9 Analytics Trends Marketers Should Expect in 2023

Analytics planning that has always been based on customers sitting in front of a browser would grow complicated as a result. Measurement planning should evolve in step with the changing customer journey.?

Marketers have to consider the impact of the below trends on their teams and clients deliverables when working out their analytics.?

1. Juggling Insights from Even More Touchpoints?

The increasing number of touchpoints in a customer journey makes analytics that focuses on static HTML content antique like the Model T. New innovations, including the rise of D2C, have decentralized where consumer engagement opportunities occur.?

The coronavirus pandemic has further shifted sales opportunities and customer journey, as people adopted work from home environments. The resultant shift in the consumer behavior, including pushing new demographics into online shopping and affording the opportunity for more people to engage with streamed media such as podcasts, offered marketers even more ways to reach customers and more regions to focus their analytics as a result.?

Some of the difficulties with analytics are: correlating in-episodic activity with digital media to where customers are present digitally so the activity has a solid connection to business value.?

The takeaway: Marketers have to look at how the metrics context is changing against sales. Analytical solutions are likely to push features that demonstrate how you can correlate tagged page events to sales activity or as an influence on customer behavior.?

2. Measurement Becoming Even Less Browser Dependent

For a long time analytics remained associated with websites, until it gradually adjusted to accommodate other media formats, such as mobile apps.?

Setting up cookie-less measures was the first step in shifting web analytics from its browser-diagnostic roots.?

Google Analytics 4 sees conversions as a percentage of individuals who trigger an event, an evolution from a percentage of individuals reaching a particular website page or section. This modification may seem Google-specific in removing bounce rate as a metric, however, it reflects a general trend of incorporating data sources that are not dependent on the browser.?

In 2023, media formats such as VR/AR, streaming services and even NFTs would introduce new measurement requirements that would refine analytics data as less web-centric.?

The takeaway: Marketers should anticipate more analytic solutions to emphasize events, meaning that they should begin planning their media strategies so the metrics better correlate to business objectives.

3. Incorporating Accessibility in Analytic Planning

Accessibility is still an essential topic in the developer community. Companies now have a chance to adjust websites for accessibility besides other analytic tasks, such as A/B testing or evaluating page speed. When a notable portion of business moved online, it only increased the need for assistive technology. Combining accessibility efforts with analytics planning can assist organizations avoid implementing accessibility features in a haphazard way so they launch websites that satisfy accessibility standards.?

The takeaway: Marketers should make sure that their website development roadmap incorporates testing for accessibility among its tag tasks.?

4. SEO Adapts to Accommodate New Search Behaviors

SEO has been a path of continuous evolution since its inception. Search engine algorithms evolved to become more sophisticated, voice search emerged, mobile-first search queries took over the search world. Marketers have had to modify their SEO strategies as a result. One of their difficulties now is setting up how search phrases may have changed over the course of the pandemic, so they have a clearer view of what users are looking for and how to provide content against search query results.?

The takeaway: Marketers should rethink content and SEO to account for almost 2 years of pandemic-influenced search, then determine how to improve their content and strategy for 2023. Look for SEO solution platforms to offer increased analytics features, such as intent optimization and semantic search options. Moreover, consider auxiliary search patterns on other platforms, like Instagram and Pinterest.?

5. Self-Service Analytics Solutions Ease Project Iteration???

Self-service analytics offer a virtual sandbox for investigating analytics concepts. Lately those sandboxes have started to increase their capabilities to make project iteration easier. These features include simple connectivity to database sources and APIs, and data visualization in interactive dashboards. No-code features are also easing the tasks of advanced analytics.?

The takeaway: Marketers must ask two essential questions when considering a shared self-service platform:

What advanced analytics process the solution improves??

How easy is it to incorporate data into the self-service environment??

Finding answers to these two questions can instantly narrow down your options, while ensuring common workflow challenges are met and individual agility to review analysis ideas and explore data is retained.

6. IT Team Reshuffling Its Analytic Responsibilities

In the past, IT teams have been responsible for backend structures like database maintenance. However, in recent years, the business teams, particularly analytic departments, adopted cloud architecture to access API services and data, freeing up IT teams in the process. Although IT is still responsible for maintaining data access – which is especially difficult during the work from home shift of the last two years – the shift in responsibilities offers a small measure of freedom to IT teams and business users alike.?

The takeaway: Marketers should try for collaboration opportunities with IT teams to improve ongoing data maintenance. The collaboration would assist analytics teams deliver better up-to-date reporting for partners and departments. It will also enhance research into tech innovations that buoy privacy and data security requirements, such as cybersecurity data mesh, as flashed by Gartner.?

7. A Central Repository for Support Materials Would Help Analytics Projects

With plenty of different open source projects employed for advanced data modeling and calculations, a central repository for support material is becoming more and more a must-have to coordinate shared knowledge within the analyst team. Support material assists analysts verify their dependencies in analysis projects correspond with the most recent data maintenance information. That QA step can have a significant impact on enhancing the data that feeds advanced models downstream and also in avoiding PII from being inadvertently inserted into a model.?

The takeaway: Marketers must look for platforms that can aid parse information instantly to support content development. The platform can range from basic common solutions like GitHub repository employed among a shared team, to in-house content management and CDP solutions.?

8. Automating Your Way to Decision Intelligence

In 2020, Gartner predicted that a third of analysts would employ decision intelligence by 2023 to improve decisions. Savvy marketers would seek systems that can be an analytics narrator, tools that can act as a help by instantly recounting events from data and spot useful insights. Using automation in the insight process can also minimize burnout from extra online work.?

Employing automation to set alerts and decisions can assist quickly scale output and decrease the stress of making workflow decisions for managers and analysts.?

The takeaway: Marketers have to look for automation innovations in different marketing domains, like using Python for automated keyword clustering in SEO.?

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

Identifying who can be the data griot – the “digital narrator” of company culture who can implement insights and traditions into data decisions – will become more difficult.? Hiring demand for analysts was already exceeding candidate availability before the coronavirus pandemic. The pressure to onboard analysts and instantly collect insights would set up further tension on retention. Other initiatives like boosting diversity on analytics teams, would also feel the effects.?

The takeaway: Marketers have to be savvy in deciding where they seek analytic talent, especially with diversity initiatives in mind. People speak of transitioning into tech, however, analytics extracts insights from different industries. HR managers would have to imagine how individuals can draw on their experiences to be the right candidate who would obtain customer experience insights from data.?

Conclusion?

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