Filed under: Managing Customer Behavior | Tags: customer decisions, customer motivation
| Notes on Customer Decision Functions |
| Customer Decision Functions are function equations that parse out the factors involved in a customer decision. By identifying the factors, and then arranging them into the form of a formula, one is able to understand better the levers for influencing the decision and the trade-offs necessary to secure a successful outcome.For example, in the case of a customer referral program that involves a reward, the function would be:
f[Referral] = (Satisfaction – Risk(reputation)) * Reward – (Effort / Attainability) This means, for example, that if satisfaction is extremely high and the potential risk to reputation low, the reward could be relatively small, provided that there was minimal effort involved in obtaining it and it was readily attainable. If satisfaction were not as high, the reward would need to be greater. If it were difficult to obtain a reward, it would either need to be substantial or satisfaction would need to be significant. An example of this last scenario would be one in which the referer gets some personal satisfaction/cache from telling others about the product/service. Other functions are below. f[Loyalty Program Participation] = Reward^Attainability – Effort^1/Investment f[Loyalty] = Satisfaction + Benefit(status quo) – Competitor Value^Ease of switching f[Satisfaction] = Reality – Expectations f[Purchase vs. Status Quo] = Benefit * Benefit Attainability – Cost + Risk(status quo) – Risk(change) f[Referral] = (Satisfaction – Risk(reputation)) * Reward – (Effort / Reward Attainability) f[Renewal Membership/Maintenance Program] = Satisfaction + Need(anticipated) + Risk(non-renewal) – Cost |
