What Does the Marketing Analytics Stack for 2021 Look Like?
With the ever-growing amount of data available and the increasing importance of analytics, marketers must be in tune with the evolving technology around them in order to take full advantage of the benefits. Research from Gartner that was conducted just before 2020 reported that the largest portion of a company’s marketing budget was dedicated to marketing technology at 26%. This trend toward optimizing marketing technology stacks will continue into 2021, with an emphasis on harnessing the power of analytics.
From the way user data is collected to how it’s processed and analyzed in the cloud, people are finding more effective and efficient ways to make sense of it all.
Continued Movement Towards ELT (as Opposed to ETL)
The use of data has significantly evolved in recent years, and in 2021, marketers will continue to move towards a more streamlined approach to data extraction through ELT (extract-load-transform). With ELT, data is stored in its raw format, then transformed appropriately on demand. Not only does this process significantly cut down on storage requirements, but because the data is raw, it allows for flexible changes to the transformation. Marketers can have all of their data ready and will be able to answer questions they never thought to ask…
This move from ETL to ELT is made possible by advances in cloud computing, with most major platforms offering reduced cost and increased scalable performance.
Bigger Investment in Server-Side Tracking
As many web browsers have stopped or plan to stop supporting third-party cookies (Google Chrome plans to phase out third-party cookies in April, while Safari and Firefox already block third-party cookies), so marketers need a new way to easily capture user identification.
This has prompted many companies to invest in server-side tracking, the act of tracking a user’s activity while visiting a website. Server-side tracking allows companies to capture information about their users directly from their sites. It also allows companies to limit the amount of data shared with third parties (e.g. Facebook, Google) and ensure their user’s data privacy is upheld while collecting more accurate data.
Humans Still Beat Machine Learning and AI
While machine learning and AI are able to draw connections and conclusions about data, they still need to be nudged in the right direction and presented with a problem to solve. The main issue is that business context is not reflected in the web analytics data. A human with the context of the business, the overarching objectives, history of data quality, and website tracking changes will always be able to generate better insights.
With the advancements in AI and machine learning, tools like Google’s GA4 are leading the way into automated insights by collecting a much wider set of data and offering a more comprehensive overview of the customer lifecycle. Google has also made it easier to leverage its cloud platform to answer specific questions that the business has.
Humans, however, must provide this context or use the results to draw final conclusions. For example, with GA4, companies still need to set up tracking in order to be able to fully leverage their measurement functionalities. This is a prime example that machine learning and AI are the tools to reach the solution, not necessarily the solution itself.
Higher Demand for Data Engineers Within Marketing Teams
With the growing increase in data volume and complexity, the need to accurately make sense of data grows too.
Dedicated data managers can be a boon to marketing teams by helping ensure the right data is being collected, the quality and reliability of that data is good, and that it is being processed and made sense of correctly.
This can help free up marketers’ time, by allowing them to focus on asking the right questions and getting the right answers without having to expend as much time and effort sifting through large pools of data.