Caille O'Leary
Generative AI has emerged as a driving force in the marketing industry. We have already seen many use cases, from content creation to campaign optimization. It is also poised to revolutionize how we gain deeper insights into our marketing data.
In this article, we’ll dive deep into how generative AI is reshaping marketing analytics and uncovering its potential to drive data-driven decisions and insights.
The best place to start is by looking at some built-in tools that currently – or will soon exist – within our data hubs and business intelligence (BI) tools.
Generative AI tools built on large language models (LLMs) explicitly for data analytics are somewhat newer and fairly rudimentary. It’s also important to consider that using an LLM requires a clean and robust underlying data model.
We will likely see the data analyst’s role shift to account for this, spending less time gathering insights, providing analyses, building dashboards and creating visualizations. Instead, the focus will be refining data models to extract these insights using natural language processing.
Take Tableau’s newest AI product, Pulse, which is slated for release by the end of 2023.
The user will select priority KPIs that Tableau will automatically generate visualizations for and alert the user when unusual trends or patterns are detected.
From there, the user can simply click on the relevant KPI to unearth the “why” behind the change in trend.
Tableau has stated explicitly that its goal is to “simplify and streamline” the existing workflows of the data analyst, which is a key idea that we will begin to see more and more.
Generative AI tools will do more of the heavy lifting. Conversely, analysts will shift their focus either to the data model development side or become a power user of a generative AI tool of choice by understanding how to construct and modify natural language queries to extract the most value out of their capabilities.
Source: Tableau.com
Funnel.io, a popular omnichannel marketing hub, takes a slightly different approach with what they have branded as Funnel AI.
While I mentioned that generative AI solutions generally require strong data models, users can extract, explore, manipulate and analyze all their marketing data in Funnel AI without leaving the Funnel platform. The one-stop-shop data pipeline approach is on the rise, aiming to eliminate data silos and democratize data, making it easily accessible for everyday use, even without the technical skills of a traditional analyst.
Funnel AI and similar tools are extremely useful for on-the-fly ad hoc analysis, rather than having to slice and dice data in an existing dashboard that requires ongoing maintenance. Funnel AI features auto-suggested queries for users unsure where to start, such as “What is the breakdown of CPC per channel in the last four weeks?”
This query delivers a bar graph of each channel’s CPC, which users can refine with additional filters or enter new queries into the search bar. Previously, obtaining this basic insight required data analysts to extract, model and visualize data tailored to specific parameters.
With generative AI, what took hours now takes seconds, freeing users for focused analysis and decision-making.
Tellius, a similar all-encompassing data platform that is more generalized, takes a similar approach through its generative AI tools to the prior examples. In the example below, their platform automatically detected a decrease in campaign conversions and immediately offered the top contributing points to explain the decrease by creating a segment analysis.
Typically, this process would consume hours of a marketer’s or analyst’s time, from identifying the drop-off to digging to determine causation.
The ability to swiftly uncover such critical insights undoubtedly provides marketers using these tools with a significant advantage, enabling them to take action promptly instead of investing time in searching for this information.
Source: Tellius.com
While we can clearly see the power and value of generative AI tools, getting widespread user adoption and trust in these models is a challenge organizations must overcome. Human oversight and data validation will still be required for the foreseeable future. Three in four CEOsbelieve that competitive advantage will depend on who has the most advanced generative AI, but 48% worry about bias or data accuracy.
This will require marketers to implement new roles and processes to ensure accountability for the AI models and transparency in AI-based decision-making. The future of digital marketing is closely intertwined with the advancement of AI capabilities. Marketers who adapt, understand best practices, and establish effective oversight gain a competitive edge.
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