How AI is Changing Marketing Analytics Today

marketing analytics

Marketing analytics has undergone a revolution in the last 20 years. In this post, we’ll dive into the data analytics challenges faced by marketing teams and how AI is transforming how data insights are discovered and utilized for better decision-making.

Challenges with marketing analytics today

Marketing analytics has undergone a revolution in the last 20 years. Marketing teams have gone from relying on surveys, focus groups, and basic web analytics to managing customer interactions across every touchpoint and reacting in real time to changing market situations.

At the same time, organizations have had the capability to process large data volumes only over the past 10 years, and this is still typically only the area of the data science team. More complex analysis, like multi-channel attribution models, predictive analytics, and recommendation systems, is pushing marketers further from answering their own questions without the help of the data team. The marketing analytics space has evolved from basic data tracking to a highly sophisticated and technical analysis.

The types of data used to analyze marketing have also become a web of various systems. In order to extract the most value from marketing, teams need to leverage internal data from interactions with customers and prospects from CRM systems and web analytics (e.g., Salesforce and Google Analytics) while also incorporating data from brand partners and external sources like demographic data, market research, and consumer surveys. Incorporating social media data can help understand emerging trends and customer sentiment. Location data provides insights into understanding foot traffic and regional preferences for targeting localized campaigns. And we haven’t even spoken about IoT data from devices and sensors in wearables and smart appliances.

Integrating these data sources comes with a variety of challenges. Organizationally, these data sources can be managed on a departmental level, making breaking down these silos a difficult proposition. In addition, departments might manage separate databases and systems that can be hard to integrate with one another. On top of this, ensuring the accuracy of data, consistency, and reliability across departments and data sources can be difficult to maintain.

Understanding the customer journey is critically important for organizations. However, with the variety of touchpoints spread across a number of fragmented data sources, it is an extremely challenging proposition. In order to gain a clear view of the customer journey, marketers must have access to data from advertising, in-store displays, online reviews, social media, and much more. These touchpoints can also be spread across multiple data sources and managed by multiple teams. With privacy concerns like GDPR and CCPA, regulations on collecting and analyzing data are making understanding the customer journey even more difficult today.

With all this data available, it’s important to focus on metrics that translate to business outcomes, but identifying those valuable metrics is challenging. Vanity metrics, like shares and follower counts, often provide a superficial view of marketing performance. These metrics do not correlate with meaningful business outcomes like revenue or customer acquisition. Vanity metrics also don’t provide insight into customer behavior or help to inform the customer journey. Understanding how customers interact with your products and services is essential to help optimize marketing strategies and improve user experience.

With the challenges of bringing together all of the available data and finding the best metrics to use, identifying insights within the data can be limited to only the most technical personnel in the marketing organization. The sheer volume of data can be overwhelming. Without a specific hypothesis or question in mind, it can be challenging to know where to start analyzing data. Marketers may also draw incorrect conclusions or make biased assumptions based on preconceived notions or expectations.

All of this is to say:

My head hurts.

Marketing analytics in 2024 is complex and difficult, and it requires a tremendous effort across the entire team to derive any value.

Challenges with legacy BI tools

While the complexity of data grows, traditional business intelligence (BI) tools aren’t keeping up with these challenges.

Outdated BI platforms cannot handle the volume and velocity of data generated by modern digital marketing channels. This slow processing time does not allow for real-time changes to promotions and in-flight marketing campaigns. As these volumes grow and usage increases, legacy systems often struggle to stay performant.

With an ever-increasing number of data sources and a mountain of historical data, leveraging a legacy BI tool can leave marketing teams behind and insights hidden.

While struggling with data volumes may be one issue, it also hampers the value derived from dashboards and reports. With the dynamic nature of marketing, static, pre-defined reports and dashboards cannot keep up with the demands of the business. If marketers need to ask new, unforeseen questions, dashboards can’t provide a detailed answer. Instead, the answer comes in the form of a new inquiry to the data team, who might take a week or more for a response.

Many legacy BI platforms also lack any ability to run advanced analytics techniques using ML and predictive modeling. While advanced analytics requires complex algorithms, ML models, and predictive analysis, traditional BI tools lack the functionality to create, deploy, and manage these models easily.

If a legacy BI tool has incorporated advanced analytical capabilities, users still require a specialized skill set in order to run customer segmentation, power recommendation systems, and leverage predictive analytics for things like churn analysis and customer lifetime value (CLTV) predictions.

While legacy BI tools have tried to incorporate more advanced functionality, the tools still are built for specialized analysis and require deep training in order to inform your marketing strategy.

Some marketers may have the time to devote to the study of statistical analysis and data science. However, due to the nature of marketing, it’s unlikely your entire team is going to get a PhD in data science.

Enter AI-powered analytics

AI-powered analytics empowers marketers to unlock the potential of data in ways that were thought to be impossible with legacy BI tools.

With modern processing engines, there are much looser constraints around the volume and velocity of data to be analyzed. Intuitive data preparation tools not only enable the data team to integrate new data sources, but also unlock this capability to analysts on the marketing team. With better processing engines, real-time analytics are now possible, unlocking the ability to react dynamically to market trends and events. Every new avenue of data can be incorporated into your analysis and explored with ease.

Natural language search mitigates the need for teams to create dashboards and reports by providing ad hoc data exploration and analysis capabilities to every marketer. Simply type in a question, and receive an answer in the form of a data visualization tailored to your analysis and the data type.

AI-powered analytics takes out the guesswork associated with creating a data visualization while making querying as simple as a Google search. Enabling domain experts to ask questions like “What percentage of customers who used a promo code made a repeat purchase?” or “Which of our competitors’ products have the highest sales from our customers?” can help to inform business strategy faster and better than before.

Beyond ad hoc data exploration and analysis, AI-powered analytics platforms can help to unlock advanced analytics to domain experts. By leveraging predictive analytics and historical data, marketers can forecast customer behavior, demand, and trends to better inform resource allocation. AI-powered analytics can also automatically segment customers based on patterns to create highly targeted and personalized marketing campaigns. With built-in recommendation models, these platforms can better suggest products, revealing potential cross-sell and upsell opportunities.

The theme here is simple: AI-powered analytics enables marketers to do more with data. AI strips away the need for many hours of training by simplifying the processes for the end user.

Putting data in the hands of domain experts in the marketing department allows for more efficient, optimized marketing campaigns, achieving a higher ROI for the business.

AI marketing analytics solutions

There are many potential use cases for AI-powered analytics within marketing. Here are some of the applications:

Digital marketing analytics

Finding the difference between the signal and the noise in digital marketing analytics can be extremely difficult. Digital marketing generates a vast amount of data, from website traffic to click-through rates and social media engagement. Managing and processing this data can be overwhelming. Analyzing the data in real time can be extremely valuable for pay-per-click advertising, but it’s a burden to manage on many legacy analytics platforms.

AI-powered analytics helps to alleviate this burden. Many AI-powered analytics platforms come with more advanced data processing engines, allowing for real-time analytics on data. Data processing engines can connect directly to CRM systems like Salesforce and web analytics providers like Google Analytics.

AI-powered analytics platforms allow marketers to leverage natural language search for KPIs like conversion rates, CTR, CPC, and more. Visualizations are generated automatically and include highlighted data points of interest. Marketers can dive deeper into the data with trend, key driver, and comparison analyses. By using search, dashboard usage can be greatly reduced, and the value of analytics projects can be improved.

Customer segmentation modeling

Optimizing content and products delivered to your customers will provide higher ROI for the business. However, this comes with its own set of challenges.

Segmentation can become overly complex, especially with the number of variables and criteria involved. Even identifying the right segmentation criteria may be difficult when deciding on which factors (e.g., demographics, behavior, or psychographics) are most relevant for your specific goals.

AI-powered analytics helps with customer segmentation by providing out-of-the-box solutions to customer segmentation modeling. AutoML capabilities enable marketers to select the right variables and create detailed segment profiles based on characteristics, behaviors, preferences, and needs automatically without the help of the organization’s data team.

Personalized marketing and recommendations

While customer segmentation is the first step in creating more personalized marketing, there are a number of hurdles to overcome.

Effective personalized marketing relies on robust customer data, which involves bringing together your data from various departments and sources in one location. Implementing personalized marketing requires an investment in technology and data analysis, as well as the technical skills of your personnel.

AI-powered analytics can help with the difficult process of pulling together disparate data sources with simple data preparation tools to create a holistic view of customer data, including data from different source systems. Marketing teams can leverage AutoML recommendation models to provide recommendations based on previous purchases or ratings.

Tailoring offers to specific customer segments helps to improve business outcomes. AI-powered analytics makes this journey easier by enabling this tool set for less technical users.

Customer lifetime value (CLTV) analysis

Predicting the value a customer will bring to a business is invaluable for any marketing organization, but it comes with several challenges.

CLTV analysis requires segmenting customers based on their value to the business. Determining the appropriate segmentation criteria and maintaining these segments as customer behavior evolves can be difficult.

AI-powered analytics helps with CLTV analysis with historical and predictive analytics. These platforms help with developing customer segments and analyzing purchasing patterns among these segments. By providing these tools to domain experts, marketers can better design loyalty programs, inform product development, and improve campaign ROI.

Marketing mix modeling

Understanding the “4Ps” of marketing is critical to improving ROI and optimizing marketing campaigns. And yet, the complexity of analysis, which often involves complex statistical models, makes it a difficult task for many marketing organizations.

Other challenges like seasonality, attribution impact, and changing market dynamics can be a tremendous burden on marketing organizations that do not have a data team on standby.

AI-powered analytics helps with marketing mix modeling by ensuring every dollar is well-spent. With these analytics platforms, marketers can accurately assess the individual contribution of each channel, including digital and traditional. This helps marketing organizations avoid inefficient budget allocation across their marketing channel and improve their return on advertising spend (ROAS).

Other AI-powered marketing analytics solutions

The above are some of the highlights of leveraging AI-powered analytics to improve marketing outcomes. However, there are many other potential use cases.

With access to predictive analytics capabilities, marketers can identify customers at risk of churning. AI-powered analytics can be used to analyze competitor pricing, demand, and other factors to adjust pricing in real time to optimize revenue and profit margins.

Things like influencer marketing can be risky, but with the power of AI, marketers can identify which influencers drive the most engagement and assess the impact of influencer collaborations.

Lastly, attribution modeling can be a difficult proposition without AI-powered analytics, but with the right tool assigning credit to different touch points in a customer’s journey, it’s made much easier.

Tellius: AI-powered marketing analytics

Tellius helps marketing organizations make better data-driven decisions more quickly. The platform helps to guide strategic decisions, optimize campaigns, and improve marketing ROI.

Tellius was designed to help upskill business users like marketers and enable easy ad hoc data exploration, analysis, and more advanced analytics use cases.

The platform includes natural language search, automated insight generation, alerting, and easy-to-use advanced analytics capabilities. The Dual Analytics Engine included within Tellius is highly scalable, enabling marketing teams to provide access to as many team members as needed and maintain performance. The platform can also directly connect to Salesforce and Google Analytics to create a single source of truth for marketing data. The Tellius AI-powered analytics platform allows marketers to get more value out of their analytics strategy.

To learn more about how Tellius helps marketing organizations, check out our marketing solutions page.


Read Similar Posts

  • 5 Common Pitfalls to Avoid When Launching a Self-Service Analytics Program
    Marketing Analytics

    5 Common Pitfalls to Avoid When Launching a Self-Service Analytics Program

    Here are some common pitfalls we've seen for organizations launching a self-serve analytics vision—and how to avoid them to maximize your odds of success.

  • Top 5 Best Practices for Self-Service Analytics
    Marketing Analytics

    Top 5 Best Practices for Self-Service Analytics

    The benefits of self-service analytics are substantial. With a self-serve model, you can start to truly scale your organization’s data-driven decision-making approach.

  • A Roadmap to Self-Service Analytics, Informed by Self-Driving Cars
    Marketing Analytics

    A Roadmap to Self-Service Analytics, Informed by Self-Driving Cars

    Self-driving cars. Self-service analytics. There are some interesting parallels to draw & lessons to be learned about the future of analytics.