Question 1

How do we triage dozens of concept ideas without missing a winner?

Accepted Answer

To sift through dozens of concepts without discarding a future winner, use a phased screening process with clear criteria and a safety net for outliers. First, run a quick evaluation round — have a team or small customer panel score each idea against key factors like uniqueness, customer need, and feasibility. This will surface the top contenders and flag the weakest. Next, cluster similar ideas so you don't accidentally eliminate an entire theme. For example, if five concepts all tackle the same problem in different ways, advance the best representative of that cluster to keep that angle in play. Then move to a second phase with more rigorous testing for the top contenders (such as a concept screen survey or quick user feedback) to gather data on appeal and potential. Finally, include a 'second-chance' step: if an idea didn't score well overall but had passionate support or intriguing feedback, give it another look before cutting. By combining systematic scoring with this safeguard for outliers, you can efficiently triage many concepts while minimizing the chance of missing a winner.

Question 2

What's a safe approach for utilities to test dynamic pricing communications?

Accepted Answer

The safest way for a utility to test dynamic pricing communications is through a controlled pilot that prioritizes customer understanding and feedback. Start by rolling out the new rate messaging to a small, opt-in group or in a trial environment - for example, sending "shadow" bills that show time-of-use pricing impacts without actually changing what customers pay. (This way, customers see how their bill could change under dynamic pricing without any risk.) This lets the utility gauge how well customers grasp the information and adjust the wording or delivery as needed before a wider launch. By closely monitoring questions, confusion, and reactions in this test group, you can refine the communication strategy. The result is that when dynamic pricing goes live, the messaging is clear, transparent, and far less likely to prompt customer pushback.

Question 3

Does usage telemetry improve calibrations for stated willingness-to-pay?

Accepted Answer

It can - combining usage telemetry with survey responses generally makes your willingness-to-pay (WTP) estimates more accurate. Stated WTP from surveys is hypothetical, so it benefits from a reality check against how customers actually use and value the product. For example, if users claim they'd pay $50 for a service but the telemetry shows they barely use key features, you might discount their stated WTP as inflated. Conversely, if another segment uses the product heavily every day, they might actually be willing to pay more than they said on the survey (perhaps they underestimated their need). By feeding these behavioral cues into your analysis - say, weighting responses or adjusting price sensitivity based on engagement levels - you calibrate the WTP model to reflect real-world commitment. The result is pricing insights you can trust more: usage data helps flag over- or underestimations in stated willingness-to-pay, leading to a better-calibrated pricing strategy.

Question 4

Are U&A studies useful after product launch?

Accepted Answer

Yes - Usage & Attitude (U&A) studies can still be valuable even after a product is launched, because they reveal how real consumers are integrating the product into their lives and what they think about it. Post-launch, you might have sales or usage analytics, but a U&A study provides context: it uncovers why people are using the product (or why not), what features they value most, and how attitudes toward your brand or category might be shifting. This insight is useful for refining marketing or product improvements. For example, a post-launch U&A could show that consumers have invented new use-cases or that certain misconceptions are holding back adoption - knowledge you wouldn't get from raw sales data alone. In short, even after launch, U&A research helps you understand the "why" behind behaviors, ensuring the product continues to meet consumer needs.

Question 5

How do we prove centralized ops lower cost without hurting local outcomes?

Accepted Answer

To prove centralized operations lower costs without hurting local outcomes, measure both the financial efficiencies and the local performance indicators before and after centralization. For example, if you consolidate regional teams into one central ops center, track the cost savings (lower headcount, reduced overhead, cost per transaction) against the baseline when each region ran its own team. In parallel, monitor local KPIs like customer satisfaction, service response times, or sales in each region. The goal is to show costs dropping while those local metrics stay steady or improve. Consider running a pilot: centralize operations for a subset of regions and keep others as-is, then compare results side by side. If the centralized regions achieve significantly lower costs with no dip (or an improvement) in customer satisfaction or other local metrics, you have strong evidence. Presenting that data — efficiency gains alongside stable/improved local performance — demonstrates that centralization delivered savings without sacrificing local outcomes.

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Market Research FAQs with Expert Insights

How do we triage dozens of concept ideas without missing a winner?

What's a safe approach for utilities to test dynamic pricing communications?

Does usage telemetry improve calibrations for stated willingness-to-pay?

Are U&A studies useful after product launch?

How do we prove centralized ops lower cost without hurting local outcomes?