Editor’s note: Ahmed Mohamed Fouad is AVP, head of customer insights, Riyad Bank, Riyadh, Saudi Arabia.  

In February 2016, Quirk’s published an article titled, “The impact of survey duration on completion rates among Millennial respondents,” which looked at a study that found there’s a major dropout inflection point among Millennial respondents after 15 minutes. 

While the study doesn’t represent all countries, the sample indicates the need for marketing researchers to keep their eyes open to apply a customer-centric approach when dealing with Millennial respondents. 

Ideally, a customer-centric approach is the suggested model to make the business successful. The approach keeps the customer at the heart of business decisions. The market research industry should apply a customer-centric approach for several reasons: 

  • insights are unlikely to be generated without respondents;
  • respondents are human;
  • respondent fatigue impacts the quality of responses;
  • respondents have to be encouraged and engaged;
  • researchers must keep respondents interested in participating in future studies; and 
  • it develops a stronger relationship with respondents that relies on honest input, active participation and eagerness to be a part of a long-term conversation.   

In some cases, survey satisfaction effects a respondent’s future participation in research. Studies show a high correlation (r2=.72) between respondent satisfaction and the likelihood for participants to accept a future survey invitation (within online panels and clients).

Surveying respondents is like conducting a chemical experiment, only researchers are mixing human characteristics (e.g., emotion, feeling and confidentiality) instead of chemicals, making the task more challenging. 

Creating an effortless and engaging experience for respondents is not only good for the participants, it improves the quality of the responses you receive. 

Studies show how important respondent experience is, as well as how necessary it is to adopt a customer-centric approach while dealing with the respondent. 

Respondent experience management (RXM) is an on-going program that measures at different stages to ensure that market research exercises are designed around respondents’ expectations. 

In order to have a positive respondent experience in place, the ERMBI model is suggested. ERMBI stands for explore, relevant, measure, build and implement. In this article I will take a detailed look at each step. Note, the model is applicable to quantitative research but may not be as effective for qualitative research. 

1. Explore the respondents 

The objective of this stage is to understand the respondent needs and preferences.

First, researchers need to understand the respondent’s point-of-view, preferences and styles of participating in market research practices and exercises. Researchers also need to come up with research initiatives for different countries and regions. This will establish an understanding of current experiences and future preferences. The study should answer the following questions: 

  • How satisfied is the respondent with his or her past MR experiences?
  • What is the satisfaction level with the value and type of incentive?
  • What is the satisfaction of the interview length?
  • What are the reasons for dissatisfaction? 
  • What would be an acceptable interview length (duration in minutes)?
  • What factors encourage the respondents to participate in the research? 
  • What factors could kill respondent intention to participate in the research? 
  • Do you think that knowing the research sponsor would affect a respondent's decision to participate in the research?  
  • What are the preferred types of incentives (cash, voucher, gift, other – specify)?

Researchers can add additional questions that relate to their particular audience. 

Not every researcher has the budget to explore the respondent experience. If this is the case, they should consider: 

  • partnering with multiple researchers in the same country/region; 
  • sponsorship agreements between research buyers and providers where research buyers can get a discount on their future research projects;
  • working with marketing research associations; 
  • partnering with statistics bureaus interested in funding the exercise.       

2. Relevant design 

The objective of the second stage is to propose research design that meets the respondent needs and preferences.

Exploring the culture and understanding the respondent is a starting point. Any design should meet the respondent expectations from stage one. 

Research buyers and internal stakeholders often pose as a barrier in this stage, hoping to implement a specific design (e.g., requesting a lot of information and exceeding the accepted interview length). Use the information you gathered in the first stage to prove the value of your proposed design. Let the stakeholder decide if they want to take the risk. 

Exploring the respondent works as a proof of your design, allowing clients and internal stakeholders to notice the maturity level of the industry and the rationale behind the proposed research design. 

It is important to highlight that sometimes, for one reason or another, the first two stages cannot be implemented. While it is not possible to propose relevant design without exploring the respondent, researchers can complete a short version of the model, building and implementing a partial ERMBI model measure.

3. Pre-live experience 

The objective of this stage is to gather respondent feedback beforehand to enhance the questionnaire design.   

The idea behind this process is to monitor our practice on the project basis to get real-time feedback. Before starting the live fieldwork, usually a pilot test takes place. During the pilot, it will be a good opportunity to measure the experience. You can ask a set of questions at the end of the pilot interview (RXM):

Variable
Label
Measurement
Effort
1. Describe the effort it took for you to complete the interview. 

□ Easy
□ Just right                  
□ Difficult  
Clarity
2. How would you rate the clarity of the questions?  
□ Clear                  
□ Unclear
□ Some clear and other unclear
Length
3. How would you describe the time taken to complete the interview?
□ Longer than expected    
□ Just right    
□ Shorter than expected  
Burden
4. Did you notice any issues during the interview that need to be addressed to improve your experience (other than any negative rating given above)? 
□ Yes                      
□ No
Triggers
5. What are the issues?
Open-ended question
Comments (if applicable) 

6. Why did you consider it a difficult interview? If “difficult” in question one. 
Open-ended question


7. Why did you say the questions are unclear or some of them are unclear? If “unclear” or “some clear and other unclear” in question two. 
Open-ended question


The RXM provides a high-level assessment of the survey design. Usually the pilot has a small sample so looking at the respondent experience metrics qualitatively helps researchers improve the questionnaire design and the experience on the project basis. 

4. Post-live experience 

The objective of this stage is to measure the respondent experience (after the live interviews) on the project level. After exploring the respondent, creating relevant design and measuring pre-live experience, it is a time to assess your effort. Establish a tracking program to record respondent feedback after every live interview. This type of assessment should be very short and include the RXM illustrated in Figure 1. The result is a respondent experience index for each project.

Apply the following formula to calculate the respondent experience index (RXI):

 RXI=(E+C+L)-B

RXI Range (-100 : +100) 

where

  • E: effort (percent of “easy” + “just right” responses)
  • C: clarity (percent of “clear” responses)
  • L: length (percent of “just right”+ “shorter than expected”responses)
  • B: burden (percent of “yes” responses)

5. Building an RX database 

The objective is to create a history of the respondent experience for all executed projects. While the tracking program (post-live experience) is running, create an internal database where you can store the respondent experience metrics along with the respondent experience index for each project (the post-live experience will feed the respondent experience database). Additionally, be sure to add the profile fields in your database:

  • Project name 
  • Client name
  • Industry 
  • Geographical coverage (city/region/country)
  • Fieldwork date (start/end)
  • Fieldwork duration
  • Target respondent
  • Planned length of interview (est. minutes)
  • Actual length of interview (average)
  • Actual length of interview (minimum)
  • Actual length of interview (maximum)
  • Incentive given (Y/N)
  • Incentive type
  • Data collection method (CAPI, CATI, CLT, etc.)
  • Effort score (percent of “easy” + “just right” responses)
  • Clarity score (percent of “clear” response)
  • Length score (percent of “just right”+ “shorter than expected” responses)
  • Burden score (percent of “yes” response)
  • RXI

It is important to use a database application that can help you to set search criteria and retrieve the data easily. 

6. Respondent experience – implement 

The objective of this stage is to utilize the respondent experience history as an optimizer for future research projects to get high-quality responses.

After exploring the respondent, measuring the real experience and building the respondent experience database, we need to put the respondent experience in the research life using the following two steps: 

Refer to your RX database. Before proposing a research design, search for any previous projects that seem similar to the one you are working on and compare the respondent experience metric and index with the implemented design and then apply the learning accordingly for the new project. The model will work as an optimizer tool for future research design. 

Another benefit of applying the model is that after a period of time (e.g., year) you will have a respondent experience index on different levels (e.g., industry, data collection method). It could be an indicator of the capabilities to engage respondents and gather high-quality responses. 

Utilize technology. Marketing researchers have to explore technology that makes the research process easy for respondents. For instance, survey platforms with a pre-loading feature that allows users to save known information for the target respondents (e.g., demographic variables or usage information) then attach the same information with the respective respondent responses after the completion. This kind of feature can reduce overall length and respondent effort. 

Beacon technology is another tool to pinpoint the location of smartphones to send push notifications or remainders based on predefined criteria for the location. Applications of this technology in the market research can vary; it can be a reminder for the respondent to participate in any research that focuses on the real-time experience for specific location or it can be a good tool to run an NPS transaction survey. This kind of technology helps researchers gather accurate responses by reducing the respondent’s effort in memorizing the things around the experience. Moreover, it creates a good feeling and enhances the respondent experience.

Understanding the respondent 

The ERMBI model introduces a holistic cycle that starts with understanding the respondent’s needs and preferences. This is followed by proposing a research design that meets respondent expectations, which ultimately prioritizes the respondent experience and allows researchers to use the respondent experience history as an optimizer for future research projects.  

The respondent experience has to be a top priority for researchers. A pleasant or at least reasonable experience is vital to gathering quality responses and delivering reliable insights and recommendations.