Unlocking the potential of generative AI for marketing success

Research indicates that 41% of marketing, sales, and customer service professionals are already utilizing generative AI. According to Deloitte, this marks the second highest adoption rate across company departments, following IT and cybersecurity functions at 46%.

If your marketing team hasn’t jumped on the Gen AI bandwagon yet due to uncertainty or a limited understanding of the technology’s capabilities, let us start with the basics.

Generative AI is a branch of artificial intelligence that focuses on creating new content based on the patterns it has learnt rather than simply categorizing existing data. It also excels at analyzing unstructured data—like customer reviews, social media posts, and open-ended survey responses—much faster than classic AI.

Gen AI differs from classic artificial intelligence by its creative capabilities. Traditional AI might analyze data to explain which types of content resonate best with a specific audience and why engagement rates have declined. Generative AI, however, goes further by creating detailed customer personas, crafting effective content, and developing campaign execution plans, automating the entire marketing funnel.

A recent Statista study revealed that 73% of the surveyed US marketers rely on Gen AI to accomplish their daily tasks. And another survey by McKinsey found that 90% of the commercial leaders anticipate increasing their use of Gen AI in the coming two years.

So, why are marketers so eager to embrace generative AI?

The answer lies in the numerous advantages this technology offers, such as:

Choosing the right generative AI approach for your marketing strategy is crucial. Let’s explore three common ways to implement Gen AI—using off-the-shelf generative AI models, customizing Gen AI, and building the model from scratch.

Off-the-shelf generative AI models are pre-trained, ready-to-use algorithms. They are created using vast datasets and are designed to handle a wide range of tasks, such as text generation, image creation, and data analysis. Popular examples include OpenAI’s GPT, Google’s Bard, and Stability AI’s Stable Diffusion.

Ready-made AI models offer the following benefits:

Off-the-shelf models are ideal for businesses that want to quickly enhance their marketing efforts without investing resources in custom development. This approach works well for general use cases, like automated email generation and basic customer sentiment analysis.

Customizing generative AI for marketing needs involves taking a pre-trained solution and fine-tuning it to better suit a company’s specific requirements. This usually implies feeding the model with proprietary data so that it generates bespoke output that aligns with brand values and the company’s unique marketing goals.

You can either re-train a commercially available Gen AI model, such as OpenAI’s ChatGPT, and only pay a fee for model access, or you can customize an open-source solution where you don’t pay licensing fees but have to provide the infrastructure to train and run the model.

This approach offers the following benefits:

Customization is a superb choice for companies that have specific needs, such as targeting niche audiences, and want to differentiate themselves in the market.

Besides development efforts, building a generative AI model from scratch involves collecting and cleaning datasets, training the modes, and continuously refining it based on feedback and performance metrics.

The expenses associated with this approach are immense. Let’s look at some examples. OpenAI spent over $4 million on building and training their large language model (LLM) GPT-3. Its more capable successor, GPT-4, cost the company a staggering $63 million. You can find more information in our detailed guide on generative AI costs. And in terms of environmental footprint, building a Gen AI model from scratch is equivalent in its carbon emissions to a lifetime of five cars.

If you are still interested in building and training a Gen AI model, you can expect these benefits:

Now that you know how Gen AI models work behind the scenes, let’s zoom in on practical generative AI applications in marketing.

The traditional gathering and analysis of market data is time-consuming and often leads to delayed insights. With generative AI, companies can tap into vast data sets in real time, uncovering key patterns and trends that might otherwise go unnoticed.

Here’s how generative AI enhances different aspects of market research:

Real-life example:

The Spanish fashion retailer Mango retrained and customized open-source and commercial large language models to produce Lisa, a conversational Gen AI platform. Lisa can support human staff in identifying trends, analyzing customer feedback, and more. It can even generate designs for clothing collections. And this entire initiative took less than nine months to carry out.

Generative AI is a powerful tool for uncovering hidden potential in the marketplace. Here’s a breakdown of this generative AI marketing use case:

Real-life example:

An Asian beverage company was looking to expand its presence to the European market. Its marketing team had to study the prospective audience to gauge people’s taste and drinking habits and come up with unique drink concepts. The traditional innovation approach would take too long, pushing the marketing team to experiment with Gen AI. They fed an AI tool information about the European preferences and traditions on beverage consumption and asked it to come up with several product concepts that will be well received in Europe. In just one day, the tool produced 30 concepts, each detailing flavor profiles and packaging designs.

Unstructured customer data, such as social media posts, customer reviews, emails, and chat interactions, holds a wealth of valuable insights. But it’s often too diverse and difficult to process for traditional methods, but not for Gen AI.

Let’s see how generative AI can help with unstructured data analysis:

Real-life example:

Netflix relies on generative AI to analyze enormous amounts of data on each customer to understand their preferences and suggest personalized recommendations. These suggestions account for over $1 billion in annual revenue for the company.

With generative AI, marketers can quickly deliver engaging and personalized content that connects with audiences in meaningful ways. They can reach the right people with the right message at the right time.

Gen AI supports marketing campaigns in several ways:

Real-life examples:

Coca-Cola’s “Masterpiece” campaign is a perfect example of deploying Gen AI to realize the full potential of human creativity. The company used DALL-E2 to generate a creative video campaign involving works of art, such as Andy Warhol’s famous Coke Bottle painting, and live humans.

With all the tasks that marketers need to accomplish, the job can get overwhelming. Marketing generative AI tools can automate some of these tasks and produce drafts that humans can work with.

Generative AI can take over the following tasks:

Real-life examples:

Las Vegas-based digital marketing and website design company Site Smart Marketing was struggling to create high-quality content at scale. Site Smart Marketing teamed up with Narrato to use their Gen AI-powered marketing tool for content creation. With generative AI, Site Smart could produce content eight times faster than before while spending only 20% of the original costs. Narrato’s platform contains personalization features, which enabled Site Smart marketers to maintain the brand’s voice of each of its clients.

In another example, an American snack company Mondelēz International teamed up with Accenture and Publicis Groupe to build its own Gen AI-powered marketing platform that will produce personalized texts and videos adhering to its brand voice.

Implementing Gen AI requires a strategic approach to ensure the technology delivers value while mitigating potential risks. Here’s a step-by-step guide to help industry leaders successfully adopt generative AI in marketing.

Before you start building an AI system, define a clear vision of how this technology will transform your marketing efforts. Outline how it can address time-consuming, costly, and resource-intensive tasks within your marketing strategy. This vision should highlight specific goals, such as improving customer engagement, automating content creation, or optimizing campaign performance.

Data is the lifeblood of generative AI. Ensuring data quality and accuracy is essential for producing reliable and meaningful results. As Accenture CMO Jill Kramer said, “Generative AI needs a corpus to work off of. If the original content is not fabulous, the generative AI-curated synopsis won’t be fabulous.”

If you have not done that yet, now it’s the time to develop your enterprise data strategy. You can hire a reliable data strategy consultant if you don’t have the expertise in-house. You will also need to invest in data management and clean and validate your data.

This is one of the main challenges of generative AI. When a model hallucinates, it produces false or misleading information that appears credible. In a marketing context, this phenomenon can lead to incorrect product descriptions, inaccurate claims, and misinformation that can damage brand reputation and customer trust.

Here are some steps that you can take to minimize hallucination risks:

In addition to the traditional data breach risks, generative AI introduces new security vulnerabilities, such as prompt injection attacks, where hackers trick Gen AI models into producing harmful content or revealing sensitive information.

Therefore, it’s important to implement strong security measures, educate teams on potential threats, and establish protocols for handling AI-generated content.

Generative AI can create copyright complexities. If a Gen AI model produces an image that resembles too closely a drawing of a human artist that it used for training without consent, then the artist can contest ownership. Some of the leading Gen AI companies are facing lawsuits for copyright infringements.

To avoid this issue, businesses can partner with responsible vendors that prioritize copyright compliance, such as Getty Images, whose Gen AI tool is trained on the company’s proprietary library of visuals.

You have seen some successful examples of using off-the-shelf AI tools in this article. A ready-made solution with a solid track record is indeed sufficient for some small-scale applications. But if you want to incorporate Gen AI across your marketing workflows and transform your operations, we advise you to consider customizing a commercial or open-source Gen AI solution to fine-tune it to your business needs.

If you are interested in model customization, opt for ITRex as your trusted generative AI development partner. Whether you want to experiment with a limited-scope use case or implement a complex system, such as AI agents, we can help. We offer an AI proof-of-concept (PoC) service that allows you to validate your generative AI hypothesis for marketing with minimal resources. You can find more information about this offering in our AI PoC guide.