The essential
- Importance of data. High-quality data is essential for accurate generative AI insights and predictions.
- Impact of personalization. Poor data quality leads to ineffective personalization of generative AI.
- Improved AI. Strong data helps AI segment, analyze trends, optimize and automate marketing efforts.
Recent research by The CMO survey The study found that while the hype around generative AI is at its peak, companies are only using AI in their marketing efforts 7% of the time. Additionally, only 10% of organizations have large language models (LLMs) in active production, and 40% of organizations have never used an LLM.
What are the challenges of using AI in marketing?
How is this possible? Well, there are still a multitude of challenges surrounding generative AI, including:
- Accuracy and authenticity of content generation.
- Security of data used by generative AI and LLMs.
- The depth of decision-making capabilities.
- Expertise in internal resources and skills.
Although many questions remain around the ultimate “transformational” capabilities of generative AI in marketing, one thing is known — Generative AI Applications hosted within an organization’s technology ecosystem are only as effective as their foundational data and analytics.
For what?
High quality data is essential for Generative AI in Marketing to generate meaningful insights and predictions. Poor data quality often leads to erroneous conclusions about the content, audiences, and activation techniques that customer engagement solutions should be provided.
This has a long-term impact on marketing strategies and their effectiveness. Consistent, well-formed and structured data that is rich from a customer profile perspective ensures that predictive modelsFrom simple propensity models to more complex AI-based models, are trained from uniform information. Improving the reliability of generative AI results should be a priority for today’s organizations.
Related article: The Unintended Consequences of Using AI in Marketing Strategies
Without reliable data and analytics, generative AI-based personalization suffers
It’s no secret that customer engagement technologies are evolving to become increasingly centered around generative AI. From suggestions on what types of audiences to create and what content to use to assistants that report on engagement results and iterative suggestions, generative AI in marketing has the potential to go from an incremental technological improvement in customer engagement to a true transformation.
However, if your personalization strategies and subsequent activities depend on data and analytics that do not accurately reflect customer behaviors, preferences and trends: downstream marketing content, campaigns, processes and results will suffer.
Related article: AI in Marketing: Balancing Creativity and Algorithms for Marketers
AI Personalization Activities
Here are some examples of how strong data and analytics support generative AI personalization activities:
- Targeting. Accurate data enables generative AI in marketing to effectively segment audiences and personalize messages to specific segments and groups, increasing engagement and conversion rates.
- Tendency. Analytics helps identify trends and patterns in customer behavior. Generative AI in marketing can leverage trend and forecast insights to predict and prescribe future consumer behaviors and needs. This results in optimized marketing strategies and proactive decision-making.
- Contextualization of content. Generative AI relies on data and analytics to determine what type of content resonates with different audiences. High-quality data ensures that content generation remains relevant and engaging. Additionally, analytics can reveal unique insights that fuel creative content generation that may not have been available before. This leads to more innovative and effective customer engagements.
- Optimization. With data and analytics, it is possible to create performance indicators and KPIs for marketing initiatives. Generative AI can use this information to refine and optimize marketing and customer engagement processes. Additionally, generative AI can serve as a marketing resource optimizer, provided you have the right input data sets.
- Automating. One of the first use cases for generative AI in marketing was to reduce the manual workload of humans. With robust data and analytics, generative AI can be used to automate tasks like content creation, audience generation, and campaign analysis. With real-time insights and updates at marketers’ fingertips, it becomes simple to adapt to changing market conditions for increased efficiency and scalability.
- Adhesion. Strong data management and analytics enable higher levels of compliance and protection, ensuring compliance with legal regulations and ethical standards. Using robust data and insights for generative AI activities enables reliable results for both the brand and the end consumer.
Related article: Generative AI in Marketing: Paving the Way for the Next Generation of Use Cases
Final Thoughts on AI in Marketing
A solid foundation of data management and analytics is essential for generative AI in marketing, ensuring the predictive insights, personalization prowess, and process compliance that so many brands want today. As generative AI becomes more commonplace in customer engagement technologies, it’s imperative to dedicate time to your brand’s data and analytics foundations.
As Forrester analyst Brandon Purcell writes in his most recent searches When it comes to customer analytics, “adding an LLM-compatible user interface to a product can help democratize it, but it doesn’t guarantee the robustness of the underlying analytics. Rather than being seduced by the facade of generative AI, buyers should look for differentiated analytics techniques under the hood.”
I couldn’t agree more.
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