Twilio’s CustomerAI Technology Enables One-to-One Customer Engagement

Technology Pairs LLM and Customer Data to Provide Granular and Complete Customer Profiles

The News:

Customer engagement platform Twilio this week announced CustomerAI, its technology that leverages the combined power of large language models (LLMs) with the rich customer data that flows through Twilio’s Customer Engagement Platform. CustomerAI is designed to help businesses pair the customer information within the data held within the company’s customer data platform (CDP), Segment, with generative and predictive artificial intelligence (AI) capabilities to help them more deeply understand and effectively serve their customers on a one-to-one basis.

You can read the full press release here.

Twilio’s CustomerAI Technology Enables One-to-One Customer Engagement

Analyst Take:

Twilio’s CustomerAI technology is pairing the power of LLMs with the customer data that flows through Twilio’s Customer Engagement Platform, with the goal of capturing a more complete picture of each customer. The technology leverages the generative and predictive AI capabilities to help them mine emails, texts, and other content, and then better understand and provide deeper value to their customers via a one-to-one marketing and engagement approach.

The Power of LLM and NLP to Drive Customer Inferences

According to the company, the combination of LLM and natural language processing (NLP) can be used to enrich Twilio Segment’s “Golden Profiles” to infer traits about a customer in real time based on customer conversations, such as messages, emails, or other content, and automatically update Segment customer profiles with these learned attributes.

The ability to update customer profiles held within a CDP with both structured and unstructured conversational data can be used to help organizations more deeply understand customers, and then interact with them based on learned predictions, including propensity to purchase, current customer sentiment markers, and even external factors. As customers expect greater levels of personalization and relevance when being served offers, the ability to quickly update customer profiles with new information is a clear win for organizations using the platform.

AI Is Improving Contact Center Agent Effectiveness

In addition to the additional functionality within the Customer Engagement Platform, Twilio’s contact-center tool Flex will leverage NLP to derive insights on trends such as top intents, hot topics, and call costs, and will utilize generative AI to create actionable recommendations about operational tasks like agent training and knowledge base updates. At the agent level, generative AI will equip agents with a summary of the customer’s history, followed by customized AI-generated responses during the interaction that are informed by the customer’s Segment profile as well as an LLM that has been trained on the knowledge base. This functionality can drastically improve the ability of the agent to understand the customer’s current situation and sentiment, and improve the accuracy, relevance, and speed of responses to the customer.

Optimized Marketing and Customer Metrics

Twilio’s CustomerAI is also designed to work in tandem with the Segment “golden profile” to streamline the ideation and execution of campaigns, while also offering guidance on the most impactful strategies for each customer. Generative and predictive AI will also make recommendations for the most optimal marketing channel, send-time, and content for that tactic, on an individual level, thereby truly enabling a one-to-one, personalized experience.

AI-powered insights and recommendations will be available via a central dashboard, which can provide visibility into predicted traits such as propensity to purchase, estimated lifetime value (LV), and churn risk. This permits businesses to make informed decisions about their engagement strategies, including customer journey, channel, and messaging, enabling one-to-one engagement.

Author Information

Keith has over 25 years of experience in research, marketing, and consulting-based fields.

He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.

In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek,, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.

He is a member of the Association of Independent Information Professionals (AIIP).

Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.


Latest Insights:

On this episode of The Six Five Webcast, hosts Patrick Moorhead and Daniel Newman discuss AWS Summit New York 2024, Samsung Galaxy Unpacked July 2024, Apple & Microsoft leave OpenAI board, AMD acquires Silo, Sequoia/A16Z/Goldman rain on the AI parade, and Oracle & Palantir Foundry & AI Platform.
Camberley Bates at The Futurum Group, reflects on NetApp’s Intelligent Data Infrastructure across hybrid and multi-cloud environments, enhancing operational consistency and resilience.
All-Day Comfort and Battery Life Help Workers Stay Productive on Meetings and Calls
Keith Kirkpatrick, Research Director with The Futurum Group, reviews HP Poly’s Voyager Free 20 earbuds, covering its features and functionality and assessing the product’s ability to meet the needs of today’s collaboration-focused workers.

Latest Research:

The Signal65 study evaluated the overall economics of a VCF environment deployed with disaggregated vSAN storage compared to the same environment deployed with a leading SAN array. To create an even comparison between storage solutions, the compute environment was kept identical when modeling both solutions. The analysis modeled both environments over a 5-year period and compared costs of hardware, support, licensing fees, and operational costs. Operational costs were calculated using an in-depth analysis of the administrative time and complexity required for each solution.
In our latest Market Insight Report, Image Generation Technology for Enterprise Use, we define the technology that enables text-to-image generation, cover the potential use cases and benefits of the technology, discuss the technical models and processes behind the technology, explore the risks involved with using the technology, and focus on the current and future commercial market for the technology.