The release of ChatGPT last November sparked an avalanche of stories about how AI is changing, and will continue to change, the way we work. From a marketing perspective, it’s been exciting to see the curiosity and creativity inspired by the possibility AI holds across the entire customer relationship.

In many ways, AI’s explosion is well timed, as 2023 has largely been defined by the need for establishing profitable unit economics and the focus on businesses driving greater efficiency in acquiring and retaining customers. According to a recent KPMG study, “companies investing in AI report achieving on average 15% productivity improvements for the projects they are undertaking” and Gartner predicts that “by 2025, organizations that use AI across the marketing function will shift 75% of their staff’s operations from production to more strategic activities.”

With new tools coming out every day, marketers may be overwhelmed by the barrage of options and how to evaluate them. As leaders of brand stewardship, marketers must be particularly careful with their AI experimentation, knowing the long-term and costly damage that can result from an AI experiment gone bad.

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With so much to consider, here are four ways to think about how AI will impact the customer lifecycle: 

1. Differentiating with big data 

Before marketers start running with AI tools, it’s important to understand that the starting point and value of any AI tool is its underlying data source. AI requires large amounts of data to inform and iterate its learning. For this reason, many of today’s tools will be based on one of the largest and most available sources – searchable data available on the web.  Access to such a robust source of data has certainly contributed to the rapid growth of AI tools.

But, keep in mind that searchable data has a few limitations. First, since it is widely available, it is effectively a data commodity. It doesn’t offer any inherent differentiation. In order to become a source of competitive advantage to a marketer, tools built upon readily available data will need to offer differentiation based on the AI programming itself – how quickly and how accurately the program can learn and adapt.

Second, as AI quickly evolves, marketers should begin looking for underlying data sources with more unique attributes. For example, they will need enterprise data that captures deep relationships (i.e., multiple interactions with a given customer) or that offers broad insights (i.e., aggregating multiple sources of reliable data) in order to meaningfully understand the customer journey. Over time, proprietary, aggregated data sets will evolve to include both of these important dimensions.

2. Putting both power and privacy in targeting   

Another way marketers can benefit from AI is in the area of privacy-friendly targeting. In the two years since Apple stunned the advertising world with its new standards for privacy, we are still seeing new and innovative ways to recreate the highly-coveted flow of information that once passed between ad platforms and advertisers. AI is already playing a role in filling the gap left by Apple’s ATT – from automated contextual advertising to models increasingly able to take large amounts of anonymized data and learn how to iteratively optimize campaign performance.  This is made possible within platforms, but also increasingly across platforms, which provide greater accuracy and speed in the machine learning and AI recommendations. These innovations put the power of targeting back in the hands of advertisers while simultaneously taking a privacy-first approach.

3. Accelerating creative iteration 

If the current trend continues, we will see a further accelerated migration from offline to digital advertising.  As efficient as digital media platforms can be in serving ads, they require a far greater volume of creative assets than what is required for a typical print or TV ad campaign. Recent innovations in generative AI will drive significant efficiency in the production of more frequent and iterative creative in response to real-time learning.

Nestle was recently profiled for using a custom AI solution to guide their creative teams to the highest performing concepts with a new metric, the Creative Quality Score. Using Meta’s Marketing Mix Model, Nestle reported that ads with a “creative quality score of above 66% had a 66% higher ROAS.” While not fully automating creativity, generative AI can both guide creativity and automate time consuming tasks, such as modifying creative to meet different platform standards. With new tools for everything from copy writing to graphic design to photo imagery, it is crucial for brands to still use human oversight to ensure creative concepts are truly brand building. Real people are still needed to filter out the types of damaging gaffes we have seen in AI’s early days.

4. Enabling a “customer-obsessed” culture

Customer service and retention tools are exploding as evidence continues to mount about the customer experience’s impact on the bottom line.  According to a Salesforce study, nearly 90% of buyers say that the experience a company provides matters as much as its products or services.  Forrester reports, “firms that have reset their tech strategy to be customer-obsessed see 2.5x more revenue growth than those that don’t.” That said, operationalizing customer obsession is not easy, and it can be particularly challenging for smaller companies. Forrester cites that four out of five teams lack some of the key skills to be successful. Many of these skills, like design, storytelling, and analytics, are areas where AI can help bridge the gap by accelerating output and improving customer service teams to balance volume with nuanced understanding.

Differentiation, targeting, on-brand creative, and customer obsession are four factors that can make or break a marketing strategy, and ultimately, they can make or break your entire business. AI is revolutionizing each of these areas in exciting, but still unproven ways. As you consider how to leverage AI in your digital marketing, and where to make the smartest investments, look for robust data sources with more unique attribution capabilities, tools that have the best targeting accuracy, learning speed and the ability to quickly iterate. Just as important, your AI tools must have engagement analytics that can help you build positive experiences for your customers along every stage of their journey. Just be sure that any AI choices you make are in line with your brand standards – and that you don’t leave quality control up to the machines.

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