Pricing & bundling
Marketing & sales

Maximizing perceived value of car customers


  • The client, a global car manufacturer, was facing hyper-competition generated by volume-driven strategies, portfolio complexity and changing purchasing behavior. With the increase of web information sharing, the customer has become a “super specialist” on specific issues, gaining a negotiation advantage versus the seller. The increasing complexity of offers and inability to target customers with traditional variables make dealers hostages for less and less controllable customers. At the same time, despite their expectations for buying-process digitalization, customers would still like a real and personal experience in the physical channel.
  • In this context, the client wanted to redesign its commercial offering through a complete process, starting from understanding customer needs and leading to building a new commercial bundle. Its objective was to meet customer needs while maximizing revenues and profits over an extended time span.


Arthur D. Little used the “choice-based” conjoint analysis to evaluate perceived value for, and select the services/product bundles sold with, the car

  • We formulated a pricing and bundling strategy based on client feedback and a solid quantitative approach
  • Arthur D. Little has identified 4 key levers to unlock the hidden value of customers in mature markets  (Illustration, cf. next slide)
  • Through behavioral clustering we identified the bundle with the highest penetration probability in the market, and designed an additional bundle to cover other client niches 


  • We minimized offer complexity and maximized value extraction: we designed different bundled offers aimed at covering 3 cluster clients and exploiting the whole service range:  
  • A “Standard” bundle to meet the most common customer behavior; 
  • A “High-Spender” bundle to extract maximum value from more demanding customers with specific add-ons to the standard bundle 
  • A “Service” bundle as an alternative bundle to satisfy the percentage of customers with less common behavior skewed toward full-service needs