Satisfaction Guaranteed: Develop Customer Loyalty Online
Domain 7, Satisfaction Guaranteed: Develop Customer Loyalty Online, tests retention, customer lifetime value, loyalty programs, service recovery, reviews, referrals, and churn. The exam expects you to see loyalty as a measurable profit driver, not just a friendly tone.
Core rules and concepts
- Retention is about keeping customers active. Repeat purchase rate, churn, and customer lifetime value (CLV) connect loyalty to economics. A simple CLV estimate is average order value times purchases per year times gross margin times average customer lifespan.
- RFM segmentation groups customers by recency, frequency, and monetary value. Recent, frequent, high-spend customers may deserve VIP treatment; lapsed high-value customers may need a win-back offer.
- Loyalty programs can use points, tiers, experiences, or early access. They work best when the reward fits the brand and is easy to understand. Discounts alone can train customers to wait for sales.
- Customer service affects loyalty. Service recovery means acknowledging a problem, fixing it fairly, and following up. Fast, human responses can turn a complaint into retention.
- Reviews, referrals, and user-generated content build trust, but incentives must be disclosed and privacy respected. NPS measures willingness to recommend; it is a relationship signal, not a revenue metric.
Worked example
A coffee subscription has an average order value of 60, three purchases per year, a 50 percent gross margin, and an average customer lifespan of two years. Estimated CLV is 60 times 3 times 0.5 times 2, or 180. If acquisition cost is 80, the CLV-to-CAC ratio is 2.25, which may be acceptable but leaves little room for discounts. The team segments by RFM, sends lapsed high-value customers a win-back survey and a free bag, and tracks reactivation and churn rather than only open rates.
Traps that cost marks
Treating loyalty as only discounts; ignoring churned customers; using NPS as proof of revenue; rewarding everyone equally; and collecting personal data for personalization without consent.