With this rapid digital growth comes the sudden need to deliver effective customer experiences, manage digital self-serve capabilities, and handle an increased volume of customer service—and that has many companies scrambling.
Digital transformation acceleration
The companies that will capture a disproportionate share of digital growth and create sustainable competitive advantage are mobilizing and prioritizing investments they may have put on the back burner over the past few years, despite recognizing the need for digital transformation and modernizing digital experiences.
Historically, moving up the digital maturity curve was a linear process that took time. However, taking advantage of the ongoing waves of digital growth will require an acceleration of this maturity. That means critical investments must be made in the people, processes, and technology that together drive the change and transformation of customer experiences and business models.
At the core of this transformation is an obsessively customer-centric operating model and a data-driven approach to ensure that every customer interaction is relevant and contributes to business value—a combination of revenue, customer satisfaction, and the growth of customer equity.
How AI can strengthen customer relationships and create immediate value
Most CTOs or CIOs have embarked on enterprise-wide AI initiatives and roadmaps focusing on use cases across the board—operations, risk, personalization, marketing, and sales. McKinsey forecasts the impact of AI in marketing and sales will contribute an incremental $1.7 trillion to $3 trillion per year in new value created across industries. Customer-centric AI applications focus on intelligent segmentation, natural-language generation and content creation, next-best action, and the orchestration, measurement, and optimization of experiences across the customer journey.
There are a number of practical and value-accretive opportunities that create relatively quick wins at scale—investments even the most risk-averse CFO can easily stand behind.
One blind spot AI can help solve is the subjectivity of content, creative, and messaging.
JPMorgan Chase, for example, uses Persado’s AI-based language platform to remove the subjectivity and bias from content creation to unlock value at scale by improving customer acquisition, customer value, and digital servicing. By leveraging natural-language generation, Chase drives between 50% and 250% higher engagement and conversion rates depending on the initiative.
Nobody would argue with the premise that words matter. In fact, there are hundreds of thousands of creative professionals who are employed to apply a career’s worth of pattern recognition, intuition, and best practices to develop content that influences customers and prospects to pay attention, engage, or buy products and services. Natural-language generation is a form of AI that unlocks value by enabling marketing and customer-experience leaders to add objectivity and mathematical certainty to the effectiveness of the words used in every customer interaction. For large enterprises with direct access to their customers, this results in tens of millions to hundreds of millions of dollars in incremental annual value.
As more companies prioritize personalization, they are also looking to AI as a core part of the solution. Vodafone found that providing truly tailored experiences for each customer was impossible without being able to tie exactly the right message to the right customer at the right time. Vodafone worked with Adobe to identify and predict customer segments and make them available for activation, and then used AI-based language to generate personalized messages for each segment, boosting customer conversions by 40%.
Tapestry, the New York-based house of modern luxury lifestyle brands, has mobilized quickly in response to the acceleration of digital adoption and e-commerce. Acknowledging that different customer groups may not share the same motivations and behaviors, Tapestry’s brand marketing teams leverage AI-generated language to develop creative concepts and unique messaging aligned to customer segments and lifecycle stages in customer journeys. For example, AI-based language insights helped the house of brands understand that certain segments of Kate Spade customers engage most with language conveying the emotions of excitement and encouragement; for Coach customers, trust, safety, and exclusivity; and for Stuart Weitzman shoppers, a fear of missing out and gratitude.
Human + machine, not human vs. machine
It will soon be part of the standard enterprise playbook for AI-based language to help balance the concurrent objectives of message performance, maintaining brand voice, and compliance with creative and legal guidelines—for every message, in every channel, at scale. And unlike many other technologies, natural-language generation doesn’t replace the human, but rather provides a powerful tool to augment the human experience. In that vein, we are at a point where, in the right context, AI can understand human emotions better than we do and can help us create deeper human connections than we can on our own.
In the early phases of a customer-centric transformation, fueled in part by AI, companies are achieving sizable quick wins with natural-language generation that help build credible support for other investments across a broader transformation. The first step is getting past the hype surrounding AI and exploring use cases where even the best intentions are prone to subjectivity and human bias. The latent value locked in that subjectivity can be staggering.