CVM (Customer Value Maximization) uses the broadest measurement model in the industry.  These measures have two primary advantages over CSM's measures.  First, by using a far broader set of measures (not just attitudinal, but behavioral, historical, threshold, cost, calibration etc.)  CVM characterizes the customer far more deeply.  Second, we use psychometrically informed approaches to collect the data. These approaches recognize that certain attitudes are typically overstated or understated by consumers.  (See the explanation of Real World Expectation below as an example.)  These two  advantages allow SatisfactionSolve™'s deep insight discovery tools to develop more risk-mitigated strategies than ever before.

Traditional CSM only links to one thing reliably; likelihood to recommend.  This finding was initially confusing for researchers. After, all it seemed reasonable that if overall satisfaction improved that Real World Outcomes (RWO's) such as sales -- should naturally follow!  The problem?  Customer Satisfaction is only a general attitude.  Structural Equation Modeling (SEM), in contrast, demonstrates that both attitudes AND behaviors are needed to change the latent factors that DO link to RWO's.  (Latent factors lie between observed factors  and real outcomes)  We therefore included latent factors in our  Real World Outcomes models within the superset of SatisfactionSolve,™ (SatisfactionSolve /DP™.)

In the real world, not the world of rarefied theory, most consumers must reach thresholds before they do anything.  That is, whatever behavior you want to elicit (initial purchase, more use of your services, cross-sell, etc.) we must provide consumers with enough "stuff" (benefit) to "get them off the dime."  That is why we measure many kinds of Behavioral Change Thresholds (BCT's) in the survey.  Quite importantly we measure these thresholds at the level of the individual respondent.  This allows our E-customers (the calibrated copy of the consumer) to respond as actual consumers do.

One of the most classic mistakes made by traditional CSM is that it asked consumers what they expected...and then naively assumed that we must deliver on stated expectation.  The problem? Stated expectation far overstates what consumers really expect. The best way to measure real expectation is to find the minimum performance level that does not create disutility (dispreference.)  In contrast, our approach uses apportioned expectation (AE)  AE focuses our efforts on things  proven to maximize marginal benefit....by improving the key drivers that cause disutility -- and hence negative consumer outcomes.

And what about ROI?  In terms of ROI, historically, over 95% of CSM engagements have actually lost money. Accordingly, let's expose another CSM myth.  CSM tells us to "exceed expectations."  This makes for a "feel good" motto - but is simply foolhardy to give consumers exactly what they want.  Doing so will bankrupt you...and barely dent Real World Outcomes (RWO's).  In contrast,  we attach ABC costing amounts to every change level of every key driver.  Thus, we know exactly how much a change will cost to implement and to maintain. This allows our models (e.g. sales, switching, etc.) to maximize those outcomes at the lowest cost to implement and the lowest cost to serve.

One of the most familiar misnomers in marketing is the concept of a "key driver." The problem with this concept?  Most drivers are not "key."  In fact, recent research has shown  that only about one fifth of the drivers measured in CSM are "key"...and those key drivers differ from individual to individual.  This is one of the primary reasons traditional CSM modeling fails.  In addition , traditional CSM modeling is aggregate modeling - it groups consumers together to determine  the best predictors.  The problem? Aggregate models wash out individual preferences.  In contrast, CVM 1) determines key drivers at the individual respondent level and 2) validates  those key drivers by calibrating them to real-world choice behavior.

Once we have a) obtained the key drivers, and b) solved for the real-world thresholds that link to real world outcomes,  and c) adding in the cost to implement and the cost to serve... then what?  Equipped with this next generation of data we can now explore what happens when we close the gap between our current performance on the key drivers and real-world expectation. CVM allows us to do "gap closure" two ways, with cost data enabled and cost data disabled.  The decisioning module that performs automatic, cost-informed  gap closure is the CSI (Customer Satisfaction Index) simulator built into SatisfactionSolve™.

SatisfactionSolve™ is not only for customer satisfaction!  In fact SatisfactionSolve™ has embedded within it ALL the capabilities of ProductSolve™.  Why you might ask? ProductSolve™ is the premier decision support platform to identify which changes to you products and services will maximize Real World Outcomes (RWO's) at the lowest cost to implement and the lowest cost to maintain.

Although SatisfactionSolve™ is an advanced decision support tool, it has been designed to be extraordinarily easy to use.  To make SatisfactionSolve™ even more immediately useful, we have added to the user manual innovative sections on theory and practice.  By including many of the actual exercises we use in our on-site training, a number of our clients have used SatisfactionSolveTM with no additional training beyond the manual itself.