Imagine a world where your marketing campaigns are precisely targeted to individual customer preferences, behaviors, and needs. Where versioning creative is easy and effortless. Where your team can produce high-performing, on-brand content with unprecedented speed and scale. Generative AI — a branch of artificial intelligence that creates new content — promises all of this, and more. But if there’s one factor that turns this promise into a pipedream, it’s issues with data.
According to S&P Global’s 2023 Global Trends in AI report, companies are finding it more costly and more difficult to start and scale generative AI projects because their data isn’t up to the task. So, if your data is disorganized, saved in different formats, siloed in different departments, or stored in disparate datasets and applications, you’re certainly not alone.
But that’s cold comfort if you’re a marketer eager to embed generative AI systems into your marketing processes today. You can certainly get started with the out-of-the-box, pre-trained models that come with third-party generative AI marketing solutions. But sooner or later, your organization’s proprietary data sets will be essential for generating unique strategic insights, original ideas, and on-brand content.
The sooner you begin building your data foundation, the better you’ll leverage the true potential of marketing AI. So, let’s explore exactly what types of data you’ll need to customize generative AI models for your specific use cases.
Before we do though, rest assured it’s not my goal to turn you into a data scientist or bog you down in detail. In this article, we’ll keep things high-level. But it’s important for any marketing leader to understand exactly what we mean when we talk about “good data,” so that you can engage in productive conversations with the data experts in your organization.
What Types of Data Will You Need?
Look for data that might inform strategic decision-making and marketing plans or allow generative AI systems to understand and replicate the ideas, messaging, style, and creative that resonate with your audience and align with strategy, identity, and objectives.
Data like:
- Customer Data: demographic, psychographic, and behavioral data about the target audience.
- Campaign Data: ad performance metrics, engagement rates, and conversion rates from past and current marketing campaigns.
- Competitor Data: data on competitors’ marketing strategies, such as ad copy, messaging, and offers.
- Content Data: high-performing content, trending content, user-generated content, and existing brand content and creative.
- Trend Data: industry trends, consumer behavior trends, and other relevant research.
- Contextual Data: data on the context in which the content will be consumed, such as the platform, device, and location of the target audience.
- Brand Strategy: values, voice and tone, brand guidelines, brand values, positioning, and messaging.
- And of course, your actual content and creative assets.
What Good Generative AI Data Looks Like
To make the most of generative AI, your marketing data should be:
- Available: This may sound obvious, but it certainly isn’t a given for many companies. Your organization needs to have the data. And it should be complete, consistent, and up to date. Poor data practices and spotty data governance over the years can negatively impact the data available for GenAI today.
- Accessible: Marketers require quick and easy access to relevant data to train, fine-tune, and even prompt generative AI models. Departmental, functional, or technical silos in your organization may make it more difficult to get your hands on the data you need. For instance, customer data or campaign performance data may be stored separately from sales data or social media data.
- Abundant: Even when data is technically available, the quantity of the data may not be sufficient. Generative AI models generally require large amounts of high-quality, diverse data to produce useful results. If certain types of data aren’t being collected, or if the data is incomplete, inconsistent, or outdated, it can significantly impact the effectiveness of these projects. Fine-tuning a pre-trained foundation model such as GPT, LLaMa, or Falcon lessens the burden of quantity, but the fact remains: Without a substantial amount of business-specific data, marketers will ultimately struggle to turn generic generative AI systems into highly differentiated, proprietary marketing difference-makers.
- Accurate: Generative AI models learn from the data they’re trained on. Accurate data ensures that the generated content and other outputs are relevant, informative, and compelling. Inaccurate data can result in misleading or irrelevant content that may harm the effectiveness of the marketing campaign or result in poor internal decision-making.
- Fresh: Freshness comes into play in two distinct ways. First, many marketing AI applications, such as chatbots or recommendation systems, need real-time data to function effectively. If data isn’t readily accessible and available, it can hinder these real-time applications. Second, generative AI models often use continuous learning to improve their performance over time. This is a distinct difference between AI-based systems and traditional software and — in fact — a key decision-making factor when determining whether your use case requires AI or if traditional data processing or analytics is sufficient. Ultimately, when you choose to deploy generative AI (or any type of artificial intelligence), a steady feed of new information is essential for maintaining and improving performance over time.
- Unbiased: Unbiased data is crucial for GenAI outputs that are fair, equitable, and inclusive, and that don’t reflect or perpetuate harmful social, cultural, racial, ethnic, or economic biases. At the same time, it’s important to understand whether your data may be slanted toward a given customer segment or scenario, in a way that might result in skewed outputs. Marketers may need to address any systematic errors in the data that can lead to inaccurate or unfair results.
- Usable: Data that’s easily understood, organized, and processed (by humans and algorithms) helps create efficient workflows, avoid inefficiencies, and minimize inaccuracies. If data is unstructured or in formats that are difficult to process, it can pose significant challenges. Furthermore, the lack of a well-structured data management and governance framework can lead to issues in data integrity and usability. Usability should also consider whether your intended use case complies with applicable regulations and even the permissions your customers opted-into when they provided their personal information.
- Useful: Useful data provides meaningful insights and actionable information that helps achieve your desired marketing objectives. It’s essential for generative AI models to produce market-facing content that resonates with your audience and achieves the desired results, and internal content that supports effective decision-making, communication, and knowledge sharing.
Build a Better Data Foundation
As a marketing leader, you’ve likely already bought into the potential for generative AI for hyper-personalized campaigns, rapid content creation, and strategic insights. However, the key to unlocking this potential isn’t just the technology itself — it’s the data that fuels it. And the truth is that your role extends beyond merely adopting AI solutions. It involves cultivating a robust data ecosystem that’s available, accessible, abundant, accurate, fresh, unbiased, usable, and ultimately, useful.
This requires a thoughtful approach to data management, breaking down silos, and fostering a culture of data literacy within your organization. While you can’t achieve this alone — you’ll need enterprise-wide commitment, and cross-functional collaboration — you can play a key role by understanding what data you’ll need, what “good” looks like, how you can use that data to drive marketing performance, and what you want to achieve by implementing generative AI as a marketing differentiator. After all, good data isn’t just an asset. It’s the key to unlocking your AI advantage.
