What is Google Analytics 360?
Differences between GA4 and GA360
Key benefits of Google Analytics 360
Summary

What is Google Analytics 360?

Google Analytics 360 is a paid version of the popular digital analytics tool, offering advanced features and capabilities that can significantly support business development. For many organizations, Google Analytics 360 provides features that are essential to deep-dive data analysis and reliable metrics management, with the premium version characterized by the lack of data collection limits and guaranteed Service Level Agreement (SLA).

Differences between GA4 and GA360

Deciding whether your organization needs the paid version of Google Analytics is crucial to understanding the differences between the free and paid versions. In today’s world, where data plays a key role in the decision-making process in companies, choosing the right tool to analyze this data is essential. In the case of Google Analytics, users face a choice between the free version and the paid version, known as Google Analytics 360 (GA 360). Differences between the two can be divided into several categories, particularly noticeable in terms of support and performance.

Firstly, the Google Analytics 360 license offers an SLA, which ensures the reliability of data collection, updates, and report availability. Additionally, the paid version differs from the free one in terms of the availability of various features, such as the number of reports available in exploration, the number of audience groups, or the number of conversions, now referred to as key events.
Differences between GA4 and GA360, part 1

From my perspective, aside from the SLA, one of the biggest differences between these versions is the event limit, which in the free version is limited to one million events per day. This limit may be a significant barrier, especially for websites with high traffic and those with mobile applications.

Additionally, the free version offers access to historical data for only 14 months, while the paid version extends this period to 50 months. For companies that rely on long-term analysis of trends and patterns, this aspect may be decisive when choosing the paid version.
Differences between GA4 and GA360, part 2

Key benefits of Google Analytics 360

As you can see, Google Analytics 360 significantly differs from its free version in many aspects. When making the decision to purchase the extended version, consider several key aspects, such as:

• Data certainty and stability
• The desire to analyze long periods of time (beyond 14 months)
• The level of advancement of your company’s digital product tracking structure
• The amount of data collected

If your analyses cover broad time ranges and you make intensive use of the interface, GA 360 will undoubtedly facilitate your work. Its advanced features allow for more efficient data processing, especially for long-term analyses.

For companies with complex organizational structures, offering a variety of sites and digital products, GA 360 enables detailed analysis through subproperties and roll-up properties. These features, which are the equivalents of views from Universal Analytics, allow for more precise tracking and data analysis.

Summary

To those who manage large volumes of data, I definitely recommend considering investing in Google Analytics 360. In this regard, I invite you to check out our guide, and if you have additional questions, I encourage you to contact us directly.

The post Does your company need Google Analytics 360? first appeared on Conversion.

The first source of truth – a reference point
Data presentation in Google Analytics
Data in Google Analytics vs. RODO
What level of data discrepancy is acceptable?
How to check the level of data discrepancy in a service?
Data discrepancies depending on the type of service
Google vs. other advertising systems/a>
How to reduce data discrepancies with changes in transaction recording
Summary

The first source of truth – a reference point

At Conversion, we most often work with e-commerce services, and as a result, the main point of reference in the projects we carry out is the warehouse and accounting system. It is the one that most often provides the previously mentioned first source of truth, relative to which we compare other data such as the number of transactions or revenue from those transactions.

When we talk about comparing data from Google Analytics to that from a company’s internal system (transactional system or CRM), we need to be aware that we are talking about two areas of data comparison – relevance and accuracy. Let’s start by clarifying these two key terms.

Relevance vs accuracy in Google Analytics

We talk about relevance when some external tool (here Google Analytics) shows exactly the same data that we see in our reference point. For the purposes of this article, let’s assume that in ecommerc’s case it is a CRM system. For example, we can find data on orders placed in our store. If Google Analytics collects data characterized by accuracy, the number of transactions will be equal to that in the internal system.

When examining the accuracy of the collected data, we no longer pay attention to the exact representation of the data in quantitative terms. Here, trends are a much more important element. If the number of transactions in our CRM is growing at a given rate during the period under study, this should also be reflected in our Google Analytics.

Data presentation in Google Analytics

Google Analytics collects – and consequently – presents data based on a couple of foundations. The first of these is JavaScript, which is embedded in the site’s source code or inserted into the page using Google Tag Manager. It is triggered when the page is loaded. Its task is to create and read cookies, which contain a unique user ID within them. In this case, we say that Google Analytics operates on the basis of JavaScript. However, not all visitors to our site have JavaScript or cookies enabled. Users also often use plug-ins that intentionally block not only ads, but also Google Analytics scripts. In such a situation, the actions performed by the user will not be tracked.

Now let’s return to the concept of accuracy. As we mentioned before, the main function of Google Analytics as a tool of the Digital Analytics class is not to show exactly the same data as the internal system. Its main purpose is to link the source of a user’s traffic (the place from which they came to the site) with their behavior on the site once they got there. It gives website managers the information they need to assess how, depending on the traffic source and behavior on the site, the user performs the actions they want – that is, they make conversions. So we need to remember that web analytics tools exist to answer questions about how to achieve the goals we have set for our site, not to collect 100% accurate data. This, unfortunately, is not possible due to the blocking of some of the information shared by users.

Data in Google Analytics vs. RODO

We live in an era of increasing concern for user privacy (GDPR). For some time now, website owners have had to take a proactive approach to obtaining user consents for the creation and use of cookies. It is obvious, then, that the more users accessing our service do not give this consent, the greater the discrepancies in the data will be. For this reason, Google Analytics will never reflect 1-to-1 the data that is collected in the internal system. That’s why it’s so important to study the trends we observe in CRM and compare them with those noted in Google Analytics 4.

Often, when working with clients, it happens that when we don’t have 100% of transactions recorded in Google Analytics, they are “sent” to it, e.g. via measurement protocol. This is not an appropriate approach to the subject of data discrepancies, for the reason that was mentioned earlier – Analytics is used to evaluate the effectiveness of traffic according to its sources or site behavior – not to collect fully complete data. If we “send” transaction data from CRM to Analytics, which it did not record due to the user’s cookie blocking, we will not have information about the related traffic source or user behavior on the site. As a result, we will not be able to take a closer look at the transaction and will not get valuable information about it.

So let’s keep in mind when using web analytics tools about their main function and not require them to be fully accurate and relevant – such a situation does not happen in real life.

What level of data discrepancy is acceptable?

Having reached this point in the article, you’re bound to wonder what level of discrepancy you shouldn’t be concerned about. Let’s assume that the data you see in Google Analytics is characterized by accuracy – the trends correspond to those seen in the CRM system. So let’s consider what level of accuracy we can consider appropriate.

In the projects we carry out, we aim for a level of data convergence in Google Analytics with internal systems of 85%. This means that for every 100 transactions recorded in the CRM system (the actual number of transactions on the site), an average of 85 should be reflected in the data in Google Analytics.

How to check the level of data discrepancy in a service?

Thankfully, there is a simple way to do this on ecommerce sites. In the internal transaction system (the first source of truth), we have data on all transactions made on the service, along with the ID assigned to the users making them. With Google Analytics configured correctly, we will see the same transactions in it, with the same assigned ID.

So the simplest thing to do is to export the data from the internal system and compare it with the data available in Google Analytics, and then see how many IDs from the CRM system are missing in Analytics.

In order to make the most valuable comparison, it is also important to pay attention to the characteristics of users, by which we can segment the completed transactions visible in CRM and invisible in Google Analytics. This will provide additional hypotheses about what the discrepancies between the systems might be due to – and that’s the first step to reducing them.

Data discrepancies depending on the type of service

We mentioned before that in most cases 85% data convergence is the level we should strive for. After delving more deeply into the subject, however, the answer is not so zero-one. A satisfying level of divergence also depends on the type of service – or, to be more precise, on the characteristics of the service’s users.

We have to realize that, as marketers, we are characterized, in general, by a higher level of awareness of Internet use. However, this does not mean that “ordinary” users are homogeneous in this respect. This is also reflected in the level of discrepancy in the data.

On websites that collect more aware users, they are more likely to have disabled browser functionalities on which Google Analytics collects data, such as JavaScripts and cookies. They can also block the transmission of information about themselves to the tool with special add-ons, blocking not only ads, but also tracking of their online activities by Google Analytics. For such sites, the level of data convergence will be noticeably lower. This is mainly the case with specialized sites, especially in the IT industry, where the level of convergence will reach “only” 40-50%.

On the other hand, for websites visited by moderately less informed Internet users, such as clothing or electronics stores, we can expect a data convergence level of 85% or higher, as mentioned before.

Google vs. other advertising systems

Discrepancies between internal systems and Google Analytics, is not the only challenge analysts face. Differences in data will also be noticeable between different advertising systems (assigned to different online marketing tools) and Google Analytics 4. This is despite the fact that these tools are based on the same JavaScript technology. So why does this happen? We should look for the answer in the attribution model used.

As an example, let’s take Facebook’s advertising system, Facebook Ads. It will strive to show the highest possible number of conversions made through ads in this system in order to attract advertisers who are encouraged by the results. On the other hand, Google Analytics receiving this data no longer has such an interest. So we can assume that the data in Google Analytics 4 should be more objective.

To illustrate this sort of war of giants between companies, let’s take a look at ads in Facebook’s mobile app. Like most users, we probably have an in-app browser installed on our phones, running on what is known as WebView. In this case, when we switch from Facebook to a third-party service, it is not displayed in the new browser, as a result of which access to data from the Google Analytics perspective is blocked. This is why the data available in Facebook’s advertising system will notice and note this action – unlike Analytics. As a result of this action, as advertisers we are encouraged to use the data in Meta’s advertising system – because that is where we will see the data.

When analyzing the data, we need to keep these nuances in mind and choose the most appropriate (reliable) attribution model for us, and it is this model that will guide us in further analysis. Each of them has its advantages and disadvantages, so it is the decision on how we want to analyze the data that is crucial – after all, we don’t want to end up in a situation where the number of conversions coming from the advertising systems used is several times higher than the actual number of transactions visible in the CRM system.

How to reduce data discrepancies with changes in transaction recording

One of the most common problems in the field of ecommerce data discrepancies are those related to transaction registration. These arise when using third-party payment gateways. In this case, transactions are registered by default when the user returns to the site. However, users very often do not return to the service after making a payment, which causes a lot of discrepancies….

How to deal with this? We recommend counting transactions in Google Analytics just before going to the external payment itself. Our observations show that there is a much higher probability that a user will not return to the service after making a payment, than that he or she will go to make a payment and abandon it immediately afterwards. This is caused by two things:

• Call to action in ecommerce – or rather, how it is phrased. In most cases it reads “place order with payment” or the equivalent, suggesting that payment will be required in the next step. Since the user is aware of this, he or she is much more likely to give up before moving on than when they get to the point where they were informed of the payment obligation.
• reason for not paying for the order – not paying for the order after going to the payment gateway is usually the result of an unexpected error or random factors such as forgetting your bank login information. However, there are mechanisms to restore the previously lost shopping cart and return to the payment, which largely eliminates the problem.

Because of this, in order to mitigate data discrepancies, we recommend setting up conversion counts just before going to the payment gateway.

Summary

When using web analytics tools, let’s remember what their purpose is and not treat them as the only source of truth. The data collected by such tools should be characterized by accuracy (not relevance) and this is what we should strive for, and a satisfactory level of convergence in most cases is 85%. However, depending on the type of service, this will not always be possible, and we should keep this in mind as well. In analysis, let’s pay more attention to the consistency of trends between Google Analytics and CRM, rather than accurately reflecting the number of transactions. In this way, informed analysis will lead us to more valuable conclusions!

The post Data discrepancies in Google Analytics – what do they stem from and how to minimize them? first appeared on Conversion.

Have you failed to deliver KPIs in the last quarter, month or year? It happens, especially in a challenging and often changing market situation. However, there is a way to get back on track with your business goals! To do so, you need to go through the process of analyzing data, drawing conclusions and making hypotheses to avoid such a situation in the future. Web analytics data will be an invaluable help in this process!

Success in e-commerce is not only a matter of providing attractive products, but also the ability to manage and analyze data. Key Performance Indicators (KPIs) play a fundamental role here, allowing you to assess the effectiveness of your strategy and identify areas for improvement. In this article, we will discuss how to improve the KPIs of an e-commerce site using online data analysis.

Revenue model in E-commerce services
Analyzing data and understanding causes
Using A/B Testing
Useful reports in Google Analytics
Summary

Revenue model in E-commerce services

Before we start any data analysis, it’s important to understand what drives our revenue. In e-commerce services, there are 3 main metrics: number of users, average order value and conversion rate.

1. Number of Users: The first element is the number of users who come to the site. In marketing efforts, we should aim not only to increase their number, but also to bring users who are most likely to make a purchase.
2. Average Order Value: Another factor is the average order value. In purpose of increasing the value of this indicator, it is worthwhile to use promotions when buying more products, or at least to recommend to users products that match those already added to the cart.
3. Conversion Rate: The third element is the conversion rate. It represents the ratio of the number of transactions to the number of users. In our activities, we should strive to increase this ratio.

The revenue that our service generates here is the result of the number of users and the conversion rate (which tells us the number of transactions) and the average order value. By multiplying these values, we get the total revenue of our store.

For example, let’s assume that our store was visited by 10 thousand users per month, generating an average order value of PLN 200, with a conversion rate of 5%. The revenue of the store will be PLN 100 thousand.

This formula, with minor modifications, can be applied not only to e-commerce, but to any service, converting average order value to average customer value over time. Similarly, by defining conversion as the desired action performed by a user on the site, we can study its coefficient and use it for analysis. In this article, however, let’s focus on e-commerce services.

Analyzing data and understanding causes

In most cases, in E-commerce services, the main KPI faced by store managers is revenue. While we already know what metrics it consists of, in order to properly analyze the reasons for lower-than-expected revenue, it is necessary to make a deeper decomposition of the component factors. As a reminder – here we will look at the number of users of the service, the average order value and the conversion rate.

The first step is to diagnose which (or which) of these 3 values is at a lower level than we assumed.

Number of users

In the case of a lower-than-expected number of users, we should take a look at the advertising campaigns being run. Based on the historical data of running campaigns, we can diagnose drops in their effectiveness. Have the current campaigns brought us the same number of users with the same budget? If not, this is a signal to decompose this area and study what is happening to the users targeted by the campaigns.

Important metrics here will be the number of page views and the CTR of each campaign. It may just be that some of them are ineffective, and these are the ones worth working on!

Average order value

With a lower-than-expected average order value, it’s worth looking at discount policies and up-selling strategies used in the store. Offering the user suggestions in an accessible way for products that match the ones they have already added to their shopping cart can prove to be a hit. It may also be a good idea to introduce promotions and discounts applicable to a specific order value, which should encourage the buyer to increase the value of the shopping cart.

Conversion rate

When we get to the point where we determine that the number of users and the average value of their transactions are at the right level, we should look at the conversion rate. This is a very capacious metric, so we should go into it in a bit more detail.

Each user, between entering our site and making a transaction, performs a huge number of intermediate actions. We can call them micro-conversions and arrange them into a kind of purchase funnel. For example – before making the final conversion (purchase), the user first had to reach the check-out. To get there, before that he probably already visited the order summary page, which he got to from the shopping cart, which he went to from the product card, and so on… Namely – the action that we should perform at this stage is the decomposition of the customer’s purchase path (that is, the previously mentioned funnel) and its in-depth analysis.

It is good practice to start this analysis “from the end”. So let’s start with the check-out of our service. To standardize, if the check-out closing ratio (understood as the ratio of the number of transactions to the number of users/sessions in the shopping cart) is below 40%, this is definitely an area in need of improvement. If it is above 70%, we can safely conclude that everything is fine at this stage of the customer path.

The next step should be to look at the ratio of users in the shopping cart to those who reached the product card. Here it is worthwhile to refer to historical campaign data and see what actions on the product card are performed by users who did not go to the shopping cart. This will allow us to diagnose what changed their decision. It may also turn out that users acquired from current campaigns have a visibly higher rate of this particular micro-conversion. This may be an indication of their inadequate customization.

In analyzing the customer path, it is worth going into as much detail as possible to effectively diagnose problems. On each subpage visited during the buying process, we can decompose separate funnels of this kind. For example, when filling out a form with personal information, it is worth looking at at which stage (after interacting with which field) the largest portion of users drop out.

A meticulous analysis will help us diagnose the problem areas on our site. But… what’s next?

Using A/B Testing

The first step of conducting an analysis based on online data is already behind us. But how to use the information gained from it? After mapping the areas for improvement, it’s time to make hypotheses about what should be done or changed to improve conversion rates at different stages of the funnel.

Once the hypotheses are set, it’s time to verify them. This is where A/B testing comes to our aid. Let’s assume that in the course of analysis we noticed that in some of the campaigns we run the creatives have changed. Coincidentally, there was also a decrease in the engagement of users who were on the product card. So the negative impact of the creative change here will be a hypothesis that we will verify.

It is A/B testing that we will use to verify this hypothesis. We will display both version A (the original) and version B (which has changes in the creation resulting from the hypothesis we are analyzing) to the recipients we are targeting with our ads. Over the course of the test, with two variants of the ad creation, one of them will go to one half of the users, and the alternative to the other. Similarly, when the tested creatives will be more. With the data from these campaigns, we will be able to determine which of the creatives results in higher audience engagement – that is, the rate of transition from the product card to further stages of the purchase process. Ultimately, this will allow us to assess the validity of our hypothesis.

If the question popped into your head whether the changes in the campaign resulting from the hypothesis can’t simply be implemented to it and compare the results of the next period with the previous one – not the best idea. We need to keep in mind that campaigns are also affected by external factors, which can differ significantly from period to period. These factors include, for example: the economic climate, the actions of competitors, seasonality, etc. The obvious conclusion, then, is that A/B testing will give us the most reliable information when verifying our hypotheses.

Useful reports in Google Analytics

When looking for the causes of under-reported KPIs in the data in Google Analytics, it is much easier to find them when we have a clearly defined problem. A completely different, but also very important topic, is analyzing data when the results are at the right level. Often there is then no clear motivation to work with the data, although there are always smaller or larger areas for improvement. It’s a good idea to start by monitoring the 3 reports described below, which definitely make it easier to analyze online data in E-commerce on a regular basis.

Funnel report

The first report we recommend is the funnel report, which will show us at what stages, after hitting the product card, users do not make further conversions. In e-commerce services it is worth paying special attention to the tightness of the checkout – if we note a value below 40% there is probably room for improvement.

To get to the report in Google Analytics 4, go to reports -> revenue generation -> path to purchase (the name may vary, depending on when the report appeared and when we set up the account). Compared to UA, in GA4 we have an important change – this report shows the flow of users who went through the next stage of the funnel, rather than the number of events (i.e., the number of additions to cart vs. start of checkout vs. purchase). We can analyze the funnel per device category, country, region, city, language and browser as standard.

Landing pages report

The second valuable report is the landing page report. Thanks to it, we will learn how users get to the site, as well as check at what level the engagement rate on specific subpages of the site is (the inverse of the rejection rate). In a situation where the mentioned coefficient is not at the right level, it is worth working on the consistency of the advertising creation with what we present on the product card.

To get to the landing page report, go to reports -> engagement -> landing page. This is one of the newest reports to appear in Google Analytics 4. If you don’t see the report here, it should be available in the library, from which you can add it to a set of reports anywhere you choose. The report differs strongly from the one available in UA, which was basically a copy of the source/medium report. In the case of the report in GA4, we have information about all users (including new users) who started a session from a particular subpage of the site or app screen. What’s missing, however, is information on rejections or engagement and the number of subsites per session, which was available in the UA report. However, nothing prevents you from adding such data by editing the report using the pencil icon available in the upper right corner. When analyzing, it is worth paying attention to the “Conversions” column and marking only those conversion events that you want to analyze.

Service effectiveness report towards technology

The third valuable report in Google Analytics is the report of the site’s effectiveness towards technology (e.g. browser or screen resolution). Sometimes at the development stage of a website, important technical elements are overlooked. This could be, for example, a call to action button placed under a page wrap, or a service element not displaying correctly in a particular browser or its version.

To access the site’s effectiveness report against technology, go to reports -> technology -> technology related details. This report will show us potential issues related to the devices and browsers the user is using.

When analyzing the web version, it is worth paying special attention to such dimensions as browser, device category, screen resolution and device make or model. It is worth remembering that in the case of Apple-branded devices, we will get very limited information about the device itself. In the case of browsers – it’s worth looking at the report more closely if any of them has recently rolled out an update (this can cause problems with the operation of our site, especially in the case of Firefox and stores on PrestaShop). New is an additional device category, smart tv, which includes TVs with built-in browsers, as well as gaming consoles such as PlayStation and Xbox.

When analyzing traffic from an app, be sure to check the version users are using – an outdated app can generate errors. Also take a peek at the Overview report in the Technology folder – there you will find additional information about the app’s stability and potential bugs in its operation.

Monitoring the previously mentioned reports will be a good start to conducting regular analysis of the site. It is also a good practice to pay special attention to the pages that generate the most traffic. When running E-commerce advertising campaigns, these will mostly be product cards. We should also pay attention to the check-out page from our store, where we can often easily diagnose a simple-to-solve problem affecting a low conversion rate. Let’s not fool ourselves – when a user reaches the very end of the purchase path, it means that he is determined to buy, and a low conversion rate at the last stage of the funnel is a clear signal that we should improve something here.

Summary

In conclusion – improving KPIs in e-commerce requires conducting meticulous data analysis and focusing on key components: number of users, average order value and conversion rate. Once the problematic component affecting revenue has been diagnosed, it should be decomposed as accurately as possible, hypotheses should be set and testing should begin. Using A/B testing to verify hypotheses and considering external factors are key aspects on the road to success.

The post Undelivered business KPIs – how to look for causes in the data? first appeared on Conversion.

If you’re on this blog, you’re surely aware of what Google Analytics’ free version is and how it works – both in the older version 3 (the so-called Universal Analytics) and in its newer variety – version 4 (which is soon to replace its predecessor completely). Therefore, let me give you a detailed discussion of the specifics of the popular “three” and instead of focusing on the principles of its operation – I will immediately start by introducing you to what it is and how the so-called paid variation of the popular tool, the New Google Analytics 360, works.

From the rest of the article you will learn what New GA 360 is, how its free version differs from the paid one, how to implement it, and – how much you will have to pay for all this. You are cordially invited!

Google Analytics 4 360 version. What is it?
What are the key differences between Google Analytics 4 and the 360 version?
New Google Analytics 360 – how much does it cost and what does the final price depend on?
Is GA 360 best option for you?
How to prepare for the purchase and implementation of New Google Analytics 360?
What if you already have 360, but in a previous version – Universal Analytics?
Summary

Google Analytics 4 360 version. What is it?

New Google Analytics 360 is an advanced analytics tool designed to offer even more value to the tool’s users than the standard version of Google Analytics 4. New Google Analytics 360 will allow you to flexibly manage your account structure, access levels, or use custom reporting to more accurately interpret and utilize data across your sites. The structure of New GA 360 allows you to take advantage of BigQuery on a wider scale, thanks to greater limits on data export, or the number of “audience lists” to create (which is especially important for creating and targeting marketing messages for remarketing).

What are the key differences between Google Analytics 4 and the 360 version?

To use an automotive metaphor: if we can compare an ordinary GA to a civilian passenger car – GA 360 is its tuned – bigger, faster, more powerful and definitely more functional version. So to put it simply – this kind of solution allows you to take web analytics to a whole new and much higher level.

It differs from its traditional counterpart, for example:

• Access to historical data – as the 360 version provides insight up to 4 years back.
• The ability to perform real-time data analysis.
• Full flexibility related to account structure management (including accesses).
• Lack of limits for exporting data to BigQuery.
• SLA, or Service Level Agreement, which guarantees the uptime and reliability of this tool.
• The option to use dozens of custom dimensions and reports that facilitate personalization and other marketing activities.

New Google Analytics 360 is also a platform that allows integration of all key tools from Google – such an option is also provided by GA 4 – including: Display & Video 360, Search Ads 360, Optimize 360, Tag Manager 360, and Data Studio.

Google Analytics 4 and New Google Analytics 360

New Google Analytics 360 – how much does it cost and what does the final price depend on?

Several factors affect the final cost of implementing and using the 360 version.
The first and most important is the number of events generated per month. It, in turn, is influenced by the number of accounts that are part of the group and are to be licensed, as well as the level of sophistication of the GA4 configuration on each account. Another factor relates to the region in which the company acquiring a particular license is located.
The necessity of the expenses to be incurred for BigQuery operations should also be taken into account during this investment. You can find the details and price list directly on Google’s website just at this address.

As you can see – the final cost impact of using GA 360 depends on several factors. If you want to know the exact price for your company then contact us!

Is GA 360 best option for you?

There is no denying it – this kind of solution is not intended for everyone. If you run a small e-business that generates minimal website traffic. On top of that, you don’t pay special attention to the data or perform in-depth analysis – probably GA 360 will not be a good solution for you.
However, if your ambition is to grow your business extensively, you realize that accessing and then properly analyzing data can improve your results, plus you want to keep growing – Analytics 360 is for you!

How to prepare for the purchase and implement New Google Analytics 360?

The process looks different, depending on what stage you are currently at. In case you are a brand new customer and this will be your first contact with Google Analytics version 360 – follow the instructions below.

• Plan your migration to Google Analytics 4 – taking into account both the higher limits and the additional functionality that the 360 version will provide you.
• Carefully map all data streams that will generate events after a full migration to GA4.
• Check the current number of generated “hits” – coming from all data streams, and then convert them to events (using a factor of 1 – 2 for this).

Example: if you generate 14 million hits from (web) data streams and estimate that the application can generate an additional 6 million or so hits – there will be a total of 20,000,000 hits. Let’s assume that the conversion factor (UA hits -> GA events 4) is 1.3. After conversion, we get 26 million events per month that can be generated by your setup after full migration to Google Analytics 4.

Ask for a quote from a company that is a licensed partner for the purchase and implementation of New Google Analytics 360. You can do it directly here – and get expert service from Conversion.

What if you already have 360, but in a previous version – Universal Analytics?

At this place I must point out that there is currently a so-called transition period. Namely, only until the end of 2022 is it possible to renew 360 for both the “older” Universal Analytics and its newer version, GA 4. In 2023, all customers will be required to have only a contract for GA 360.

So, if you have an older GA and want to “switch” to its newer version – here are simple instructions:

• Plan the migration of your existing tool to Google Analytics 4 – taking into account the higher limits and additional functionality that the 360 version offers you.
• Map out in detail all data streams that will generate events after full migration to GA 4.
• Turn on and test GA 360 in beta version. This is very important to avoid possible errors and potential loss of valuable data.
• Use the built-in Bill Preview feature used to estimate the monthly number of events based on the data available in the current GA 360 configuration.
• Contact the company and ask for a quotation – choose a brand that is a licensed partner for the purchase and implementation of New Google Analytics 360.

Summary

Google Analytics 4 version 360 is an advanced tool aimed primarily at companies and individuals who want to “extract” far more from their data than before. If you base your business mainly on online activity and want to collect, aggregate and process information in a way that will allow you to maximize your financial results – you should definitely consider replacing classic Google Analytics with its paid and definitely more developed 360 version.

At Conversion, as web analytics specialists, we will be happy to help you through the entire process. We will check what the implementation of GA 360 will look like in your case, enable a smooth purchase, and then be responsible for the preparation and final implementation.
If you want to start your adventure with GA 360 or have additional questions – please contact us. We will be happy to provide you with all the necessary information. I invite you – on behalf of myself and the whole team!

The post Google Analytics 4 vs. New Google Analytics 360. Is it worth switching to the paid version of GA4? first appeared on Conversion.