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Artificial intelligence in marketing can be increasingly used more than before when the phrase was “Half my advertising spend is wasted; the trouble is, I don’t know which half.” Attributed to John Wanamaker (1838-1922), fast forwarding to our times, many companies still struggle with this blockage.

Given the organizational context, it can be normal for small businesses not to measure their marketing initiatives beyond the obvious clicks, impressions, and sometimes sales, since they run isolated campaigns and in this way, they manage, but they do not have operational processes to measure.

Despite this context of small businesses, medium and large companies take care to set their systems to collect data to decide with more structured information. In this case, there is no excuse, as Google Analytics 4 (the free standard edition) allows a configuration that, if done professionally, enables keeping up with these new times (measuring all the campaigns that interact on the website and App such as:

– Traffic sources and campaigns

– Most viewed and most interacted with pages

– Context of the articles viewed and sold (using behaviour and more data on the context of the action ex: size, color, temperature )

– Allowing to offer the customer what they are searching through integration with the Google Marketing Platform (Google Ads)

– Sophisticated analysis of the consumer journey and the possibility to segment and create audiences. (People who bought a certain type of items)

Unfortunately, when migrating from Google Analytics Universal Edition to Google Analytics 4, many of the settings and functionalities were demoted to a third plan and sometimes forgotten, on the other hand, user training was not considered. For this reason, users are not familiar with the potential of the tool and find the platform strange, and since they do not have a professional configuration, they have problems with settings and they are lost.

But to move to digital maturity, we need to start with the basics, i.e., a solid and documented configuration (using dataLayer) that is appropriate for the organization and the changes and evolutions that are needed in the future.

In this way instead of just using what Google Analytics 4 allows, and especially using churn prediction and the probability of purchase for e-commerce websites (see what we propose in our data-driven marketing framework) which allows evolution by stages and with consistency.

In one of these stages, we use Google Analytics 4 to perform a direct export to Google BigQuery, which is a data warehouse that allows collecting raw data from Google Analytics 4. Using this functionality, we can use the data integrated between the website data with the CRM and other data sources. We can model the data for different business objectives allowing for better efficiency of means. Some of these objectives may be:

– Lead scoring allows obtaining value through interaction and interest in certain pages and actions

– Market basket analysis, allows joining items that are more sold together)

– Purchase frequency and the possibility of buying the suggested item

– By business rules (complaint, bad payment list)

– Customers with a high-value churn potential.

Join us! We are supporting organizations from different verticals to extract value from data-driven approach to marketing.

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