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Blog / How To Use AI in Data Analytics To Drive Marketing Growth

How To Use AI in Data Analytics To Drive Marketing Growth

The launch of ChatGPT in November 2022 brought AI into mainstream digital marketing, and the last 18 months have seen a rapid progression in AI technology's capabilities and its adoption within marketing teams. 

A lot of the AI buzz has been around generative AI — using large language models and image-generation tools to support the generation of copy, content, and visual assets.

But AI is also transforming other areas of marketing, such as the automation of PPC campaign management, identifying improvements for on-site SEO, and enhancing data analytics. 

In this article, we’ll explore how AI is changing the way marketing data is analyzed and how you can leverage this to drive business growth. 

The role of AI in marketing data analytics

With the rapid improvements and mainstream acceptance of AI in recent years, there have been a number of media outlets that have predicted job losses in certain industries.

So it’s completely understandable if some data analysts are harboring concerns about their job security with the rise of AI data analytics. 

However, these concerns are likely unfounded.

While AI is great at automating certain tasks and can provide a lot of value in analyzing large datasets, it cannot replace the business context, intuition, experience, and knowledge that human data analysts bring to the table.

Data analysts will always be essential for understanding the specific needs of a business, asking the right questions of data, interpreting complex analyses, and providing tailored strategic recommendations.

So, instead of replacing data analysts, AI is more likely to change the way they work. 

The future of marketing AI data analytics is likely to involve a synergy between technology and human expertise, with data analysts working hand in hand with AI tools to ensure the quality of data, process large data sets, uncover insights from unstructured data, identify trends more quickly, and make data-driven decisions more efficiently. 

Our view is that while AI will certainly transform the marketing data analytics landscape, it’s unlikely to make human data analysts obsolete. 

Instead, it will be used as a valuable tool to streamline data processing and analysis tasks, empowering analysts to spend more time on strategic recommendations to help drive marketing growth.

How marketers and data analysts can use AI to drive marketing growth

The future is certainly exciting for AI data analytics. 

But if we look at the here and now, AI is already influencing how we process and analyze data, and it can be found in a number of different platforms and tools. 

Let’s take a look at five main areas where AI can currently support businesses with their data processing and analysis to help drive business growth. 

Improving accuracy in data integration

The quality and accuracy of your business data are the foundation of effective marketing analytics.

Without high-quality data, your business runs the risk of making ill-informed optimizations, developing misguided strategies, and wasting business time and resources. 

Businesses often use ETL tools and data integration platforms to help ensure the quality of data, reduce the likelihood of manual errors, transform data so it’s standardized and consistent, and enrich it so it aligns with specific business needs. 

Leading data integration platforms are incorporating AI into their data transformation processes to further ensure that your business data is of the highest quality and exactly as you need it. 

An upcoming feature within Adverity is the AI-powered enrichment copilot, which allows users to use natural language to explain the type of data enrichment they seek. The system will then automatically write the code and provide step-by-step instructions to achieve the data enrichment objective. 

Accessing insights more quickly

One of the biggest advantages of AI in data analytics is its ability to process vast amounts of data more quickly than would be possible with manual analysis or traditional tools.

Given that the amount of data that businesses have is increasing all the time, this is likely to be a welcome application of AI for many data analysts.

By using AI features within marketing analytics tools, analysts and marketers can access valuable insights more quickly. 

This faster access to insights can lead to more agile decision-making and give businesses a competitive edge to deliver marketing growth. 

Unlocking the power of predictive analytics

AI and machine learning can improve how marketing teams approach forecasting and planning. 

Historically, predictive analytics within marketing was a cost-prohibitive process for many businesses — often relying on complex Media Mix Modelling (MMM) analysis to generate relevant insights. 

The advent of AI data analytics has changed all that, with a range of predictive analytics tools now available. 

By analyzing historical data and building sophisticated data models, AI can help predict future trends in marketing performance with a high degree of accuracy, and at a price point that’s accessible for most businesses. 

This predictive capability of AI in data analytics also opens up the possibility of ‘what if’ analysis, allowing marketers to make decisions by simulating different scenarios and looking at the likely outcome. 

This promises to be a game-changer for marketers and data analysts, enabling them to optimize their strategies and tactics more effectively to achieve business growth.

Gaining insights from unstructured data

As a bit of a recap, businesses have two main types of data: structured and unstructured data. 

Structured data is the type of data that typically first comes to mind when you think of marketing analytics — organized, formatted, and tabulated data that is easily queried and can be used to populate marketing reports and dashboards. 

Unstructured data, on the other hand, is less organized and comes in various formats, such as videos, emails, social media posts, and customer reviews, making it less easy to query and analyze. 

Effectively analyzing unstructured data has historically been challenging, as it’s not formatted in a clear, logical structure, and there’s a lot of it (it’s estimated that 90% of business data is unstructured).

However, recent advances in AI-based data analytics can make analyzing unstructured data much easier for businesses.

Using techniques like natural language processing (NLP) and sentiment analysis, AI data analytics can help marketers unlock valuable insights from unstructured data, allowing them to better understand customer preferences, market trends, and brand perception.

Improving data democratization

Data democratization is the process of making your data accessible and understandable to all stakeholders within your business, so they can use it without any outside help. 

Organizations that prioritize data democratization are more likely to have a data-driven culture, which can improve transparency, collaboration, and overall business performance. 

Although many businesses would love to improve the democratization of their data, it’s often easier said than done. Many marketing analytics tools historically have a steep learning curve to access valuable insights. 

The good news is that recent advances in AI data analytics have made data democratization easier to achieve than ever. 

Natural language searches are now becoming more commonplace in tools like GA4, allowing users to access insights by simply typing their query in a search box rather than needing extensive technical knowledge. 

Implementing AI data analytics

AI is evolving at a rapid pace, with new developments and breakthroughs happening all the time.

Many popular analytics platforms already include AI features, such as the natural language search, anomaly detection, and predictive analytics found in GA4, and Einstein Discovery which is the AI-powered predictive modeling tool found within Tableau. 

As businesses recognize the potential of AI data analytics to drive marketing growth, we anticipate that an increasing number of platforms will enhance their offerings with AI. 

At Adverity, we’ve built AI features like our enrichment copilot into our solution to enhance our market-leading data integration platform.

Find out more about the exciting new features coming soon in our YouTube video, or book a demo with one of our team. 


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