6 predictions for the Analytics Industry in 2022.

As we’re nearing the end of 2021 tradition requires us to reflect upon the year that has passed. The team at Piwik Pro however, asked us to do something different. They asked us to predict what we believe 2022 would look like. More specifically, what the world of analytics would look like going forward. They gave us 1500 characters to express that vision. These ideas got bundled in their “Marketing, Technology & Privacy Forecast for 2022”. We realised that their question sparked quite the conversation in our team, but also that 1500 characters wasn’t much. So we sat down, poured some coffee and started fleshing out some of our forecasts more in detail. You’ll find these ideas in this article. So here are our 6 predictions for the analytics industry in 2022:



1. Event based tracking will be the primary way to track web behavior


The measurement model is changing. From hits, sessions and visitors to a more granular measurement model called event based tracking. Event based tracking uses event-property (or attributes, parameters, etc. Whatever you would like to call it) combinations that allows you to customize measurement entirely and contextualise every event to your personal context. Even though a number of products were already focused on event based tracking methodologies it is only in the last two years that this trend has accelerated. Mainly because some major analytics players (read Google Analytics) have overhauled their tracking methodology to an event based model. This forced a large number of organisations and their data analysts to follow.


That being said - many organisations are struggling with this fundamental change in measurement system and methodology. Event based tracking requires not only more implementation effort but also requires everyone to actually become a better analyst. To be able to ask the questions and build the hypothesis before diving into the data. It’s a scary thing for many web analysts that have been doing their work by scrolling through pre-built reports. However, it’s a change that comes with more benefits than drawbacks and is driving the way the analytics industry is evolving.



2. Web analytics will evolve in a multitude of fields tailored to the needs of different stakeholders


For a very long time the term web analytics covered the use cases relevant to it. Today, more and more, the field of analytics is evolving, adapting and morphing into different fields each with their proper tooling and roles that cover a number of different use cases. This shift is being facilitated due to the increasing adoption of the event based measurement model.

Marketing analytics

This field is addressed explicitly to marketers. It’s the most traditional form of web analytics and focuses on understanding how users reach websites and if they reach certain goals during their session(s) in order to optimise marketing spend. It’s heavily tied to performance marketing KPIs and reporting and therefore resembles the original form of web analytics the most.



Product analytics

The field of product analytics adheres, even though its name would suggest, not only to product teams, product managers and product owners but also people working in the field of UX, A/B testing and development. These roles focus on understanding which product features drive adoption, monetisation and stickiness and how this can feed a next iteration of product (website or app) improvement. These teams will gain more and more traction as performance marketing teams focused on acquisition through media buying are being put under pressure due to the evolution of that industry (increasing privacy regulation, pressure on cookies, the rise of walled gardens, etc).


This means that every website prospect is becoming increasingly more valuable and that the website (considered as a product) needs to perform at its best in order to transform that prospect into an actual client. It’s the core responsibility of the product team to ensure the product experience (and thus client experience with that regards) is top-notch. Enter data-driven product development as a philosophy.



Customer analytics

Customer analytics is, under this form at least, a relatively new field within the world of analytics. It focuses on increasing the understanding of who the users actually are, how they can be segmented and what the best personalised approach is. Even though it sounds trivial and something that BI teams have been doing in some companies, the fact that this field is actually being named as customer analytics is extremely important. It indicates that the customer is finally being put at the center.


It’s also the field that is linked to technologies such as Customer Data Platforms (or CDPs) or reverse Extract Transform & Load (or reverse ETL, LTE) solutions that sit on top of Data Warehouses such as Google Bigquery. And we all know these are fundamentally changing the way things are being done.



3. The role of the analytics team will change. From being a service desk to facilitating data democratisation


When the field of analytics started to develop it was only occupied by a handful of people minding their own business using their own tools in a corner of the office building. Quite obvious, as the digital space was very young and attracted only believers, techies and people interested in measuring what was going on on these platforms.


Today analytics teams have evolved into full fledged teams that, still all too often, work in their corner and use their own increasingly complex tools. They’re being perceived as the wizards that solve problems and answer questions by wielding the wand of data. No one really understands how their dark art works, but all too often no one is interested either. Data is complex and scary. People interacting with these teams just want their problems to go away. These problems are often taking the form of tickets that require a report as an answer.


However, that is changing. From where we are sitting, at Human37, we see a fundamental shift that is happening. Slowly but surely we see that analytics teams are becoming facilitators for data analysis rather than the only ones executing data analysis. As more and more roles and departments are seeing the benefit of using data (see the above fields such as marketing, product and customer analytics) to make informed decisions and the pace at which this access to data is required is increasing these roles and departments are starting to consume the data themselves. Therefore the role of the analytics team is changing from purely solving problems and answering questions to facilitating data access, tooling and educating teams and users to properly surface insights correctly.


As such they are transforming from this team that is the only one capable of working with the data and digging for insights to a team that is facilitating data democratization.



4. Analytics is no longer a standalone solution, it’s part of a stack


With the evolution of analytics into different fields, analytics can no longer be seen as a standalone solution. It’s part of what we call an analytics or MarTech stack. More and more analytics tools are being used in parallel where each platform is being used by a different type of stakeholder (market, product, etc) and can even be fed through a central data pipeline such as a CDP.


These analytics platforms can then be linked to activation channels such as Email Service Providers (ESPs) or advertising channels in order to activate certain segments or cohorts of users and drive personalised experiences. We finally come to a point where data is being activated, and not just being analysed or reported on. The same goes for the marketing tools. They too become part of something bigger. It’s part of a best of breed stack building philosophy.



5. The rise of a new KPI - the identification rate.


While performance marketing focused on media based acquisition is under pressure, brands are being told to leverage their first party data. And more specifically, their customer data.


Why? Because this data can be used for building custom audiences and lookalike audiences in many of the walled gardens. Next to that it allows companies to reach out directly to customers and build a more personal relationship (taking the required consents into account). It also means that organisations need to start thinking about the value exchange they offer to anonymous users in order for them to identify themselves by creating an account or signing up for a newsletter.


For many organisations this will become front and center when designing communication and marketing campaigns and results in an entirely new KPI: the identification rate. This KPI represents a conversion rate focused on transforming anonymous users into known prospects or even customers.



6. As much as I would like to be wrong: Building dashboards or reports and looking at them will still be the primary way of “doing analytics” for the majority of companies in 2022.


Data only has value when acted upon or when it’s being activated. Marketing data should be used to optimise marketing campaigns, make informed decisions and drive personal marketing experiences. Product analytics data should be used to understand where users get stuck or what draws them in, what features should be optimised, which A/B tests should be built and what the product roadmap as a whole should look like. Customer analytics is the core to focusing on the customer. What does your customer require during the current or next interaction with your organisation, how can we anticipate that and delight the customer. Data is only useful if you use it to guide your next decision, iteration and when it’s activated. Not when its final purpose is to sit on a slide during yet another meeting. However, for many organisations that call themselves data-driven this is the end of the line for most of the data they have in their hands. And this too will still be valid in 2022. Even though I would love to be wrong about this prediction.


Wishing you a data-rich 2022


So here they are. Our 6 predictions for 2022 in the world of analytics. There are many more things to discuss than these 6. We realise that. Do you recognise yourself struggling with any of these or do you have questions about them? Reach out. Happy to discuss with you.