How to Establish Trust and Unlock Customer Data to Turn the AI Promise into Profit
May 15, 2024

Kathryn Murphy

Share this

Businesses are dazzled by the promise of generative AI, as it touts the capability to increase productivity and efficiency, cut costs, and provide competitive advantages. With more and more generative AI options available today, businesses are now investigating how to convert the AI promise into profit.

One way businesses are looking to do this is by using AI to improve personalized customer engagement. In fact, seven out of 10 companies say they are already using AI to personalize content and marketing.

AI makes it possible to deliver truly unique and individualized experiences for every single customer, building loyalty and increasing efficiency along the way. Using AI, businesses can move away from the one-to-many marketing strategy and provide truly individualized, one-to-one experiences.

With any emerging technology, there are also major risks — including lack of access to the right data, transparency, and trust. In order to gain a return on AI customer experience investments, businesses must maximize their data and prioritize trust.

The Customer Data Disconnect

While businesses have put a greater focus on collecting data, many are struggling to put this data to work, especially when it comes to AI.

According to Twilio's 2024 State of Customer Engagement Report, which surveyed over 4,700 executives and 6,000 consumers, only 16% of businesses strongly agree that they have the data they need to understand their customers. This lack of data is a huge and potentially costly setback for businesses. Without the right customer data, businesses risk building experiences and communications that are at best generic or at worst wrong. With more and more businesses actively considering how to incorporate AI into their experiences, having the right customer data is essential to success.

In order to best understand where customer experience data gaps exist, businesses should start with these tactics:

Conduct a customer journey audit to find out what's missing and identify the pain points preventing conversion. This audit is the foundation for a holistic customer journey map that will help businesses activate their data to provide better customer experiences.

Unlock warehouse data. While most businesses have a wealth of information in their data warehouses, few can actually activate it. To solve this problem, businesses can connect their customer data platform (CDP) data with the data in their warehouses, promoting a deeper understanding of each customer and their interactions with the brand.

Integrate AI directly into the contact center to collect and share data in real time, including events across platforms leading up to the moments prior to a customer calling a contact center. This helps customer service agents provide personalized experiences without getting overwhelmed by the deluge of information, and ultimately resolve customer issues faster and more effectively.

AI opens the opportunity to analyze the large volumes of collected customer data to gain actionable insights and deliver personalized experiences to customers. Taking stock of what data and potential barriers exists will help businesses capitalize on the AI opportunity in a way that meets customer expectations.

Navigating Today's AI Trust Landscape

However, with emerging malicious use cases like AI-generated images diminishing consumer trust in AI, it's essential that companies improve data transparency and security when delivering customer experiences powered by AI. Failure to do so puts customer trust and loyalty at risk.

Consumers want to know exactly how businesses leverage their data. Almost half (49%) of consumers say they would trust brands more if they openly disclose the use of customer data in AI-powered interactions. The problem, however, is that businesses believe they are already doing this well. According to this same report, while 91% of brands say they're transparent with customers around how AI uses their data, only 48% of customers agree. The bottom line for businesses is consumers need to experience personalization in a trusted way. It has to be clear to the consumer that the brand knows certain information and is leveraging it to personalize for the end users benefit. There is a thin line between too much and awesome — brands need to be wary to not cross it.

AI's value is unlocked when businesses can securely harness customer data to develop a deep understanding of each customer and improve each interaction over time. But to build customer trust, businesses need to be open and clear with their customers on how they are using customer data in AI-powered experiences. Twilio introduced a clear and concise way for businesses to provide greater transparency in how they use this data, called AI nutrition labels. These AI nutrition labels operate like the labels on everyday grocery items and give an inside view into exactly what data went into creating each AI-powered experience, how exactly it is being used and more. This transparency helps to instill trust and shows a commitment to the privacy of customers.

Unlocking the AI ROI Promise

When businesses are able to build trust through transparency and activate their data, using AI in customer engagement stands to impact the bottom line. According to the report, consumers will spend 54% more with a brand that personalizes to them. AI can help power the personalization that secures this loyalty — 48% of consumers say they've made a repeat purchase from a company based on the level of personalization they received.

Businesses who use AI to power personalized customer experiences are reaping the rewards. Seven in 10 companies already leverage AI to personalize content and marketing and are realizing a number of benefits, including higher customer satisfaction scores (45%), better data-driven decision-making (41%) and improved market segmentation and targeting (41%).

AI and generative AI have the potential to transform how businesses connect with and retain their customers. The businesses that invest in trust and in building a strong data foundation stand to not only maximize their AI investments, but also maximize their potential profits.

Kathryn Murphy is SVP of Product and Design at Twilio
Share this

The Latest

May 23, 2024

Hybrid cloud architecture is breaking the backs of network engineering and operations teams. These teams are more successful when their companies go all-in with the cloud or stay out of it entirely. When companies maintain hybrid infrastructure, with applications and data residing across data centers and public cloud services, the network team struggles. This insight emerged in the newly published 2024 edition of Enterprise Management Associates' (EMA) Network Management Megatrends research ...

May 22, 2024

As IT practitioners, we often find ourselves fighting fires rather than proactively getting ahead ... Many spend countless hours managing several tools that give them different, fractured views of their own work — which isn't an effective use of time. Balancing daily technical tasks with long-term company goals requires a three-step approach. I'll share these steps and tips for others to do the same ...

May 21, 2024

IT service outages are more than a minor inconvenience. They can cost businesses millions while simultaneously leading to customer dissatisfaction and reputational damage. Moreover, the constant pressure of dealing with fire drills and escalations day and night can take a heavy toll on ITOps teams, leading to increased stress, human error, and burnout ...

May 20, 2024

Amid economic disruption, fintech competition, and other headwinds in recent years, banks have had to quickly adjust to the demands of the market. This adaptation is often reliant on having the right technology infrastructure in place ...

May 17, 2024

In MEAN TIME TO INSIGHT Episode 6, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network automation ...

May 16, 2024

In the ever-evolving landscape of software development and infrastructure management, observability stands as a crucial pillar. Among its fundamental components lies log collection ... However, traditional methods of log collection have faced challenges, especially in high-volume and dynamic environments. Enter eBPF, a groundbreaking technology ...

May 15, 2024

Businesses are dazzled by the promise of generative AI, as it touts the capability to increase productivity and efficiency, cut costs, and provide competitive advantages. With more and more generative AI options available today, businesses are now investigating how to convert the AI promise into profit. One way businesses are looking to do this is by using AI to improve personalized customer engagement ...

May 14, 2024

In the fast-evolving realm of cloud computing, where innovation collides with fiscal responsibility, the Flexera 2024 State of the Cloud Report illuminates the challenges and triumphs shaping the digital landscape ... At the forefront of this year's findings is the resounding chorus of organizations grappling with cloud costs ...

May 13, 2024

Government agencies are transforming to improve the digital experience for employees and citizens, allowing them to achieve key goals, including unleashing staff productivity, recruiting and retaining talent in the public sector, and delivering on the mission, according to the Global Digital Employee Experience (DEX) Survey from Riverbed ...

May 09, 2024

App sprawl has been a concern for technologists for some time, but it has never presented such a challenge as now. As organizations move to implement generative AI into their applications, it's only going to become more complex ... Observability is a necessary component for understanding the vast amounts of complex data within AI-infused applications, and it must be the centerpiece of an app- and data-centric strategy to truly manage app sprawl ...