Marketers’ GenAI Spending Surges, Readiness Lags

Studies show that marketers are cautiously investing in generative AI (GenAI) to enhance their marketing technology (martech) stacks. However, a gap between investment and understanding continues to hinder the adoption of GenAI as a tool for improving marketing efficiency and effectiveness.

According to research released at the end of last year, 90% of organizations planned to invest in GenAI for marketing this year. However, 90% of CMOs admitted they do not fully understand GenAI or its potential impact on their business.

Researchers found that three-quarters of marketers currently use GenAI in their day-to-day work. This widespread usage makes marketing the leading business function for GenAI adoption, ahead of areas like IT. They utilize GenAI for simple tasks such as copywriting, editing, and content creation.

For more sophisticated tasks, only 19% use GenAI for audience targeting, 18% to build audiences, 16% for customer journey mapping, and 14% for price optimization, according to Jon Moran, head of martech solutions marketing at SAS. A major culprit in the widening gap is the lack of GenAI education and training.

“For many organizations, senior management’s lack of GenAI understanding is restraining them from experiencing its full potential. So too is concern over privacy and trust,” he told CRM Buyer.

Building GenAI Skills for MarTech Adoption

Martech is an all-encompassing term for software and tools marketers use to plan, execute, and measure the success of their marketing campaigns and activities. The evolving technology helps them streamline and optimize marketing efforts.

Martech differs significantly from traditional advertising technology, also known as adtech. Adtech is used to influence buyer behavior by promoting offerings. Martech, on the other hand, refers to digital tools that help create, communicate, and deliver product offerings.

“Marketing teams need to have the necessary skills, including those related to data, AI, and GenAI. Of course, it is not AI coming for your job. Rather, it is a marketer with AI skills,” Moran said of the newest addition to the martech stack.

The so-called stack is overrun by an AI revolution of sorts. AI is transforming modern marketing into a combination of machines that analyze data, predict customer needs, and deliver personalized experiences in real time.

AI Tools Drive Smarter Marketing Decisions

The new goal is to streamline marketing operations, enhance data-driven decision-making, and deliver a seamless and personalized customer experience. With AI-driven decision-making and digital engines emerging as a core platform, businesses of all sizes are leveraging cutting-edge tools to streamline their operations, improve customer engagement, and enhance ROI.

Moran predicts that GenAI will move beyond content creation into orchestrating the creation, design, and execution of customer journeys. Skills in traditional and generative AI will prove critical for marketing success. He likes to think of AI as a teammate or accelerator helping people be more productive and achieve better outcomes.

“With AI, we can go well beyond content creation, continually looking for ways it can help us creatively differentiate our brand and responsibly boost productivity,” he suggested.

Optimizing Customer Journeys With GenAI

The new CRM approach combines traditional AI-based optimization with GenAI and customer routing technologies. The technique, known as customer journey optimization, can improve marketing outcomes, such as conversions, customer acquisition costs, and customer lifetime value, Moran opined.

Customer journey optimization focuses on guiding customers through personalized paths to conversion rather than relying on generic, brand-defined routes. By analyzing historical and real-time customer data, AI (reinforcement learning) can identify patterns and predict the most effective pathways for customers.

“As these paths are determined, copy and content can be contextualized and appended with GenAI,” he noted.

Moran offered an example of customer journey optimization: reinforcement learning can compare a consumer’s abandoned shopping cart with parallel patterns of other customer journeys that resulted in a conversion.

“Applying the freshest next-best-action tactics to achieve the highest rate of success for every micro and macro goal defined for this journey will result in higher conversion rates,” he said.

He admitted that organizations have not widely used this technology yet, but he fully expects it to be prevalent within the next five to 10 years.

Combining GenAI and AI for Predictive Insights

An offspring of GenAI is synthetic data generation, the ability to generate artificial data that can supplement customer profiles or datasets. For AI and machine learning models to be accurate and practical, the model input data must be complete and of excellent quality, explained Moran.

“Organizations can use synthetic data generation to fill gaps in existing data sets to improve model output scores,” he added. “GenAI can improve predictive analytics and decision-making by enhancing the data that is fuel for martech. GenAI can do this through the generation of synthetic data to enhance AI and machine learning models.”

These data enhancements provide customer experience (CX) teams, such as sales, service, and support, with better insights on which to act. Moran added that these insights can come from propensity, forecasting, demand, optimization, and content-generation models.

A common challenge in synthetic data generation is determining the proper parameters to produce high-quality, relevant data. Proper setup and implementation early in the generation process overcome this challenge, he clarified.

An example of synthetic data in action is its use for look-alike modeling. The trick is to create artificial data that mimics actual customer data in its features, structures, and attributes. Brands can model for and identify potential new audiences that look like existing successful micro-segments and then target them.

Tackling GenAI Integration in Marketing Tech

According to Moran, integrating GenAI with existing CRM and CX tools and platforms is a worthwhile but exacting process. It includes process-related (CX), technological (integration), and cultural (team adoption) challenges.

“GenAI has the potential to fundamentally change how marketers work. Marketing workflows will be more streamlined, and GenAI will support more marketing strategies and associated customer journeys,” he surmised.

Flexibility will be crucial. Marketing teams must be able to experiment with GenAI and related technologies, try novel approaches, and, when necessary, go back to the drawing board.

“In marketing, one size does not fit all. That goes for AI and GenAI applications. Each marketing organization must find technologies that match its use cases and meet its own specific needs,” cautioned Moran.

How AI Is Shaping the Future of CRM

Investing in AI technologies, such as synthetic data generation and AI-based customer journey optimization, can offer substantial returns for marketing departments. By leveraging these tools, organizations can enhance customer interactions, optimize data use, and improve overall marketing effectiveness, according to Moran.

Increasingly, brand-to-consumer interactions will involve AI components. Currently, this involvement is primarily in the back office, with AI supporting customer understanding initiatives alongside and, at times, within CRM systems, he suggested.

“However, in the future, we are going to see agentic AI increasingly used, with agents handling interactions, engagement, and decision-making across multiple interaction channels. These will include ATMs, kiosks, point of sale, contact centers, web, email, and social touchpoints,” he predicted.

AI will continue to evolve CRM. Staying ahead of these trends and investing wisely in these areas will be crucial for maintaining a competitive edge and achieving long-term success.