Artificial intelligence for marketing results

Can AI add value in predicting marketing outcomes?

Peter Desmyttere
December 6, 2023
⏱ 4 min. read

A recent marketing benchmark report on artificial intelligence (AI) indicated that 61.4% of marketers are already using AI to optimise their marketing activities. The majority (44.4%) use AI for content production. The next step will undoubtedly be the use of AI to determine, via marketing analytics, which content is or is not successful. The result of these analyses will then probably lead to an adjustment of the content marketing plan. But using AI to analyse marketing results will go beyond content. The results of, say, digital advertising campaigns, landing pages for SEO or the number of registrants for an event will also be quickly and clearly mapped by AI. AI will thus become an ally for marketers who want more focus on analysing data, spotting trends, and providing valuable insights. In this article, I map out the basics of ‘measuring marketing results through AI’ for marketers.

Roadmap for marketing results analysis

The biggest danger in setting up a project to measure marketing results is setting an overload of KPIs. I wrote that in an earlier article 'Analyse, structure and manage marketing results'. There is a huge amount of marketing data available, but it is so scattered across a multitude of data platforms that it is impossible for the average marketer to get started.

The technology company Gartner identifies 4 stages in the process of doing marketing analysis:

  • analyse what happened (descriptive analyses)
  • analyse why it happened (diagnostic analyses)
  • analyse and predict what will happen (predictive analyses)
  • analyse what should happen (prescriptive analyses)

The difference between these 4 phases briefly explained:

  • descriptive analyses are used to identify the results of ongoing or past marketing campaigns.
  • diagnostic analyses examine why those particular marketing results came about.
  • predictive analyses use both previous analyses to make predictions on marketing results.
  • prescriptive analyses are the 'nec plus ultra' of marketing analyses; they indicate what needs to be done to achieve predictive results based on historical data, external data, and various sources for recommendations

Obviously, covering all stages is unfeasible and unaffordable today. However, it is far from a dream scenario that marketers should soon be able to use AI to drive descriptive analytics (what happened), to diagnose (what is the cause or driver of this outcome) and to do predictive analytics that influences marketing planning.

Predictive analytics may be the target at the date of writing this article. For example, you can use these analytics to determine the best time to launch a campaign for a new product, to market a promotional campaign, or to plan a particular event. But equally, predictive analytics will provide recommendations to content marketers to determine the appropriate timing of blogs, videos, or social media and will be able to intervene in the content calendar. AI as a soothsayer for marketers, in other words.

Today, there are a lot of tools and solutions in the pipeline. But there are also strategic tools already incorporating AI into their marketing analytics. The most obvious analytics tool - and probably the most used by marketers - is Google Analytics.

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What AI applications does Google Analytics offer?

Many of the data-driven insights in GA4 that you use every day are already driven by AI. A free marketing analytics tool that covers the start of your marketing analytics project right now. Great right? 

Google Analytics offers built-in artificial intelligence capabilities such as insights and recommendations. Insights are unusual changes, emerging trends, and other insights about your site or app. Recommendations offer tailored suggestions to help you get the most useful and accurate data and take advantage of new, relevant features as soon as they become available.

In their video Artificial Intelligence capabilities in Google Analytics, you will learn how to use and customise these capabilities to suit your business needs.

Automated insights into marketing results

One of the most prominent AI-driven features in GA4 is automated insights. Suppose organic search traffic suddenly accounts for more than 50% of conversions, GA4 will uncover this anomaly as an automated insight. Another example: suppose social media posts would account for a remarkable -50% drop in conversions, GA4 will immediately share this insight with you.

So you no longer have to, like a human analyst endlessly analysing statistics to discover variations, look for statistically significant changes that affect your marketing. AI in GA4 does that for you and learns itself which insights are best aligned with your business based on your feedback. You do this by giving a thumbs-up or thumbs-down to each insight, then training the algorithms on what is valuable to you.

Tailor-made insights into marketing results

However, Google Analytics offers much more than just automated insights. With custom insights, you can show exactly those metrics or chart KPIs that are important to you. Some descriptive analytics you can set up yourself:

  • send a notification if more than 75% of your visitors are browsing via mobile, to avoid over-targeting your campaigns to desktop
  • send a notification if more than 10% of sales come from a new country
  • send a notification if one ad campaign accounts for more than 25% of the sales on an online shop
  • send a notification if the conversion cost for a certain keyword is too high, so you can adjust the ad budget or stop the ad

Thus, Google Analytics becomes a kind of analytics assistant by your side, making recommendations along with clear steps to implement suggestions.

When measuring marketing results, automated insights reveal meaningful changes in marketing data. Customised insights make it possible to accurately track key metrics. And recommendations provide an analytics assistant to make better decisions. No wonder Google Analytics is the first tool you should use if you want to use AI for marketing results.



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