To gauge impact, leverage tools like the Net Promoter Score (NPS) to assess loyalty and satisfaction trends. By combining NPS results with repeat purchase data, I’ve identified patterns that informed a loyalty program I implemented in a sales optimization project, boosting retention by 15%. Use text analytics on feedback to uncover recurring themes and address root issues. A great resource is “Measuring Customer Satisfaction and Loyalty” by Bob Hayes, which dives into actionable metrics. Analyzing after-sales data is like solving a puzzle; each piece reveals a clearer picture of what makes customers stay. ???? Do follow for more insights like this! ??

é essencial segmentar seus clientes, entender o que realmente importa para cada grupo e oferecer solu??es que atendam suas necessidades. Depois, escolha os canais de comunica??o certos e capacite sua equipe para transformar esses dados em resultados reais e satisfa??o.

one thing i found it helpful is customer lifetime value CLV which is the total worth to a business of a customer over the whole period of their relationship with the brand

Devil’s in details & data! Monitoring any after-sales data & customer behaviour is nonsensical without knowing what is the “Expected Customer Behaviour”, which in the Automotive world, is a very simple, service schedule and engagement rates on different touch points, predominantly physical and increasingly digital once’s. Once you do that, any dispersion from this ECB could be an opportunity to sell or to gain trust or to facilitate a customer. We could get that granular level of data because we spent a lot of effort using AI/ ML to clean & integrate data from 70+ DMS’s & 10 legacy platforms & measuring the impact of all little actions, using Customer LifeCycle Value & a Predictive CRM to drive engagement & push loyalty & repurchase.

By ASKING! Data is numbers, and results -- but is no substitution for actual communication with the end user, and correlating it. Full stop!