Market Research Frequently Asked Questions
_____________________________________________________________
When is a qualitative research methodology appropriate?
A qualitative research study is appropriate when you need to tap into the hearts and minds of the customer.
A highly subjective research discipline, qualitative research is specifically designed to look "beyond the percentages" to gain an understanding of the customer's feelings, impressions and viewpoints. Such intuitive, highly subjective personal input can only be obtained through qualitative research.
Strengths:
- Small samples, sharp focus: Qualitative research is laser-focused, dealing only with smaller samples.
- Probing interviews: Expert moderators, unencumbered by the time constraints of a quantitative survey, use a multitude of techniques during lengthy interviews to obtain in-depth information.
- Rich responses: The interviews, which last as long as two hours, allow the moderator to elicit extremely candid, highly complex responses.
 Back to top
When is a quantitative research methodology appropriate?
When you need to forecast customer attitudes, behavior and performance, quantitative research is an excellent tool. Real-world examples have shown the effectiveness of quantitative research in measuring product awareness, establishing customer profiles and determining market size.
Strengths:
- Projectable results: Quantitative research is a scientific, statistics-based methodology designed to yield data that is projectable to a larger population.
- Quantifiable data: Because it is so deeply rooted in numbers and statistics, quantitative research has the ability to effectively translate data into easily quantifiable charts and graphs.
- Cutting-edge design: Quantitative research continues to improve by employing innovative, highly advanced experimental designs.
Back to top When are both quantitative and qualitative methods beneficial?
Certain types of projects benefit from the strategic application of quantitative and qualitative methodologies. A recent study we completed for a computer monitor manufacturer illustrates this point.
CASE STUDY: COMPUTER MONITOR MANUFACTURER
Objectives:
- Understand and identify the relative desirability of a flat-screen monitor for our client's target consumer market.
- Identify the effect price or feature modifications may have on the client's unit market share.
- Profile the high-end consumer market.
To secure the necessary information and the distinct voice of potential customers, we designed a study where both quantitative and qualitative methods were utilized.
Qualitative Phase:
A small portion of respondents participated in probing focus groups. The focus groups obtained detailed feelings, impressions and viewpoints regarding new flat-screen monitors. Quantitative Phase:
Participants were recruited to a research facility and educated on product concepts, attributes and survey procedures. At the facility they were able to see, touch and feel the various monitors, then complete the quantitative survey.
Results:
Upon completion of both the quantitative and qualitative phases, key product styling/feature concepts were verified and customer behavior patterns were clearly established.
Back to top How can I determine product demand after a change in price, features and/or distribution channels?
Answers Research's simulation software is designed to analyze and answer these types of "what if?" scenarios. By changing the product's price or features, the simulation software will automatically show the resulting price share of your company's product and all competing products. The simulation software is custom written based on the results of a choice modeling survey.
With the simulation software, we can see the incremental change in share gained from a change in product, as well as which competitors' products lose the market share your company gains. This is quite valuable in assessing possible competitor responses.
Strengths:
- Easy-to-use: Simulation software is user-friendly.
- Runs everywhere: Designed to run on any PC capable of running Microsoft Excel.
- Versatile: Can handle any number of products. Typically, we design it to accommodate 6-10 client products with 15-20 additional competitor products.
Why use a conjoint methodology?
Conjoint or choice modeling is the most accurate and projectable methodology available to collect information on what is important to customers. Why? Choice modeling derives rather than asks customers to tell directly what is important to them.
To derive this information, we would implement a conjoint study where respondents are asked to choose between several products. This way, we never ask a respondent to tell us how important price, brand or a particular feature is when they make a purchase.
The resulting conjoint "utility scores" tell us relative importance. We do not recommend directly asking respondents to rate importance (for example, asking them to rate the importance on a 1 to 10 scale where 1 = "Not at all important" and 10 = "Very important"). Many times, direct ratings of importance result in everything being important, thereby obscuring those few issues that clearly delineate winning products.
Back to top What are the available choice modeling alternatives?
There are several types of methodologies available for conjoint studies. These include discrete choice, full-profile, adaptive, self-explicated, and hybrid methodologies such as our Complete Conjoint™. Each has its advantages and limitations, but usually one method is most appropriate for a given situation.
Discrete choice works best for products where consumers purchase multiple products distributed over many brands over the course of a year in proportion to the relative desirability of those products.
Strengths:
- A discrete choice model does not require that every attribute/level combination "make sense."
- Has the ability to show certain features with one brand and a different set of features only with another brand.
- Discrete choice has the ability to measure interactions between two attributes, such as between price and brand.
- Works best for products where consumers purchase multiple products over the course of a year, and where they typically distribute their purchases over a number of brands.
Limitations:
- The greatest limitation of a discrete choice model is that it can only be analyzed at an aggregate level. This eliminates the ability to measure brand cannibalization.
- Typically not able to generate accurate market share due to the inability to make respondent level adjustments for volume purchasing, unequal awareness and/or distribution.
Full-profile conjoint is the methodology of choice for purchases where a person typically purchases a single product (such as a car, a wide-screen TV, or a home computer monitor), or where they standardize on one model (corporate purchasers of desktop PCs).
Strengths:
- Full-profile conjoint has the ability to show full product designs, which replicates the way products are actually purchased.
- Full-profile conjoint can generate models per respondent (e.g., for each individual taking the survey). This information allows the marketer to identify a "most preferred" product for each respondent that more closely reflects real-world purchase behavior.
Limitations:
- A key limitation is that full-profile conjoint has a pragmatic limit of about six attributes, with each having no more than 5-9 levels. This limitation does not allow highly complex products to be fully described.
- Full-profile conjoint assumes, by definition, that all attributes are independent of one another.
Adaptive Conjoint Analysis (ACA) is a sophisticated computer implementation of traditional conjoint. In essence, ACA creates a custom conjoint questionnaire for each individual respondent, implemented in real-time on a computer. ACA focuses on the attributes and levels essential to the respondent, then extrapolates this information to determine the relative importance for all possible combinations of attributes and levels.
Strengths:
- ACA is a powerful conjoint technique because it can handle a relatively large number of attributes (15+) and levels (7+), while keeping the survey simple. This process allows marketers to replicate fairly complex products and mirror the real-world purchase process.
- Similar to full-profile, ACA is designed to calculate a model-per-respondent.
Limitations:
- ACA must be implemented by computer.
- Currently, it cannot be implemented over the Internet.
Self-explicated conjoint also generates models for each individual taking the survey. It is exceptionally adept at measuring preference for more complex products with large-numbered attributes (i.e., 20+).
Strengths:
- Self-explicated conjoint offers potentially greater accuracy in modeling respondents' choices when many attributes are in the design.
- Can handle many more attributes and levels than full-profile conjoint or discrete choice models can, allowing the marketer to model more complex and more realistic products.
- Like discrete choice, self-explicated conjoint does not insist that all combinations of attributes "make sense." This is beneficial if one feature is offered by only one manufacturer.
- Allows the possibility of choosing "none." In other words, the model does not insist that all products must have some element of each attribute.
- Allows for the measurement of interactions between two attributes such as price and brand.
Limitations:
- The limitation of self-explicated conjoint is that is does not mirror a real-world purchase process. Why? Because it directly asks respondents to rate attribute performance, self-explicated conjoint has the potential to inflate the importance of price.
Complete Conjoint™ is a robust methodology that allows for accurate analysis of complex products. Developed in conjunction with Stanford University, Complete Conjoint™ combines the strengths of discrete-choice, full-profile conjoint and self-explicated conjoint. Strengths:
- Complete Conjoint™ allows for more than 15 products at nine different levels to be modeled, compared, and the purchase process to be replicated.
- Has the ability to predict actual brand share within a few percentage points by accounting for brand loyalty effects, unequal awareness and distribution, volume purchasing and brand standardization.
- Capable of generating a model for each respondent.
- Can measure interactions between pairs of attributes such as price and brand, thus accounting for subtle brand loyalty affects.
Limitations:
- Complete Conjoint™ currently cannot be implemented over the Internet.
- It must be implemented with a computer.
Back to top How can I optimize a product for maximum market and profit share?
Generally, it is extremely difficult to determine which set of features will maximize market share because of the multiple combinations of features that need to be individually simulated and analyzed. As a result, Answers Research developed a simulation software tool called Optimizer™ which has the ability to:
- Automatically identify a new product's optimal features given a pre-determined price
- Identify the optimal features, including price, to maximize gross profit across an entire product line
- Identify the product mix that will give a company maximum share or profit given any number of products. This helps understand the incremental share or profits that can be derived with more or fewer products in the product line
- The Optimizer™ integrates seamlessly into our simulation software to give clients the ability to maximize market share for multiple products concurrently.
Back to top How can I capture customers' spoken and unspoken needs to develop tomorrow's breakthrough products today?
When developing new products, it is important to determine customers' spoken and unspoken needs-needs that they are unable to express but would love to have satisfied. Through a cutting-edge methodology called Latent Needs Analysis™ (LNA), we are now able to obtain this essential information.
LNA combines qualitative and quantitative methodologies to obtain a prioritized inventory of customers' spoken and unspoken needs. LNA has the ability to:
- Identify breakthrough product ideas and specific sets for new products
Provide a sustainable competitive advantage by understanding customers' needs and motivations at a subconscious level
- Understand customers' needs and anticipate what they will buy, even if they cannot consciously describe what it is they want
LNA involves two phases. The first is a series of qualitative one-on-one interviews among respondents designed to generate a list of customers' spoken and unspoken needs. This is accomplished through the use of a proprietary method, MindProbe™, that is based on neurolinguistic programming (NLP) and cognitive psychological theory. The second phase is quantitative in nature. We employ a proprietary technique called Thought Synthesis™ in which we use a unique survey approach to assess the incidence and importance of the needs identified in phase one. This generates a concise list of statistically valid subconscious needs and the features which customers feel deliver them. We then determine subconscious needs using a pattern recognition algorithm. As part of our analysis, we also identify specific features that will meet these needs.
LNA can be an excellent technique to use for becoming more customer focused and overcoming the limitations of traditional product development techniques.
Back to top
|
