How To Unlock Customer Centricity Through AI-Powered Processes

In this data rich environment, Debbie Braney, SVP of Global Marketing at digital customer experience analytics provider Glassbox, believes it’s important to integrate essential datasets for analysis and then apply machine learning and Gen AI to efficiently extract critical insights. Below, the Demand Gen Report team spoke to Braney to learn more about her thoughts on AI, data and customer insights.

Demand Gen Report: It’s no secret there are billions of datapoints available — how do you suggest practitioners navigate them in an effective way?

Debbie Braney: Living in a time of limitless data is a double-edged sword. While the proliferation of data helps deliver better, more personalized experiences, it also makes finding actionable insights more challenging, often relying on technical or analytical resources.

To establish an efficient path to critical insights, companies need to focus first on integrating essential data sets to be available for analysis. Then, they can apply machine learning to drive efficiency of their analysis and GenAI to deliver insights to business users with or without data and analytics expertise. 

Now you have the foundation for making data-driven decisions in real time across all parts of the organization. 

DGR: Now that we have the data foundation in place, can you share some insights on how practitioners can responsibly use AI and navigate consumer hesitation?

Braney: Trust is a critical element of brand loyalty, which is fleeting in most industries today. Embracing AI can be a win-win for the business and the customer with the right safeguards AND transparency. First, organizations must maintain the same standards they use for data privacy and security elsewhere to their adoption of AI. There should be no shortcuts in the name of innovation or cost-savings. Second, they would be wise to publish their stance and/or policies on the use of AI where they can be seen by concerned customers.

DGR: We know a major part of leveraging data is to create highly personalized experiences that increase customer experiences (CX). In previous conversations, you’ve mentioned the concept of “Autonomous CX” — can you elaborate on that?

Braney: Autonomous CX is a concept that embraces AI to revolutionize how organizations understand and deliver digital customer experiences. Rather than focusing on bots and conversational AI as the direct interface with customers, it focuses on using data to drive analysis, insight and ultimately actions that optimize digital experience.

The journey to fully Autonomous CX has three phases:

• Using machine learning to analyze large data sets more efficiently to get to insights much faster;
• Using Generative AI to democratize access to those insights, freeing knowledge from technical constraints; and
• Using data and advanced AI models to progress from insight to action with self-optimizing digital applications.

It’s important to note that Autonomous CX is not about eliminating the human component of customer interactions. Rather, Autonomous CX uses AI to bolster customer experience while freeing digital and product teams to devote their time (and brain power) towards more strategic work.

DGR: While we’re on the topic of using AI to supercharge CX strategies, do you have anything else you’d like to share?

Braney: AI can help to address one of the biggest barriers to delivering great CX — the limitations of customer feedback, aka Voice of the Customer (VoC). With traditional VoC programs only capturing feedback from 4%-8% of customers, organizations are making key decisions with a very narrow understanding of customer sentiment. Add to this the fact that customer feedback is not always actionable, coming in the form of numerical ratings or written feedback that is vague or emotional.

Instead, practitioners have to focus on the Voice of the Silent (VoS), an AI-powered platform that seeks to connect the feedback of a few customers with the similar experiences of 100% of customers to multiply the volume, accuracy and actionability of feedback. With the rollout of VoS, coupled with GIA, companies can close the customer feedback gap and align customer sentiment with actual product and brand experience, supercharging CX, meeting evolving customer demands and driving revenue.

DGR: What do you see for the future of AI personalization?

Braney: Contrary to what many think, AI personalization encompasses so much more than just recommending products based on past behavior. It involves leveraging AI algorithms to tailor experiences to different individual users. 

Imagine a scenario where websites equipped with AI can immediately recognize the accessibility needs of their visitors. For instance, if an individual with colorblindness accesses a website, AI can automatically adjust the website's color scheme to ensure it improves and individualizes their digital experience. This level of customization is a prime example of how AI personalization can transcend traditional boundaries and cultivate long-term customer loyalty. 

When customers feel understood and catered to on an individual level, they are more likely to forge enduring connections with brands. This loyalty translates to sustained patronage and positive word-of-mouth, amplifying the benefits for businesses.

In essence, the potential of AI personalization extends far beyond mere product recommendations. It heralds a future where technology seamlessly integrates with human needs and preferences, transforming customer experiences into tailored, efficient — and ultimately — more rewarding interactions.