Question 1

How do we best introduce new tracking KPIs alongside legacy ones?

Accepted Answer

To introduce a new tracking Key Performance Indicator (KPI) alongside legacy ones, roll it out gradually by running it in parallel with the old metrics and clearly explaining its purpose. Start by reporting the new KPI alongside the old instead of replacing anything immediately. During this overlap, communicate why the new measure is being added, how it differs from the old one, and provide context or training so everyone understands it. For example, if you add a customer effort score to a dashboard that previously only had customer satisfaction, show how a low effort score might align with high satisfaction but also highlight pain points that a satisfaction score alone could miss. As stakeholders see both metrics together over time, they'll learn to interpret the new one relative to the familiar benchmark. Once there's confidence in the new metric, you can consider phasing out the legacy KPI if it's redundant, or retain both if each offers distinct value. Throughout, maintain transparency and educate teams so they have time to adapt to the new KPI.

Question 2

Will category entry points reshape how we model mental availability?

Accepted Answer

Yes — incorporating category entry points (the situational cues that prompt brand recall) is reshaping how mental availability is modeled by adding context to brand salience. Traditionally, mental availability was measured broadly (how easily a brand comes to mind), but now marketers map specific occasions or triggers when consumers think of the brand. For example, instead of just tracking general awareness, a coffee brand might quantify how often it's top-of-mind for "morning commute" vs. "afternoon pick-me-up." By modeling these distinct category entry points, brands get a granular view of where and when they hold memory real estate. This approach refines mental availability models: success is not only being known in general, but being salient in the key situations that drive purchase decisions. Ultimately, brands are focusing on owning relevant entry points to enhance their overall mental availability.

Question 3

When is video analytics more effective than real-time observation in research?

Accepted Answer

Video analytics tends to be more effective than real-time observation when you need to capture and analyze complex behaviors at scale or in fine detail. For instance, if a study involves watching multiple shoppers in a store or users interacting with a product over an extended period, recorded video allows researchers (and even AI tools) to review footage repeatedly, slow it down, and quantitatively code patterns that a live observer might miss. Video analysis also shines when consistency is crucial: it can apply the same criteria across dozens of sessions, reducing human bias and fatigue. In contrast, real-time observation is limited by an individual's immediate perception and note-taking, so whenever the research demands thorough, repeatable analysis of behavior — or coverage of many events simultaneously — video analytics is the more effective choice.

Question 4

When are conjoint results more convincing than sales team's pricing instincts?

Accepted Answer

Conjoint analysis results become more convincing than the sales team's gut instincts when you need objective, customer-backed evidence to settle pricing debates. Sales instincts are valuable, but they often come from a handful of big deals or personal impressions, whereas a well-run conjoint study quantifies preferences across a broad market sample. If conjoint data shows, for example, that customers are willing to pay a premium for certain features (or that a mid-priced bundle is optimal), you can use those numbers to challenge or confirm a sales team's hunch in a concrete way. It's especially powerful in situations where instincts conflict or stakes are high - like launching a new product or entering a new segment - because conjoint provides a simulation of market choices rather than anecdotes. By illustrating trade-offs (perhaps revealing that customers would drop an ancillary service to get a lower price, contrary to what sales assumed), the conjoint results give a fact-based narrative that can sway skeptics.

Question 5

How do I quantify ROI from maintaining an in-house research panel?

Accepted Answer

Quantifying the ROI of an in-house research panel involves measuring how it saves money and adds value compared to not having one. Start by tallying cost savings: calculate what you would have spent on external sample or recruitment fees for all the projects where you instead used your panel. Those avoided costs directly contribute to ROI. Next, factor in speed and efficiency gains — if the panel allows you to complete studies faster (say, cutting a typical project timeline from eight weeks to four), consider the business benefits of those time savings (faster decision-making or quicker product tweaks that might prevent revenue loss). You can also quantify improvements in data quality or engagement: for instance, higher response rates and the ability to run longitudinal studies with the same panelists can lead to better decisions, which translates to avoided mistakes or seized opportunities. By assigning dollar values to these savings and benefits (e.g., cost avoided per project, value of time saved, improved outcomes), you can demonstrate a strong return on investment for maintaining your in-house panel.

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Market Research FAQs with Expert Insights

How do we best introduce new tracking KPIs alongside legacy ones?

Will category entry points reshape how we model mental availability?

When is video analytics more effective than real-time observation in research?

When are conjoint results more convincing than sales team's pricing instincts?

How do I quantify ROI from maintaining an in-house research panel?