Only 7% Use AI in Marketing?
The Gist
- Data importance. High-quality data is crucial for accurate generative AI insights and predictions.
- Personalization impact. Poor data quality leads to ineffective generative AI personalization.
- AI enhancement. Strong data supports AI in segmenting, trending, optimizing and automating marketing efforts.
Recent research from The CMO Survey found that while generative AI media hype is at an all-time high, companies are only using AI in marketing activities 7% of the time. Additionally, only 10% of organizations have large language models (LLMs) in active production, and 40% of organizations have not used LLMs at all.
What Are the Challenges of Using AI in Marketing?
How can it be? Well, a multitude of challenges still exist around generative AI, including:
- Accuracy and authenticity of content generation.
- Security of data used by generative AI and LLMs.
- The depth of decision-making capabilities.
- Internal resource and skillset expertise.
While many questions exist around the ultimate “transformational” capabilities of generative AI in marketing, one thing is known — generative AI applications housed within an organization’s tech ecosystem are only as good as its data and analytics foundation.
Why?
High quality data is essential for generative AI in marketing to generate meaningful insights and predictions. Poor data quality will often lead to incorrect conclusions about the content, audiences and activation techniques that customer engagement solutions should make.
This has a long-term drain on marketing strategies and their effectiveness. Data that is consistent, well-formed and structured and rich from a customer profile perspective ensures that predictive models, ranging from simple propensity models to more complex AI-based models, are trained on uniform information. Enhancing the reliability of generative AI outputs should be top of mind for organizations today.
Related Article: The Unforeseen Consequences of Relying on AI in Marketing Strategies
Without Strong Data and Analytics, Generative AI Based Personalization Suffers
It’s no secret that customer engagement technologies are being transformed to become more and more generative AI centric. From prompted suggestions on kinds of audiences to create and what content to use, to wizards reporting on engagement results and iterative suggestions — generative AI in marketing has the potential to move from an incremental technology enhancement within customer engagement to a truly transformative one.
However, if your personalization strategies and subsequent activities hinge on data and analytics that don’t accurately reflect customer behaviors, preferences and trends — downstream marketing content, campaigns, processes and results are going to suffer.
Related Article: AI in Marketing: Balancing Creativity and Algorithms for Marketers
AI Personalization Activities
Here are just a few examples of how strong data and analytics support generative AI personalization activities:
- Targeting. Accurate data allows generative AI in marketing to segment audiences effectively and customize messages to specific segments and groups, increasing engagement and conversion rates.
- Trending. Analytics helps identify trends and patterns in customer behavior. Generative AI in marketing can leverage trend and forecast information to predict and prescribe future consumer behaviors and needs. This results in optimized marketing strategies and proactive decision-making.
- Content Contextualization. Generative AI relies on data and analytics to determine what type of content resonates with differing audiences. High quality data ensures that content generation remains relevant and engaging. Additionally, analytics can uncover unique insights that fuel creative content generation that may have been previously unavailable. This leads to customer engagements that are more innovative and effective.
- Optimization. With data and analytics, performance metrics and KPIs for marketing initiatives can be created. Generative AI can use this insight to refine and optimization marketing and customer engagement processes. Additionally, generative AI can serve as an optimizer of marketing resources, given the proper input data sets.
- Automation. One of the first use cases for generative AI in marketing was to reduce manual task completion by humans, and with strong data and analytics, generative AI can be used to automate tasks such as content creation, audience generation and campaign analysis. With real-time insights and updates at the fingertips of marketers, adapting to changing market conditions for increased efficiency and scalability becomes simple.
- Adherence. A solid data management and analytics foundation leads to greater levels of compliance and protection, ensuring legal regulation and ethical standards are met. Using strong data and insight for generative AI activities results in outputs that can be trusted by both the brand and the end consumer.
Related Article: Generative AI in Marketing: Unlocking the Next Generation of Use Cases
Final Thoughts on AI in Marketing
A strong data management and analytics foundation is indispensable for generative AI in marketing because it ensures the predictive insight, personalization prowess and process compliance that so many brands desire today. As generative AI becomes more mainstream within customer engagement technologies, it’s imperative to spend the time on your brand’s data and analytics foundation.
As Forrester analyst Brandon Purcell writes in his most recent research on customer analytics, “Slapping an LLM-enabled user interface on a product may help with democratization, but it does nothing to ensure the robustness of the underlying analyses. Instead of being beguiled by generative AI window dressing, buyers should look for differentiated analytical techniques under the hood.”
I couldn’t agree more.
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