Table of contents

1. Extended data storage time
2. Countering data sampling
3. Quick update of data in reports
4. Advanced data access management
5. The full picture
6. Summary

1. Extended data storage time

Imagine a situation in which you are asked to answer a question about whether users used mobile devices, tablets, and computers simultaneously in 2022. To do this, you can use the template in exploratory reports. Thanks to the fact that the data retention period in GA4 360 is 50 months you will be able to answer this question in a matter of minutes. This would not be possible in the standard version of the tool because the maximum data retention period is 14 months, meaning that in October 2023 we will not have data available for the entire year 2022.

The extended retention period of up to 50 months will also be indispensable for analyzing multi-year trends or seasonality.

Note that the data retention period is limited only by the exploration section. Standard reports do not rely on restrictions regardless of the version of the tool.

2. Countering data sampling

Data sampling is a statistical procedure used in Google Analytics 4 to generate data in reports based on representative groups rather than the total information collected. It is mainly used to speed up the tool. We can find out whether the report is sampled from the statement top message in the exploration section:

Data sampling in GA4 can be encountered mainly in exploration reports. However, with a very large number of users or the use of secondary dimensions, filters, or segments, it can also occur in standard reports.

The sampling procedure can affect the accuracy of analyses. The example below shows data over 30 days for a comparison of the hostname dimension with the display metric for the same site but two different versions of the tool. You can see that the difference in the number of impressions reaches 12%.

GA4 360 gives us two options for dealing with sampling in the tool panel:a) Adjusting the level of accuracy and speed. By selecting the more detailed results option, we can get an unsampled exploration.

a) Adjusting the level of accuracy and speed. By selecting the more detailed results option, we can get an unsampled exploration.

b) Request for unsampled results. Alternatively, if unsampled data is not available right away, there will be an option to send a request for an accurate report. Results will be available in most cases after about 30 minutes.

It is worth mentioning that in the standard version of the system, the only way to counter-sampling is to change the population size, for example, by shortening the date range.

3. Quick update of data in reports

In today’s fast-changing world, access to up-to-date data is very important. For example, if you’re running marketing campaigns that last for several days, your team needs to be able to analyze the results in real time. This will be possible, only in GA4 360, which in most cases (for Premium Normal and Large services) will be able to process data from 1 to 4 hours. From experience, we know that for standard services this time is a minimum of 24 hours. Efficient data updating can also allow us to implement corrective actions. For example, with up-to-date data, we can detect a sudden drop in the number of users or conversion rate caused by the publication of a new version of the site, and instantly take action to improve the situation.

4. Advanced data access management

For companies with diverse operations who want to manage data access in an advanced way, the functionality for creating subproperties will be helpful. These are simply subsets of the data that are in the master service. For example, you can separate a subproperty for each market in which you do business, for each brand, making it easier to analyze the data. A subproperty created for a marketing agency running a campaign on a specific platform will ensure that we are not forced to present all revenue data to partners, they will only get the information they need to run their optimizations. Note that adding the service mentioned above will involve an increase in license costs due to the processing of additional events, for example, attribution is modeled independently for the source and subproperties.

5. The full picture

Reviewing the data in the standard reports, you may come across a row with a dimension (other). The data is presented this way due to exceeding the limits of rows in the table. This is affected, among other things, by the types of reports (some reports have higher limits) or the complexity of the data set (additional dimensions, filters).

By hiding some of the data in a single line, we do not have the full picture. This can cause problems, for example, when analyzing the effectiveness of product categories. With complex category trees, data on some elements will be “hidden” in a row (other) and, as a result, we may not know which types of categories to eliminate from promotional activities. The lack of a complete picture can lead to the creation of erroneous marketing strategies. You will solve the problem of aggregating data to a single row with the functionality of automatic or manually configured expanded datasets.

Increased data limits, compared to standard GA4, will also help obtain full information on user behavior.

Thanks to the functionality of developed datasets and increased limits of processed information, we are able to fully understand our audience and make more accurate business decisions. Importantly, with the support of Full Stack Experts consultants, we are assured that the quality of the data we collect in the tool is at the highest level. This allows us to take full advantage of the potential of Google Analytics 4 360 and increase the effectiveness of our marketing efforts.

Tadeusz Skrzypczak, Head of Product&User Intelligence at Ringer Axel Springer Poland

Summary

Of course, some of the problems described in the article can be solved by substitute methods, for example, by creating custom scripts that minimize sampling and pass data to Big Query or another warehouse with the help of data science specialists or programmers. On the other hand, if we want all departments in the organization to speak the “language of data” we need to guarantee quick and easy access to it directly in the tool panel for every person involved in site optimization or marketing activities. Google Analytics 4 360, when properly configured, will give us such an opportunity, allowing more people to have an impact on increasing business efficiency.

Why attribution? Why data-driven?

But first, why is data-driven attribution the best solution actually? To know that you need to understand what is the aim of attribution and how it is achieved by rules-based models. Certainly you know that sessions aren’t equally valuable and not every user who entered a website will convert. Evaluating campaign performance by a number of impressions, clicks or CTR never shows precisely its impact on goals associated with generating conversions or revenue. However, results presented as distribution of conversions between channels enable objective evaluation of channel efficiency. And consequently whether a budget is spended optimally.

The easiest way to make attribution is to create a heuristic model based on simple rules of credit distribution across channels that occured in the path. On the infographic below you will find a comparison of the most important features of these models and the data-driven one. 

Common advantage of rules-based models is the fact that it is easy to understand the algorithms used to distribute conversion between channels. However, every model has its disadvantages resulting from too cursory research of clients paths. Imagine a quite frequent scenario where a user starts their journey from acquisition channels, then returns to the website thanks to remarketing campaigns and finally, when they decide to convert, they do it after entering the website using a brand channel (for example after typing shop’s name into a search engine). Linear model is the only rules-based model that doesn’t make assumptions about higher significance of particular elements of the user’s path – every step gains equal credit for bringing conversion. Sessions aren’t equally important though, some channels are more effective than others, but linear model don’t take up a challenge to identify those channels. Other heuristic models assume higher significance of channels placed in particular path phases making some channels underrated or overestimated. 

So the challenge here is to identify – in the fair way – which channels are the most efficient in encouraging clients to make a conversion. This challenge is taken up by data-driven models. In contrary to rules-based models, they use machine learning algorithms to evaluate the impact of every channel to conversion. These models take into consideration such factors as number of interactions, order of channels in the path, relations between channels and both converting and non-converting paths. In the end, a data-driven model can answer a question of the real added value of a channel when it appears in the user path. Common algorithms used in business are Markov chains model and Shapley value model.

Data-driven model is the best… but in which tool?

In every GMP tool data-driven attribution uses the same logic and is based on the same algorithm – Shapley value. In Blueattribution we have a slightly different approach – our model is based on Markov chains algorithm which performs better when business has many various channels. Another thing, which may be even more important than choosing a data-driven algorithm, is selection of data used to evaluate channels performance. That factor differs between GMP tools. 

On the one hand we have Search Ads 360 and Google Ads – tools that are very strong in actionability. Data-driven attribution results can directly improve campaigns, since they can be easily used to bid adjustment. Data-driven model will credit campaigns from every stage of the funnel based on their actual impact, so switching attribution model to data-driven may lead to more accurate ROAS calculation and better performance of the campaigns in the long run. The disadvantage here is the fact that these tools aren’t comprehensive, since they contain only a portion of marketing channels.

On the other hand we have products sharing a more complete story of marketing efforts. Data-driven attribution in Campaign Manager measures effectiveness of paid channels considering all conversion paths, but attributes to maximum 200 touchpoints. Unlike other tools, CM takes an advantage of third-party cookies so can also take impressions into consideration in the attribution model. It may be a huge benefit when you have display campaigns which don’t necessarily drive you clicks, but are important in building brand awareness. However, you have to be aware of inaccuracies caused by elimination of 3rd party cookies in popular browsers.

Campaign Manager shows us only paid channels results. To keep track with all marketing channels, including for example organic and social channels, we can use Google Analytics. First-party cookies enable click-based attribution. Although it is possible to use other data sources (Campaign Manager 360, Google Ads or Display & Video 360) to add impressions, it would introduce a bias to the model, since other channels, such as organic and social, wouldn’t have impression data in the model. Google Analytics offers two data-driven models – Attribution (beta) and attribution in Model Comparison tool for GA360 users only. The main difference between these approaches is the limitation put on data used in attribution models. Model Comparison tool considerates all paths but uses only the last four touchpoints of the path no matter how long ago they occured, whereas Attribution (beta) uses all touchpoints in the lookback window which can be set to at most 30 days. 

Investigating boundaries put on both modules we can conclude that businesses which clients need more time to make a converting decision, but don’t interact with website many times, should consider attribution in Model Comparison in GA360. In contrast, businesses which clients convert quickly, can use Attribution (beta) module. It seems that GA doesn’t offer a suitable solution for businesses with customers paths longer than 30 days and consisting of many interactions. To meet this need we recommend data-driven attribution in Blueattribution that is more flexible and can be adjusted to business needs. 

Read more:
https://www.fullstackexperts.eu/
https://attribution.bluerank.com/

We are currently observing the growing maturity of the organization in the area of ​​using analytics to achieve business and marketing goals. Maturity at the same time translates into a growing demand for enterprise-class tools. Who needs them? Contrary to the suspicions, it is not only large organizations, potentates of their industries. More and more often, aggressive startups reach for such tools as Google Analytics 360. Young, dynamically developing technology companies, innovative, often supported by global investment funds. In addition, the client and supplier meet across borders of countries and regions, which is also an evidence for the search for the best solutions and services, regardless of theoretical geographical limitations.

Why do companies choose Google Analytics 360?

• A specific level of Service Level Agreement, along with a constant scope of support from the Google Marketing Platform partner
• Increased limits and no data sampling – for an extensive data architecture
• Integration with BigQuery – for advanced data analysis and integration with BI
• Integration with SalesForce – for combining marketing and sales data
• Integration with the marketing platform (Display & Video 360) – for accurate reporting and better campaign targeting

Increasingly, companies are reaching for, not one but a set of analytical tools from the Google Marketing Platform. 360 Analytics for accurate measurement, Data Studio for convenient reporting, Optimize 360 ​​for constant content optimization.
We are pleased to see how the Google Marketing Platform responds to the further needs of our clients.

More aboutj Analytics 360