Rajan Samandam, TRC
Idea generation from survey respondents is strongly dependent on incentive. Introducing competition strengthens the quantity and quality of creative responses. TRC provides examples of smart incentives in this white paper.
How often have you done surveys where open-ended questions soliciting new product/feature ideas have produced poor response? If your answer is “innumerable times,” then it is time to consider smart incentives. Smart incentives are a way of motivating survey respondents to think about the questions asked and to generate the best ideas they can.
Consider what happens in a regular consumer survey. Respondents are qualified and asked to participate in a study. Often an incentive is used and while they vary in type (e.g., cash incentive, entry in a lottery, points redeemable for rewards), they all have one thing in common: The incentives are for participation in the survey, not for the quality of the responses given. The primary aim of the respondent is to complete the survey rather than provide the most thoughtful response. While this may be reasonable in some types of studies (say, measurement of customer satisfaction), it is not very fruitful when the objective is idea generation.
Innovation is the engine of growth. Yet idea generation is a difficult task for companies in all industries. Significant numbers of ideas are generated internally, but there is no denying the fact that consumers can and should participate in the process. This is where smart incentives come in. Smart incentives are a way of rewarding respondents for coming up with the best ideas and they work as follows:
Inform respondents to the survey that their ideas will be evaluated and that the best idea will receive a significant reward. Then simply allow the magic of free market competition to work as respondents compete with each other to produce the best idea.
That is all it takes. Since each respondent is providing ideas in isolation (as opposed to collaborating as in a focus group), the potential for a large and varied set of ideas to emerge is quite high. Research has shown that when a large number of reasonably well-informed people generate ideas independently, the outcome is much more positive. In other words, this method overcomes the problem of “group think.” Apart from producing good ideas, the task itself is more interesting for the respondents because they are now competing to be the best.
What kind of results can we expect from using smart incentives? We ran an experiment using respondents to a Web panel in which two random groups of people were asked two simple questions. The first question was about checking accounts and asked respondents to suggest one new feature or modify an existing one. The second question was similar and related to Internet service providers (as this was a Web panel all respondents were familiar with ISPs). One group was offered a standard participation incentive, which in this case was entry in a lottery for a chance to win one of several $20 gift certificates. The second group was offered the same participation incentive and was also offered an additional incentive of $200 if their idea “won.” The winning idea, these respondents were told, was to be picked by another group from the same Web panel.
When the results came back it was quite clear that the smart-incentive group was responding quite differently as seen by several measures.
- The number of non-responses was higher for the participation-incentive group for both questions. That is, the smart-incentive group was more motivated to answer the questions.
- The smart-incentive group was following instructions more accurately in suggesting only one idea per question (as asked).
- When it came to the question of actual number of ideas, the smart-incentives group (across the two questions) produced 100 percent more responses than the participation-incentive group.
Looking at the ideas it was also clear that the smart-incentive group was trying harder and providing more “meat” around each idea. Frequently, they provided more elaborate descriptions of their ideas, sometimes running into paragraphs. On the other hand, the participation-incentive group’s answers tended to be single phrases or single sentences.
Checking-account ideas from the participation-incentive group tended to be fee- or access-related. Examples include free checking and bill payment, ability to access account and check status online, and e-mail notification. Ideas from the smart-incentive group covered these topics, but also went beyond them and were more creative. A points/rewards system for maintaining minimum balances, linkage of bank card to grocery store card, online account balancing and various biometric recognition ideas were some that came from this group.
ISP ideas from the participation-incentive group were generally related to control (blocking pop-ups) and security (spam). Again the ideas for the smart-incentive group covered these topics and went beyond. Bundled services, phone service over Web, automatic link to best dial-up connection in area, and portable e-mail addresses were some ideas that came from this group.
Second Phase
Of course, not all ideas are created equal. The process described so far is primarily useful for identifying a variety of ideas. To truly understand if one or more of these ideas will prove to be popular, a second phase of research is necessary. In this phase, ideas that are deemed practical and innovative are tested to gauge market response.
In our experiment another wave of data collection was conducted with the Web panel. In this case, respondents were asked to rate the ideas generated in the first phase. The idea that got the highest rating was declared the winner. In each product category, the winning idea was suggested by a single respondent. This is interesting because in each case a solitary respondent was able to come up with an idea that ultimately proved to be the most popular. If smart incentives were not provided or if the second phase of the research were not conducted, it would not have been possible for these ideas to emerge as winners. Of course, the client is under no obligation to just go with the winner. It is entirely possible that the client may like other popular ideas that did not win because of feasibility or other reasons. But the point is that there are many more ideas to choose from and they have all been put to a popularity test that provides a good indication of what the market thinks of them.
Needless to say, the two respondents who won were very glad to receive the $200 cash prize!
Better returns
What we see here is the power of a very nominal smart incentive to provide substantially better returns. There are different directions in which this research can be taken including additional stages for fine-tuning ideas, and addressing other objectives such as concept testing and enhancement. But the basic idea of motivating the respondent to be thoughtful remains the same. At a time when new products are failing at a high rate and companies are looking for any sort of differentiating edge, smart incentives can provide a very effective and economical boost to the idea generation and product modification processes.
This article was written by Rajan Sambandam of TRC, a full-service market research provider located in Fort Washington, PA.
[Nov 24, 2009]
Other Resources By TRC
Better Questions For Segmentation: Use of MAX-DIFF | White Paper
Rajan Sambandam, TRC
 | Using Maximum Difference Scaling as a method in designing surveys may ensure more useful results in your market research. It is a comparative method based on importance that sidesteps the problems associated with traditional importance scales. TRC explains the mechanics behind this method through a detailed example in this white paper. | Read White Paper » |
Database Scoring with Object Based Segmentation | White Paper
Rajan Sambandam, TRC
 | Segmentation created from company databases are often lacking the rich segmentation schemes formed by attitudinal surveys. A new approach is Object based segmentation that uses database variables at the basis for forming attitudinal segments, leaving both markets classifiable with clear demographic segments. TRC compares traditional segmentation analysis with Object based. | Read White Paper » |
Asymmetry in Product Features: Use of the Kano Method | White Paper
Rajan Sambandam, TRC
 | The presence or absence of product features strongly affect consumer satisfaction with the design. Comparing these features using asymmetry analysis can help identify satisfiers and dissatisfiers from among the features of a product. The Kano method is similar but results in categorizing each respondent's answers. TRC presents this essential method of deciding new product features in detail. | Read White Paper » |
Conjoint Analysis versus Self-Explicated Method: A Comparison | White Paper
Rajan Sambandam, TRC
 | Determining feature importance in a product can be divided into two techniques - top-down methods where a customer evaluates the whole product at once, and bottum-up methods where features are evaluated individually or in sets. The former method, Conjoint Analysis, is more common while the latter method, Self-Explicated Method, is not widely used but has practical advantages. TRC compares the two methods in this white paper. | Read White Paper » |
Product Configurator | White Paper
Rajan Sambandam, TRC
 | To help customers purchase the right product, companies often use product configurators - tools that let customers design their purchase before ordering. This method is employed as a market research technique, similar to conjoint analysis but without some of the constrictions. This white paper from TRC explains an appropriate use of the product configurator method. | Read White Paper » |
Market Segmentation: One Method, Four Examples | Case Study
Rajan Sambandam, TRC
 | Effective market segmentation requires an understanding of the market and the skilled art of finding the appropriate segments. TRC gives four examples of this method's application with results. | Read Case Study » |
How to Measure the Value of a Brand | White Paper
Rajan Sambandam, TRC
 | Brand name evokes an inherent value; finding a way to reliably measure that value is crucial in determining product development. A technique called discrete choice conjoint analysis is described in this paper by TRC. | Read White Paper » |
Asymmetry Analysis | White Paper
Rajan Sambandam, TRC
 | Asymmetrical relationships among variables in satisfaction research have been increasingly investigated in the last decade. However most of the work has been published in academic journals (such as Marketing Science and Journal of Marketing Research), which may not always be accessible to practical market researchers. The objective of this article is to both provide a simple introduction to this topic and add to the existing body of knowledge. | Read White Paper » |
Deriving Value from Research: the Use of Conjoint Analysis for Product Development | White Paper
Rajan Sambandam, TRC
 | Marketing research has been used by firms over the last several decades to provide information for decision making. Over time, increasingly sophisticated statistical methods have been developed and deployed in the service of this goal. This article focuses on one such method - conjoint analysis - and its application to product development. We will briefly look at what conjoint analysis is and a real life example of its application that provided true value to a company. | Read White Paper » |
Cluster Analysis Gets Complicated | White Paper
Rajan Sambandam, TRC
 | Segmentation studies using cluster analysis have become commonplace. However, the data may be affected by collinearity, which can have a strong impact and affect the results of the analysis unless addressed. This article investigates what level presents a problem, why it's a problem, and how to get around it. Simulated data allows a clear demonstration of the issue without clouding it with extraneous factors. | Read White Paper » |
Identifying Feature Importance: A Comparison of Methods | White Paper
TRC
 | Understanding what customers want is fundamental to the new product development process as well as to the process of keeping existing products fresh and relevant. To be successful in this area we need to be able to correctly identify what features are important to consumers. Feature importance can be measured using a variety of methods of differing effectiveness. In this paper we will deal with the following methods: Importance Scales, Pick data, Pairwise Comparisons, and Max-Diff.
| Read White Paper » |
Monadic Price Testing vs. Price Laddering | White Paper
TRC
 | Compares two popular pricing methods to understand the difference in take rate information. | Read White Paper » |
New Product Development: Stages and Methods | White Paper
Rajan Sambandam, TRC
 | TRC identifies the best methods for each stage of the product development process, from Idea Generation through Feature Development, Product Development and Product Testing. | Read White Paper » |
Understand Choice in Banking: Use of Discrete Choice Conjoint Analysis | White Paper
TRC
 | Conjoint analysis provides incentive for survey respondents to determine which features must not be omitted in their final purchase. The method closely mirrors decision-making in the real world, and as shown by TRC in this white paper, is applicable to many situations including how customers choose their bank. | Read White Paper » |
An alternative method of reporting customer satisfaction scores | White Paper
Rajan Sambandam and George Hausser of TRC
 | Though customer satisfaction evaluations are widely used, reporting of these scores has varied from one study to another. This is likely the result of each method’s advantages and disadvantages, as well as the personal preferences and habits of the researcher. We recently had the opportunity to report customer satisfaction scores in a unique format that assimilates the advantages of various methods and provides the manager with a clearer picture of where to take action. In this article we review various reporting methods and outline our method with an example. Further, we also discuss a type of reporting that is becoming increasingly common especially in the health care arena, i.e., the issue of comparing the performance of various facilities or centers that belong to a single network or organization. We show how our method can be applied for this purpose and why it is advantageous. | Read White Paper » |
Identifying the Key Drivers of Brand Image | Service
TRC
 | Measuring brand image requires looking at direct effects as well as indirect effects of a company's performance. TRC compares traditional multiple regression with SatiscanTM, a method that can review all possible path models. | Read Service » |
Improving Call Satisfaction: A Case Study | Case Study
TRC
 | TRC presents a case study of analyzing and improving a call center as an on-going data collection process. | Read Case Study » |
Improving Claim Satisfaction: A Case Study | Case Study
TRC
 | A case study on applying full-service market research to help an insurance company improve their client satisfaction with claim handling. | Read Case Study » |
Non-Response Bias In Survey Sampling | White Paper
TRC
 | Market research accounts for many scenarios to ensure high quality of data. One of the most overlooked problems is non-response bias. TRC describes ways to reduce its effects through survey design and data adjustment in this white paper. | Read White Paper » |
Segmentation Success | White Paper
Michael Sosnowski, TRC
 | This paper explains the basic building blocks of the segmentation process and its implementation. | Read White Paper » |
Survey of Analysis Methods Part I | White Paper
Rajan Sambandam, TRC
 | Practical marketing research deals with two major problems: identifying key drivers and developing segments. In this two-part series TRC looks at key driver analysis and segmentation. | Read White Paper » |
Survey of Analysis Methods Part II | White Paper
Rajan Sambandam, TRC
 | This is Part II of a series looking at aspects of practical marketing research: identifying key drivers and developing segments. This content describes specific segmentation methods: cluster analysis, neural networks, self-organizing map (SOM), and mixture models. Included is a discussion on ideas for developing good segments. | Read White Paper » |
Validating Satiscan Using A Split Sample Approach | Service
TRC
 | TRC's SatiscanTM model is tested for validity using call center data and a split sample approach. This shows that SatiscanTM produces similar models when run on random halves of an energy industry dataset. | Read Service » |
Satiscan and Regression Analysis: A Comparison | Service
TRC
 | The comparison shows the advantages of SatiscanTM, an analytical method from TRC, over regression in identifying the correct and cost efficient action steps. | Read Service » |
TURF: New Methods for Implementation | White Paper
Westley Ritz, TRC
 | TURF is a long-established and quite useful marketing research tool, but not everyone is familiar with how it works, or with the latest developments that can make TURF even more effective. The purposes of this paper are twofold: (1) to explain the technique and (2) to describe the latest methods for implementation.
| Read White Paper » |