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SIMALTO Method
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 benefit provisions of a local authority ,state or national government in their annual budget, and their costs, (tax and spend issues) would meet with the ‘maximum’ popularity of a target population.

Survey design
SIMALTO is based on creating a matrix of the options that can combine to form the product or service. Each row of the matrix represents an attribute and the matrix columns are the various options (alternative features, levels of service, benefits) of that particular row attribute. Each option is associated with ‘cost points’ which indicates how much more or less that option costs to deliver than the other options on the matrix. The cost points may reflect the actual price in currency, say, of a consumer or industrial product option, or, more commonly in service applications, the relative costs to the supplier of delivering the different benefit options. Respondents complete a series of tasks on this matrix. These include some, but not necessarily all of, indicating the option on each row he currently perceives he experiences in the product or service he has now, any options he would consider to be ‘unacceptable’ for the application of the product or service he requires, and his expectation of an ‘affordable ideal’ service. But the main tasks completed on the matrix are the respondents prioritisation of the options within total ‘constrained’ budgets. The respondent is given a total amount of ‘cost points’ which he allocates to the options on the matrix to ‘design’ his preferred total specification within that total given cost constraint – called his first priorities. Then he is given more cost points to ‘improve’ his first budget allocation to show his second priorities. Typically there are a minimum of three prioritisation stages and rarely more than five. These priorities, and extra relevant data that may be collected in the interview such as relative alternative brand values, if applicable, and the respondents preference between the different priority scenarios he created, can be used to create market models that estimate potential market share of alternative product or service specifications. SIMALTO data collection in the format described was first developed by John Green while he was the international market research manager at Xerox in London in the mid 1970’s. But a simpler format 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 in Detroit in the late 1940’s, and more recently has been used by many manufacturers on their web-sites to collect a given respondents preferred specification. 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 cluster analysis were first created in the early 1980’s, with major improvements and extended capabilities introduced by 2000. If applicable brand can be regarded as a product attribute, but for SIMALTO market simulations brand value information is not collected in the same way as the trade-off options of the product or service specification. Rather a variant of Brand-Price Trade-Off is used. Total specification price or cost is not treated as one of many specification variables, as in conjoint studies, but treated mainly as a constraint. SIMALTO Modelling is part of the set of trade-off analysis tools used for systematic quantified analysis of decisions. These tools include the various forms of conjoint trade-off, discrete choice modelling and brand-price trade off approaches.

Survey administration
Presentation of the matrix of attributes and their options is the key component of SIMALTO studies. Originally these were presented on paper, often in A3 size in Europe or ‘Legal’ size in the USA. The larger than usual paper size was required to comfortably show all options simultaneously to the respondent. The simultaneous element is important because trade-off priorities should be collected in the context of the total specification and each options cost portion of the constrained total cost should be ‘in view’. Respondents used different coloured pens to indicate their different stages of priorities. The questionnaire could be presented by an interviewer face-to-face with the respondent, or in a ‘focus-group’ situation where all participants individually completed the various SIMALTO stages under the guidance of a single moderator. The limit on the number of attributes is not constrained by mathematical issues as in conjoint approaches, but rather dictated by common sense of what a respondent can sensibly deal with in a particular product field and in a reasonable time period. Experience shows that up to 20/25 attributes, each with up to 4 or 5 options generally works very well. However in studies that have fairly simple options with short descriptions of alternatives, e.g. banking charge cards or contract options for telecoms services, up to 35 attributes have been successfully included. Studies including attributes where differences between options require more detailed explanations, perhaps with illustrations, mean that 15-20 attributes might be a practical limit. Since 2000 the majority of SIMALTO studies have been completed via the web. This reduces data collection costs considerably but has the disadvantage that the whole of a matrix with 10 or more attributes cannot be viewed simultaneously without scrolling the matrix on the screen. This disadvantage means that the user should err on the side of fewer attributes wherever possible. Experience suggests 20 is a realistic maximum attribute target on the web, unless respondents are sufficiently motivated (by product interest and/or incentives) to spend longer than 20 minutes completing the questionnaire.

SIMALTO analysis
A huge benefit of the SIMALTO approach is that the data collected is of high quality and directly useful in itself. The sequential process of building up a specification reflects real life evaluation of products or services. It mirrors respondent decisions such as ‘what options must I have, what options are nice to have, what are they worth to me and which options are unnecessary for me’. Simply counting how many respondents choose a particular option in preference to another provides an unambiguous quantification of each option’s relative popularity. 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 are: Of competing specifications facing an individual respondent. For similarly priced specifications, the one that contains more of my high value priority options and fewer of my low value priority options is likely to be the one most preferred.

The one that has the fewest options I consider to be ‘unacceptable’ will be preferred to those with more unacceptable options. The one that is priced (costed) nearest the price I want to pay for this product is likely to be preferred to those that are either over-specified, and therefore too expensive, or those that are under-specified and therefore unlikely to satisfy my needs.

A logical 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.

Application of rules such as these enables predictions of potential market shares between competing specifications, determination of those options in an ‘optimum’ specification at a given total specification price/cost, a hierarchy of preference for each option relative to other options and for ‘needs-based’ cluster analysis which can find groups of respondents with similar values/priorities.

Advantages
The direct SIMALTO data is of high quality and meaningful in itself. The non-boring non-repetitive approach engages the respondent and ‘educates’ him if required about competing options availability and their likely relative prices/costs. The respondent is clearly not completing a ‘tick-box’ survey simply asking for ‘top-of-the-mind’ response. Because the respondent evaluates each option on many occasions the resulting data variance is more akin to that of averages rather than individual observations.

The straightforward nature of the survey questioning mean respondents understand the survey objectives and the research clients readily understand the information reports they receive.

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 often not realised by, the more simplistic trade-off techniques. Simulation preference predictions are made for each respondent individually – there is no averaging of forecasts across respondents required by those trade-off 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 which could distance the findings from reality. Product price or service cost is not treated as a trade-off variable but rather price and cost are more considered as 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 in the trade-off part of the survey, 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, availability, customer inertia etc.) and so brand value is included in the forecasting process directly on its own terms.

On occasion the client of a piece of market research may not want to accept the research findings for whatever reason. If situations of this type arise then the direct un-modelled SIMATO data can be used to explain why the simulation forecast was as it was.

Disadvantages
Between 5% and 10% of ‘educationally challenged’ 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 helps resolve this. For web based surveys a helpful interviewer is usually unavailable. However those respondents that find the SIMALTO process too demanding would in all probability find other methods of trade-off also beyond them, but, in some trade-off approaches disinterested respondents, for whatever reason, are able to provide answers with consequentially possibly spurious validity. The SIMALTO process is more rigorous when completed and therefore less likely to be subject to random response.

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.

External link
Conferences:
 * Nigel Hill and Jim Alexander, The Handbook of Customer Satisfaction and Loyalty Measurement : Edition 3, 2 mars 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
 * 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