Using AI in Marketing to Supercharge your Data Analytics and Customer Engagement

The majority of marketers have identified data analytics as a top priority and generative AI can play an important role in data analytics. Since ChatGPT launched in 2022, companies and especially marketers have used generative AI to help create content. Recent developments have shown that this technology can also improve decision making through three important capabilities: 1) summarization, 2) autonomous deep agents, and 3) synthetic data generation.

Let’s take a look at each

1) Summarization

Generative AI is adept at summarizing large amounts of information and identifying trends and anomalies that might otherwise be overlooked. This has been especially true in regulated industries where analysts have access to filings and reports but may not have sufficient time to sift through all the information such as summarizing stock trades and regulatory filings.

Marketers can benefit from generative AI’s summarization capabilities in similar ways. Large language models can distill user surveys, product reviews, marketing reports, social media posts, and other datasets to detect trends and user preferences. Marketers may use LLMs to analyze social media comments, images, and videos to identify new product opportunities.

2) Autonomous Deep Agents

Most businesses are still plagued by data silos that make information inaccessible. For marketers, data comes from websites and mobile devices, email campaigns, social media platforms, digital advertising networks, call centers and many other systems. Marketers know this information could be useful, but rarely have the time or technical expertise to access, format and run analytics on the data.

Generative AI is changing the situation dramatically. Large language models can power deep agents that interpret user requests and integrate with multiple data sources to produce meaningful answers. Many technology companies already offer autonomous agent solutions that can handle multiple tasks, including supporting analytics.

To use an autonomous agent, users first grant the agent access to tools such as databases, APIs or other business software. The AI-powered agent can then break down a user’s request into a series of steps that it maps to specific tools. The agent orchestrates calling the tools to produce a final response that fulfills the user’s request.

A generative AI-powered agent may analyze requests and recognize that it can use a specific database and web analytics API to solve a specific problem. It could then generate a graph with all the information using data visualization software.

Generative AI and autonomous agents represent a significant change in how users access information. Large language models can now serve as a bridge that connects users to data sources and analytics tools.

3) Synthetic Data

A major problem with business analytics is the lack of good user data. Conducting user surveys is expensive, time consuming and response rates are generally low.

While some expectations may be overblown, there is strong evidence that synthetic data can significantly change market research. Synthetic data is already widely used in fields like biomedical research, insurance and financial services. It also makes sense that generative AI can produce marketing data because the underlying large language models have been trained on datasets created by real humans. Again, data quality matters.

By ingesting product reviews, social media posts, blogs and other writings, the models have learned people’s perceptions of and preferences for specific brands and products. The models can thus use this information to predict how real people would respond to marketing questions.

Of course, people’s opinions shift over time. In order to work well, large language models need be fine-tuned periodically with new information for successful AI in marketing. Rather than conduct expensive user surveys, however, marketers can refresh their models with information from more readily available sources, such as social media and other first party data that captures the behaviors and preferences of users.

Building a Data-Driven Culture

First, companies need to adopt a data driven culture, where they are willing to make decisions based on data and invest in the right roles and tools to be successful.

Once resources are available, companies need to develop a coherent data strategy. What questions do you want to answer? What is the business value? Where is the data you need to answer those questions? Who is responsible for managing and collecting that data?

The last step is implementation, which is when you invest not only in generative AI systems, but all the other tools needed to collect, store and distribute data within your organization. During implementation, companies should start small with well-defined goals. They should also plan for rapid iteration. Any project is only the first phase in a continuous process of refinement and improvement.

Generative AI can help you analyze your data and improve your decision making through its ability to summarize, power autonomous agents and create synthetic data. It is simply a tool that augments your abilities.

For generative AI in marketing to be effective, you need to define the business questions you want to answer and invest in the data foundation that will support the new technology.