Example Service Optimisation Analyses

1)  Summary Benchmark Perception and Priority Information (Express Parcel Deliveries)

Attribute: Frequency of Damaged Parcels 

 An unambiguous measurement benchmark of current activity is obtained, and, if a competitive sample was interviewed, a direct comparison could be made.

 Clear targets could be passed on to staff responsible for parcel transit of, say, only 25% of customers experiencing a 1 in 100 or more damage rate.

Example Service Optimisation Analyses

 2)  Relative Importance to Obtain Some Given Level of Service (Automobile Servicing)

 Example 1 showed the extent of SIMALTO data information on 1 particular attribute.  This example shows how one can compare priorities to achieve a given level of service across attributes

 So it is much more important to have no more than one hour wait for an oil change (85%) then it is to be able to interact with the technician while he services your car (32%).  However if the wait for oil change level was reduced to not more than 5 minutes, then only 24% prioritise to this high level of this attribute (most being content with a 10-15 minute wait).  Then “interaction with technician” is now a higher priority (for most drivers) than a 5 minute or less wait.

Example Service Optimisation Analyses

 3)  Relative Customer Priority Of Making Single One-Box Shifts Of Improvements From Some Given Level Of Service (Town Council Services to the General Public)

 A second improvement on some items is more important than a first improvement on others.

Example Service Optimisation Analyses

 4)  Comparison of 2 or more different service specifications to find which would meet with most customer approval

 (A) Continuing the previous Council Services Example

 Having reviewed the SIMALTO priority information, two factions of the council disagreed as to the best way of enhancing services by an annual household cost of approximately $30..  One-box improvements on each attribute, from the current perception, in either of the following set A or set B attributes cost the same amount.

The simulation predicted that Set A improvements would be preferred to Set B improvements by 64% to 36% of ratepayers (and by men, 72%:28%: women 57%:43%).

Example Service Optimisation Analyses

 (B) Insurance Claim Handling

 On a SIMALTO grid containing 23 attributes, part of which is shown below, respondents indicated their perception of current service and those priority improvements from a pre-circled position shown.  This latter was deliberately set as approximately to or marginally below what the insurance company thought it offered today.

The pre-circled offer totalled 32 points on the total grid.  The median perception of current service was 49 points.  If a “best package” of 17 points was created from respondent priorities (as example 3 above), then this “pre-circle offer plus best 17” was preferred to the current median perception (of the same cost to supply) by 85% to 15% of the customers.

 Clearly, customers reinstated some of the benefits they thought they currently enjoyed (in a 17-point priority spend) to reach or exceed their current perception.  But other benefits they thought they currently had, they did not reinstate.

 The insurance company now knows which benefits it does, or is thought to offer, are really valuable, and which others are “nice-to-have” only.

 5)   SEGMENT PERCEPTION AND PRIORITY COMPARISON e.g. LOYAL VERSUS VULNERABLE Customers (Store cards). 

 The grid below is a simplified version of part of a store card SIMALTO sheet.

PROPER ONE TO BE INSERTED FROM PAGE 28 OF DOCUMENT.

The circles show the median perception of these two segments.  The arrows show the simulated top priority improvements for each segment – (both totalling the same amount on grid costs)

 Clearly the best investments to increase the loyalty of the “loyalty segment” are on different attributes than the best investments to CONVERT vulnerable customers to becoming loyal.

 The supplier might ask himself which group’s enhanced loyalty would bring the most profit.  Also were differences in perceived median performance real differences, or were they halo effects from some other cause of satisfaction/dissatisfaction.
Example Service Optimisation Analyses

6)  CLUSTERING – FINDING GROUPS OF CUSTOMERS WITH DIFFERENT BENEFIT

NEEDS/PRIORITIES (Fleet Service Management)

 Managers of vehicle fleets were asked to prioritise the service levels they wished to receive from the garages appointed for vehicle servicing. All respondents began prioritising from the same base position.  About 90% of managers seemed to cluster into either of 2 equi-sized groups.  The other 10% seemed to have no common priorities with each other or these 90%.  The first group prioritised “Driver Convenience” services, e.g. 

 The second group prioritised “Corporate Confidence in the Service”, e.g.

The discovery of clusters of different need priorities may enable the client to better understand and meet the challenge of a rival service company, which may appear to be succeeding in certain market segments, or, identify a cluster himself which he may be able to exploit by certain service level enhancements, or promotion, or both.

Example Service Optimisation Analyses

 7)  Indication of Return on Investment for Alternative Levels of Investment In Service

 A)     Air Versus Rail Travel Services

 Loyalty/ Competitive Switching Predictions

 Travellers were interviewed as to how likely they were to choose between Rail and Air Travel between two given cities approximately 200 miles (300 km) apart.  Respondents had to have used both modes of transport in the previous 12 months.  A 6-point scale of degree of choice was used.  After indicating their current choice for Rail or Air, they were allowed to improve their perception of Air in two stages on the SIMALTO grid and after each one they again indicated their degree of choice between Rail and this “new improved Air”.  The results were as follows:

The Airline deduced the return on its first bonus investment could be up to 8%, (10+22+9) – (4+14+15), increase in preference over Rail, and up to 20% extra passengers if it invested both first and second bonus amounts.  The loyalty of its own current preferrers would also increase dramatically with this investment from 18% definite today to 44% definite after both improvements.

Armed with this encouraging response, it cherry-picked from among the potential customers most popular improvement “requests”, those that it was best able to provide.  Two alternative investment specifications were proposed with similar costs to provide.  The model simulated choice distribution for these was as follows:

 The research indicated B to offer better potential than A in attracting rail passengers to air travel.

 B) Fleet Services Management Segments (identified in Example 6)

Different Action Plans by Customer Segments

 The managers of vehicle fleets discussed in example 10 above were asked three ‘action’ intention questions after each of the 3 bonus point spends on the SIMALTO grid.  These questions were:

1. Would you pay more than today for the improved level of service, and if so how much?
2. Would you be more prepared to recommend this service maintenance contract to your colleagues?
3. Would you place more of your leased vehicles with the leasing company that offered this better maintenance contract?
   

Example 6 showed that the respondents segmented into those prioritising ‘Driver Convenience’ and those prioritising ‘Corporate Confidence’ options.  What was also noteworthy was that these two segments had quite different reactions to these 3 intention questions.

The Driver priority cluster was more willing to pay more and recommend to their colleagues, whereas the Corporate priority cluster was more likely to increase its share of business with this provider.

 The client created two distinct benefit packages based on this research – an efficiency/convenience pack and a customer care/courtesy car pack.  At the time of writing it was beginning to calculate the likely ROI from these alternative offerings, together with the financial terms/ conditions of its leasing contract.

SUMMARY

  'Service Optimisation' SIMALTO benefits are: