Marketing AnalyticsAs everything is becoming more and more data-driven, there has never been a greater need for high-quality marketing analytics.

Despite this need, there are still many companies who’s marketing analytics still only consists of a few dashboards and a weekly report.

However, more forward-thinking organisations have been pushing ahead with new analytics processes and tools. Below are some of the trends in marketing analytics set for this year.

New Data Sources

Traditionally marketing analytics have relied on big centralised-managed data servers or data warehouses to help companies make important business decisions. This has recently changed and now companies have found that analysts also need to search for data stored in many ‘mini’ data warehouses.

Apart from the usual internal data repositories, marketing analysts also should be pulling data in from other separate systems such as:

  • Google Analytics.
  • CRM provider such as Salesforce.
  • Email service provider.
  • SEO platform.
  • Social media platforms.
  • Chat applications.


When these data sources are combined they provide much better insights for marketing and sales than the internal systems on their own, helping businesses to optimise pricing, deliver an improved customer experience, and generate extra consumer interest.

This information shows that analyst must now do more than just analyse, they also need to identify where important data resides, determine what needs to be extracted, and devise a strategy for using new data sources to drive business decisions.

Artificial Intelligence

The speed of data coming into organisations has increased to the extent that it is now not possible for human analysts to process it all.

To solve this issue, firms are offering marketing analytics with baked-in-artificial intelligence. By using machine learning and other AI techniques these systems can then help analysts find patterns in customer data, allowing non-professionals to access complicated analytics using simple languages and elicit recommendations for optimising performance.

An example of this can be seen with Hyper Anna. This is an AI-powered data analyst that takes in company data and returns high-impact use cases. This means that marketing data such as financial performance, customer interactions, and supplier activities can be uploaded, and Hyper Anna will provide information about revenue forecasting, supply chain management information and upsell and cross-sell opportunities.

Storytelling Analysts

Because of the new data sources and AI tools, this year will see the traditional skill set of the analyst change. Skills such as SQL, Excel and business analysis will still be important, but analysts will be expected to do much more than just crunch data and produce reports. Some of new tasks analysts will be expected to carry out include:

  • Obtain data from non-traditional sources.
  • Use programming languages such as Python to clean data.
  • Use visualisation tools to polish the data and create attractive charts and graphs.
  • Transform data into easy-to-understand stories, so non-analysts can understand emerging trends and opportunities.


Analysts are going to have to focus more on their customers this year – simply sending out a weekly dashboard and a weekly report will not be enough anymore.

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