Question 1

How are mixed-mode designs evolving in response to declining phone survey?

Accepted Answer

As phone survey response rates decline, mixed-mode designs now rely on online and text as the primary channels while using phone calls more selectively. Researchers often begin with a web survey link or SMS invitation because people are far more likely to respond digitally than answer an unknown call. The phone hasn't disappeared — it's used as a follow-up for those who don't respond online (for instance, some older adults who still prefer a call). Mixed-mode strategies also increasingly give respondents a choice: a survey invite might let people pick between an online survey or calling in to complete it, so everyone can use their preferred method. This flexibility tends to boost participation. On the backend, improved integration and weighting ensure that data from different modes aligns, maintaining quality. In short, phone surveys have become just one part of a broader toolkit: we reach most respondents via online/text for efficiency, and use phone outreach in a targeted way to cover the gaps, adapting to modern communication preferences.

Question 2

How can AI improve recruitment of hard-to-reach B2B respondents?

Accepted Answer

AI can improve recruitment of hard-to-reach Business-to-Business (B2B) respondents by identifying and engaging niche professionals more efficiently through data analysis and automation. Machine learning algorithms can sift through large data sources — like professional networks (LinkedIn), industry publications, or web content — to find individuals with the specific roles or expertise needed for a study, even if they aren't in your existing contact lists. For example, an AI model could analyze job titles and keywords to surface hidden experts (say, engineers or procurement managers in a very specialized field) who fit your criteria. AI tools can also personalize outreach at scale: intelligent email campaigns or chatbot assistants can tailor messages and follow-ups based on each person's profile and likely availability, improving response rates. By automating the search and initial engagement, AI reduces the time and effort required to locate qualified B2B respondents and helps connect with them where traditional methods might fail.

Question 3

How should we handle multi-stakeholder decision units in sampling?

Accepted Answer

When dealing with purchase decisions made by a group of stakeholders, make sure your sample represents all the key roles in that decision unit. In a B2B context, for example, one deal might involve an end user, a technical evaluator, and a financial decision-maker. If you only survey one of them, you'll get a one-sided view. So you might recruit multiple people from each client company or tag respondents by role (user, influencer, buyer, etc.) and ensure you have each role covered per decision. That way, you can see where opinions align or conflict — maybe the technical team loves a solution but the finance exec has reservations. Understanding those dynamics is crucial. You could also consider qualitative methods like conducting a group interview with representatives of each role to watch how they collectively discuss a choice, although coordinating that can be challenging. The bottom line is to design your research to capture input from every stakeholder who has a say. Then, analyze both individual perspectives and the combined picture to truly understand how the group will decide.

Question 4

How can I research underrepresented populations cost-effectively?

Accepted Answer

Researching underrepresented groups affordably requires creative recruitment and lean methods that still respect those communities. One effective tactic is partnering with community organizations or trusted local influencers who already engage the target population-for example, working with a community center or advocacy group lends credibility and recruits participants without costly mass outreach. Digital approaches like mobile-friendly surveys, social media targeting, or online focus groups cut travel and venue costs while reaching dispersed participants. Additionally, snowball sampling-where initial respondents refer others-and tapping into existing panels or public datasets can boost sample diversity cost-effectively. Offering culturally appropriate yet modest incentives (like local gift cards or community recognition) encourages participation without straining the budget, and ultimately it's crucial to meet people where they are via tailored outreach rather than high-cost recruitment drives.

Question 5

How can loyalty research capture younger consumers' shifting brand expectations?

Accepted Answer

Loyalty research can capture younger consumers' shifting brand expectations by focusing on the new factors that drive their loyalty, such as authenticity, shared values, and unique experiences. Traditional surveys about points and discounts might miss what matters to Gen Z and younger millennials, so studies should also ask how things like a brand's social responsibility, personalization, or community involvement affect their willingness to stay loyal. For example, you might measure emotional connection or the importance of a brand aligning with their values, not just satisfaction scores. Methods like qualitative interviews or social media listening can uncover emerging expectations--like the desire for more two-way engagement or instant rewards--that would help loyalty programs evolve. By broadening loyalty research beyond old-school rewards metrics, you capture what really makes younger consumers stick with or switch away from a brand.

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Market Research FAQs with Expert Insights

How are mixed-mode designs evolving in response to declining phone survey?

How can AI improve recruitment of hard-to-reach B2B respondents?

How should we handle multi-stakeholder decision units in sampling?

How can I research underrepresented populations cost-effectively?

How can loyalty research capture younger consumers' shifting brand expectations?