Conjoint Analysis Slide 1 There are many research methods I could review with you that are important to marketing research and are especially important to research in sports marketing; for example, survey research and questionnaire design. One especially valuable technique is conjoint analysis. Slide 2 What is conjoint analysis? It’s the family of techniques that model choice by decomposing overall preference and evaluation—in terms of the relative values or components of your product’s attributes—of respondents. Conjoint analysis isn’t just one approach; it’s multiple approaches for optimizing product offerings. Toothpastes have different components, such as teeth whitening, breath freshening, and the like. Hotels offer different features, such as free continental breakfast, free newspaper, free phone calls, or pay per view. To design the optimal product for patrons at the maximum profit, it helps to know the monetary value that customers assign to each product component. Slide 3 In marketing, how might one typically use conjoint analysis? It could be used to design new products. What features should be included? For a new brand in an existing product category, this is more readily determined. Conjoint analysis can help design a new product, provided it shares features with existing products. It can help to fine tune the marketing mix, especially pricing. It can help identify segments of consumers who have similar product preferences. Finally, it can simulate post-product-modified market shares. If a new brand is introduced, how might it compete against current and likely competitor brands? Conjoint analysis allows market simulations for estimating market shares for various configurations of a newly introduced brand. Slide 4 Here are a few examples more specific to sports marketing. For equipment design, think in terms of designing a set of golf clubs or large piece of exercise equipment. What features do consumers find important? It’s profit sapping to include features that consumers don’t want, as their additional willingness to pay won’t cover the cost of those features. To maximize features, marketers try to identify features that people want and will purchase. Golf clubs—drivers and putters, for example—are now adjustable or offer weighting systems that allow users to modify the club. What features do consumers want in exercise equipment such as treadmills? Current treadmills differ by top speed, programming (for exercise sessions), and maximum inclines. Conjoint analysis can indicate what people are willing to pay for different levels of those features. Sporting facilities include country clubs and sports arenas. What services should a country club community offer? What kind of meal service? How should membership fees be structured? What locker room features should be included? What type of golf course should be built? What types of residences should be built around the golf course? Is a gated community preferred? Will guards man the entrances? All these questions are necessary to determine what potential residents want in that community. Consider possible amenities for a sports arena. Is it an indoor or outdoor sporting facility? What is the quality of the food service? Are the restrooms clean? What are the quality and size of seats? Is the lighting adequate? Is there a jumbotron? These are important questions for Page | 1 patrons who pay top dollar to attend such arenas and sporting venues. Ticket prices will reflect preferences for those amenities. If you have a swimming pool inside an arena and no one uses it, then that’s a needless expense. Here at NMSU, the availability of alcohol at sporting events is an ongoing issue. That’s an amenities issue that conjoint analysis could address. Finally, consider sports broadcasts. Team ownership must decide which media to use and in what way. Now, most sports teams maintain an official website. During games, fans can use the website to vote on the game MVP. Whether broadcasting games over network television, satellite, radio, or Internet, teams face many decisions about the main and supplemental media channels. The announcer talent is an important consideration, as an analysis like would include fans’ willingness to watch. Pricing is one step removed, as ratings and viewership determine sponsors’ willingness to pay for 30 and 60 second ad spots during broadcasts. Was it worth it for NBC to pay for John Madden to announce football games? Is it worth it for FOX to pay Joe Buck to announce baseball games? Apparently, the networks seem to think so. The networks also pay reporters to walk around and interview the players and coaches; are viewers interested enough in these interviews to justify the reporters’ salaries? Finally, broadcasts are supported by many pieces of high-tech equipment. For example, new high-tech cameras offer dazzling images that seem to attract viewers; hence, advertisers seemingly are willing to cover the cost of this high-priced equipment. Slide 5 Now that I’ve raved about the value of conjoint analysis for making optimal product and service design decisions, let me illustrate the method with a few basic marketing examples. Don’t judge conjoint analysis by the complexity of these examples, which are limited by this PowerPoint medium. I’ve linked roughly 20 articles to the lecture that indicate conjoint design decisions. Slide 6 This example entails determining the optimal design for packaged soup. For pedagogical ease, it’s limited to four attributes, with a couple levels for each attribute. The soup can come in one of three flavors: onion, chicken noodle, or country vegetable. The calories per serving can be 80, 100, or 140. The soup may or may not be salted. Salt provides an inexpensive flavor source; in contrast, salt-free soup would require more expensive ingredients to produce a sufficiently flavorful soup. Soups in this market are sold at two price points: $1.19 per can and $1.49. So, what’s the optimal soup to market given customer preferences? Slide 7 One way to collect conjoint data from consumers is to provide them what’s called full-profile stimulus cards. In this example, these cards would show flavor, calorie level, salt content, and price. Researchers could ask respondents to rate each combination of soup features on a 1 to 10 scale. Here, respondents would be asked to rate 36 different combinations. Slide 8 This slide summarizes the ratings for the 36 combinations derived from three different flavors, three different calorie levels, ‘yes’ or ‘no’ on salt freeness, and two different price levels (3 x 3 x 3 x 2 = 36). The slide show ratings as low as ‘1’ (least preferred) for ‘chicken noodle, 140 calories, no salt, and sells for $1.49’. Other combinations are rated ‘9’; for example, ‘onion, 80 calories, salt-free, and $1.19’. Conjoint analysis allows researchers to aggregate such ratings from many respondents. Page | 2 Slide 9 This slide shows utility functions. The beauty of conjoint analysis is it allows meaningful comparisons among features measured on vastly different scales. In other words, it allows comparing apples to oranges (or in this case, flavor versus calories versus salt freeness versus price). Salt freeness is ‘yes’ or ‘no’, price is in dollar terms, and calories are something different, so how can researchers identify how much consumers would value each level of each one attribute? Conjoint analysis. In this example, the highest utility score is assigned to country vegetable flavor at 3.66 units. Country vegetable and onion flavor are extremely important and roughly equal in value to consumers. Chicken noodle is of no value. Calories are important: limiting the soup to 80 calories is worth something, but only half as much as country vegetable flavor. Dropping from 100 calories to 80 calories increases the utility relative to calories fourfold, to 2.03 units. In contrast, dropping from 140 to 100 calories only increases utility by 0.46 units. Salt freeness also has some value, roughly in accord with 80 calories. Price isn’t as important as salt freeness, 80 calories, or most preferred flavor. Utility scores are ratio-scaled data, which almost no other research method creates. As a result, it’s possible to make claims like ‘reducing the price $0.30 is only 1/5th as important as avoiding chicken noodle flavor’. Hence, it possible to make informed decisions about the least expensive soup that would provide the greatest utility to potential customers. Utility scores are additive, so the soup with the highest value to customers would have these characteristics: country vegetable flavor, 80 calories, salt free, and a $1.19 price. In contrast, the least desirable soup— the one with no value to customers—would have these characteristics: chicken noodle flavor, 140 calories, salted, and a $1.49 price. The cost of production would now determine the most profitable soup to produce. It would be absurd to sell a soup for $1.19 if production costs $1.29. Slide 10 This is a graphical representation of the utility scores from the previous slide. The elbows or breakpoints in the graphs indicate key attribute levels. The curve for calories provides an excellent example; that elbow reinforces (1) the value of decreasing from 100 to 80 calories, and (2) the value of decreasing from 140 to 100 calories is worth far less to consumers. Slide 11 Here’s a graphical summary for PC attributes: different price points, memory capacities, and different versatilities (in terms of number of possible tasks). Here, decreasing the price from $2000 to $1500 is worth only 0.1 unit of utility; in contrast, decreasing the price from $1500 to $1000 is worth 0.4 unit, or four times more utility gain to customers. Seemingly, people are willing to pay a good price for a computer if they want it and it will perform needed tasks; dropping the price several hundred dollars has little impact on the computer’s perceived value. Conversely, some PC users are price sensitive, so price point is critical to them; for the, dropping the price several hundred dollars may bring them into the market. For versatility, increasing from 4 to 8 functions is worth a lot, but after 8 functions there isn’t much gain in additional versatility. If increasing from 8 to 12 tasks requires increasing the price from $1000 to $1500 (to cover higher production costs), then it’s not worth it to consumers; this PC would lose 0.4 units of price utility (from .8 to .4 units) but gain only 0.1 units of versatility utility. This conjoint (also called ‘tradeoff’) analysis allows marketers to understand the tradeoffs that people make when deciding to buy one brand versus another brand. Page | 3 Slide 12 This example shows coffee makers with three attributes: brewing time, capacity, and price. Brewing time is 3, 6, 9, or 12 minutes, the capacity is 4, 8, or 10 cups, and the price is $18, $22, or $28. This matrix summarizes many people’s preferences for these attributes. Here, the lowest number denotes the least preferred combination of attributes and the highest number denotes the most preferred combination. This example is a bit odd because the most preferred combination has a longer brewing time than another model with the same price point and capacity. Regardless, the $18, 10 cup, and 6 minute brewing time combination is most preferred, and the $28, 4 cup, 12 minute brewing time combination is least preferred. So, how does conjoint analysis convert this rank-order preference data into utility scores? Slide 13 This slide suggests how conjoint analysis software creates utility scores from rank-order preference data. Assume initially that all utilities are equal for capacity, price, and brewing time. As conjoint analysis assumes an additive model, total utility is the utility for chosen capacity plus price plus brewing time. This additive model is simple to understand. The software then adds the utility scores for each set of attribute levels to create a matrix of total utilities. Next, the software compares that summed utilities matrix to the initial rank-order preference matrix (the one with numbers 1 through 35). If the relative summed utility matrix doesn’t correspond well to the initial rank-order preference matrix, then the software modifies the utility scores for one of the attribute levels, recomputes the summed utility matrix, and recompares it to the initial rankorder preference matrix. The software continues this modification-comparison routine until the two matrices correspond at some acceptable level. In essence, the software is generating different utility scores and then comparing the preference and utility matrices for acceptance correspondence. For the coffee maker, the most preferred attribute combination has a utility score based on summing 0.6 units of utility (for an $18 price), 0.5 units of utility (for a 10 cup capacity), and 0.4 units of utility (for a 6 minute brewing time), which equals 1.4 units of utility. The highest utility goes to the largest capacity, the cheapest, and the fastest machine, at 1.6 units of utility. What the software is doing is to create a matrix with utility scores that correspond as closely as possible to the initial rank-order preference data. Slide 14 I hope the previous examples have given you a basic idea about the data generated from a conjoint analysis, how you might use that data to make an informed decision about the combination of features you might design into your product, and how the computer takes respondents’ rank-order preference data and converts it into part-worths or utility scores. What other things would you need to worry about? I assume you’re a consumer of conjoint analysis and not a do-er, so you’re not running the software or collecting the data. What things should you be doing to help your researcher to design a conjoint study that will help you make the best possible decision about the design of your product or service? The first thing to think about is the attributes. The product attributes should to be determinant, which means those attributes completely determine what people buy or don’t buy. If you include an irrelevant product feature in a conjoint analysis, then you’re discovering nothing about the ideal product configuration. The attributes should be easily measured and easily communicated. The attributes in the examples—such as soup flavor, number of calories, price point, cost of PC, number of Page | 4 cups capacity, and brewing times—are all objective, easily communicated, and easily measured. To be avoided are attributes like ‘plush styling’ or luxurious or any vague descriptor that’s difficult to communicate in a way that all respondents understand. Such attributes can’t help marketers design better products. The attributes should be controllable by the company. If government has mandated that certain features be incorporated in a product, it’s superfluous to ask consumers if they want that feature. Why ask people if they are willing to pay $100 for legally mandatory seat belts? The attributes should be realistic. It’s silly to ask people if they would buy a car for $5.00 that gets 200 miles to the gallon, as such a vehicle is technologically impossible at present. Only possible combinations of features should be presented to respondents. People should differ in their preferences for different levels of each attribute. If people are indifferent among soup flavors, then it’s superfluous to ask them their preferences on this attribute. The attributes should be compensatory, which is consistent with conjoint analysis as an additive model. Recall that total utility is the sum of utilities for each included attribute level. Compensatory means that a product may be acceptable if its low utility score on one feature is offset by high utility scores on other features. Also, attributes should not be complimentary, like DVDs and DVD players. If attributes are complimentary, then both are required, which precludes a compensatory model. The attributes should not be redundant, which is an important issue because price often is included in a traditional conjoint analysis. The problem with price, to some extent, is that it incorporates the other features. For example, respondents will make assumptions about the quality and features of a low-price PC. If people are asked to rate the same attribute more than once, then their utility scores for that attribute will be split between the redundant attributes; as a result, the attribute will seem less important. Capacity is not redundant of brewing time and flavor is not redundant of calories. Slide 15 Here are some other kinds of considerations that are important for two reasons. Don’t overload the software. Asking about too many attributes at too many levels makes it difficult for the conjoint analysis software to parse the utility scores for each level of each attribute. Respondents will burn out if asked about too many attributes at too many levels. There are ways to sidestep this problem—even with full profile methods—with balanced designs that limit respondents to a subset of possible product configurations. Attribute levels and their ranges must be considered to ensure they are meaningful, informative, and realistic to both respondents and marketers. Bizarre configurations should be avoided because they cause respondents to discount the research as silly. Remember, adding one or two attributes levels, depending on the conjoint method used, can increase the complexity of the respondent’s task markedly. Page | 5 Slide 16 If at this point you’re enthused about using conjoint analysis to identify the optimal design of goods and services, then I hope you’ll read (or at least skim) some of the articles I’ve provided from Sawtooth Software, as they are free and should be accessible to most readers. Although Sawtooth Software produces user-friendly conjoint analysis software, I’m not advocating you buy it from them. However, some Sawtooth website content can help you to understand what using this software entails. The full profile method discussed previously is limited. The examples in this lecture imply a full profile approach. Full profile conjoint analyses should be limited to six or seven attributes on only a few levels. A full profile method with four attributes, two at three levels and two at two levels, requires respondents to consider 36 combinations. Seven attributes at four levels would require 4 to the 7th power (4 x 4 x 4 x 4 x 4 x 4 x 4) combinations, which clearly is unworkable. In essence, the full profile approach yields good utility data, but respondents only can process a few attributes on a few levels. There are other possibilities, such as asking respondents to rate pairs of attributes in a matrix, similar to the coffee maker example. The problem with that task is it lacks realism, which can produce inconsistent responses and suspect data. Instead, I use Sawtooth Software’s Adaptive Conjoint method, which allows people to initially rate the desirability of each attribute at each level. That rating data is used to generate preliminary utility scores, which are fine tuned by subsequently asking respondents to indicate their relative preferences for different attribute combinations. This hybrid approach works fairly well and provides good quality data. There’s also choice-based conjoint, which I’ve yet to use. Regardless, the Sawtooth Software articles discuss all these approaches and when to use them. Slide 17 Finally, if you’d like to walk through some conjoint exercises, Dr. Tom Novak of Vanderbuilt University has created these two exercises. I found these on the Internet and they are offered to you free, from Tom and me. Either the airline travel preferences or movie theatre preferences exercise are similar to, but slightly different from, the three examples I gave in this lecture. Page | 6