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 to the target market to justify the cost necessary to add the feature. Using conjoint analysis, companies can now answer that question.
Most products consist of a variety of features and benefits. 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 or not giving customers enough reason to buy your brand. A quantitative guide to answering what’s you part-worth comes from conjoint analysis, which tells firms how much consumers are willing to spend for each feature they’re considering adding to their brand. Part-worths also tell a firm if consumers are willing to exchange one feature for another.
How does the product development process work
- A brainstorming session (or sessions) are conducted to develop ideas for new products — hopefully building on customer needs with the idea of solving a customer problem.
- Ideas are evaluated for their fit with the firm’s competitive advantages, its market opportunities,
- Top innovative ideas are fleshed out so they have a degree of concreteness.
- Next, firms make a business case for the product — identifying features and benefits consumers are most likely to want in the new product, how much it’ll cost to make the product, how much customers will spend on the product, and how many units of the product the firm might expect to sell.
- Among other types of marketing research conducted in earlier stages of the product development process, a conjoint analysis might be used to determine which bundle or bundles 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. These bundles don’t have to be physically created, this is simply a tool for consumers to envision a new product. The bundles do not have to be feasible either, at least not all of them. 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 necessary data for analysis.
- Data are collected on how members of the target market rank each bundle.
- Data are analyzed 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.
- A prototype is built and tested 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 into each 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, 3 levels of each feature you would have 27 bundles. Once we add in a couple of levels for price (use something that represents your normal price points), we increase the number of bundles (it’s a factorial problem to determine the number of bundles you need). 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 what’s your part-worth or data might be inaccurate.
Problems calculating what’s your part-worth
- Conjoint designs are relatively complex and may be difficult to implement 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 a value to each. This is especially difficult for features they’ve never encountered before or that they haven’t used, like self-driving options. Features based on the 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.
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