Why do the tools for processing data from marketing channels show me different numbers? / Part 2

  • Business Strategy, Data Analytics, Resources

Why do the tools for processing data from marketing channels show me different numbers? / Part 2

In the following article, you will learn not only which tools you can monitor data on user behavior on the web and the cost of obtaining them. But at the same time, you will gain an awareness of how this data is processed and where its boundaries end to discover the necessary facts.

The first source of data that is very important to you is Google Analytics. If you have an e-shop from Shoptet, you have its implementation very well-tuned and you can start merrily analyzing it. If you have another e-shop platform and you are not sure that your GA measures correctly, start by auditing your data measurements. Finally, in Google Analytics you will get answers to the following questions in particular:

  1. Where did the customer come from to your website (from what marketing source/campaign)?
  2. What pages and products did they visit on your site?
  3. Whether they also bought something from you and, if so, what products were they.
  4. And a lot of other useful information.

 

Unfortunately, in Google Analytics, you will basically not find information on the profitability of your e-shop and in most cases, how much the acquisition of traffic costs you. You can easily link Google Analytics to your Google Ads account (where you are most likely to invest in Google paid search campaigns), but the cost of campaigns on other platforms (Facebook, Sklik, etc.) is no longer so easy to get into GA (For a start, I definitely recommend the solution from Czech developer Standa Jílek).

However, even if you import costs from all marketing platforms into GA, you still won’t make your business profitable. First, Google Analytics is not 100% reliable (by default it measures 5% – 10%, so the actual number of orders in GA and in your Shoptet transaction database will be slightly different). Furthermore, there are no returns by default (and if someone convinces you that you have to send a return to GA, please refuse it politely, but very emphatically).

Within GA, you can also monitor the volume of sales excluding VAT, and by default, there is no margin information for transactions. As with returns, this information can be retrieved from GA, but in most cases, it doesn’t make sense. Working with data is now at a completely different level and it makes sense to process data in cloud services such as Google Cloud Platform, Amazon, Snowflake, or Keboola (if someone tries to discourage you with the fact that this is extremely expensive, then it’s only because they do not understand how to use this :-). Simply put, Google Analytics is used to collect data on user behavior on the web and not for data transformations (engineering work with data), quantification of your company’s profitability, and e-shop data management in general. Honestly, if you want to succeed in the field of e-commerce nowadays and you are serious about doing business in this area, without a data engineer who will give you at least a few hours a month and smart data work well beyond the possibilities of Google Analytics, you can no longer get by without it today.

What is important for the identification of important business data and the resulting information (professionally referred to as KPIs)?

For simplicity, we can divide the main business KPIs into two categories:

  1. Profitability (= the goal is to generate the maximum net profit of the company).
  2. Growth in sales volume (= the goal is to generate the maximum turnover of the company even at the cost of negative profitability).

 

“But that goes hand in hand, doesn’t it? When my sales grow, logically does my profit grow?”

However, this judgment cannot be applied strategically, because according only to the set goals can the equation be supplemented by one unknown. You can read about the relationship between profit and growth in the next article focused on the customer approach.

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Predictive analytics in GA4

One of the new and exciting features of GA4 is called “predictive metrics” – with these you can learn about your customers and their shopping behavior. These are added automatically to your data and are based on Google’s machine learning expertise.

GA4 currently includes the following predictive metrics:

  • Purchase probability: The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
  • Churn probability: The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
  • Predicted revenue: The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.

You can create audiences based on these values and leverage them in your advertising campaigns – for example, you could exclude users who have a high purchase probability as you may assume that these users will buy anyways and you don’t have to spend additional marketing dollars on them (test it) or try to win back an audience with higher churn probability with a special campaign or communication.

Data export to BigQuery

Google BigQuery is a cost-effective and highly-scalable cloud data warehouse optimized for high performance on very large data sets. With GA4 you can export all your event-level data to BigQuery for additional analytics or data science initiatives.

Example use cases might be

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

Example use cases might be

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

You will need to have a Google Cloud account set up and maintained for this purpose. That is why we are here and can solve all the infrastructure for you and with you!

Free data driven attribution in GA4

Since 1/2022 GA4 has made a data-driven attribution model available to all users – unlike in Universal Analytics where the DDA model was only available to GA360 customers.

DDA is an algorithmic attribution model that quantifies the value of each touchpoint in the user journey (such as campaign click) and it does so by smart modelling behind the scenes without human bias that is always present in rule-based models.

The DDA model in GA4 is also better than the one on GA360 as it takes into account up to 50 touchpoints in the user journey vs. the GA360 only took 4 touchpoints.

It is also possible to set the default attribution model to DDA (or another model) from the old Last Non-Direct Click. And then all your reports and data exported to BigQuery will use the newly selected model as default.

DDA in Google Analytics 4 excludes almost all direct visits from receiving credit – you may or may not like that, but as it’s been always the case with attribution we recommend validating any model using marketing experiments.

World without Cookies and GA4

GA4 is built for a world where more and more users opt out of cookie consent and other methods for data collection.

Google uses machine learning modeling to fill in the data gaps – some of these are already in the current GA4 others will be deployed in the future.

For example, modelling conversions allows GA4 to properly attribute conversions without user identification – this is crucial for optimized advertising campaigns and automated bidding. This covers situations such as some browsers limiting the time window for first-party cookies, conversions for unconsented users, Apple’s App Tracking Transparency (ATT) impacts, cross-device user behaviour, and others.