Find your business potential through marketing data / Part 1

Find your business potential through marketing data / Part 1

The content of the following articles was created with the aim of creating a comprehensive overview of the most important data sources, which we recommend for e-shop management using the most well-known external tools. When working with data, a real-world context is very important in the first instance. For this reason, the author of the article Milan Merglevský describes the use of data in business examples from his practice.

In the realm of e-commerce business, you can have two best friends who will show you the way to success – online marketing and data. Based on results and analyses, you can find the right key to fulfilling your business potential.

So what data is most important for e-shoppers? -> The obligatory answer to an obligatory question: “It depends :-)”.

Author’s tip: if someone answers you with the phrase “it depends” and does not immediately follow up with “it depends on what’s the issue?”, do not talk to them anymore, it is a waste of time and money.

Why should every e-shopper track data about user behavior on the web?

Simply put, an e-shop works like a classic store. So a customer comes to your store, wanders between the shelves for a while, and with a little luck they buy something from you. With a classic store, it is very difficult to determine exactly from what source the customer came to your store (we know that they came through the door off the street, but it is already very difficult to acquire more details). An attentive salesperson can watch the customer move around the store (large players such as Ikea even have detectives in their stores who track the movement of selected customers and optimize the routes and locations where the goods are displayed based on the data collected), however, processing information obtained by observation is essentially mission impossible for most smaller retail stores.

On the contrary, the advantage of an e-shop is perfect knowledge of the source (marketing channel) from which the customer came to you, what products and pages they visited on your website and how many times before they finally ordered something. The advantage of an e-shop is that expensive rent does not have to be paid for renting a space in a busy place with a high potential for the arrival of customers. The disadvantage of an e-shop is the fact that each visit (even the visits from an organic search) costs you something. For a paid search (Google / cpc, Seznam / cpc) the price for one visit is usually between 2 – 15 CZK and from social networks, it is around 20 CZK.

It is necessary to realize that a visit, for which we paid 25 CZK thanks to a marketing campaign on social networks, does not mean that you paid 25 CZK for gaining a customer. Out of 100 people who come to your website, on average 2-3 people will buy something (= conversion ratio 2% – 3%). If you paid an average of approximately 6 CZK for each visit, one order would cost you 300 CZK at a 2% conversion rate.

Is 300 CZK per order (CPA) a lot or a little? In answering this question, it is first necessary to clarify:

  1. What is the average value of the order?
  2. What is the average margin on my order?
  3. What are the other operating costs associated with the order?
  4. What percentage of orders will be returned? (= return ratio).

If we have an average order value, for example, of 1,200 CZK, with a margin of 35%, we will be left with after deducting the marketing costs of obtaining an order (CPA) from the margin of 120 CZK (1200 X 0.35 – 300). If 120 CZK is enough to cover other operating costs (your salary, PPC campaign manager, packaging and transport costs, warehouse rent, etc.), you have earned a profit on the order. If 120 CZK is not enough to cover operating costs, you will end up with a company operating at a loss at the end of the month (which is unsustainable in the long run).

There’s no point in lying to yourself. The price for getting a visit to your e-shop is quite high nowadays (the range of 3 – 6 CZK is from dozens of domains for which we make analytics a fairly standard level depending on the competition of the selected segment of offered products/services.) and it should be taken into account that this price will continue to increase in the future. Always calculate the profitability of investments into individual marketing sources well. There is no value in getting orders at a loss (unless you do not have a correctly calculated “customer lifetime value” and are not driving your business in this direction, which is a professional discipline in itself). Less is sometimes more.

Calculate it, and calculate it well. Otherwise, you’re in a fight with evil. E-commerce is a tough industry. Mistakes are not easy to forgive.

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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.