In 2022, marketers may feel as though short order chefs are criticizing customer orders. Except in the case of marketers, the demands will be the needs of the customers spread over a variety of areas. The confluence of different technology and environment trends — privacy, the elimination of cookies, changing customer journeys and uncertainty about long-standing measurement standards — will only drive the madness further.
Analytics planning, which has long relied on clients sitting in front of the browser, will get more complex as a result. Measurement planning must evolve in parallel with the evolving customer journey.
Marketers should consider the impact of the following trends on their teams and customer deliverables when planning their analytics.
1. Juggling visions from more touch points
The increasing number of touch points in customer journeys deliver analytics focused on static HTML content as old as the Model T. New innovations, including the advent of D2C, are becoming decentralized as consumer engagement opportunities occur.
The COVID-19 pandemic has changed the customer journey and sales opportunities even further, as people have adopted working from home environments. The shift in consumer behavior that has resulted, including pushing new demographics to shop online and allowing more people to interact with streaming media like podcasts, has provided marketers with more ways to reach customers — and more areas to focus their analytics as an outcome.
Some of the challenges of analytics include connecting incidental activity in digital media to where customers are digitally so that the activity has a clear connection to business value.
What does this mean: Marketers should consider how to change the context of metrics against sales. Expect analytics solutions to drive features that show how tagged page events can be linked to sales activity or as an impact on customer behavior.
2. Scaling is less browser dependent
Analytics has long been associated with websites, although it was eventually modified to fit other media formats, such as apps.
Creating a measurement without cookies was a first step in moving web analytics solutions at the root of browser diagnostics.
As I explained in my bounce rate post, Google Analytics 4 defines conversions as a percentage of people who launch an event, which is an evolution from a percentage of people accessing a specific page or section of a website. This change may appear to be specific to Google in excluding the bounce rate metric, but it does reflect a general trend of incorporating non-browser data sources.
In 2022, media formats like AR/VR, streaming services and perhaps even NFT will introduce new measurement needs that will redefine data analytics as less web-centric.
What does this mean: Marketers should expect more analytics solutions to emphasize events, which means they should start planning their media strategies so that the metrics correlate better with business goals.
3. Include Accessibility in Analytical Planning
Accessibility remains an important topic in the developer community. Businesses now have the opportunity to modify websites for accessibility along with other analytical tasks, such as evaluating page speed or A/B testing. When a significant portion of businesses moved online, this increased the need for assistive technology. Pairing accessibility efforts with analytical planning can help organizations avoid haphazard implementation of accessibility features until websites that meet accessibility standards are launched.
What does this mean: Marketers should make sure that their website development roadmap includes accessibility testing within their tag assignments.
4. SEO adapts to new search behaviors
SEO has been on a continuous path of evolution since its inception. Search engine algorithms grew more complex, voice search arrived, and the first search queries on mobile devices dominated the search world. Marketers had to develop SEO strategies as a result. One of their challenges now is to determine how search terms have changed over the course of the pandemic, so that they have a clearer view of what people are searching for and how to present content against search query results.
What does this mean: Marketers must rethink search engine optimization (SEO) and content to take into account nearly two years of search impacted by the pandemic, and then determine how to enhance their content and strategy for 2022. Look for SEO solutions platforms to provide increased analytics features, such as optimizing Intent and semantic search options. Also consider additional search patterns on other platforms, such as Pinterest and Instagram.
5. Self-service analytics solutions facilitate project iteration
Self-Service Analytics provides a default sandbox for exploring analytics concepts. These sandboxes have recently started increasing their capabilities to make project iteration easier. These features include data visualization in interactive dashboards and simple connectivity to database sources and APIs. The no-code features also make advanced analytics tasks easier.
What does this mean: Marketers should ask two main questions when considering a common self-service platform:
- What advanced analytics does the solution handle?
- How appropriate is data integration in a self-service environment?
Answering these two questions can quickly narrow down your options, while ensuring that you meet common workflow challenges and retain individual flexibility to review analysis concepts and explore data.
6. Redistribute its analytical responsibilities
IT teams have always been responsible for back-end structures such as database maintenance. However, in recent years, business teams, especially analytics departments, have adopted cloud architectures for data access and API services, freeing up IT teams in the process. While the IT department is still tasked with maintaining access to data—which has been particularly challenging during the work-from-home shift in the past two years—the shift in responsibilities offers a small amount of freedom for business users and IT teams alike.
What does this mean: Marketers should look for opportunities to collaborate with information technology to enhance ongoing data maintenance. Collaboration will help analytics teams better deliver up-to-date reports to departments and partners. It will also improve research into technology innovations that support privacy and data security needs, such as the Cybersecurity Data Network, as identified by Gartner.
7. Central repository of support materials will help analytics projects
With so many different open source projects used in advanced data modeling and computations, a central repository of support materials is increasingly essential to coordinating shared knowledge within analyst teams. Supporting materials help analysts verify that their dependencies in analysis projects are consistent with the latest data maintenance information. This quality assurance step can have a significant impact on improving the data that feeds into advanced forms downstream and also in preventing personally identifiable information from being inadvertently entered into the form.
What does this mean: Marketers should look for platforms that can help analyze information quickly to support content development. Platforms can range from simple common solutions like the GitHub repository used among a common team, to internal content management and CDP solutions.
8. Automate your way to smarter decision
In 2020, Gartner predicts that by 2023 a third of analysts will use decision intelligence to increase decisions. Experienced marketers will seek systems that act as narrators for analytics, tools that can be of assistance by quickly recounting events from data and identifying useful insights. Leveraging automation in the insight process can also reduce burnout from excessive online work.
Insights-Based Automation is an automatic adoption of “So what?” Ruling that analytics supporter Avinash Kaushik has been rumored for more than a decade. Using automation to set alerts and decisions can help scale production quickly and reduce the stress of making workflow decisions for analysts and managers.
What does this mean: Marketers should look for automation innovations in different areas of marketing, such as using Python to automate keyword clustering in SEO.
9. Fewer pilot opportunities for platforms
One of the biggest competitive differences for dashboards lies in their user interface. Make it simple right out of the box and win over exhausted strategists as a client. Maybe launch a half-baked beta, and you’ll hear from analysts. Platform providers realize that the window for marketers to gain approval through beta versions is narrowing. For example, Google made major changes in GA4 but according to the Search Engine Journal, some analysts were outspoken in their dissatisfaction with some of the features. Service providers must prove ease of use from the start.
What does this mean: Marketers should look to dashboard updates, such as the drag-and-drop interfaces in Amazon SageMaker Studio Labs and Azure ML, that simplify previously mentally stressful tasks. It will filter out even for the simplest of dashboards. Google Analytics 4 mirrors features that were previously exclusive to GA360. More improvements are on the way, with major competitors like Adobe Analytics not far behind.
10. Keep up with global privacy policies
Analysts will spend a fair amount of their time finding innovative ways to get the right privacy, as companies juggle their digital presence with their global presence for compliance. Legislation passed in a few US states has added to the complexity. All of this created a tension between the need to access data to unlock business value and the push to restrict access to protect data. Many companies are still playing a role in catching up with GDPR compliance, nearly four years after it came into force, Digiday reported.
What does this mean: Marketers should increase focus in training on data operations to play privacy, so that marketers can determine how data flows through the organization and where it affects privacy management and privacy protection.
11. A major resignation will have a major impact on analyst retention
Figuring out who can be the data owner — the “digital narrator” of company culture who can apply tradition and ideas to data decisions — will be more difficult. Recruitment demand for analysts was already far outpacing the availability of candidates prior to the COVID-19 pandemic. Putting pressure on analysts to board and gather insights quickly will create more stress about retention. Other initiatives, such as increasing diversity in analytics teams, will also feel the effects.
What does this mean: Marketers should be aware of choosing where to look for analytical talent, especially with diversity initiatives in mind. People talk about switching to technology, but analytics derives insights from various industries. HR managers will have to imagine how people can leverage their experiences to be the right candidate who will gather customer experience insights from data.
Pierre Dubois is the founder of Zimana, a small business digital analysis consultancy. It reviews data from web analytics and social media dashboard solutions, then makes web development recommendations and actions that improve marketing strategy and business profitability.