I recently read Courtney Trudeau’s article about journeyless marketing and the concept of moving away from emphasis on customer journey orchestration. As marketers, we’ve become so focused on the customer journey and trying to drive consumers down the purchase funnel that we stopped thinking about customers as individuals. Embracing “human centricity” vs. “customer centricity” means there is no one-size-fits-all path to purchase or engagement with a brand, which requires organizations to adapt to each customers unique’s needs and journey. 
 

Finding the Right Tools

When we, as marketers, start to implement journeyless marketing, we need to rapidly identify key moments and touch points and dynamically influence them at scale. Harnessing AI capabilities is key to making it possible.

Note that we’re not necessarily talking about some disruptive generative AI application or fantastical models – we’re talking about using AI to help you do your job more effectively by maximizing the engagement you're creating with your customers. Do it faster. Do it better. Do it at scale.
 

How to Implement AI for Journeyless Marketing

So, what does that actually mean for us as marketers? How can we harness AI to supercharge our marketing technology?

New generative and advanced AI capabilities are already being commoditized and baked in the tools/suites we already work with. The first step we recommend is taking inventory of the capabilities you have access to in your tech stack. For example, if you’re currently leveraging Salesforce’s Einstein capabilities for machine learning and data processing, is there a way you can leverage them in other parts of the tech stack to perform faster audience analysis and segmentation? Adobe's Firefly capabilities that came out earlier this year can similarly help you rapidly test new options without having to acquire a new suite of tools.

However, before implementing new AI processes and tools, there’s a much more foundational need: rich, clean first-party data. Everyone has heard the old cliché on data usage: “garbage in, garbage out.” The same is absolutely true when it comes to AI: the outputs are dependent on the inputs that you provide to your AI models. You need to have a foundation of rich and clean first-party data to valuable outputs.
 

Where in the Tech Stack Does AI Sit?

At Merkle, we describe the tech stack as made by six foundational layers to help us understand and outline the capabilities that contribute to the overall consumer experience. When we look at AI, we don't see it as a single line item, platform, or capability that sits in one area of the tech stack. AI can and should show up in multiple areas of the tech stack.

We are seeing brands’ adoption and application of AI rapidly mature, transforming many areas of their tech stacks and influencing, for example, what a modern data stack should look like.

That said, there are a few specific areas where we see brands use AI most frequently, and as we explore it we find it useful to distinguish between discriminative and generative AI:

• Discriminative AI: Sentiment analysis; predictive models; image classification; analysis of unstructured data. Models that are designed to classify input data into categories based on data features. These models make predictions based on patterns in the input data, but do not create new data. Discriminative AI can help brands process more data, faster – leading to new insights and quicker activation of those insights for experience optimizations.
  
  
• Generative AI: Content production and manipulation (text, image, video, audio, code); conversational experiences (chat bots, self-serve). Models that are designed to create new data by learning and understanding probability distribution of data. They leverage training data to generate new content that is similar.
  
  Generative’s AI efficiency in developing personalized content makes this an attractive use case for many brands to power ambitious personalization visions. The conversational experience delivered by chat bots and machine learning is evolving to deliver more automated or self-service options for customers to solve issues, check order status, process refunds, or more quickly find the product that they're looking for – all of which create a more streamlined customer service experience. New capabilities are pushing the boundaries beyond chat and text to voice, video and audio.
  
  We’ve developed an offering around this capability called GenCX – visit our landing page to learn more.
   

Conclusion

The maturity of AI capabilities is advancing at an unprecedented pace, offering a unique opportunity for brands to supercharge their tech stack and realize game-changing advantages.

The perfect recipe leverages unique brand data assets and makes them accessible through a combination of specialized models and modalities that become applications. These new applications build intelligence into the processes our internal teams manage and the experiences we deliver to our customers.

First, grounded in your strategic objectives and vision, identify which new capabilities could unlock the greatest value based on the type of experience you’re trying to curate for your customers. Then, prioritize your use cases and start small, as experimenting is key to drive the adoption of more advanced AI/ML at scale. What use cases can you comfortably test within your current tech stack? Where are you looking to expand capabilities going forward? If you need a partner to guide you on your AI journey, reach out – we’d be happy to help!