Reducing Cancellations and Improving Retention with AI Insights

The service operation faced critical retention gaps that directly impacted revenue and customer loyalty:

• High cancellation rates, often linked to changes in customer conditions (e.g., reduction in covered people or financial constraints)
• Limited ability to retain customers during interactions, with weak or inconsistent argumentation from agents
• Low use of counter-arguments, with agents accepting objections instead of exploring alternatives
• Lack of flexibility in adapting plans, creating friction when customer needs changed
• Unclear communication around policies, especially cancellation rules, penalties, and conditions
• Insufficient probing of root causes, limiting visibility into true customer motivations

At the same time, customers were actively considering competitors, increasing the risk of permanent churn.

Using Atento AI Advanced Insights, we analyzed interaction data to connect customer intent, agent behavior, and operational gaps into a structured retention improvement model.

The AI Insights process

1. Identify opportunities

• • Analyzed call transcripts to identify the main drivers of cancellations
  • Quantified key reasons, including:
    • Reduction in covered people (~40%)
    • Perceived high penalties (~30%)
    • Limited payment options (~20%)
    • Communication gaps (~10%)

2. Create prompts (to uncover root cause and behavior gaps)

• • Designed prompts to evaluate how agents handled objections and cancellation scenarios
  • Assessed whether teams:
    • Attempted to retain customers
    • Presented counter-arguments
    • Offered alternatives (e.g., discounts or adjustments)
  • Identified patterns where opportunities to retain were missed

3. Translate insights into actions

• • Defined targeted actions to strengthen retention conversations
  • Introduced clearer guidelines for:
    • Argumentation and negotiation
    • Handling objections
    • Explaining policies in simple terms
  • Highlighted moments where proactive retention actions should occur

4. Accompany applicability and results

• • Worked with operations to embed new behaviors into daily interactions
  • Tracked improvements in retention attempts, communication clarity, and customer responses
  • Refined actions based on real interaction outcomes

The client moved from reactive cancellations to a more structured and proactive retention approach.

People (frontline execution)

• Stronger argumentation skills to retain customers during cancellation requests
• Increased use of counter-arguments and alternatives instead of passive acceptance
• More effective probing to uncover real reasons behind cancellations
• Improved empathy and communication during sensitive conversations

Process (operational clarity)

• Clearer communication of cancellation policies, penalties, and available options
• Better alignment between business rules and how they are explained to customers
• Identification of key retention moments within the interaction flow
• Increased consistency in how cancellation scenarios are handled

• +22% increase in retention effectiveness (more customers reconsidering cancellation during interaction)
• +35% increase in use of counter-arguments and negotiation techniques
• -18% reduction in cancellations linked to avoidable causes (communication gaps and lack of alternatives)
• +25% improvement in clarity perception, reducing misunderstandings around penalties and policies
• +30% increase in proper root-cause identification during calls

Retention stopped depending on individual agent effort and became a structured capability, with AI-driven insights guiding when to act, how to respond, and how to improve outcomes consistently.