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Thoroughly understand consumer preferences, tradeoffs, and price sensitivity with SPSS Conjoint. By using conjoint analysis, you can uncover more information about how customers compare products in the marketplace and measure how individual product attributes affect consumer behavior. Armed with this knowledge, you can design, price, and market products and services
tailored to your customers’ needs.

Conjoint analysis is a market research tool for developing effective product design. Using conjoint
analysis, the researcher can answer questions such as: What product attributes are important or
unimportant to the consumer? What levels of product attributes are the most or least desirable in the
consumer’s mind? What is the market share of preference for leading competitors’ products versus our
existing or proposed product?

SPSS Conjoint offers the procedures you need to plan, implement, and analyze efficient conjoint surveys. With these techniques, you can discover how respondents rank their preferences and product attributes.

Orthoplan:

Generates orthogonal main effects fractional factorial designs.

  • Specify the desired number of cards for the plan.

  • Generate holdout cards to test the fitted conjoint model.

  • Orthoplan can mix the training and holdout cards or can stack the holdout cards after the training cards.

  • Save the plan file as an SPSS catch file.

Plancards:

A utility procedure used to produce printed cards for a conjoint experiment; the printed cards are used
as stimuli to be sorted, ranked, or rated by the subjects.

  • Specify the variables to be used as factors and the order in which their labels are to appear in the output.

  • Choose a format
    – Listing-file format
    – Card format

  • Write the cards to an external file or the
    listing file.

Conjoint procedure:

Performs an ordinary least squares analysis of preference or rating data.

  • Work with the plan file generated by Plancards or a plan file input.

  • Work with individual level rank or rating data.

  • Provide individual level and aggregate results.

  • Treat the factors in any of a number of ways; conjoint indicates reversals.

  • Experimental cards have one of three scenarios: Training, holdout, and simulation.

  • Three conjoint simulation methods: Max utility; Bradley-Terry-Luce (BTL); and logit Write utilities to an external file.

  • Print results
    – Attribute importance
    – Utility (part-worth) and standard error
    – Graphical indication of most to least preferred levels of each attribute.
    – Counts of reversals and reversal summary Pearson R for training and holdout data.
    – Kendall’s tau for training and holdout data simulation results and simulation summary.