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Customer Experience
80 posts about Customer Experience. Every summary links to the original.
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From resolutions to outcomes: Evolving how Fin delivers value
Fin is changing its pricing metric from “resolutions” to “outcomes” as its capabilities extend beyond fully autonomous support conversations. Historically, a resolution meant Fin solved an issue end to end without human involvement; the model gave teams a clear ROI comparison, and Fin’s average customer resolution rate now stands at 76% across more than 7,000 teams. Customers increasingly deploy Fin on complex, multi-step work involving external systems, policy constraints, and human oversight, where Fin may gather context and act before handing off for confirmation. An outcome counts when Fin successfully completes the action it was configured to perform, including a full resolution or a Procedure. The change keeps pricing simple while recognizing collaborative workflows, supporting compliance, and providing a metric that can scale as Fin takes on more roles beyond service.
Transformation in action: When support becomes the blueprint for company-wide AI
The post describes how businesses are extending AI beyond customer support across the customer lifecycle, with support teams increasingly positioned to lead that work. Research cited in the 2026 Customer Service Transformation Report says 52% of respondents plan to scale AI to other departments in 2026, while 32% say customer service teams lead their AI transformation strategy. WHOOP deployed Fin on its Join page after prospective buyers faced waits of more than 10 hours; Fin resolved 84% of inbound questions and helped drive a 130% increase in attributable sales. The post warns that isolated departmental agents can fragment experiences, and presents a unified Customer Agent with shared memory and business knowledge as the intended direction. It argues support can provide shared standards, guardrails, operating practices, and implementation expertise as AI expands into sales and other functions.
New book: Intercom on Jobs
Intercom launched its fourth book, Intercom on Jobs-to-be-Done, to document lessons from applying the Jobs-to-be-Done framework to product, design, and marketing for software. The book addresses a gap: although the framework is nearly 30 years old and often discussed through physical products, few materials cover its application to software companies. Intercom says the framework helped it understand the needs customers addressed with its product and improve their experience. Rather than offering a prescriptive, step-by-step method, the book presents hard-learned lessons intended to be timeless and universal, including customer switching, real competitors, product scope, exact jobs, Job Stories versus personas, and software research.
CX Score: How we built a metric support leaders can defend
CX Score was built to give support leaders a defensible view of customer experience across conversations, addressing whether an automated metric can be trusted. The team grounded its criteria in how experienced support professionals assess strong, mixed, and poor interactions, then created a dataset of thousands of conversations reviewed by experts, with two reviewers where possible and disagreement resolution. It operates as a production system using specialized prompt engineering, support-focused model configuration, neutral defaults for ambiguous evidence, and multi-stage evaluation across LLM models. Precision, recall, F1, regression, and bias checks set a required F1 above 0.8, while field testing with real customers examined varied tickets, languages, agent types, and conversation lengths. The stated conclusion is that CX Score is designed to be inspected, explained, and defended rather than treated as a black-box output.
The evolution of support starts now
Intercom introduces Resolution Bot, a machine-learning support chatbot designed to resolve customer issues rather than merely direct people to help-center articles. In the Intercom Messenger, it provides tailored resolutions, can use integrations such as Shopify to surface order status, and proactively suggests answers while customers type. The update adds six languages—Spanish, French, Italian, Brazilian Portuguese, Russian, and German—along with demographic targeting based on factors such as spend, business type, and location. New reporting shows answer-level sends and resolution rates, identifies underperforming answers, and lets teams filter conversation metrics for bot interactions. Intercom reports that the bot automatically resolves 33% of common support questions and saved businesses an average of 109 hours of customers’ time over the last year, positioning the release as a way to provide faster, more consistent support at global scale.
Defining customer support in 2025: Why it's key to your long
Customer support has shifted from a perceived cost to a differentiator as expectations rise and companies focus on retention, advocacy, and product feedback. Modern support aims to resolve issues, troubleshoot problems, and help customers get more value through personal, contextual, knowledgeable, and proactive interactions. The post argues that human+AI partnerships can improve speed and efficiency: AI agent Fin handles common questions in more than 45 languages, while human teammates address complex queries empathetically. It also explains why ticketing became necessary for tracking asynchronous work, internal ownership, and customer updates within an all-in-one support platform. Examples from Hospitable and Rebag report faster responses, reduced response times, and higher NPS, while the broader guidance emphasizes clear expectations, robust processes, escalation paths, and a clear route to human help.
The AI deployment gap is widening, don’t get left behind
The 2026 Customer Service Transformation Report examines how organizations are adopting AI in customer service, based on a survey of over 2,400 global customer service professionals. It identifies a widening difference between narrow deployments that handle simple questions or workflow steps and mature deployments that integrate AI into critical operations, assign it more responsibility, and improve it continuously. Although 62% of respondents report improved customer-service metrics after implementing AI, mature teams report this at 87%, while only 10% say deployment is mature. The report also finds mature deployment associated with higher quality and consistency, clearer ROI measurement, and less time spent handling support volume; 43% report higher quality and consistency. With customer experience becoming the top priority for 58% in 2026, support is positioned as a proving ground for expanding AI into customer success, marketing, and sales, with 52% planning that scale.
2026 customer service planning series: Vol. 05
AI-first customer service makes capacity planning less predictable because automation changes volume, work mix, human responsibilities, and performance over time. The proposed planning model puts automation rate—AI Agent involvement rate multiplied by AI Agent resolution rate—alongside inbound volume, human output for complex remaining work, and occupancy. It recommends ambitious automation assumptions only when backed by named ownership, content and systems investment, work-type targets, and staged improvement, while modeling lower cases closed per person and more time for off-queue work. Finance should treat the plan as a revisable set of assumptions, review it at least quarterly, and avoid cutting headcount before automation is proven. Surplus capacity should be redirected to AI improvement, product feedback, new channels, or proactive customer experience work rather than automatically reduced.
2026 customer service planning series: Vol. 04
After defining team roles, an operating model is needed to make progress part of customer-support work and keep an AI Agent improving over time. The stated goal is that “the first time you answer a question should be the last,” with each resolution improving the system so fewer issues repeat, quality compounds, and support scales. The post emphasizes clear ownership, risk-aware guardrails, feedback loops, and a culture that values improvement work; it identifies ambiguity as a common cause of performance plateaus. An AI ops lead, often located in support operations or developed from an existing role, owns performance and drives change, illustrated by Dotdigital’s dedicated specialist after resolution volume held at about 2,800 conversations per month for three months; lightweight governance is presented as a way to keep iterating without excessive bureaucracy.
More context, more confidence: The new CX Score explained
CX Score was created to give support teams a broader view of customer experience than survey-based measures such as CSAT and NPS, which capture only a subset of interactions. Its latest iteration evaluates more of each conversation, combining answer quality for Fin and human teammates with customer effort, strong emotion, product or service feedback, and policy feedback. The update also expands scoring to shorter and more transactional threads, adds reasons behind individual ratings, and enriches summaries with the issue, handling, and drivers of the score. The stated goal is to help teams identify operational problems, route conversations for review or follow-up, and move from measurement toward continuous improvement. The new version is rolling out gradually to all customers and is scheduled to be in workspaces by December 3rd.
2026 customer service planning series: Vol. 02
When AI Agents resolve most customer conversations, support organizations need to move beyond tiered models centered on queues, handoffs, and volume-based productivity. The proposed AI-first operating model distributes four foundational responsibilities: an AI operations lead monitors and improves performance, a knowledge manager maintains structured content, a conversation designer shapes communication and handoffs, and a support automation specialist builds executable workflows. Existing enablement, QA, workforce management, and leadership roles also evolve, with player-coach leaders managing both AI performance and people. Examples from Dotdigital, Clay, Lightspeed, and Intercom illustrate dedicated roles and an Intercom structure organized around Human Support, AI Support, and Support Operations and Optimization.
Customer Experience: The 2026 Marketer's Guide
Customer experience (CX) spans every interaction across the journey, from advertising and purchase through support, product use, and advocacy, rather than referring only to customer service, UX, or customer success. The guide argues that AI has raised expectations for fast, personalized responses, while privacy-first marketing increases the importance of consented zero-party and first-party data, voice-of-customer programs, and social listening. Its framework maps awareness, consideration, purchase, post-purchase, and loyalty or advocacy; combines social, survey, support, and review data; prioritizes fixes with an impact-versus-effort matrix; and establishes workflows, response SLAs, and ongoing measurement. It recommends metrics including NPS, CSAT, CES, sentiment, and churn rate, alongside competitive benchmarking and continuous iteration. Examples describe using Brandwatch's Data Upload API and Social Panels to identify product issues, customer questions, and improvement opportunities before broader launches or operational changes.
Hollywood demos vs. real
AI demonstrations fall into two categories: polished “Hollywood demos” and raw “real-world demos,” with the latter showing how customer-service AI agents perform under messy, complex interactions rather than edited presentation. Voice provides an especially demanding test because timing, tone, turn-taking, latency, interruptions, and real-time adaptation all shape the experience. At Pioneer, Paul Adams called Fin Voice live; within 90 seconds, it verified identity, retrieved account data, handled an interruption, offered options, completed the workflow, and sent a follow-up email. The demonstration exposed brief pauses for backend work, natural conversational flow, tone changes, and unscripted behavior, while later updates added voice customization, escalation guidance, deployment controls, integrations, support for 28 languages, and roughly 30–40% lower latency since launch.
Headlines from Pioneer 2025: Fin 3, the vision for a unified Customer Agent, and what’s next for customer experience
Pioneer 2025 presented Intercom’s latest customer-service AI plans, centered on Fin 3 and a longer-term vision for a single Customer Agent covering the entire customer experience. Fin 3 adds Procedures for complex, policy-controlled resolutions, Simulations for pre-release testing, Slack and Discord deployment, improved Voice, and Insights features such as CX Score Reasons, Topic Trends, and AI-powered Suggestions. The proposed Customer Agent would combine specialized roles, user-defined goals, persistent memory, business knowledge, and interoperability across tools, systems, and channels. Customer examples described phased rollouts, rapid feedback, broad scaling, and support-team involvement, while Anthropic reported two specialists enabling nearly 50 full-time employees’ worth of coverage in its volume. The event framed AI as a force for converging products, roles, and organizational structures, with the Customer Agent future described as arriving quickly.
Preparing for the Customer Agent future
At Pioneer 2025, Eoghan McCabe presented a plan for Fin to become a unified Customer Agent spanning the entire customer experience, from first contact through purchase, support, success, and upsell. The proposal responds to the risk that separate AI Agents for sales, marketing, success, and service will use conflicting priorities, disconnected information, and inconsistent interactions. Fin is intended to evolve from a task-based system into an agentic system with fluid roles, user-defined goals, persistent customer memory, comprehensive business knowledge, and interoperability across tools, systems, and channels. The post argues that customer service leaders are positioned to guide this wider AI rollout because they have experience, data, and operational insight from early AI adoption. Early activity includes WHOOP using Fin for sales conversations, where it is resolving 84%, while new capabilities are expected to roll out over the coming months.
Navigating the AI storm: How to harness the power of AI without being crushed by it
Support leaders are being asked to preserve customer experience and operational performance while becoming AI experts and leading high-lift, cross-functional implementations. Before AI, support was often treated as a cost center; now executives may demand aggressive AI metrics without understanding the resources, risks, and work still required. The proposed response is to harness the change by learning AI deeply, testing multiple vendors through trials, choosing a durable partner, and defining benchmarks and phased implementation plans. Support leaders should also explain why deflecting 50% of inquiries does not justify cutting staffing by 50%, given coverage, service-level redundancy, growth, attrition, and work beyond direct inquiries. The article ultimately argues that visible ownership, concise project management, and weekly or bi-weekly executive updates can reposition support as a value driver rather than a cost center.
Announcing The AI Agent Blueprint
The AI Agent Blueprint is a strategic guide for support, customer experience, and AI transformation leaders navigating how to adopt and scale AI Agents. Its first part, “Launch it,” covers building a business case, deciding whether to buy or build, evaluating an agent with a four-step framework, planning deployment, and continuously training, testing, deploying, and optimizing performance. The second part, “Scale it,” focuses on redesigning customer experience, organizational and system design, and economics through AI-first journeys, new roles and governance structures, and revised metrics, ROI models, and reinvestment strategies. The Blueprint argues that launching an agent is only the beginning: sustainable value requires treating AI as infrastructure and support as a potential growth lever, and it is live now.
Faster and Local Shopping on eBay Just Got Simpler
eBay has introduced UX enhancements that make local listings, fast-shipping items, delivery estimates, and retail standards easier to find on Search item cards. Because its inventory comes from thousands of sellers in a global, decentralized network, eBay uses artificial intelligence that considers customer proximity, chosen shipping service, seller shipping history, and real-time data synchronization to improve estimate accuracy and coverage. Item cards now show delivery ranges for all fast-shipping items, whether delivery is paid or free, while the “Shipping and pickup” filter supports fast shipping, Free shipping, Local pickup, and Click & Collect options. For local pickup, cards display the exact distance between the buyer and item location. In Germany, the eBay Top-Service badge identifies items meeting standards including fast and free shipping, easy returns, and trusted sellers, simplifying overlapping retail signals.
Intercom vs Zendesk: Two AI agents put to the test
The post compares Intercom’s Fin AI Agent with Zendesk’s AI agent, focusing on answer quality, complexity, and resolution performance. In a test using 48 fictional bed-and-breakfast help articles and 200 questions, both systems answered the same dataset, while four AI models judged responses and an Elo rating system selected winners. It reports that Fin produced better answers in 80% of cases, answered 96% of multi-source questions versus Zendesk’s 78%, and delivered responses averaging 120 words versus 50. The analysis also found no statistical difference in hallucination levels and estimated that Fin was 66% more likely to provide a complete resolution when both agents answered. The post notes limitations including simulated data, AI judging, and differing product features, then describes Fin’s integrations and 99¢-per-resolution pricing.
Announcing Intercom’s new program for venture-backed startups
Intercom announces a startup program aimed at fast-growing, venture-backed Seed and Series A companies, working with venture capital, accelerator, incubator, and technology partners. The program provides its main customer communication solutions at no cost in the first year, 50% off in the second, and 20% off thereafter, covering tools for bots and content, in-product messaging, onboarding, and conversational engagement. It extends Intercom’s early-stage program, which offers a 95% discount and has helped thousands of pre-seed startups, while focusing more closely on companies scaling with venture backing. Intercom says more than 5,000 startups from leading incubators use its products and cites Spendesk, Notion, Monzo, Frame.io, and Aircall among startup customers; it also plans webinars and events and invites partners to apply.
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