Example Product Analyses

 1.   Simple count of customer wanting each benefit at a given bonus scenario - Colour Printers

This output gives an immediate summary of respondent priorities.  When more complex comparisons are made between competing complete printer specifications, this factual data may help explains why one specification was preferred over rivals with different combinations of benefits.

2.   Evaluating Bundles of Specification Changes

(A) Fast Food

 Evaluating Bundles of Specification Changes

One cannot optimise products or services by improving features in isolation.  Consider the following “fast-ish food” restaurant data:

                                                                                                                                                                                                                                                                   Preference

 Adequate Meal + 5 Minute Wait                        45%

or

So it would seem that meal quality should improve at the expense of slightly longer waiting.  If the food quality did improve and now the comparison was

                                                                                                                                                                                                                                                             Preference

Inviting Meal + 5 Minute Wait                    61%

                     or

Excellent Meal + 10 Minute Wait                39%

then it seems that improving food ‘beyond’ inviting to excellent was less rewarding to customers than the shorter wait.  An illustration that desire for a given option depends on the levels of performance delivered by other features.

B) Car Options

 A  French manufacturer was losing share to some new body style introductions from competition.  To stem the loss of sales until his own new body styles were to be introduced 18 months later, he decided to offer ‘bundles’ of feature improvements at 40% of their normal showroom prices.  The question was which bundles of 3 or 4 options would be most appreciated.

SIMALTO analysis suggested a combination of power steering, central locking and remote operation mirrors would have most appeal to a certain segment of the car market.  The manufacturer himself preferred to offer a sunroof, tinted glass and a better quality radio at a similar price equivalent.  The hierarchy of individual feature option preferences clearly supported the SIMALTO recommendation, and to demonstrate the clarity of its findings, four priced specifications were presented to the respondents’ data for preference simulation:

The manufacturer, who preferred the sunroof pack, was disappointed at these predictions, and was only convinced it was not an aberration of the ‘black box’ computer methods when he examined for himself the original SIMALTO Sheet respondent information on what drivers said they wanted and would pay for.  It is almost certain that conjoint utilities/ regression coefficients would not have had sufficient user friendly appeal to overcome this senior manager’s scepticism of black box output that did not agree with his own views on this issue.

 Note that 54% above preferred none of the enhanced packages.  This is because they would have preferred the extra ‘3 000 FF’ to have been spent on other option combinations, and so the above were not sufficiently ‘valuable’ for them compared to the status quo.

3.    Clustering Customers with different product benefit priorities

(A) Automobilies

Grouping responses with similar patterns of priorities reveal customers groups choosing:

Trying to optimise the option benefits/ price increases for an average motorise would result in a car which matched none of these different groups priorities.  Products must be created for each commercially viable segment of the business.

 (B) Desk Top Personal Computers

Several distinct priority need segments were identified based on the options selected.  Because PC product specifications change so quickly the options were not described in terms of megabytes/ speed of operation/ screen size, refresh rates, etc. but rather in terms of the benefits these options can enable.  Needs clusters included:

In addition, the respondents in each of these clusters were identified and the type of work they did on their PCs  linked to the benefits prioritised.

  (C) Health Care Insurance

 A complex market with different life stages -  young/old, with and without  dependants, and having different personal budgets available to spend on the provision of their family health plan.

 Respondents chose the benefit levels and options they desired, cognisant of the relative costs each incurred.  They were then asked how much they would pay for each ‘bonus stage of choices’.

 Linkage of this level of payment with options chosen enabled grouping of respondents, both in terms of overall quality/cost of service product – silver, gold, platinum, - and the options each of these contained, and in addition what other option benefits were prioritised across quality levels within lifestage clusters.

(D) Video Telephones for the Residential Market

 This SIMALTO project was conducted before such products were available.  Therefore 3 brief concepts were shown – as $1000, $2000 and $4000 benefit packages.  Respondents, pre-recruited to be sympathetic to purchasing this type of product, than self-allocated to which of these (if any) they felt most suited their requirements.

 Then a complete specification was pre-circled on a 20-feature grid corresponding to which of the low, medium, high priced options they chose.  Respondents then redesigned and allocated bonus improvements to this pre-circled position.  A final range of specifications was deduced using the model which effectively refined the initial three-way price segmentation, bringing it closer to market needs.

4.   Price Sensitivity Curve

 Predicting the Impact of Price changes on Preference for a Specification (Price Sensitivity)

 A PC Manufacturer had decided most of the benefits it was going to incorporate in a new PC specification.  However, a bundle of software and support options could be added instead of further hardware enhancements.  The manufacturer wished to gauge the likely preference between these two alternatives, and how price sensitive this preference was.

 When both alternatives were priced equally, 62% preferred the software/ support and 38% preferred extra hardware.  The table below shows how this 62% preference changes as the software/ support alternative changed to price relative to the hardware alternative.

 The above price sensitivity can be plotted to form a typical ‘S-curve’.  Within reason, however cheap the software/support pack is made, there is still a hard core (!) or around 22% of customers who prefer the hardware package.  And even with a 20% price premium, there are 42% of customers still opting for the software package.

 Many customers were fairly price sensitive around the equi-price scenario.  This (and other) data led the manufacturer to introduce both options into the market place.

 A similar set of calculations was carried out for a fax machine manufacturer.  A segment of his market provided their priorities for improvements of one of his current machines costing $995.  The manufacturer could meet a subset of these demands for a total price of $1120,  but he could charge $1180 before his new machine reduced its value preference to that of the current fax.

 5.    Best Specification Within a Price Constraint

 The hierarchy of single improvements as illustrated by the Customer Satisfaction Analysis, Example 3, can be created for product option inclusion for a given segment/needs based cluster.  Each of these improvements has its own cost increment to find the best return for a given addition of x price units, those top hierarchy elements whose individual prices cumulate to x will serve as a first estimate.

 Replacement of the one or two individually least important of these with those most important options that were not in the top x cumulated price first estimate can create alternative specifications costing x units.  Testing pairs of these using SMALTO simulations will reveal the optimum combination.

 They of course could be compared with a rival brand specification – costing x units.  The winning specification against a rival may be different from the optimal one if no rival were present.  This is due to the method optimising for individuals, not the average.

If the oval represents customer values in n-space, then without competition the best place to be is at M.  If a rival brand specification is at A, then if B positions itself also at A it will share the market equally, all other things (Brand Equity, Availability, etc) being equal.  If B positions itself at M then it will still face a challenge from A for those customers to the ‘left’ of M, and not be an ideal product for customers to the ‘right’ of M.  Positioning B as shown might be the strongest place for B to be (!).

 SIMALTO analysis allows this with/without competitors to be evaluated and client main player, niche player strategy can be involved with subsequent positioning decisions. 

 6.   Brand Overlay

Brand cannot usually be traded off against features, since many products can have any features, irrespective of who brands the features.  (Few feature options are brand specific in competitive markets.)  But Brand does convey a relative value to a product, e.g. consider the following information from a SIMALTO brand simulation of two competing products and the same specification in the Telecoms field.

i.e. A has to reduce price by $5 before it approximates to getting 50% of the value preference of this market segment.

If all 7 brands are compared on these average relative values then the following results apply:

Note, on average, Business Users thought A worth more than S, but the reverse was true for Home Users.

 In the SIMALTO simulations, the relative value of the brand is added to the relative value of the specification.  Then those ‘floating voters’ who marginally preferred specification X to slightly different specification Y, may be convinced to switch their preference to Y, if Y is made by a brand they have a higher relative value for than X’s brand.

 It is this CONVERSION of floating voters, that uses the SIMALTO model simulation work at the individual customer level, that is so important to its superiority over “averaging models”.

 7.   Customer Activity Forecasting

Bank Current Account Benefits Cards

A bank has decided to upgrade its package of benefits that was offered to those of its current customers who choose to pay an extra monthly fee for this package.  Respondents indicated which options of over 30 attributes they most wanted as part of this package in a normal SIMALTO questioning process.  They then indicated on a 6-point scale, of which the “top two boxes” were very likely and definitely, their intention to buy the packages they had prioritised after each bonus spend.  The intention to buy was asked in the context of a monthly fee of £10, £8, £6 and £3.

After assimilating the information, the bank proposed the contents of two new levels of card benefits, and used the activity model simulation to predict the percentage of customers who would be very likely or definitely likely to buy these cards.

The forecast results of % acceptance versus price was as follows:

Other forecasts were made for customers of rival banks “Gold Current Account” cards in order to estimate the chances of the planned offerings being attractive enough to encourage them to switch to the client bank. (See also example 7 of the Customer Satisfaction Analyses)