The Intersection of Generative AI and Marketing Data

Using AI for content is a little like using a crowbar for a hammer. Yes, it can get the job done, but it’s going to be a messy process with uneven results. AI is a great tool for researching content, even generating outlines and rough drafts, but it should be used sparingly on the content drafting side of things.

Where AI really shines in marketing is in data analysis. AI and machine learning algorithms are very good at spotting trends in large data sets. 

As we marketers lose some of our most useful data tools, AI and machine learning can help us pick up the pieces. 

Here’s the current state of generative AI for marketing data, and how it looks to evolve in the near future.

How generative AI unlocks the potential of marketing data

Marketers have no shortage of customer data on hand — quite the opposite. The challenge is to: 

• Analyze massive amounts of data for meaningful insights.
• Put these insights to work in a timely fashion.

Fortunately, generative AI can help with multiple aspects of these challenges.

Insight generation

AI algorithms can generate insight from data more efficiently and thoroughly than people can. AI can analyze massive data sets to uncover hidden patterns that might not show up in traditional analytics tools. 

As AI grows more sophisticated, it is also able to take on unstructured data that historically would have required human analysis. Text, images, and behavioral markers can all be a quantifiable part of your customer data set. 

Advanced behavior-based segmentation

Traditionally, marketers have relied on demographic attributes to create segments, with a reliance on third-party data. Generative AI algorithms can take a more nuanced approach by analyzing customer behavior to identify segments that are likely to convert given a specific intervention.

For example, the algorithm might detect a pattern that 75% of people converted after going to a particular page on your site, then receiving a specific series of follow-up offers.  You could market directly to this new segment, testing new offers that fit with the pattern of those who have already converted. 

Behavior-based segmentation gives marketers more insight into the who and why of their customers that goes far beyond age, gender or job title. 

Personalization in real-time at scale

Personalization is the cost of entry for marketers now. A recent study from Adobe found that 73% of customers expect personalization before and after making a purchase. But personalization at scale and in real-time requires superhuman capabilities. 

By analyzing vast amounts of data, AI algorithms can identify patterns and preferences unique to each individual or persona and identify trigger points. Then, AI-powered tools can dynamically generate personalized content and deliver it automatically when a trigger is spotted. 

Whether it’s hyper-relevant personalized product recommendations, dynamic email content, or targeted ad campaigns, generative AI makes the superhuman super possible.

Predictive analytics

We’ve all heard of the 80/20 rule: 80% of your results come from 20% of your activities. Or, to put it another way, 80% of our time is virtually wasted. The trick is to discover what your most profitable 20% is and focus efforts there. That’s where predictive analytics come in. 

Generative AI uses machine learning algorithms to analyze historical data and generate predictive models. These models help reduce our 80% overhead in a variety of ways, including:

• Forecasting customer lifetime value for various behavior segments
• Developing a data-driven ideal customer profile
• Identifying customers at risk before they churn
• Ranking leads by their potential lifetime value 

Automation

The rise of automation has revolutionized marketing operations, streamlining processes and freeing up valuable time and resources. Generative AI plays a pivotal role in this automation revolution, powering chatbots, virtual assistants, and other AI-driven tools that handle routine tasks with speed and efficiency. By automating repetitive processes such as customer support inquiries, lead scoring, and content generation, businesses can focus their human resources on more strategic initiatives, driving innovation and growth.

What’s next for AI in marketing

The capabilities of AI are evolving fast. Marketers will find a bumper crop of new ways to know their audience, understand their journeys, and deliver the right messaging at the right time. Here’s a look at what’s next.

Enhanced customer experience

Many brands are already experimenting with AI-powered customer experience, from personalized chatbots to virtual shopping assistants. Expect to see these experiences become more immersive on the customer side, and easier to orchestrate and deliver on the marketing side. 

Hyper-personalization (without cookies!)

We’ve seen that AI can help identify unique opportunities for personalization, and deliver on these opportunities in real time. These capabilities will only grow more useful over time. As AI combs data from social media, browsing behavior, history of engagements with your brand, and more, you’ll be able to deliver highly resonant and specific content, one-to-one, at scale.

Voice and visual search

Voice search — spoken language search queries directed at an AI assistant — is taking up an ever-increasing percentage of search queries. Research found that 72% of people have used voice search in the past six months.

As AI grows more sophisticated, visual search is the next frontier. Android customers can already use their phone cameras to search for products, translate text, and much more. 

Marketers will need to account for the growing number of non-textual searches as they create content and design campaigns.

Augmented analytics

Presenting results to the C-suite is not anyone’s favorite part of marketing. It can be challenging for marketers to tell the story of their data in an understandable, meaningful and relatable way. 

In the near future, AI will help marketers quantify their results by:

• Providing deeper insight into customer journeys
• Implementing more accurate, data-driven attribution
• Helping create a narrative for the data
• Creating data visualizations that make it easy to see the narrative

More flexible and adaptable marketing

Increased efficiency and automation of manual tasks will help marketers become more adaptable and agile. Marketers will be better equipped to respond to changing market dynamics, optimize their campaigns on the fly, and capitalize on emerging opportunities with greater speed and precision.

For more about AI and marketing, read Age of SGE: How Will AI Affect Search Traffic in the Next Decade?

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