Follow these five steps to capitalize on the power of AI.

Artificial intelligence (AI) and advancing technologies have revolutionized marketing. In the digital economy, businesses around the world are turning to data-driven marketing to gain deeper insights into customers. The magic lies in personalization – understanding the unique preferences of each customer and delivering targeted content, offers and experiences that resonate deeply. 

One successful example is Unilever’s Dove company. It created stickers for social media to directly connect with consumers, which they shared via Line, a messaging app popular across Asia. They particularly focused on Thailand, where the company’s stickers were downloaded 6 million times. As a result, the corporation’s database for Thailand now encompasses nearly one-third of the population. 

Unilever’s case offers valuable insights for other companies who likewise want to leverage AI in their marketing strategies, whatever their sector. Here is a five-point blueprint for businesses to consider and follow when navigating the current AI marketing revolution. 

1. Foundation: gathering the data 

The journey toward AI-driven marketing begins with the foundational stage. Companies must focus on collecting the data required to fuel AI-powered initiatives. The key differentiator here is the source of the data – first-party data, gathered directly from consumers. 

This approach offers a competitive advantage, especially in regions like Europe, where stringent data protection rules – such as the General Data Protection Regulation (GDPR) – limit the use of third-party data. By directly engaging with consumers, companies can build robust foundations for their AI-driven marketing efforts.

A good example of the benefits of the foundational phase can be found with The Coca-Cola Company, which gathered data through its Freestyle dispenser – an innovation that enabled customers to personalize their soda by mixing and matching various flavors. The data generated by this customization was sent to corporate headquarters, paving the way for successful product development, such as Sprite Cherry, and the more advanced Powerade Power Station, which offers one-to-one personalization.

2. Experimentation: refining the consumer experience 

Once the foundation is established, the second stage involves experimentation. Here, companies embark on a journey of trial and error, conducting a plethora of experiments aimed at enhancing the consumer experience. These experiments can span various objectives, such as attracting new customers, retaining existing ones, stimulating growth and fostering advocacy. 

Consider Starbucks and its highly-rated rewards program. Through experimentation, the coffee chain gathered a wealth of data, including customer contact information, purchase behaviors, redemption of offers and even mobile device ID. Starbucks refined its approach over a decade of experimentation, ultimately shifting from sending 30 handcrafted emails per week to employing AI to send 400,000 hyper-personalized emails in real time. 

The critical aspect at this stage is not only to innovate, but also to keep a watchful eye on the financial returns. Experimentation allows businesses to refine their strategies, aligning them more closely with consumer expectations. 

3. Expansion: a holistic approach to personalization 

As companies gather valuable insights from their experiments, they enter the third stage: expansion. This is where businesses grow their understanding of consumer behavior and preferences. The aim is to extend personalization beyond one specific aspect of the consumer relationship. The focus shifts to tailoring various touchpoints in the consumer journey. Companies build on the knowledge gained during experimentation to create a more seamless and personalized consumer experience.

Thread, now owned by the British retailer M&S, exemplifies the expansion stage. Leveraging AI, the fashion brand personalized wardrobes by combining recommendations from personal shoppers with algorithms distilling data from various sources. This approach allowed Thread to serve tens of thousands of customers with a personalized service, far more than human personal shoppers could have done alone. 

4. Transformation: all-encompassing personalization 

The fourth stage, known as transformation, represents the pinnacle of AI maturity in marketing. At this point, companies aim to personalize every facet of consumer engagement using AI. This requires substantial investments including the development of in-house capabilities or even acquisitions of specialized companies. Transformation ensures that every interaction with the consumer is finely tuned and customized to their individual preferences, further strengthening the brand-consumer relationship. 

Disney’s MagicBand is one example. This RFID wristband enables monitoring of the theme park visitors’ movements and buying patterns, with each device promptly transmitting its data to Disney. It simplifies the customer experience by helping the company to predict customer actions, adapt staffing levels, and encourage guests to visit other attractions. The results are improved efficiency, increased operating margins and strong footfall.

5. Monetization: expanding opportunities 

In the final stage of AI maturity in marketing, companies have honed their skills and capabilities to an exceptional level. With a solid foundation, a wealth of data and advanced AI expertise, businesses can now explore monetization. 

This may involve developing a new business model, offering AI as a service to other companies, or creating revenue streams by capitalizing on the AI capabilities they have cultivated. Monetization opens new horizons, enabling companies to extend their AI prowess beyond their own operations and into the broader market.

Alibaba serves as a good case study. Starting as an online marketplace for buying and selling goods, Alibaba has evolved into a data-centric ecosystem that links together vendors, advertisers, service providers, logistics firms, manufacturers and financial service providers. Underpinning this transformation is Alibaba’s “smart business” model, which incorporates machine learning, enabling businesses with common goals to make strategic decisions together within a digital network. 

By following the five stages of AI maturity, companies can leverage AI and data-driven strategies to create deeply personalized experiences, setting the stage for a more engaging and impactful future in marketing. 

Rajkumar Venkatesan is Ronald Trzcinski professor of business administration, University of Virginia Darden School of Business.

Together with Jim Lecinski, clinical associate professor of marketing at Northwestern’s Kellogg School of Management, Professor Venkatesan recently co-authored The AI Marketing Canvas, A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing, Stanford University Press.Tags