Imagine you work for a company in their development department and your job involves developing a new product to help increase sales for your company. Among the myriad of decisions you face, your engineering team suggested they can offer a variety of features in the new product. Of course, the more features you add to the new product, the more the product costs your company to manufacture so you likely need to set the consumer price higher to recoup the manufacturing cost. If only there was a tool you could use to determine whether consumers value a feature enough to pay extra for it. Well, there is. It involves calculating the part worth of each feature under consideration.
Most products consist of a variety of features that benefit the consumer. Adding a clock to a TV plugin like a blu-ray device provides the benefit of being able to tell the time but how much will consumers pay to include a clock? Since studies show most consumers never take the time to set their clock or reset it once there’s a power outage, the likely answer is $0. Add an internet connection to the device so users without a smart TV can download content from streaming services and you have a feature consumers might pay extra for. The perennial question is: how many features should you add to your product? Guess wrong and you lose sales — either by over-engineering your brand (resulting in overly complex products that are hard to use or too expensive) or not giving customers enough reason to buy your brand, thus lacking a competitive advantage in the marketplace or, worse, falling behind the competition in offering desired features. There’s a lot riding on the decision about which features to include on a specific model. Your target market, its needs, its ability to pay, and the utility of the feature in question all play into your decision.
A quantitative guide to answering what’s your part worth comes from conjoint analysis, which tells firms how much consumers are willing to spend for each feature they consider adding to their product. Part-worths also tell a firm if consumers are willing to exchange one feature for another. Want to know what the part worth of a feature? This is a critical question in marketing research when companies need to determine whether the cost of adding a feature/benefit to a new or improved product creates enough value for the target market to justify the cost necessary to add the feature. Using conjoint analysis, companies can now answer that question.
How does the product development process work
- Conduct a brainstorming session (or sessions) to develop ideas for new products — hopefully building on customer needs with the idea of solving a customer problem.
- Evaluate the ideas for their fit with the firm’s competitive advantages, market opportunities, mission, and competencies. Eliminate those that don’t pass muster.
- Flesh out the top innovative ideas so they have a degree of concreteness. Eliminate more ideas that don’t offer a great opportunity for the firm.
- Next, firms make a business case for the product — identifying features and benefits consumers want the most in the new product, how much it costs to make the product, determining how much customers are willing to spend on the product, and how many units of the product the firm might expect to sell. This analysis makes the business case for the potential profitability of the product.
- Among other types of marketing research conducted in earlier stages of the product development process, a company might use conjoint analysis to determine which bundle or bundles of features represent the highest potential for the business, if any. Conjoint analysis is usually implemented using computer software such as SPSS or SAS and requires a structured marketing research plan, as well as carefully collected data. Here are the steps involved in determining what’s your part worth:
- Bundles are built — each bundle differs from the next bundle by one feature. You don’t create these bundles as physical products, this is simply a tool for consumers to envision a new product. Don’t worry at this point if all the bundles are feasible, you can eliminate those that aren’t feasible in a later step. Importantly, the software needs data on all possible bundles to perform the calculations correctly. For instance, some bundles might represent combinations that the firm can’t produce for what customers want to spend on the product. Despite the feasibility of some bundles, it’s important all are represented in the study to provide the necessary data for analysis.
- Researchers collect data on how members of the target market rank each bundle.
- Researchers analyze data using SPSS, SAS, or some other statistical package. The statistical package creates output containing the part worth for each feature of the bundle.
- Now, is the time to re-evaluate the bundles since only those that are feasible move to the next step.
- The company builds a functional prototype of the winning bundle and tests it through marketing research as well as quality and functional testing.
- The firm test markets the product. Often this involves setting up the sale in a limited geographical area or in test stores.
- If everything looks good, the firm rolls out production, distribution, and marketing.
New product development – an Example
Let’s say you are an automobile company interested in creating a new car. You might brainstorm features based on existing options, as well as market research with consumers to identify unmet needs in existing cars produced by your company and its competitors. Remember, at this point, we suspend our need for reality. We’ll worry about how to create the features and whether we can do so profitably after we determine which features consumers want most. Let’s say you end up with this list:
- Electric power
- Gas power with high MPGs
- Hybrid drive
- Self-driving car
- Leather seating
- Cloth seating
- Heated seating
- Burlwood finish
- Teak finish
- Pearl inlaid finish
- … the limit is your imagination
Now, we create bundles (ensuring we add pricing to each bundle based on estimates of how much you would need to charge to make a profit with each product bundle).
- 1. Electric, leather, teak
- 2. Electric, leather, Burl
- 3. Electric, leather pearl inlay
- 4. Electric, cloth, teak
- 5. Electric, cloth, Burl
- 6. Electric, cloth, Pearl inlay
- 7. Electric, heated seating, teak
- 8. Electric, heated seating, Burl
- 9. Electric, heated seating, pearl inlay
You get the idea. So, with 3 features and 3 levels of each feature, you would have 9 bundles. Obviously, the more features and levels for each feature, the larger the number of bundles you must create to express every combination. It’s not really possible to ask respondents to answer about their preferences across so many options, so you break them up so each respondent answers across a more reasonable number of bundles. For conjoint analysis to work, the bundles must represent all the possible combinations of the features and levels. If you don’t include all possible bundles, you won’t know the part worth for each feature or your data won’t be accurate.
Here is the real data for a part worth problem related to the options considered for a new mobile plan. In an earlier iteration of this data, the researcher eliminated the other options considered for the price point since the results suggested over 90% of the respondents wanted a price of $30/month. I won’t bore you with the other data displays provided in conjoint analysis because this is the most important when it comes to making your decision about what the new plan looks like.
In this chart, we see that, when ranked by value to customers, the plan that includes unlimited data, 300 minutes of international calling, and unlimited text messages. But, we can also see that unlimited minutes is least important to our respondents since we see only a small drop in value with other options including 90 minutes and 0 minutes of international calling. If I were the marketing manager on this project, I would offer the bundle without international minutes to maximize corporate profit since we will likely gain nearly the same number of subscribers with this plan as the top-ranked plan that costs the company more money. However, reducing the data or the unlimited text messaging options means we won’t attract as many subscribers, even though we gain more profit. I would reject these options.
Problems calculating what’s your part-worth
- Conjoint designs are relatively complex and it takes highly trained researchers to implement conjoint analysis effectively if there are a large number of proposed features, levels of features, or both.
- The cognitive load on consumers evaluating the bundles is high For this reason, often individual consumers may only rank a subset of the total number of bundles. If not implemented correctly, the resulting data is useless.
- Consumers often have difficulty envisioning products or features necessary to assess the value of each bundle. This is especially difficult when proposing features they never encountered before or that they haven’t used themselves, like self-driving options. Features based on technology they don’t understand can also cause them to reject options as being unrealistic.
- Consumers have a hard time evaluating their response to features situated within a social context. For instance, what is the value of having a feature similar to one valued by members of their social group? What is the emotional worth of having a feature desired by your family? etc. Since consumption decisions are embedded in a social milieu, no survey/ experimental type study can effectively model the actual behavior of consumers in a more naturalistic setting. That’s why most research finds a less-than-perfect relationship between behavioral intentions and actual behavior.
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