Differentiated actions on customer behavior

One of the simplest steps most oth can do is to segment their customers behavior data ( purchase, payments, claims, complaints, clickstream behavior etc )to find out what natural groupings exist and how this can be leveraged to drive segment specific interventions.

In the context of customers behavior segmentation a lot has been discussed around

a) CUSTOMER DATA DIMENSION : Experiences in the quality of the input customer data
- missing zip codes
- wrong customer classifications
- match merge customer identifiers
- enrichening customer profiles with credit rating and demographic information from experian etc
- identify derived behavior metrics
- rfm etc

b) STATISTICAL PROCESS DIMENSION : If the data hygiene dimension is stabilized, then the focus shifts to the segmentation process -
- Do we use K means cluster vs SOM/Kohonen method vs Hierarchial clustering etc ?
- How many clusters are ideal ?
- How many variables should we use to segment ?
- Which variables should we use ?
- How do we characterise the clusters ?

Assuming we get the data dimension and the segmentation process right, there needs to be a lot more conversation around ACTIONS to undertaken once this customer behavior segments are created. There have been situations where the customer behavior data was segmented but on account of poor 'actionability' framework the whole exercise collapsed.

Increasingly we are seeing 'ACTION' post the segmentation process being the weak link in the whole process.

Here are some actions we have seen working from a customer segmentation intervention point of view ( It has a predominantly retail and travel flavor :-) ...
1) Bundling multiple products and offering a discount to selective segments exhibiting certain behavior
2) Building multiple cross sell models to increase wallet share from certain segments
3) Reallocating marketing $ to run different kinds of campaigns for each behavorial segments.
4) Decisions on what kind of promotional stimuli to use for each behavorial segment. Some may respond well to a temporary price reduction, others may respond well to a coupon, some segments may respond well to a gift off on a product while others maybe open to an exclusive 'in store' event.
5) Each of these behavorial segments could be treated by different call center agents . For example the most valuable customers can get routed to the call center agents who are ranked high on performance . The cross sell customers can get routed to the agents who have a good track record of cross sell conversions etc
6) Channel decisions: Low value customers can probably be moved to the internet channel and the high value customers can have a dedicated relationship manager to offer personalized experience
7) Personalized gift with name printed could be offered to selective segments.
8) Steeper discount to customers who have generated value in other lines of products but have not tried out a new line of product
9) Personalized online portal for customers exhibiting certain behavior with customized recommendations

Since ,Data + segmentation + Segment intervention = Successful segmentation exercise
Wanted to understand from others their experential inputs on what segmentation interventions have worked for them .
1) What differentiated actions on customer behavior segments have worked to impact business outcome? ( Retail or Finance or Travel or any other industry is fine )
2) Is their a generic "actionability framework" which can be created which gives a menu card of differentiated actions to undertake on various segments ?
3) Are some interventions for customer behavior segments more effective than others.Are their innovative or effective actions which have been undertaken on segments discovered by segmenting customer behavior whichresulted in lift in sales or some business outcome metric ?