11 Marketing Analytics Trends for 2022

In 2022, marketers may well feel like short-order cooks slammed with customer orders. Except in the case of marketers, the orders will be customer needs spread across a variety of domains. A confluence of various tech and environment trends — privacy, elimination of cookies, changing customer journeys and uncertainty around long-time measurement standards — will only add to the frenzy. 

Analytics planning, which has long been based on customers sitting in front of a browser, will grow complicated as a result. Measurement planning must evolve in step with the evolving customer journey.

Marketers should consider the impact of the following trends on their teams and client deliverables when planning their analytics.

1. Juggling Insights From Even More Touchpoints 

The growing number of touchpoints in customer journeys render analytics that focus on static HTML content as antiquated as the Model T. New innovations, including the rise of D2C, have decentralized where consumer engagement opportunities occur. 

The COVID-19 pandemic has further shifted the customer journey and sales opportunities, as people adopted work from home environments. The shift in consumer behavior which resulted, including pushing new demographics into online shopping and affording the opportunity for more people to engage with streamed media such as podcasts, provided marketers with even more methods to reach customers — and more areas to focus their analytics as a result.

Some of the analytics challenges include correlating in-episodic activity with digital media to where customers exist digitally so the activity has a clear connection to business value.

What This Means: Marketers should look to how the metrics context is changing against sales. Expect analytic solutions to push features that demonstrate how tagged page events can be correlated to sales activity or as an influence on customer behavior. 

2. Measurement Becoming Even Less Browser Dependent

Analytics was long associated with websites, though it eventually adjusted to accommodate other media formats, such as apps. 

Establishing cookie-less measurement was a first step in moving web analytics solutions from its browser-diagnostic roots.

As I explained in my bounce rate post, Google Analytics 4 identifies conversions as a percentage of people who trigger an event, an evolution from a percentage of people reaching a specific website page or section. This change may seem Google-specific in eliminating bounce rate a metric, but it reflects a general trend of incorporating data sources that are not based on the browser.  

In 2022, media formats like AR/VR, streaming services and potentially even NFTs will introduce new measurement needs that will redefine analytics data as less web-centric.

What This Means: Marketers should expect more analytic solutions to emphasize events, which means they should start planning their media strategies so the metrics better correlate to business objectives.

3. Including Accessibility in Analytic Planning

Accessibility remains an important topic in the developer community. Businesses now have an opportunity to adjust websites for accessibility alongside other analytic tasks, such as evaluating page speed or A/B testing. When a significant portion of business moved online, it only heightened the need for assistive technology. Pairing accessibility efforts with analytics planning can help organizations avoid implementing accessibility features in a haphazard way so they launch websites that meet accessibility standards.

What This Means: Marketers must ensure that their website development roadmap includes testing for accessibility among its tag tasks.

4. SEO Adapts to Accommodate New Search Behaviors

SEO has been on a path of continued evolution since its inception. Search engine algorithms grew more sophisticated, voice search arrived, mobile-first search queries dominated the search world. Marketers have had to evolve their SEO strategies as a result. One of their challenges now is establishing how search phrases may have changed over the course of pandemic, so they have a clearer view of what people are looking for and how to deliver content against search query results.

What This Means: Marketers must rethink SEO and content to account for nearly two years of pandemic-influenced search, then determine how to strengthen their content and strategy for 2022. Look for SEO solution platforms to provide increased analytics features, such as intent optimization and semantic search options. Also consider auxiliary search patterns on other platforms, such as Pinterest and Instagram.

5. Self-Service Analytics Solutions Ease Project Iteration   

Self-service analytics provides a virtual sandbox for exploring analytics concepts. Lately those sandboxes have begun to augment their capabilities to make project iteration easier. These features include data visualization in interactive dashboards and simple connectivity to database sources and APIs. No-code features are also making advanced analytics tasks easier.

What This Means: Marketers should ask two key questions when considering a shared self-service platform:

• What advanced analytics processes the solution enhances.
• How convenient is it to incorporate data into the self-service environment?

Answering these two questions can quickly narrow down your choices, while ensuring common workflow challenges are met and individual agility to review analysis concepts and explore data is retained. 

6. IT Reshuffling Its Analytic Responsibilities

IT teams have traditionally been responsible for back-end structures such as database maintenance. Yet in recent years business teams, especially analytic departments, adopted cloud architectures to access data and API services, which freed up IT teams in the process. While IT is still tasked with maintaining data access — which is especially challenging during the work from home shift of the last two years — the shift in responsibilities provides a small measure of freedom to business users and IT teams alike.

What This Means: Marketers should look for collaboration opportunities with IT to enhance ongoing data maintenance. The collaboration will help analytics teams deliver better up-to-date reporting for departments and partners. It will also improve  research into tech innovations that buoy privacy and data security needs, such as cybersecurity data mesh, as flagged by Gartner.

7. A Central Repository for Support Materials Will Aid Analytics Projects

With so many different open source projects used for advanced data modeling and calculations, a central repository for support material is increasingly a must-have to coordinate shared knowledge within analyst teams. Support material helps analysts verify their dependencies in analysis projects correspond with the latest data maintenance information. That QA step can have a big impact on improving the data that feeds advanced models downstream and also in preventing PII from being inadvertently inserted into a model.  

What This Means: Marketers should look for platforms that can help parse information quickly to support content development. The platforms can range from simple common solutions like a GitHub repository used among a shared team, to in-house content management and CDP solutions. 

8. Automating Your Way to Decision Intelligence

In 2020, Gartner predicted that one third of analysts will use decision intelligence by 2023 to augment decisions. Savvy marketers will seek systems that act as an analytics narrator, tools that can act as an aid by quickly recounting events from data and identify useful insights. Leveraging automation in the insight process can also minimize burnout from excessive online work.

Automation based on insights is an automated adoption of the “So what?” rule that analytics advocate Avinash Kaushik popularized over a decade ago. Using automation to set alerts and decisions can help quickly scale output and reduce the stress of making workflow decisions for analysts and managers.

What This Means: Marketers should look for automation innovations in various marketing domains, such as using Python for automated keyword clustering in SEO.

9. Fewer Beta Opportunities for Platforms

One of the biggest competitive differentiators for dashboards lies in their user interface. Make it simple right out of the box and win pandemic-weary strategists as a customer. Release a perhaps half-baked beta, and you will hear from the analysts. Platform providers are realizing the window to gain marketer buy-in through betas is growing smaller. For example, Google rolled out major changes into GA4 but according to Search Engine Journal, some analysts were vocal in their displeasure with some of the features. Providers must prove ease of use from the get-go.

What This Means: Marketers should look for dashboard updates, like the drag and drop interfaces in Amazon SageMaker Studio Labs and Azure ML, that simplify tasks that were previously mentally taxing. These will filter down to even the simplest dashboards. Google Analytics 4 reflects features once exclusive to GA360. Further refinements are on the way, with main competitors like Adobe Analytics not far behind.

10. Catching Up on Compliance With Global Privacy Policies

Analysts will spend a fair amount of their time finding innovative methods to get privacy right, as firms reconcile their digital presence against their global presence for compliance. Legislation passed in a few U.S. states has further raised complexity. All of this has created a tension between the need for data access to unleash business value and the push to restrict access to protect data. Many companies are still playing catch up with GDPR compliance, nearly four years after it went into effect, as Digiday reported.

What This Means: Marketers should increase the focus in data operations training to play up privacy, so marketers can identify how data flows through the organization and where it impacts privacy management and privacy protection.

11. The Great Resignation Will Have an Outsized Influence on Analyst Retention 

Spotting who can be the data griot — the “digital narrator” of company culture who can apply traditions and insights into data decisions — will get harder. Hiring demand for analysts was already far outstripping candidate availability before the COVID-19 pandemic. The pressure to onboard analysts and quickly gather insights will create further tension on retention. Other initiatives, such as increasing diversity on analytics teams, will also feel the effects.

What This Means: Marketers must be savvy in choosing where they seek analytic talent, especially with diversity initiatives in mind. People speak of transitioning into tech, but analytics draws insights from various industries. HR managers will have to imagine how people can draw on their experiences to be the right candidate who will glean customer experience insights from data.