Question 1

Can AI forecast which innovations will scale successfully in the market?

Accepted Answer

AI can help predict which innovations have a higher chance of scaling successfully by finding patterns in data that humans might miss, though it isn't a crystal ball. Algorithms analyze factors like past product launches, market trends, and real-time consumer feedback to flag promising ideas; for instance, an AI might detect a growing niche need or an early adopter buzz around a concept that correlates with products that later became hits. This data-driven insight can surface potential winners faster than traditional market research alone, giving teams a head start. However, forecasting innovation success is complex and uncertain: AI predictions should be handled with caution because they can overlook intangible factors like sudden cultural shifts, regulatory changes, or simply luck. The most effective strategy is to use AI as an early indicator--pointing to innovations worth further exploration--and then follow up with human judgment and small-scale market tests to validate those signals. In short, AI makes picking likely winners more informed, but it can't guarantee any new product will definitely scale up.

Question 2

What triggers business investment in research of hard-to-reach populations?

Accepted Answer

Companies typically invest in researching hard-to-reach populations when a business decision or opportunity demands insights from those specific groups despite the higher effort and cost. For example, if a firm plans to enter a niche market or develop a product for an elusive demographic (say, a medical device for a rare patient group or a service for C-suite executives), it will fund targeted research to understand that audience. Sometimes external pressures such as new regulations or public scrutiny over diversity and inclusion can trigger studies focusing on marginalized or seldom-heard populations. Another common trigger is a performance gap: if sales or engagement are lagging with a particular segment that isn't well covered in general research, leadership might green-light a special study to diagnose the issue. In short, a clear strategic need - like a new market opportunity, compliance requirement, or correcting a blind spot - justifies the extra investment to reach those hard-to-engage audiences.

Question 3

When should MMM reconcile with platform conversion-lift studies?

Accepted Answer

You should reconcile your MMM with platform conversion-lift studies whenever you have credible lift data for a channel — especially if the MMM's findings differ from what those experiments show. Platform lift studies (like Facebook's or Google's conversion lift tests) provide hard evidence of incremental impact. So, for example, if your MMM says social media contributed X%, but a well-run Facebook lift test shows a different contribution, that's a flag to align the two. Typically, this reconciliation happens during MMM updates or recalibrations: you feed the lift results into the modeling process as calibration points or constraints. In practice, many firms do this annually or quarterly. After big campaigns, if a platform provided lift metrics (say, YouTube showed a 10% sales lift from ads), you'd use that to tweak your MMM so it doesn't over- or under-credit YouTube going forward. In short, whenever high-quality lift studies are available, use them to sanity-check and refine your mix model. It ensures your MMM stays anchored in experimental reality, improving its accuracy and the trust stakeholders place in its guidance.

Question 4

Does higher likeability always predict binge completion or share?

Accepted Answer

High likability doesn't always guarantee that viewers will binge-watch a show to completion or share it with others. Content can be enjoyable on the surface yet not compelling enough to drive a marathon viewing session or inspire recommendations. Factors like narrative suspense, emotional investment, and peer buzz often play a bigger role in whether someone keeps watching or tells friends, beyond just liking the characters or premise. For example, a gentle, feel-good series might be well-liked but only watched occasionally, whereas a suspenseful show with slightly lower likability can trigger binge behavior thanks to cliffhangers. In practice, a likeability score is only one indicator; truly binge-worthy content usually combines being likable with hooking viewers via intrigue. Research teams should therefore track engagement metrics (completion rates, shares) and qualitative feedback alongside simple liking ratings to get the full picture.

Question 5

When should we use dubbing versus subtitling when testing concepts globally?

Accepted Answer

It depends on the audience and the importance of the original audio. Use dubbing when you want participants in each country to experience the concept as if it were made for them, without the extra effort of reading text. Dubbing is often best if the visuals are complex or the emotional tone in voice is critical - it helps ensure comprehension because viewers can focus on the imagery and message in their own language. On the other hand, subtitling can be effective when you need to preserve the original audio (say the voice of the original actor or spokesperson is important to the concept) but still provide translation. Subtitles are usually quicker and cheaper than producing multiple dubbed versions, and in markets where people are used to subtitles, it won't be a big barrier. So, if native-language immersion and ease of understanding are top priority (and budget allows), lean towards dubbing. If authenticity of execution or speed is more important - and the audience can handle reading - subtitling is a solid choice.

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Market Research FAQs with Expert Insights

Can AI forecast which innovations will scale successfully in the market?

What triggers business investment in research of hard-to-reach populations?

When should MMM reconcile with platform conversion-lift studies?

Does higher likeability always predict binge completion or share?

When should we use dubbing versus subtitling when testing concepts globally?