How to set basics business goals? / Part 3

From economic terminology, I would like to use the term “marginal utility theory” here. It simply describes a development where each additional profit unit purchased is more expensive than the previous unit. In other words, you usually buy your most loyal (VIP) customers from the region of your business and for a significantly lower price than the remaining customers.
 
 
Visualisation of marginal utility theory, source: https://xplaind.com/

Visualisation of marginal utility theory, source: https://xplaind.com/

 
“So how do I know that I want to run my business towards profit rather than sales volume, or vice versa?”
 
The difficulty of answering this question is equivalent to the question: “What should I do to be happy in life?”. So, it’s nice to have someone you can consult about your plans, ask them such a challenging question and use their practical experience, but it’s important to realize that the final decision and especially the real work is 95% up to you. No one will gain happiness for you and no one will build your business for you. Your business (e-shop) is reflected a lot in your personality, dreams, and goals.
 
“Hmm, so I don’t know where to start now, it’s just getting complicated.”
 

Business plan

In my experience, the first step to a successful data-driven e-shop is a business plan. From dozens of different online businesses and hundreds to thousands of hours spent on business planning, we have gradually put together a template based on the free Google Sheets technology (freely available later in this article).
 
In our simple template, you can start managing your e-shop (in terms of technology, Google Sheets and spreadsheets are generally just the beginning of the journey and the know-how you have to go through).
 
Template for simple planning of operational and marketing investment of an e-shop
Basically, it is necessary to put together the company’s operating costs (salaries, rents and other fixed items), marketing costs and margin information (at least the overall average) in one place. You will get an overview of how many orders you need for your company to generate the required amount of cash flow at the end of the month (or fiscal period). You can download the template for planning within Google Sheets as our best practice tip for beginning here for free.
 
Author’s tip: When working with data, try (even if you’re at the very beginning and only have 10 orders a day) to work with scalable cloud technologies like Google Sheets, the general Google Cloud Platform, and other tools that you’ll read about later in this article. Always keep in mind: everything I do must be easily available for collaborative work online and ideally automated, otherwise the competition will overwhelm me. It is best to completely avoid locally stored Excel files and similar customs from times past. This “innovative” approach is more important (and simpler and cheaper) than it might seem at first glance. For me, one of the keys to the success of effective e-shop management or “every saved second of time counts”.
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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.