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Without real-time intelligence, support agents are forced to manually probe customers to verify claims of damaged items or missing packages. This investigative work is slow, inefficient, and detracts from their primary mission of serving legitimate customers, all while trying to meet KPIs.
Related Insights
Many brands have data-driven insights but struggle with the time and manual work required to implement changes across many SKUs and retailers. This execution gap, not a lack of strategy, is the primary performance challenge that agentic AI aims to solve.
AI can analyze a customer's support history to predict their behavior. For instance, if a customer consistently calls about shipping delays, an AI agent can proactively contact them with an update before they reach out, transforming a reactive, negative interaction into a positive customer experience.
The common frustration of a dropped customer service call is often not an accident. Call center agents are measured on "average handle time" and are penalized if calls are too long, incentivizing them to hang up on complex calls to avoid punishment.
Accurately identifying legitimate customers allows brands to move beyond just stopping abuse. This data empowers CX teams to confidently offer "surprise and delight" moments, like instant refunds, turning a potential service issue into a powerful, loyalty-building experience.
Instead of replacing humans, Aviva uses AI to anticipate *why* a customer is calling about a claim. The agent receives this prediction and relevant data upfront, skipping lengthy verification and improving the customer experience.
The goal of AI in customer service isn't human replacement. Instead, use AI agents to handle predictable, repetitive queries instantly. This strategy frees up human staff to focus their time on complex, empathetic problem-solving where a personal connection is most valuable.
The common practice of offering "premium" human-only support is counterintuitive. These customers often wait longer for a response compared to lower-tier users who receive instant, accurate answers from an AI agent, resulting in a poorer overall experience.
Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.
Unlike the instant feedback from tools like ChatGPT, autonomous agents like Clawdbot suffer from significant latency as they perform background tasks. This lack of real-time progress indicators creates a slow and frustrating user experience, making the interaction feel broken or unresponsive compared to standard chatbots.
Brands have heavily fortified the point of sale, shifting the primary vulnerability to the post-purchase experience. The most significant margin leakage now comes from exploited return, refund, and support policies, which are often managed across fragmented systems and teams.