The intersection of generative AI and marketing data

The intersection of generative AI and marketing data
Using AI for content is a bit like using a crowbar as a hammer. Yes, it can do the job, but it will be a complicated process with uneven results. AI is a great tool for researching contenteven generating outlines and drafts, but should be used sparingly in the content writing portion.
Where AI really shines in marketing is in data analysis. Artificial intelligence and machine learning algorithms are very good at detecting trends in large data sets.
As marketers lose some of our most useful data tools, artificial intelligence and machine learning can help us pick up the pieces.
This is 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:

  • Analyze massive amounts of data to gain valuable insights.
  • Put this knowledge into practice in a timely manner.

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

Knowledge generation

AI algorithms can generate insights from data more efficiently and comprehensively than people. AI can analyze massive data sets to uncover hidden patterns that might not appear in traditional analysis tools.
As AI becomes more sophisticated, it can also take on unstructured data that historically would have required human analysis. Text, images, and behavioral markers can be a quantifiable part of your customer data set.

Advanced Behavior-Based Targeting

Traditionally, marketers have relied on demographic attributes to create segments, drawing 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 where 75% of people converted after going to a particular page on your site and then receiving a specific series of follow-up offers. You could market directly to this new segment, testing new offers that fit the pattern of those who have already converted.
Behavioral segmentation gives marketers more information about the who and why of their customers, which goes far beyond age, gender or job title.

Real-time personalization at scale

Personalization is now the cost of entry for marketers. A recent Adobe study found that 73% of clients Expect customization before and after you make a purchase. But personalization at scale and in real time requires superhuman capabilities.
By analyzing large amounts of data, AI algorithms can identify patterns and preferences unique to each individual or person and identify trigger points. AI-powered tools can then dynamically generate personalized content and automatically deliver it when a trigger is detected.
Whether it’s highly relevant personalized product recommendations, dynamic email content, or targeted ad campaigns, generative AI makes the superhuman possible.

Predictive analysis

We have all heard about 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 practically wasted. The trick is to figure out what your most profitable 20% is and focus your efforts there. That’s where predictive analytics comes into play.
Generative AI uses machine learning algorithms to analyze historical data and generate predictive models. These models help reduce our overhead by 80% in a variety of ways, including:

  • Forecast customer lifetime value for various behavioral segments
  • Develop a data-driven ideal customer profile
  • Identify at-risk customers before they churn
  • Ranking Leads by 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 critical role in this automation revolution, powering chatbots, virtual assistants, and other AI-powered tools that handle routine tasks with speed and efficiency. By automating repetitive processes such as customer service inquiries, lead scoring, and content generation, companies can focus their human resources on more strategic initiatives, driving innovation and growth.

What’s next for AI in marketing?

AI capabilities are evolving rapidly. Marketers will find a host of new ways to know their audience, understand their journeys, and deliver the right message at the right time. Here’s a look at what’s next.

Improved 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 (no cookies!)

We have seen that AI can help identify unique personalization opportunities and leverage these opportunities in real time. These capabilities will only become more useful over time. As AI combines data from social media, browsing behavior, interaction history with your brand, and more, it will be able to deliver highly resonant and targeted content, one-to-one, at scale.

Visual and voice search

Voice search (spoken language search queries directed to an artificial intelligence assistant) is taking up an increasing percentage of search queries. The research found that 72% of people have used voice search in the last six months.
As AI becomes more sophisticated, visual search is the next frontier. Android customers can now use their phone’s cameras to search for products, translate text, and more.
Marketers will need to take into account the growing number of non-textual searches as they create content and design campaigns.

Augmented analytics

Presenting results to senior management is no one’s favorite part of marketing. It can be difficult for marketers to tell the story of their data in a way that is understandable, meaningful, and relatable.
In the near future, AI will help marketers quantify their results by:

  • Provide deeper insight into customer journeys
  • Implement more accurate, data-driven attribution
  • Help create a narrative for the data.
  • Create data visualizations that make it easy to see the narrative.

More flexible and adaptable marketing

Greater 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 accuracy.
To learn more about AI and marketing, read SGE Era: How will AI impact search traffic in the next decade?

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