Question 1

Can AI pricing models outperform traditional conjoint in predicting purchase?

Accepted Answer

AI-driven pricing models can sometimes predict actual purchase behavior better than a traditional conjoint study, especially when they have rich real-world data to learn from. Conjoint analysis relies on what people say they'd choose in a hypothetical scenario, which isn't always how they behave when it comes time to buy. An AI model can analyze large amounts of actual sales data and customer behavior, picking up nuanced patterns (like how demand shifts with time of day, or how different customer segments respond to price changes) that a conjoint might miss. In areas like e-commerce or retail with dynamic pricing, such AI models can continuously adjust and often outperform the more static conjoint-based predictions. However, AI models need that data history. For a new product with no sales track record, conjoint is still very useful to estimate price sensitivity. In practice, these approaches can complement each other: conjoint might guide initial pricing, and then AI refines it once the product is in market. So under the right conditions, AI pricing tools can outdo conjoint in accuracy, but both have their place.

Question 2

When should insights teams be embedded in change programs?

Accepted Answer

Insights teams should be embedded at the very start of a change program and remain involved throughout its implementation. By including researchers from the planning phase, the organization can ground its change strategy in data--such as baseline employee sentiment, customer feedback, or performance metrics illustrating the need for change. Early involvement also allows the insights team to set up clear success metrics and gather input that shapes the program's design. As the initiative rolls out, the embedded team can continuously gauge reactions and adoption rates (via surveys, interviews, etc.) and give real-time feedback to leadership. In short, having the insights function integrated from day one ensures the change program is informed by evidence at every step and can be adjusted in real time, rather than only evaluated after the fact.

Question 3

How do I quantify ROI from automating recurring analysis tasks?

Accepted Answer

Quantifying ROI from automating recurring analysis tasks means capturing time and cost savings and tying them to business outcomes. Start by calculating how much manual effort is eliminated: for example, if automation saves an analyst 10 hours a week, multiply those hours by the hourly rate to get a yearly cost savings. Factor in quality gains as well — fewer human errors can mean avoided rework or compliance penalties, which have their own dollar value. Also consider the benefit of speed: faster analysis might enable quicker decisions that capture revenue or reduce risk (even if these are estimates). Add up the labor cost saved, error-related savings, and any opportunity gains, then subtract the cost of the automation tool or development. The result is a concrete ROI figure that can be expressed as money saved per year or an efficiency percentage improvement.

Question 4

When should search data serve as proxy for salience or mental availability?

Accepted Answer

Search data can serve as a proxy for brand salience or mental availability when consumer interest naturally translates into online searches. In practice, this works best in categories where people habitually research products or brands — if consumers tend to Google a solution when it's on their mind, then rising search volumes for a brand or keyword indicate it's becoming more top-of-mind. For example, after a big ad campaign or news event, a spike in branded search queries often signals increased mental availability (people are hearing the brand and looking it up). Marketers should use search as a proxy mainly when there's a strong link between awareness and search behavior; for a low-consideration product that nobody bothers searching for, search volume isn't very telling about salience.

Question 5

How should we select a small set of RTBs from a large set of credible options?

Accepted Answer

To choose a few powerful "reasons to believe" (RTBs) from a long list of credible points, focus on the ones that most strongly reinforce your core promise and resonate with your audience. Start by filtering out any RTBs that are redundant or tangential — you want each selected reason to cover distinct ground in supporting the product's main benefit. It often helps to test or gather quick feedback on the large set of RTBs (through a survey or stakeholder input) to identify which points actually increase a customer's interest or trust the most. Then select a small number (usually two to three) that together provide a compelling, non-overlapping proof set. By spotlighting only the most convincing and relevant reasons, you avoid diluting the message and give your audience a clear, memorable case for believing in the offering.

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Market Research FAQs with Expert Insights

Can AI pricing models outperform traditional conjoint in predicting purchase?

When should insights teams be embedded in change programs?

How do I quantify ROI from automating recurring analysis tasks?

When should search data serve as proxy for salience or mental availability?

How should we select a small set of RTBs from a large set of credible options?