The Gist:

  • Keeping up. In 2023, digital analytics solutions will be more versatile, integrating insights from various data sources, including APIs, and using AI for forecasting. Marketers need to keep up with these trends for better decision making.
  • Finding cost-effective solutions. With budget cuts looming, marketers must find cost-effective solutions for data management and analysis. Meanwhile, organizations must adopt a collaborative approach to analytics. 

If analytics were a person in 2023, its name would be Jack … as in Jack-of-all trades. It’s likely your “Jack” — aka, an analytics solution — is pulling multiduty.

Digital analytics is meant to connect insights to action, but these days insights through data are being derived from more sources than a website page. Incorporating many data sources via database integration and application programming interfaces (APIs) have raised the number of ways marketers connect data stories to decisions. Add a splash of AI, and you have several ways to forecast a given key performance indicator (KPI) or metric of interest.

How can marketers stay up-to-date on all analytics trends? Keeping track of analytics trends is complex, but savvy marketers can use targeted continuous intelligence to stay ahead of customer experience trends. Here are the top trends that will ignite discovery in 2023.

Expect More Budget Debates for Advanced Analysis

Budgets for advanced analysis are on the chopping block. Many managers are interested in data, but looking to reduce spending in all marketing activities, especially with an economic outlook overshadowed by high-profile tech layoffs and potentially reduced ad campaign spend in many sectors. 

Marketers should expect bigger fights in justifying the budget for advanced analytics projects. These projects can be time-consuming, requiring significant setup time for data training and analysis to gather strategic insights. Some businesses may not have the budgetary runway to support weekly analysis. Additionally, comparing metrics across media channels, such as customer acquisition cost (CAC), can be difficult to understand.

The Takeaway: Marketers will keep their ears open for new cost management tools for data management, coordinating access and analysis workflow. Expect more inspection of proxy metrics that represent data usage. 

Related Article: Why Now Is a Good Time to Adjust Your Analytics Workflow

We Don’t Need Another Data Hero. We Need Data ‘Avengers’

Organizations that have relied on a single person or small team to handle their entire analytics strategy are at risk of falling behind. Initially, this approach may have been effective in getting analytics up and running, but it fails to foster the distribution of data access and analysis management throughout the organization. To achieve scalable, valuable results, it's important to spread access and leverage the strengths of multiple teams to create a sustainable, collaborative culture. Marketers need a team of "Data Avengers," rather than just a single "Data Luke Skywalker."

The Takeaway: Marketers must coordinate the usage of dashboards, solutions and frameworks among teams to raise effective team collaborations more frequently. 

Know Your Data Lineage Leading to KPIs and Customer Experience

Real-time data has always been part of the analytics workflow. Analytics practitioners frequently use real-time data to confirm that their analytics tags were working within the website. Real-time data is now becoming increasingly important in the development of machine learning models that can adapt to changing conditions. Models filter and sift through the data to identify the relationships that may be valuable for driving KPIs. 

KPIs will reflect advanced statistics. KPIs are usually meant to be a ratio or benchmarking number, but either case you’re looking to benchmark performance of a model against a known standard. By applying advanced analytics through machine learning, it’s possible to determine if these benchmarks are being met or if performance is trending towards a target. So, while real time data has become table stakes to executing strategy, data has also become complex, making it difficult to draw true value.

The Takeaway: Marketers should look for how real-time data and subsequent advanced analytics and machine learning models align with narratives that describe a company's performance relative to KPIs. This will provide clarity on the impact of these models on return on investment (ROI). The narratives shape the interpretation of data storytelling for KPIs.

Related Article: How to Get Attribution in Analytics Right

The Fight Against API Sprawl

A technical challenge for marketers is the risk of API sprawl, the spread of API access around a given platform. Dashboards rely on open-source data accessed through API portals. However, as marketers create more dashboards and panel features, the need to keep track of APIs becomes a source of technical debt if the underlying applications are no longer needed or become deprecated over time.

API sprawl is a common challenge for marketers who work with federated gateways, an ecosystem of tightly integrated services used by developers or B2B partners. These gateways increase the number of APIs required and introduce different types of operational, customer and application data within the supporting documentation, leading to more aspects of the martech stack to address when updating.

The Takeaway: Marketers need to manage and audit the types of data sources being used. External data sharing is also strategic and can drive API sprawl as well. Marketers should champion ways to consolidate the tech stack through simplifying integrations.

Optimize the Supporting Workflow for Privacy 

No question that achieving data privacy is on everyone’s minds, meeting compliance with regulations that are coming into play in the United States and other jurisdictions. However, the Interactive Advertising Bureau (IAB) has noted that the regulations may make it difficult to understand audience reach. To stay compliant, teams must understand their role as data controller or processor according to the specific jurisdiction. Identifying this role will determine the company's exposure to regulatory privacy noncompliance risk.

The Takeaway: Marketers should expect to quickly come up to speed on roles and responsibilities within an organization. 

Learning Opportunities

Back in 2015 Analytics and privacy expert Aurelie Pols made the observation in her Smartcon presentation that data “is infinitely transferable without decay.” Expect marketers to spend considerable resources to identify and mitigate the risks associated with data transfers, as well to understand when these risks can spiral out of control.

Decrease the Time for Cross Functional Data to Cross the Whole Organization

Cross-functional data teams have emerged in recent years to handle the growing volume of data. This reflects a data-driven culture being adopted across organizations, resulting in increased in-house data access and no longer limiting data usage to just one analyst. The challenge now is to provide timely access and quickly act on the insights derived from the data.

The Takeaways: Marketers should determine the types of frequent analysis to monitor the frequency and speed of data access by teams. This analysis can help determine what data is being requested, the reason for the request, how it is being used and where it is stored. This information can be used to improve the efficiency of data retrieval.

Plan Data Strategy for External Partners Alongside Internal Stakeholders

External data sharing is strategic. The data products managed encompass various types and have varying operational impacts, affecting decision-making systems.

The Takeaway: Set an enterprise data strategy that outlines its technical processes and assigns responsibilities for the workflow of these processes.

The Rise of Data Clean Rooms

Clean rooms have become essential for advanced analytics models as they enable users to create data-based audiences or segments from the data without disclosing personally identifiable information. Marketers can use this to meet the needs of various stakeholders by providing access to the data for a range of analysis

The Takeaway: Marketers should expect to look for centralizing tools like data fabrics and data mesh that can blend new user interfaces and old databases for convenient analysis. 

Related Article: Customer Data, Analytics Top Priorities for Customer Service Leaders

Statistics Will Be Emphasized in Advanced Analysis

Advanced models are increasingly incorporating statistical and probabilistic analysis to evaluate the likelihood of an event. Previously, models would focus on website loading within a browser, but with customers interacting with websites in new ways, there is a growing reliance on models that predict the likelihood of a conversion or visit. Today there are more touchpoints than ever before — I predicted this back in last year’s analytics trends post. Prediction and measurement are becoming less browser dependent. 

The Takeaway: Marketers must brush up on the statistics, and look at models being developed within data science, and ask how those models can be applied within a marketing construct. 

Exploring Artificial Intelligence Against a Budgetary Backdrop

ChatGPT has raised the profile of AI in business solutions. Analytics is no exception. eMarketer reports that marketers are experimenting with AI with various campaign activities ranging from digital ads to segmentation. Segmentation is part of predictive analytics, so marketers will likely leverage AI more frequently to enhance customer experience. This will free up analytics to tackle larger strategic issues. However, it’s unclear if those automations will lead to better ROI initially.

The Takeaway: Questions remain on the value of ChatGPT, but distinct adoptions such as the Microsoft integrations and Google’s competing Bard chatbot makes terrific new workflow and use cases possible. Marketers should evaluate these opportunities as they develop through 2023.

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