How to tune data sources to the resounding epoch of your e-commerce business?/ Part 4

Data sources for e-shop management and planning

The main data parts that make up an e-shop business plan

  • Sales without VAT
  • Margin % without VAT
  • Operating (company) costs
  • Marketing Investment

Thanks to the decision to use Shoptet as a platform for your e-shop, obtaining basic data is very easy and available for you directly via administration


1. Sales excluding VAT and margin %: source is Shoptet administration

  • DO NOT use Google Analytics as a source of your total sales (of course, they also have their place in your data ecosystem, we will get to this later). Inaccuracies always arise in GA for various reasons, working here with returns (or importing other data into GA) is already a remnant of times gone (as well as saving local Excel files, as mentioned above). In 2020, even a small e-shop has to truly work with data at a real level if it wants to succeed in an extremely competitive e-commerce environment.
  • In addition, sales data can be easily exported from the Shoptet interface, thanks to which you can connect to other data sources and gradually obtain a more detailed and comprehensive picture of your Internet business. In my opinion, connecting data and searching for business opportunities is one of the keys to success in the e-commerce scene (Czech and global) in the long run. The second key is a unique service or product that has the potential to gain loyal customers with repeat orders, however, we will not address this topic in today’s article).

 2. Operating (company) costs

Unfortunately, your company’s operating costs cannot be easily exported from any system. Simply because it’s largely your unique business know-how. Personally, the option to keep these costs at least in the “high-level” segments already in the aforementioned Google Sheets template has proved most successful for me. Firstly because of collaboration with other colleagues, automatic cloud versioning of the document, and last but not least because the data stored in Google Sheets can be further connected to other data sources and thus effectively puts the data into context. 

3. Marketing Investment Volume

How much is required to invest in marketing? The easiest way to answer this question is to use a simple equation: “traffic x conversion rate x average order value x cost per visit.” Are the resulting sales without VAT sufficient? Well, it depends on what business plan you have and what you want to achieve, see the chapter on KPIs.

These metrics are available for free in Google Analytics. Thanks to Shoptet, the implementation of Google Analytics includes an enhanced e-commerce section (will discuss EE next time in related articles), so just log in to your GA account, go to the most used Acquisitions -> Source / Medium report (see the screen below) and you have easily accessible data.

 Why are these business planning metrics missing the number of orders and the volume of revenue excluding VAT? They are not missing, only these metrics are already so-called “calculated metrics”. This means that if you plan the volume of visits and the conversion rate % of your e -shop, you already have a planned volume of transactions (transaction = traffic x conversion rate %). Add to that the average value of an order by multiplying the number of transactions to get revenue volume.

 Searching for connections in data or how to succeed in the field of e-commerce, which is dominated by in the Czech Republic and by Amazon abroad?

As I mentioned above, not even all the data in the world can compensate for the business “power” of the uniqueness of your product or service, the positive customer approach, and the quality of work done well. At the same time, even the best product in the world will not sell if no one knows it exists. What marketing channels, platforms, and technologies and people should you invest in? The answer to this question lies in the interconnection of data sources important for your business.

 What data sources can, for example, support the growth of your e-shop?

-> Data from Google Analytics, where thanks to the well-thought-out architecture of queries to the GA API, we can also conjure things up from the standard (unpaid) GA for things that even the GA 360 does not have to be ashamed of

-> Data from your ad platforms, such as:

  • Google Ads (even data that are not available as part of GA integration, not even in the Google Ads API)
  • Facebook
  • Sklik
  • RTB platforms
  • Affiliate systems
  • Mailing systems
  • Manually entered data
  • And more..

-> Transaction database

  • Here, very valuable business information is stored not only about your orders and actual margins, but also about repeat purchases from customers (according to the hash of the e-mail or phone number).

-> SEO data of a technical nature, e.g., very detailed information from regular crawling of your and your competition’s website.

-> SEO data of a business nature such as selected keyword positions, your market share as well as the competition and other information (one of the data sources for us in this case is or

-> Google Search Console, which also contains very valuable information not only about the status of organic searches on Google.

-> Data about your competition, which is freely available and it is important for you to process this data regularly and use it for strategic decisions within your business.


-> And much more…

But how do you know your way around such a massive amount of information and when to find the time for it?

The solution, in this case, is to use more advanced methods of data processing and automation, thanks to which you “manually” go through only those parts of your data in which an opportunity worthy of your attention has been identified.

 Unfortunately, advanced analytics and data handling are very often compared to the visualization of Google Analytics data in Google Data Studio dashboards. In better cases, at the very least use the basic data blend function (that is, merging data from multiple sources together). Unfortunately, there are a large number of “specialists” in the current market who only visualize data from one source in another visualization tool (e.g. Google Analytics data visualized in Google Data Studio) without any further plan to work with the data, enrich it and actually use it for the development of your business. Unfortunately, at first glance, a layman does not recognize the difference between amateur and engineering data processing at the level of visualization.

 What is the difference between visualizing data from Google Analytics that flows through a professionally created data infrastructure, compared to visualizations that are created with a native GA connector that is available directly in Google Data Studio? About the same as between a graphic designer who works in Adobe Creative Cloud and a graphic designer who works in Windows Paint :-).

 A huge part of the work and know-how needed for professional data processing will be done for you by the platform, thanks to which you can process business-critical data from various sources to create a scalable, long-term sustainable data ecosystem (data model) that will enable you to compete with “small” e-shops as well as such giants as and for a fraction of the investment they make in the development of their data ecosystem. How is it possible? David also defeated Goliath, as his agility and sharpness were enough. You are small and in their eyes “below the distinctive level”, but that does not mean that you can not be better for a fraction of the cost :-).

 You can then use the data model built, for example, within for customer segmentation, automation of advertising campaigns both at the level of targeting and at the level of architecture for advertising accounts, to identify business opportunities and inefficient investments.

 Does that sound too complex? Yes, in recent years e-commerce has become a very complex world, in which it is necessary to include a large amount of knowledge from several different fields. How can we achieve this? Collaborate with the right partners in areas where you do not feel strong yourself (eg. if you are not a web developer, set up an e-shop on Shoptet) and instead focus on those where you can build your unique position in the market.

 Data visualization tools

All the data that is currently important for your business is already connected in one place. But where to visualise the information obtained? Personally, I basically prefer to start with Google Data Studio.

Why use Google Data Studio?

  1. It’s free!
  2. It enables very simple collaboration and sharing of reports and dashboards (for me, a key function for which GDS has no competition).
  3. Simple work with visualizations and their editing.
  4. A plethora of community templates (more here).
  5. It allows you to basically link data from multiple sources using the Data Blend feature (although this section may be more detrimental, as you read in the previous paragraph).

Other visualization platforms and their advantages

  • PowerBI -> runs locally, thanks to which it has enough computing capacity to process a larger volume of data. It is possible to use it for free.
  • Tableau -> a very powerful tool for local processing of large data cubes (BigData), only paid (from approx. 20,000 CZK / year).
  • And more…

What views of data are absolutely essential for e-shop management?

Personally, I can no longer imagine managing investment in an e-commerce project without these 4 key views of data:

  1. A look at the cost and profitability at the level of the operating profit of individual marketing channels, including fixed operating costs (= order costs) as well as the cost of campaign managers, agencies, freelance consultants, and more.

  1. Customer analytics, thanks to which you will know the acquisition chart of your e-shop (i.e., how many new customers you acquire at a given time) and especially how much these new customers cost you (according to marketing channel) and also how much repeat orders cost you.

  1. Attribution modelling according to a different methodology than the one offered by Google Analytics

    Mi Data-Driven Attribution Analysis – Bar Chart Visualization
  1. Product analytics, thanks to which I know which categories and which specific products are currently sold via which channels, what differences are in the year-on-year comparison, etc.

Each of these topics would suffice for its own series of articles, maybe someday will be enclosed here or you can just get in touch with us to work together on your data-driven ecosystem. For a specific idea of what the visualizations of the above information may look like, I am enclosing screenshots from our platform for data management of e-shops

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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.