The essentials
- AI-powered insights. AI is revolutionizing marketing by providing deeper insights into customer behavior through advanced data analytics.
- Customization on a large scale. AI enables marketers to create personalized experiences for customers, improving engagement and conversion rates.
- Streamlining decision making. With AI-powered data analytics, businesses can make smarter, faster decisions that drive better marketing results.
Spotify has launched a data driven marketing campaign in 2016 that leveraged user data to highlight strengths and trends in specific markets. The campaign went viral and included many memorable creative assets, such as one aimed at the New York market that read: “Dear Theater District person who has listened to the Hamilton soundtrack 5,376 times this year. Can you get us tickets?”
Spotify’s campaign demonstrated that data could engage users, create personalized experiencesand run effective marketing campaigns. What was true in 2016 is still true today. According to Marketing WeekNearly half of marketers have identified data analytics as a priority investment for the remainder of 2024.
What we often forget is that Generative AI can play an important role in data analysis. Since the launch of ChatGPT in 2022, businesses and especially marketers have been looking into Generative AI to create engaging contentRecent developments have shown that this technology can also improve decision-making through three important capabilities: synthesis, autonomous agents and synthetic data generation.
We’ll look at each of these features in turn.
Abstract: The Role of Generative AI in Modern Marketing
Generative AI is able to summarize large amounts of information and identify trends and anomalies that might otherwise be overlooked. This is especially true in regulated industries where analysts have access to documents and reports but may not have enough time to sift through all the information. Nasdaq, for example, has developed a generative AI assistant that increased the productivity of its analysts by up to 33% by summarizing stock market transactions and regulatory filings.
Marketers can leverage the summarization capabilities of generative AI in a similar way. Large language models can distill user surveys, product reviews, marketing reports, social media posts, and other data sets to detect trends and user preferences. L’Orealfor example, uses generative AI to drive its marketing efforts. Among its initiatives, it uses large language models to analyze comments, images and videos on social media to identify new product opportunities.
Autonomous Agents: Breaking Down Data Silos
While data is a critical asset for decision-making, most businesses are plagued by data silos that make information inaccessible. For marketers, data comes from websites and mobile devices, email campaigns, social media platforms, digital ad networks, call centers, and many other systems. Marketers know this information can be valuable, but they rarely have the time or technical expertise to access, format, and analyze the data.
Generative AI is a game changer. Large language models can power autonomous agents that interpret user requests and integrate with multiple data sources to produce relevant responses. Many technology companies already offer autonomous agent solutions that can handle multiple tasks, including supporting analytics.
To use an autonomous agent, users must first grant it access to tools such as databases, APIs, or other enterprise software. The AI-powered agent can then break down a user’s request into a series of steps that it associates with specific tools. The agent orchestrates the invocation of the tools to produce a final response that satisfies the user’s request.
For example, let’s say a company has a database with data about email campaigns (campaign names, descriptions, email addresses, open rates, click-through rates, etc.) and an API to access web analytics. A marketer might ask an autonomous agent, “Give me a chart from last month showing the effectiveness of our email campaign named X.”
The AI-based generative agent would analyze this query and recognize that it can use the campaign database and web analytics API to solve the problem. It could then write and run a query against the database to find all users who received a campaign email and clicked a link in the message in the last 30 days. The agent could then call the web analytics API to track what those users did on the website, including whether they made a purchase. It could then generate a graph with all of this information using data visualization software.
Generative AI and autonomous agents represent a significant shift in how users access information. Large language models can now serve as a bridge connecting users to data sources and analytics tools. A researcher at MIT recently noted that with generative AI, users can “move from simply querying data to asking questions about patterns and data.” AI in Marketing at its best.
Related article: Stay Ahead: Adopt Generative AI in Marketing Now
Synthetic data: the power of market research
One of the biggest problems with business analytics is the lack of reliable user data. User surveys, for example, are expensive, time-consuming, and response rates are typically low. But is user research really necessary? What if you could use generative AI to produce so-called synthetic data?
In April And December In 2023, researchers published papers on using generative AI to conduct market research. Specifically, they asked large language models to answer user survey questions to determine brand perceptions and preferences. In both studies, the results correlated very closely—up to 90%—with the answers provided by human respondents. One group of researchers even demonstrated that they could ask the models to provide insights for specific demographic groups.
The results of this research have been so promising that several marketing firms and startups have begun offering market research services that generate synthetic data. In a matter of hours, these companies can produce marketing data for target demographics that would have taken weeks to compile through traditional means. Marketing Week Editor enthusiastically: “The era of synthetic data has arrived.”
While some expectations may be exaggerated, there is strong evidence that synthetic data can significantly change market research. Synthetic data is already widely used in fields such as biomedical research, insurance, and financial services. It also makes sense that generative AI could produce marketing data, since the underlying large language models were trained on datasets created by real humans.
By ingesting product reviews, social media posts, blogs, and other writings, the models learned people’s perceptions and preferences for specific brands and products. The models can then use this information to predict how real people would respond to marketing questions.
Of course, people’s opinions evolve over time. To work well, large language models need to be periodically refined with new information for effective AI in marketing. Rather than conducting costly user surveys, however, marketers can refresh their models with information from more readily available sources, such as social media and other proprietary data that capture user behaviors and preferences.
Related article: Top 10 AI-powered Marketing Analytics Tools
Creating a data-driven culture for AI success
To use Generative AI for Data Analysisyou need to establish the foundation for success. First, businesses must adopt a data driven a culture where they are willing to make data-driven decisions and invest in the right roles and tools to succeed.
Once the 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 final step is implementation, which involves investing not only in generative AI systems, but also in all the other tools needed to collect, store, and distribute data across your organization. When implementing, companies should start with well-defined goals. They should also plan for rapid iteration. Any project is just 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 synthesize, power autonomous agents, and create synthetic data. But it’s not a magic wand that will instantly solve your problems. It’s just a tool that augments your capabilities.
For generative AI in marketing to be effective, you need to define the business questions you want to answer and invest in the database that will support the new technology.
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