"Complex and conceptual measuring into an analytical platform",[35,953,954],{},"Reliable and uniform measuring of (clean) data",[35,956,957],{},"Automatic error tracking",[35,959,960],{},"Fast and easy integration of new platforms",[27,962,964],{"id":963},"common-problems-when-not-having-measurement-hub-or-when-do-you-need-it","Common problems when not having Measurement Hub (or when do you need it)",[11,966,967],{},"Often, the question of what is behind Measurement Hub implementation, especially in the case of a bigger e-commerce website, when setting up TMS can be done internally. This is true, however, the advantage is not solely in TMS itself. TMS is a means to an end, which in this case can be better user segmentation, targeting, and consistent data structure that supports it all. It requires technical background and knowledge along with business and user understanding to be able to adjust the settings for current and future marketing purposes; to be able to think ahead and from a certain perspective.",[146,969,971],{"id":970},"incorrect-data-duplicity-different-formats-unstructured-missing-data","Incorrect data: duplicity, different formats, unstructured, missing data...",[11,973,974],{},"Measurement Hub provides a set of codes that are implemented into TMS to make the most out of it but consistently and without duplicity. Without Measurement Hub the data is pushed in different formats causing duplicity or lost information. It can be almost impossible to put it all together after it is being pushed incorrectly.",[146,976,978],{"id":977},"website-design-changes","Website design changes",[11,980,981],{},"If website content changes progressively, the data layer gets disrupted or disappears totally and the measurement in the analytical platform cracks and then becomes incorrect, absent, or delayed, requiring substantial changes in settings. Thanks to Measurement Hub's automatic error tracking, the broken data layer is detected on time, can be fixed without a delay and the data damage will be minimal.",[146,983,985],{"id":984},"one-time-help-but-not-bringing-long-term-impact","One-time help but not bringing long-term impact",[11,987,988],{},"An easy and fast solution for a one-time project is not helping the company to grow. Opting for faster and simpler box solutions for smaller problems can help with the project. Measurement Hub looks at the whole picture, understanding the goal of the project and taking into consideration company growth. The elaborate conceptual approach harvests much more benefits in the long run and solves many problems.",[146,990,992],{"id":991},"replacing-platform","Replacing platform",[11,994,995],{},"In case of a need of replacing any current platform (especially analytical) for the new one, the entire measurement can be significantly damaged. Many platforms have their own standard data layer that is developed for their own use but is not compatible with the other platforms. Therefore, its change is extremely difficult and lots of data gets lost in the process. Leveraging Measurement Hub's own data layer, which is flexible and vendor-agnostic, the substitution happens smoothly without interruptions.",[146,997,999],{"id":998},"change-in-the-platform-api","Change in the platform API",[11,1001,1002],{},"Measurement Hub has its own DL standards that work for many different analytical and marketing platforms, are very flexible towards specific platforms and their changes. It is the first one to know when there is a change in the API of each platform.",[146,1004,1006],{"id":1005},"consent-management-ready","Consent management ready",[11,1008,1009],{},"With the personal data law enforcement, customer consent needs to be integrated transversally on the entire website. Most TMS lack this extension, especially with GTM it can be almost impossible to execute. Measurement Hub is ready for any Consent Management System, the most common we use is Wecoma, which is in compliance with personal data protection laws, such as GDPR in the EU or CCPA in the US, or any other regulation.",[27,1011,1013],{"id":1012},"what-can-be-achieved","What can be achieved",[11,1015,1016],{},"The ultimate reason for choosing Measurement Hub is generally improved marketing strategy achieved by:",[32,1018,1019,1022,1025,1028,1031,1034],{},[35,1020,1021],{},"campaign automation and triggering",[35,1023,1024],{},"better customer experience and personalization",[35,1026,1027],{},"advanced segmentation and micro-segmentation",[35,1029,1030],{},"managing marketing audiences for targeting and retargeting",[35,1032,1033],{},"competitive advantage",[35,1035,1036],{},"exponentially higher marketing investment returns",[27,1038,1040],{"id":1039},"why-its-different","Why it's different",[11,1042,1043],{},"On top of all the benefits named above, Measurement Hub and the team executing it solve problems holistically for the company, not for one project. Experiences showed that the conceptual approach should not be forsaken for the seemingly longer execution on the contrary it needs to be prioritized.",[11,1045,1046],{},"Every third-party analytical and marketing tool has a different way of sending and collecting data, as well as different expectations on when and where their tags should be placed. We understand each platform and its parameters hence we know exactly how to implement them all properly. Measurement Hub is created to match the parameters therefore platform integration is simple and preserves data consistency.",[11,1048,1049],{},"The service also contains a detailed explanation of the whole process precise definitions of tailored requirements, documentation, and consulting.",[27,1051,1053],{"id":1052},"who-can-benefit","Who can benefit",[11,1055,1056],{},"Website data measuring is important for every e-commerce company. However, when e-commerce is the core business or brings the most lead and marketing spending is quite significant, simple measuring and analysis are usually not sufficient. Eloquently big companies with complicated and extensive website data architecture tend to neglect the importance of consistency and structure. Reaching higher levels of personalization and accomplishing better marketing campaign results might take even more effort and resources when done manually. Just shaping it together along the way may seem adequate, in the short-term, but eventually, it will catch up later.",[27,1058,1060],{"id":1059},"how-does-it-work","How does it work",[11,1062,1063],{},"Measurement Hub’s logic in TMS serves its purpose as a middleman between the website and each platform. Some key properties of Measurement Hub:",[32,1065,1066,1069],{},[35,1067,1068],{},"It is running in the browser of a user visiting your web. Therefore, there is no need for any other server and infrastructure.",[35,1070,1071],{},"It functions as a real-time stream middleman, thus by itself, it has no database or any storage.",[11,1073,1074],{},"In general, Measurement Hub takes the data from the data layer in real-time, transforms, and augments them, and immediately sends them into marketing and analytical platforms.",[11,1076,1077],{},"The whole data flow proceeds as follows:",[1079,1080,1081,1103],"ol",{},[35,1082,1083,1086],{},[18,1084,1085],{},"Main entities are pushed into the data layer",[32,1087,1088,1091,1094,1097,1100],{},[35,1089,1090],{},"User – information about the user",[35,1092,1093],{},"Page – information about the context",[35,1095,1096],{},"Session – information about the current session",[35,1098,1099],{},"Order – information about things which user just ordered",[35,1101,1102],{},"And so on...",[35,1104,1105,1108],{},[18,1106,1107],{},"Event is pushed into the data layer",[32,1109,1110],{},[35,1111,1112],{},"Page – it tells us that pageview happened (but it could be also any other event e.g. Add to cart, Product like, etc.)",[652,1114,1118],{"className":1115,"code":1116,"language":1117,"meta":583,"style":583},"language-json shiki shiki-themes material-theme-ocean","{\n  \"page\": {\n    \"type\": \"list\",\n    \"trail\": \"marketing\u002Farticles\",\n    \"list\": {\n      \"pageNumber\": 2,\n      \"filters\": {\n        \"years\": [\"2020\", \"2019\"],\n        \"keywords\": [\"affilates\", \"seo\"]\n      }\n    },\n    \"locale\": \"cs-CZ\",\n    \"currencyCode\": \"CZK\",\n    \"countryCode\": \"CZ\"\n  },\n  \"session\": {\n    \"machine\": \"external\",\n    \"deviceType\": \"mobile\",\n    \"env\": \"prod\"\n  },\n  \"user\": {\n    \"username\": \"tester123\",\n    \"id\": \"66oc39119520732e1s1f23ead6c57\",\n    \"segment\": \"customer.premium\",\n    \"transactionCount\": 2,\n    \"transactionValue\": 799.99\n  },\n  \"event\": 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 },\n",[1121,1390,1392,1394,1396,1398,1400],{"class":1123,"line":1391},16,[1121,1393,1133],{"class":1127},[1121,1395,125],{"class":1136},[1121,1397,1140],{"class":1127},[1121,1399,1143],{"class":1127},[1121,1401,1146],{"class":1127},[1121,1403,1405,1407,1410,1412,1414,1416,1419,1421],{"class":1123,"line":1404},17,[1121,1406,1151],{"class":1127},[1121,1408,1409],{"class":1154},"machine",[1121,1411,1140],{"class":1127},[1121,1413,1143],{"class":1127},[1121,1415,1162],{"class":1127},[1121,1417,1418],{"class":1165},"external",[1121,1420,1140],{"class":1127},[1121,1422,1171],{"class":1127},[1121,1424,1426,1428,1431,1433,1435,1437,1440,1442],{"class":1123,"line":1425},18,[1121,1427,1151],{"class":1127},[1121,1429,1430],{"class":1154},"deviceType",[1121,1432,1140],{"class":1127},[1121,1434,1143],{"class":1127},[1121,1436,1162],{"class":1127},[1121,1438,1439],{"class":1165},"mobile",[1121,1441,1140],{"class":1127},[1121,1443,1171],{"class":1127},[1121,1445,1447,1449,1452,1454,1456,1458,1461],{"class":1123,"line":1446},19,[1121,1448,1151],{"class":1127},[1121,1450,1451],{"class":1154},"env",[1121,1453,1140],{"class":1127},[1121,1455,1143],{"class":1127},[1121,1457,1162],{"class":1127},[1121,1459,1460],{"class":1165},"prod",[1121,1462,1382],{"class":1127},[1121,1464,1466],{"class":1123,"line":1465},20,[1121,1467,1388],{"class":1127},[1121,1469,1471,1473,1476,1478,1480],{"class":1123,"line":1470},21,[1121,1472,1133],{"class":1127},[1121,1474,1475],{"class":1136},"user",[1121,1477,1140],{"class":1127},[1121,1479,1143],{"class":1127},[1121,1481,1146],{"class":1127},[1121,1483,1485,1487,1490,1492,1494,1496,1499,1501],{"class":1123,"line":1484},22,[1121,1486,1151],{"class":1127},[1121,1488,1489],{"class":1154},"username",[1121,1491,1140],{"class":1127},[1121,1493,1143],{"class":1127},[1121,1495,1162],{"class":1127},[1121,1497,1498],{"class":1165},"tester123",[1121,1500,1140],{"class":1127},[1121,1502,1171],{"class":1127},[1121,1504,1506,1508,1511,1513,1515,1517,1520,1522],{"class":1123,"line":1505},23,[1121,1507,1151],{"class":1127},[1121,1509,1510],{"class":1154},"id",[1121,1512,1140],{"class":1127},[1121,1514,1143],{"class":1127},[1121,1516,1162],{"class":1127},[1121,1518,1519],{"class":1165},"66oc39119520732e1s1f23ead6c57",[1121,1521,1140],{"class":1127},[1121,1523,1171],{"class":1127},[1121,1525,1527,1529,1532,1534,1536,1538,1541,1543],{"class":1123,"line":1526},24,[1121,1528,1151],{"class":1127},[1121,1530,1531],{"class":1154},"segment",[1121,1533,1140],{"class":1127},[1121,1535,1143],{"class":1127},[1121,1537,1162],{"class":1127},[1121,1539,1540],{"class":1165},"customer.premium",[1121,1542,1140],{"class":1127},[1121,1544,1171],{"class":1127},[1121,1546,1548,1550,1553,1555,1557,1559],{"class":1123,"line":1547},25,[1121,1549,1151],{"class":1127},[1121,1551,1552],{"class":1154},"transactionCount",[1121,1554,1140],{"class":1127},[1121,1556,1143],{"class":1127},[1121,1558,1222],{"class":1214},[1121,1560,1171],{"class":1127},[1121,1562,1564,1566,1569,1571,1573],{"class":1123,"line":1563},26,[1121,1565,1151],{"class":1127},[1121,1567,1568],{"class":1154},"transactionValue",[1121,1570,1140],{"class":1127},[1121,1572,1143],{"class":1127},[1121,1574,1575],{"class":1214}," 799.99\n",[1121,1577,1579],{"class":1123,"line":1578},27,[1121,1580,1388],{"class":1127},[1121,1582,1584,1586,1589,1591,1593,1595,1597],{"class":1123,"line":1583},28,[1121,1585,1133],{"class":1127},[1121,1587,1588],{"class":1136},"event",[1121,1590,1140],{"class":1127},[1121,1592,1143],{"class":1127},[1121,1594,1162],{"class":1127},[1121,1596,1137],{"class":1165},[1121,1598,1382],{"class":1127},[1121,1600,1602],{"class":1123,"line":1601},29,[1121,1603,1604],{"class":1127},"}\n",[1079,1606,1607,1620],{},[35,1608,1609,1612],{},[18,1610,1611],{},"When some event is pushed into DL, logic in TMS is triggered and starts consuming all data in DL.",[32,1613,1614,1617],{},[35,1615,1616],{},"First data are transformed and augmented if needed.",[35,1618,1619],{},"Then logic for every marketing and analytics platform is triggered and data are transformed and send according to the platform's requirements.",[35,1621,1622],{},[18,1623,1624],{},"Data is processed and saved on servers of specific platforms",[27,1626,1628],{"id":1627},"how-is-it-implemented","How is it implemented",[11,1630,1631],{},"The process starts with the definition of project requirements and is finalized by the implementation of measurement into platforms and proper settings of the analytic platform. The biggest and most important part is revolved around the data layer, its specification, implementation, and validation.",[11,1633,1634],{},"The implementation process of Measurement Hub consists of a series of steps and may take from a few weeks to several months, depending on the project requirements.",[11,1636,1637],{},"Implementation process in steps:",[1079,1639,1640,1653,1666,1676,1686,1695,1705,1715],{},[35,1641,1642,1645],{},[18,1643,1644],{},"Consultation on the measurement requirements with the client",[32,1646,1647,1650],{},[35,1648,1649],{},"This is the phase where we need to gain knowledge of your business and understand your needs. The main measurement concept is created, and critical parts of the measurement are identified. It will define data requirements in marketing and analytics platforms on a general level.",[35,1651,1652],{},"The requirements are specified by the Measurement Hub team together with a responsible person from your company, usually from the analytical team or performance team.",[35,1654,1655,1658],{},[18,1656,1657],{},"Creating the data layer specification based on the client’s requirements and website structure",[32,1659,1660,1663],{},[35,1661,1662],{},"This is the main part of the process. Every entity and every parameter, which could appear in Data Layer, must be properly defined. All events must be defined there.",[35,1664,1665],{},"It is processed mainly by the Measurement Hub team but with a consultancy with your team.",[35,1667,1668,1671],{},[18,1669,1670],{},"Specification adjustment with web development",[32,1672,1673],{},[35,1674,1675],{},"There is a difference between what an analytics or performance team wants, and how difficult it would be to implement it, if it is even possible and last but not least, how much it would cost. At this stage, developers can alert us to parts of the specification that would be too difficult to implement or that do not logically match, for example, the database structure of the website. The Measurement Hub team then incorporates the decision into the specification.",[35,1677,1678,1681],{},[18,1679,1680],{},"Data layer implementation",[32,1682,1683],{},[35,1684,1685],{},"In this phase, the client's developers must implement the whole DL according to specifications.",[35,1687,1688,1690],{},[18,1689,693],{},[32,1691,1692],{},[35,1693,1694],{},"The data layer is validated manually by the Measurement Hub team. We are checking if events are happening when they should happen and if all parameters are containing what the specification defines. If there is some bug in the implementation, we are giving it back to developers for a fix.)",[35,1696,1697,1700],{},[18,1698,1699],{},"Implementation of the codes into the preferred tag management system",[32,1701,1702],{},[35,1703,1704],{},"Measurement Hub team will implement the content of the tag management system with codes for all marketing and analytics platforms which client want to use according to measurement specification)",[35,1706,1707,1710],{},[18,1708,1709],{},"Analytical platform settings",[32,1711,1712],{},[35,1713,1714],{},"Measurement Hub team prepares full settings of Analytical platform according to measurement specification, including custom dimensions, metrics settings, proper account structure settings, proper campaign grouping, filtering out testing, bots and other not wanted traffic.",[35,1716,1717,1720],{},[18,1718,1719],{},"Validation of measured data in the analytical platform",[32,1721,1722],{},[35,1723,1724],{},"Measurement Hub team validates measured data in analytical data to approve the right implementation of everything above. Also, some errors in DL there could appear due to hidden parts of the web or use case, which was not possible to find out with manual validation.",[27,1726,1728],{"id":1727},"price","Price",[11,1730,1731],{},"Measurement Hub's price depends on the website’s complexity, a number of marketing and analytics platforms, and individual requirements. Price ranges widely and it needs to be individually defined. There is a one-time payment just for the implementation without further fee for using Measurement Hub. Additionally, we can agree on a support fee for change requests in the specification, measurement codes, and so on.",[27,1733,1735],{"id":1734},"faq","FAQ",[729,1737,1739],{"id":1738},"what-if-we-do-not-have-any-tag-management-system","What if we do not have any tag management system?",[11,1741,1742],{},"That is not an obstacle. We can help you to pick the right one for you and include its implementation into the specification for developers.",[729,1744,1746],{"id":1745},"does-measurement-hub-also-work-for-mobile-applications","Does Measurement Hub also work for mobile applications?",[11,1748,1749],{},"Yes, from a technical point of view it is different, but the concept is almost the same. There is just one main difference in the implementation process - the implementation of marketing and analytics platforms. In the application, the implementation needs to be done fully by your developers. However, we provide full specifications on how to implement Measurement Hub in the mobile application, consistently aligned with website measurement.",[729,1751,1753],{"id":1752},"what-if-we-have-already-implemented-something-in-our-tms","What if we have already implemented something in our TMS?",[11,1755,1756],{},"That is not a problem. We can choose from several approaches, depending on the quality of the content of your current TMS and your preferences. We can investigate the functionality in your current TMS and propose solutions for improvement. A few options that can be either use part of it or all of it, or just overwrite it all.",[729,1758,1760],{"id":1759},"what-if-our-developers-do-not-cooperate-they-are-overloaded-with-other-higher-priority-tasks-or-they-cannot-help-us-for-a-different-reason","What if our developers do not cooperate, they are overloaded with other higher priority tasks, or they cannot help us for a different reason?",[11,1762,1763],{},"This can be a huge complication, which will definitely have an impact on the quality of the result. But we can help even in this case. It is possible to detect many things directly in TMS without developers' DL, which means without developers' help. We can develop some workarounds that can help you to get as much as possible from your website without developers.",[729,1765,1767],{"id":1766},"what-if-i-already-have-implemented-the-data-layer","What if I already have implemented the data layer?",[11,1769,1770],{},"We always prefer to create a new property for the new data layer to not be in conflict with the old one. The best practice is to also create a new TMS container for Measurement Hub and in that case, we have totally separated the old and the new measurement.",[729,1772,1774],{"id":1773},"what-about-the-continuality-of-my-data","What about the continuality of my data?",[11,1776,1777],{},"We are always preparing the specification with respect to your current measurement. We are discussing with you where we can break continuality for better measurement usability and the final decision is always on you.",[577,1779,1781],{"link":579,"button":1780},"Get in touch with us!","\nWant to know more about Measurement Hub and how you can benefit from it?\n",[1783,1784,1785],"style",{},"html pre.shiki code .sAklC, html code.shiki .sAklC{--shiki-default:#89DDFF}html pre.shiki code .sJ14y, html code.shiki .sJ14y{--shiki-default:#C792EA}html pre.shiki code .s5Dmg, html code.shiki .s5Dmg{--shiki-default:#FFCB6B}html pre.shiki code .sfyAc, html code.shiki .sfyAc{--shiki-default:#C3E88D}html pre.shiki code .sx098, html code.shiki .sx098{--shiki-default:#F78C6C}html pre.shiki code .s-wAU, html code.shiki .s-wAU{--shiki-default:#F07178}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}",{"title":583,"searchDepth":584,"depth":584,"links":1787},[1788,1789,1790,1793,1794,1800,1808,1809,1810,1811,1812,1813,1814],{"id":837,"depth":584,"text":838},{"id":850,"depth":584,"text":851},{"id":864,"depth":584,"text":865,"children":1791},[1792],{"id":882,"depth":592,"text":883},{"id":889,"depth":584,"text":890},{"id":896,"depth":584,"text":897,"children":1795},[1796,1797,1798,1799],{"id":903,"depth":592,"text":904},{"id":922,"depth":592,"text":923},{"id":929,"depth":592,"text":930},{"id":936,"depth":592,"text":937},{"id":963,"depth":584,"text":964,"children":1801},[1802,1803,1804,1805,1806,1807],{"id":970,"depth":592,"text":971},{"id":977,"depth":592,"text":978},{"id":984,"depth":592,"text":985},{"id":991,"depth":592,"text":992},{"id":998,"depth":592,"text":999},{"id":1005,"depth":592,"text":1006},{"id":1012,"depth":584,"text":1013},{"id":1039,"depth":584,"text":1040},{"id":1052,"depth":584,"text":1053},{"id":1059,"depth":584,"text":1060},{"id":1627,"depth":584,"text":1628},{"id":1727,"depth":584,"text":1728},{"id":1734,"depth":584,"text":1735},"\u002Fupload\u002Fmeasurement-hub-article-title-picture.webp",{},"\u002Fen\u002Fblog\u002Fzoom-in-on-measurement-hub","2020-06-25T12:00:00.000+00:00",15.86,"16 min read",[1822,1823],"content\u002Fen\u002Fblog\u002Fnotes-on-new-features-of-google-analytics-reporting-api-v4.md","content\u002Fen\u002Fblog\u002Fnew-data-api-for-google-analytics-4.md",{"title":832,"description":583},"en\u002Fblog\u002Fzoom-in-on-measurement-hub","Make the data clean, focused, and make all marketing campaigns more effective. Leveraging the collected data is a competitive advantage that generates exponentially higher marketing investment returns.","RHzQSC5vtEVUzNdvkmdpxHJCHBOl3wg9Lro8zMPmQM0",1789131822675]