{"id":4380,"date":"2023-03-05T14:11:40","date_gmt":"2023-03-05T17:11:40","guid":{"rendered":"https:\/\/www.quantiz.com.br\/?p=4380"},"modified":"2023-03-07T14:19:32","modified_gmt":"2023-03-07T17:19:32","slug":"conjoint-analysis-supporting-price-decisions","status":"publish","type":"post","link":"https:\/\/www.quantiz.com.br\/en\/conjoint-analysis-supporting-price-decisions\/","title":{"rendered":"Conjoint Analysis Supporting Price Decisions"},"content":{"rendered":"

Article written by Dario Sales and Henrique Souza and published in the Journal of the Professional Pricing Society (PPS).<\/p><\/blockquote>\n

Customer perception-driven pricing has become an important tactic to pricing in numerous\u00a0 businesses. A dominant methodology for this approach is Conjoint Analysis, a research method that contemplates a cohesive understanding of a costumer\u00b4s preferences and purchasing\u00a0 decisions. Any product or service consists of a bundle of attributes that a costumer jointly\u00a0 considers when making a decision. Because it is a method that seeks to understand the\u00a0 preference of one attribute in comparison to others, a keyword for this analysis is the term\u00a0 trade-off.\u00a0<\/span><\/p>\n

In previous projects, we have applied Conjoint Analysis and came to several findings that were\u00a0 not only common sense to our clients, but also had great potential to generate business value\u00a0 through pricing strategies. We highlighted a project that showed a different willingness to pay\u00a0 across different segments for the main product of our client\u00b4s portfolio, thus allowing to\u00a0 capture more value from the segment that was less price sensitive for this solution.\u00a0<\/span><\/p>\n

Another case pointed out that one of the most valued attributes by the business\u00b4s\u00a0 management was not so valued by the customer. The action plan was to work on the product\u00b4s\u00a0 communication in order to create value for this attribute and, by applying future research, we\u00a0 were able to charge for this specific attribute. Finally, another interesting case was about\u00a0 whether the firm\u00b4s technical assistance was valued by its customers to be set as an additional\u00a0 charge. The study found that in general customers valued this attribute and captured how\u00a0 much they were willing to pay for it. The action plan was to add technical assistance to the\u00a0 company’s offer and grant discounts to those customers who did not value this service, instead\u00a0 of charging an additional fee to those who valued it.\u00a0<\/span><\/p>\n

One can tell from the examples above that there are several ways to work with the results of a\u00a0 Conjoint Analysis and generate value for a company. However, an important question still\u00a0 remains, how can we translate this powerful tool into business added value? First, we will go\u00a0 through the concept behind this study and then we will proceed with a structuring and\u00a0 application guide.\u00a0<\/span><\/p>\n

While there are other research techniques that also seek to understand the perceived value of\u00a0 products or services, the Conjoint Analysis method indirectly addresses the costumer and\u00a0 therefore bypass biases from direct research methods that make it harder to correctly model\u00a0 consumer choice behavior. For example, a research on credit card preferences could address\u00a0 the \u201cannuity\u201d attribute by asking if a low annuity rate is important to the customer. Most\u00a0 likely, following this rationale, the answers to any attribute would always be \u201cvery important\u201d.\u00a0 However, if the question relies on the comparison of low annuity rate and credit limit over\u00a0 $10,000, which one is more important? In this case, we would work the respondent\u00b4s\u00a0 tradeoffs.\u00a0<\/span><\/p>\n

There are various Conjoint Analysis methods one can use, such as Choice-Based Conjoint (CBC)\u00a0 or Conjoint Value Analysis (CVA). The first, CBC, is the most common method for pricing\u00a0 purposes. In many literatures, it is also known as Discrete Choice Modeling (DCM). The use of\u00a0 each will depend on the characteristics of the product or service, sample size, number of\u00a0 attributes raised and how the survey is going to be applied. In general, these methods can answer in different ways many questions related to pricing. Among them, the following stand\u00a0 out:\u00a0<\/span><\/p>\n

– <\/span>Relative importance of the attributes: <\/b>What are the most relevant attributes of a given\u00a0 product or service?\u00a0<\/span><\/p>\n

– <\/span>Market segmentation: <\/b>How do different customer groups perceive a given attribute?<\/span><\/p>\n

– <\/span>Evaluation of new attributes \/ services: <\/b>Does it make sense to include a new attribute in\u00a0 a product \/ service package?\u00a0<\/span><\/p>\n

– <\/span>Price sensitivity and price range suggestion: <\/b>What is the price range that the costumer is\u00a0 willing to pay?\u00a0<\/span><\/p>\n

– <\/span>Competitive positioning: <\/b>How do costumers perceive our product or service compared to\u00a0 the competition?\u00a0<\/span><\/p>\n

Structuring the study, as well as the application of the research, however, is a critical point for\u00a0 obtaining results with greater accuracy and confidence. The following is a step-by-step guide\u00a0 to help you build a well-designed Conjoint Analysis: <\/span><\/p>\n

    \n
  1. Attribute and level selection\u00a0<\/strong><\/li>\n<\/ol>\n

    In order to select the attributes to be analyzed, best practices suggest in-depth interviews with\u00a0 the client’s management team or professionals from the industry in question. Going back to\u00a0 the credit card example which discusses consumer preferences when purchasing a card, we\u00a0 could raise a number of attributes, such as: a) annuity fee; b) card\u00b4s interest rates; c) credit\u00a0 limit; d) benefits program; e) card flag; f) card\u00b4s premium level; g) call center; h) card\u00a0 acceptability; i) ties with philanthropic institutions, among others. These attributes may have\u00a0 more than two levels of response, as is the case with the benefits program, which may be a\u00a0 Mileage Program, Insurance Coverage, Concierge, among others. However, literature\u00a0 references suggest choosing at most between six and seven attributes. Above this number, it is\u00a0 difficult to keep the respondent motivated to answer the survey as it becomes lengthy and\u00a0 complex, a matter further discussed in more detail in the following topic. The researcher\u00a0 should also avoid inaccurate or subjective criteria for defining the levels of each attribute, such\u00a0 as low, medium and high. For instance, a low annuity for one respondent may be an average\u00a0 annuity for another.\u00a0<\/span><\/p>\n

    To better understand how the number of attributes and levels impact a Conjoint Analysis, let’s\u00a0 take an example of a 4 attributes survey with 3 levels:<\/span><\/p>\n

    \"\"<\/p>\n

    This format can generate 81 different types of combinations (3x3x3x3). However, for this\u00a0 analysis an orthogonal design approach is used, using statistical software such as SPSS,\u00a0 Minitab, Sawtooth, which reduces the number of combinations to the minimum possible. In\u00a0 this case, the profile combinations could be reduced to 16. These profiles are also called cards.\u00a0 An example card can be seen below:<\/span><\/p>\n

    \"\"<\/p>\n

    This set of minimal combinations enables the measurement of what is referred to as main effects,\u00a0 when the levels of the same attribute are relativized, not considering the interactions between\u00a0 different attributes. Consequently, each attribute would have its utility or perceived value\u00a0 estimation and it would show the relevance of each of the levels for each attribute. For\u00a0 example, considering that the benefits program contemplates one of the following three\u00a0 benefits: mileage program, insurance coverage or concierge. The main effect chart would show\u00a0 the utility for each of these benefits in the respondent\u00b4s view. The results for this hypothetical\u00a0 example will be shown on the last topic of this paper.\u00a0<\/span><\/p>\n

      \n
    1. Metrics for establishing the results\u00a0<\/strong><\/li>\n<\/ol>\n

      As mentioned, Conjoint Analysis research is used to understand consumer preference or\u00a0 purchase intent. Thus, the methodology requires respondents to rate the options shown by\u00a0 either grading them or sorting their preferences. The first uses a metric scale, in which the\u00a0 respondent rates the cards independently, ranging for example from 1 (would certainly buy) to\u00a0 5 (would never buy). The second approach is known as ranking, in which the respondent ranks\u00a0 the cards according to his preference. The latter is considered more reliable, but as the\u00a0 number of profiles to be sorted increases, it becomes more difficult for the interviewee to fully\u00a0 differentiate them because the combinations become more alike. Therefore, for a high\u00a0 number of profiles, the metric scale approach may be more appropriate. Another form of\u00a0 contingency for a research with a high number of profiles is the adaptive conjoint method. It is\u00a0 a software-based modality that, based on the initial answers, the tool adapts to the\u00a0 respondent’s preferences and can eliminate unnecessary questions, thus reducing the survey\u00a0 time.\u00a0<\/span><\/p>\n

        \n
      1. Survey application and its sampling\u00a0<\/strong><\/li>\n<\/ol>\n

        Literature references show that samples for Conjoint studies range from 100 to 1,000\u00a0 observations, most commonly from 300 to 500. In any case, it is of utmost importance that\u00a0 profiles or attribute cards are presented very clearly and simply. The application of the survey\u00a0 through personal interview is indicated. However, it takes longer and it is more expensive. If\u00a0 applying via online survey platforms or by any method that does not involve the presence of\u00a0 the interviewer, it is important that pre-testing be done internally as well as with potential\u00a0 respondents.\u00a0<\/span><\/p>\n

        It is important to measure the response time during the pre-test. Since it may be a complex\u00a0 survey, it is common that the time required for answering is longer than what the respondents\u00a0 are accustomed to, and this can lead to withdrawals. In addition, a relevant step before\u00a0 applying the survey is to segment it according to the purpose of the study, so it is possible to\u00a0 evaluate behaviors and trends by various criteria, for example, by socioeconomic profile or by\u00a0 region.\u00a0<\/span><\/p>\n

          \n
        1. Validation of results\u00a0<\/strong><\/li>\n<\/ol>\n

          There are a few ways to validate the answers from the survey, among them we highlight the\u00a0 creation of a profile with the worst levels of each attribute. Going back to the credit card\u00a0 example, it would be a profile with the most expensive annuity rate, the lowest credit limit and\u00a0 all the other attributes being negative. If the respondent ordered this profile as preferred or\u00a0 would certainly buy, it is evident that there was no discernment at the time of choice, resulting\u00a0 in an invalid answer. However, there is no need to create an additional profile to test the\u00a0 integrity of the response in case there is an objectively less desired profile than another.<\/span><\/p>\n

          Results\u00a0<\/strong><\/p>\n

          The following are hypothetical examples of how we view the results of a Conjoint study. The\u00a0 first and second graphs show the interaction of the main effects, that is, of each attribute\u00a0 independently. The graphs show the utility between different levels of the same attribute,\u00a0 which in this example is the credit card benefits program. What sets them apart is the\u00a0 socioeconomic segmentation.<\/span><\/p>\n

          \"\"<\/p>\n

          Results show that respondents with an income below $3,000 perceive insurance coverage as\u00a0 more valuable than a mileage program and concierge. For a wealthier respondent, the mileage\u00a0 program becomes more relevant. In both cases, the concierge attribute has the least relevance\u00a0 in decision making.\u00a0<\/span><\/p>\n

          The following chart is also one of Conjoint’s results and shows the relative importance of each\u00a0 attribute. In this hypothetical example, we highlight the importance of each attribute when\u00a0 purchasing a credit card.<\/span><\/p>\n

          \"\"<\/p>\n

          The Utility functions and the relative importance of the attributes allows many considerations\u00a0 about consumer behavior without the need to ask detailed questions about each attribute. It is\u00a0 a reliable technique that can replace traditional customer satisfaction or Voice of the Customer\u00a0 (VOC) surveys. This study has the ability to demystify the customers\u00b4 trade-offs, thus\u00a0 supporting management with great strategic tool. However, medium and long-term strategic\u00a0 business awareness is essential, which are often not derived solely from research or\u00a0 quantitative analysis. A clear strategy is very important to address planning, assembly and\u00a0 application of research methods.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

          Article written by Dario Sales and Henrique Souza and published in the Journal of the Professional Pricing Society (PPS). Customer perception-driven pricing has become an important tactic to pricing in numerous\u00a0 businesses. A dominant methodology for this approach is Conjoint Analysis, a research method that contemplates a cohesive understanding of a costumer\u00b4s preferences and purchasing\u00a0 […]<\/p>\n","protected":false},"author":5,"featured_media":3510,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[30],"tags":[],"class_list":["post-4380","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-process"],"acf":[],"yoast_head":"\nConjoint Analysis Supporting Price Decisions - Quantiz<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.quantiz.com.br\/en\/conjoint-analysis-supporting-price-decisions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Conjoint Analysis Supporting Price Decisions - Quantiz\" \/>\n<meta property=\"og:description\" content=\"Article written by Dario Sales and Henrique Souza and published in the Journal of the Professional Pricing Society (PPS). 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