Question 1

Will agentic interviewing give me comparable depth to human moderation?

Accepted Answer

Agentic interviewing (using an AI interviewer) can deliver a good level of depth on structured topics, but it usually doesn't match the nuanced probing of a skilled human moderator. AI-driven agents are very consistent and can ask follow-up questions based on answers, but they might miss subtle emotional cues or opportunities for empathy and improvisation that humans catch. For example, a virtual agent will diligently cover all scripted questions and even some dynamic prompts, yet it might not notice when a respondent hesitates or needs gentle encouragement to elaborate. In practice, many organizations treat AI interviewing as a complement — using it to cover the basics or scale up the number of interviews — while relying on human moderators for deeper exploration and sensitive topics where personal rapport is key.

Question 2

How can path-to-purchase research address gaps in measuring online-to-offline?

Accepted Answer

Path-to-purchase research can fill the online-to-offline measurement gap by linking consumers' digital interactions to their in-store actions through smarter data integration and tracking. One approach is to use unique identifiers across channels: for instance, a loyalty account or coupon code can tie a customer's online behavior (like clicking an ad or reading a review) to their eventual store purchase. Mobile location data also helps; if a shopper who saw a promotion online later visits a physical store, geolocation analytics can flag that journey. Surveys and diaries play a role too — asking customers what online resources influenced their store visit can provide qualitative confirmation of the digital-to-physical path. Advanced attribution models now combine e-commerce clickstream data with point-of-sale records to estimate how online ads or social media engagements drive offline sales. By employing these methods, path-to-purchase research creates a more complete picture of the customer journey, ensuring that the effect of online touchpoints on brick-and-mortar outcomes isn't missed.

Question 3

How do we motivate teams to use automated QA checks and standard playbooks?

Accepted Answer

Motivating teams to actually use automated QA checks and playbooks requires demonstrating value and making the process seamless. First, show concrete benefits: for instance, share how an automated data check caught errors that saved hours of rework, or how following a playbook led to a successful project outcome. When teams see that these tools reduce their headaches, they're more inclined to adopt them_x000D_. Also integrate the QA checks into existing workflows (maybe as simple checklists or software prompts) so it's easier to comply than to skip. It helps to have leadership support and peer champions — if project leaders consistently expect playbook use and recognize teams that adhere to it, it reinforces the habit. Finally, gather feedback and keep the playbooks useful and up-to-date; if employees help shape them, they'll take more ownership. Making quality the path of least resistance and celebrating its practice will encourage consistent use.

Question 4

How can I prove ROI from in-store merchandising research?

Accepted Answer

To prove return on investment (ROI) from in-store merchandising research, link the insights to tangible improvements in sales or efficiency and quantify the before-and-after impact. For example, if the research suggested a new shelf layout and you implemented it in select stores, track the sales lift or basket size increase in those test stores versus control stores that kept the old layout; a significant uptick in revenue, after accounting for costs, directly demonstrates ROI. If sales data isn't straightforward, look at related metrics like improved product visibility (measured by shopper observations) leading to higher conversion rates, or time saved by staff due to a better stocking system. The key is isolating the effect of the merchandising changes: use a control vs. experimental setup or compare pre- and post-change figures while keeping other factors constant (no big promos skewing the results). By translating the research outcomes into dollar figures — like a percentage sales increase or cost saving attributed to the new display — stakeholders can clearly see that the study paid off and was worth the investment.

Question 5

Can AI tools improve efficiency in CATI or online survey administration?

Accepted Answer

Yes, AI tools can streamline both CATI (phone surveys) and online survey administration by automating repetitive tasks and enhancing data quality. In CATI, AI-powered voice agents can handle initial calls or simple survey questions, reducing interviewer workload - for instance, an AI could dial and ask screening questions, then pass the call to a human for complex parts. AI can also assist live interviewers by suggesting follow-up probes based on responses or flagging inconsistent answers in real time. For online surveys, AI chatbots can engage respondents in a more conversational way, potentially boosting completion rates. On the backend, machine learning algorithms quickly clean and categorize open-ended responses (text analytics) and can detect low-quality data (like straight-lining or random responding) faster than manual review. The net effect is faster fieldwork and analysis without sacrificing quality. Researchers just need to ensure these AI systems are well-tested to avoid any bias or robotic interactions that might put off respondents.

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Market Research FAQs with Expert Insights

Will agentic interviewing give me comparable depth to human moderation?

How can path-to-purchase research address gaps in measuring online-to-offline?

How do we motivate teams to use automated QA checks and standard playbooks?

How can I prove ROI from in-store merchandising research?

Can AI tools improve efficiency in CATI or online survey administration?