Presented by Twilio
In their earliest days, digital brand communications, like business-to-customer text messages, were revolutionary. Consumers benefited from direct access to deals and news about the brands they loved, and the brands welcomed a low-cost, efficient way to reach their customers. But as channels have multiplied, digital communications have become more chaotic for consumers. On my own phone now, for example, I use text messages, calls, email, WhatsApp, LinkedIn Messenger and applications to connect with brands.
These experiences clearly are not optimized for customers, who have trouble knowing which channels to prioritize and which brands to trust with their personal information and access.
Here is the twist: This experience isn’t optimized for businesses trying to reach customers, either. On the other side of these texts, calls and emails are marketers, salespeople and contact center teams frustrated with incomplete information captured in disjointed tech stacks.
The good news: now that we’re squarely in the Industrial Revolution of AI, where AI has accelerated the creation and value of products we build at scale, we can use the data and tools available to us to put the customer experience back at the center of innovation. We have the technology to filter the noise, finally enabling simple, smart, and trusted communications.
There’s a simple formula organizations can follow to do this: apply AI technology toward three key goals -- knowing the customer; sending smarter, rather than more, communication; and upholding customer trust.
Know your customer with a simple, unified profile
Authentic communication starts with understanding your audience, requiring a direct relationship with your customer. A constellation of data points helps paint a picture of your customer as a three dimensional human with evolving needs and conditions, for instance:
Whether the consumer is on Wi-Fi or a slower cellular network
Which times of day the consumer is most active on that channel
Which channels the consumer prefers
The sentiment of the consumer’s last interactions with a given brand
It’s important for businesses to rely on first-party data (information collected directly from your customers' interaction with your brand), rather than third party data (information collected by external companies that do not have a direct relationship with customers) to create the most accurate, real-time understanding of their audience.
Research shows that 92% of businesses are already using AI-driven personalization to drive growth. That’s a good start. Yet AI is only as good as your data. The data and context from each customer interaction serve to fine-tune the data models, simplify the customer experience, and inform organizations’ future engagement with customers.
Even with a direct customer relationship, stitching data points together into a cohesive view of your customer can be a challenge, thanks to unwieldy tech stacks with data silos. Customer Data Platforms make this easier, enabling brands to turn disparate data sources into unified customer profiles that update in real time. This way, you truly know who you’re talking to and have the full history of how unique customers have interacted with your brand.
Smarter, not more, communication with omni-personalization
Instead of casting the widest net to reach customers, focus on orchestrating better customer experiences to enable better outcomes, like higher purchase conversion and customer loyalty.
We can do that by combining a unified profile of our customers with new information we learn as we interact in real time. AI layered on top of this unified profile makes it simpler to deliver timely and streamlined communication across all channels and anticipate what your customer may want or need in the future. For instance, we can prioritize deliverability (how many messages are being delivered and read), insights about which messages aren’t delivered and why (time of day, channel, etc.), and an awareness of what types of content generate engagement. This drives results: brands using AI to deliver personalized communications are seeing higher customer satisfaction scores (45%) and improved market segmentation and targeting (41%).
Say your customer who typically avoids the phone has called to return a product and expresses dissatisfaction with the service during the phone interaction. The brand can update that information in the customer’s unified profile, and a customer support specialist can then follow up with that customer using a messaging app to recap the conversation and offer a promotion code for products with a higher rating. The customer then uses the code and buys the product, all while your customer service agent is equipped to know in the future which products the customer uses.
Context from each interaction informs the brand's next outreach. When customers engage, you get more data to optimize future interactions. This is how the flywheel of good customer experience spins, resulting in more engagement, loyalty and ultimately, conversions. This personalization and applied context from across channels is what I call “omni-personalization.”
Prioritize trust
Of course, this guidance applies to legitimate communication that consumers value and have consented to receive. The reality is that a lot of the noise that consumers face is spam or fraud, and that leads them to distrust many digital communications. As an industry, we need to rebuild trust by sending only consented communications that consumers actually want and have opted into receiving.
Generative AI adds a new level of complexity to things, and will introduce new security and fraud threats. However, while it’s true that generative AI will contribute to the noise, I believe that it can and should be a powerful tool for fighting spam and fraud.
A good example of this AI complexity comes from Bluesky, a social media app that recently launched publicly after months of being invite-only. The company used Twilio’s Verify API and AI-powered Fraud Guard to ensure a secure sign-up process for new users (over 800,000 in one day). Bluesky was able to block out fraudsters while ensuring that new users received secure authentication, increasing customers’ trust -- and saving the company hundreds of thousands of dollars.
Another positive signal of innovation toward trusted communications: Apple recently announced it will support RCS, the next-generation messaging standard that brings branding, rich media,] and more to text messaging for iOS 18. RCS and other branded communication ensure that messages are delivered and build consumer confidence that the messages come from legitimate sources. Businesses that embrace trust-centered tools and practices gain a competitive advantage in an era of distrust. Consumers are ready to exchange their loyalty for transparency and credibility.
Imagine a world where a company knows exactly who the consumer is, why they’re calling, has predicted the problem they’re trying to solve based on what they know about the consumer, and can seamlessly deliver the solution to that consumer on the channel of their choosing based on where they are in their day. Layer in the power of AI throughout to assist both employees and consumers, and you now have an omni-personalized and contextualized experience that leads to better outcomes for everyone. That’s innovation with consumer experience at the center.
In this same world, marketers, salespeople and customer support teams have contextual information about every customer, the engagement in their customer lists has doubled or tripled, and their staff is far more productive, happy, and engaged. Brands are no longer blasting consumers with messages and hoping for a few clicks. Instead, they’re understanding the impact of every email, message, and phone call and using those insights to start the flywheel spinning.
That’s a future worth aiming for.
Inbal Shani is the Chief Product Officer, Communications for Twilio, responsible for R&D, product innovation, and resiliency and trust in the Communications business. She is among the pioneering technologists who applied AI to solve complex technical and business problems.
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