Simalto

SIMALTO – SImultaneous Multi-Attribute Trade Off – is a survey based statistical technique used in market research that helps determine how people prioritise and value alternative product and/or service options of the attributes that make up individual products or services.

A particular specific application of the method is in political science. It can be applied to predicting which of the alternative combinations of optional service benefits provided by a local authority, state or national government in their annual budget would meet with the ‘maximum’ approval of a target population.

Survey design

Simalto Matrix example

Example SIMALTO Matrix : To improve from 8 hours service response time to 2 hours would ‘cost’ an extra 10 points. This would be twice the cost of improving from 6–10 days wait for spare parts to a 3-5 day wait.

This staged prioritization of options was first developed by John Greene while he was the international market research manager at Rank Xerox in London in the mid-1970s. A simpler questionnaire, where the respondent only allocated a single total budget across many of the various matrix options available, to build his ‘personal specification’, was used by Ford Motor Company in Detroit in the late 1940s. More recently this single stage budget allocation approach has been used by many manufacturers on their web-sites to collect a given respondents preferred specification having been shown the costs of different options. Also this single budget allocation, without the multiple prioritisation stages, is a part of some variants of modern conjoint trade-off analysis. The algorithms required for the modelling predictions of SIMALTO data enabling potential market share calculations and needs-based analysis were first created in the early 1980s, with major improvements and extended capabilities introduced in 2000.

SIMALTO analysis

However this direct data is insufficient to deduce the most popular total specification when there are more than 5 or 6 attributes, or to predict preference shares between competing specifications at different prices/costs. Therefore, to satisfy these requirements modelling capabilities must be applied to the raw SIMALTO data. The method most frequently used is based on expert system rules linked to neural net logic and genetic algorithm theory. Examples of these rules applied to competing specifications facing an individual respondent:

• For similarly priced specifications, the one that contains more of the respondents high value priority options and fewer of his low value priority options is likely to be the one most preferred.
• The specification that has the fewest options he considered to be ‘unacceptable’ (if asked on the questionnaire) will be preferred to those with more unacceptable options.
• The specification that is priced (costed) nearest the price he wanted to pay for this product is likely to be preferred to those that are either over-specified, and therefore likely to be too expensive, or those that are under-specified and therefore unlikely to satisfy his needs.
• The SIMALTO modeling analysis expects that the respondent will seek the best ‘bargain’. That is the difference between the value of the specification to him compared to the price it is sold at.

Advantages and disadvantages

Advantages

• The questionnaire sequence engages the respondent and ‘educates’ him if required about competing options/benefits availability and their likely relative prices/costs. The respondent is not completing a ‘tick-box’ survey simply asking for ‘top-of-the-mind’ response.
• Because the respondent evaluates each option on many occasions and the modeling uses these several data inputs, the variance of reported findings is less than usual with individual observations.
• The SIMALTO modelling analysis is ‘cause and effect’ based and does not rely on equations that might make statistical demands on data distribution and attribute independence assumed by, but may not be realised by, regression based trade-off questionnaire approaches. Simulation preference predictions are made for each respondent individually – there is no averaging of forecasts across respondents required by methods that can only show a subset of all the options to an individual respondent when there are more than 7 or 8 attributes.
• Needs-based cluster analysis is carried out directly on individual respondents and does not require complex statistical Bayesian analyses.
• Product price or service cost is not treated as a trade-off variable but rather price and cost are more considered to be a constraint, better reflecting the real-life situation.
• Brand value, if included in a survey, is not simply treated as another trade-off attribute. In many product fields most major manufacturers can produce most of the options on the SIMALTO matrix, so to trade off a brand with one or more options is not realistic. But brand does have a relative value (due to brand image factors, promotion, availability, customer inertia etc.) and so brand value is included in the forecasting process directly on its own terms.

Disadvantages

• Between 5% and 10% respondents find the initial appearance of many attributes and options rather daunting which can deter them from completing the survey. A one-on-one interviewer/respondent situation may help resolve this, but for web based surveys a helpful interviewer is usually unavailable.
• For a product or service with many features, designing the Simalto matrix can be time-consuming.
• With too many options, in a web-based survey some respondents may not read all the possible options as they will have to scroll down.

Practical applications

SIMALTO has been used in a wide range of product fields – it is suitable wherever there are choices to be made between product options or levels of service at different prices/costs. Applications in consumer durables, financial services, transport and distribution, utilities, telecoms and medical equipment have been the most frequent, together with the specialised application in budget allocation optimisation for local and national government.

See also

• Advertising
• Marketing
• Marketing research
• New product development
• Quantitative marketing research
• TURF analysis
• Value-based pricing

External links

• Nigel Hill and Jim Alexander, The Handbook of Customer Satisfaction and Loyalty Measurement : Edition 3, 2 March 2017, pages 127, 132, 134, 156
• John Green, Can you fix the economy?, London Evening Standard Newspaper, Web Edition; 14 April 2010
• D. Douwes and R. Giebels, Waarop zou de gemiddelde Nederlander bezuinigen? (How would you allocate the Dutch Budget?), de Volkskrant (Dutch daily newspaper), 20 September 2011
• B. Chudy and R. Sant, Customer driven competitive positioning, Marketing and Research Today; September 1993
• M. DiSciullo and M. Horowitz, Taking SIMALTO Online: A Case Study of Advanced Choice Model Methods Completed Online., Advanced Research Techniques Conference, Chicago 2002
• M. Kilner and John Green, Budget Policy SIMALTO Modelling, Local Authorities Research & Intelligence Association; June 2003

Conferences

• John Green, SIMALTO – A technique for improved product design and marketing, ESOMAR conference, Oslo 1977
• J. Jones and G. Miles, Rail Roading Ahead: applying an established approach to a new field, Southgate P., Market Research Society conference 1982
• John Green, SIMALTO computer assisted product design and marketing planning, IMRA conference May 1986
• J. Crane and P. Macfarlane, The only real measure of customer satisfaction is market share, ESOMAR conference, Prague 1992
• John Green, IMRA's Advice to the next Chancellor of the Exchequer, IMRA conference May 1986
• John Green, Optimising Product Specification and Customer Services using SIMALTO, ITMAR conference Brussels 1989
• J. Green and E. Goldsmith and C. Parish, The SIMALTO approach to Optimal Product Specification, American Marketing Association, Advanced Research Techniques Conference, Colorado 1991