Question 1

How do confidentiality concerns inhibit employee candor & how do we relieve it?

Accepted Answer

When employees worry that their survey responses or comments could be traced back to them, they tend to hold back, so assuring confidentiality is critical to getting candid feedback. Confidentiality concerns can absolutely inhibit candor — people might soften criticism or avoid mentioning problems if they fear retribution or being singled out. To relieve this, companies often use third-party survey platforms or anonymized reporting methods that strip identifying info, making it impossible (or at least very difficult) for managers to link comments to specific individuals. Leaders should communicate clearly that honest feedback is valued and will not trigger negative consequences, and then prove it by responding to input constructively rather than shooting messengers. Over time, consistently protecting respondent anonymity and acting on feedback in good faith builds trust, encouraging employees to speak up honestly because they believe their voices truly are safe and impactful.

Question 2

Will relationship NPS decline as journeys fragment across channels?

Accepted Answer

As customers engage with brands across more channels (store, web, mobile, social), relationship NPS can suffer if those experiences aren't consistent. Fragmented journeys often lead to pain points: maybe the mobile app is smooth but the call center is a nightmare, or a customer has to repeat their issue in each channel. Those frictions erode overall loyalty and drag down NPS - a customer's sentiment is only as strong as their worst experience. However, a fragmented journey doesn't have to mean a lower NPS. Companies that integrate their channels (for example, sharing customer data so interactions feel seamless) often maintain or even improve their NPS. Customers appreciate not having to start over each time they switch channels. So yes, there's a risk NPS will decline if channel experiences are siloed and uneven, but if you focus on making the multi-channel journey cohesive, you can protect or even boost your NPS.

Question 3

How do we robustly validate new co-viewing models?

Accepted Answer

To robustly validate a new co-viewing model (which estimates multiple viewers watching together), you need to test its predictions against known ground truth and across diverse scenarios. For example, if the model estimates how many people are jointly watching a stream, you'd compare its output to real observations - like data from households or lab tests where actual co-viewing counts are known - to gauge accuracy. It's important to evaluate the model with different content types and audience segments: a model might perform well for family TV viewing but should also be validated on live sports or kids' shows, for instance. Use statistical checks (error rates, confidence intervals) and benchmark against any existing co-viewing metrics or prior models. In short, treat the new model like a hypothesis: continuously compare its estimates to reality and refine it until it reliably reflects real-world co-viewing patterns before rolling it out widely.

Question 4

Is color-coding the key to making CX dashboards understandable and actionable?

Accepted Answer

Color-coding is a great visual aid in CX dashboards, but on its own it isn't enough to guarantee understanding and actionability. Using colors (red, yellow, green) to signal performance is helpful for scanning, yet if the dashboard relies solely on color with little context or explanation, users might misinterpret what needs attention. To make a dashboard truly effective, pair color cues with clear labels, trend indicators, or alerts that tell the user why a metric is colored red and how to investigate further. Also consider accessibility: ensure patterns or icons accompany colors so color-blind users aren't left out. In essence, color-coding should complement a well-structured, annotated dashboard; it's a key piece of the puzzle, but the real clarity comes from providing context and guidance alongside those traffic-light colors.

Question 5

When should foresight research influence portfolio strategy in research?

Accepted Answer

Foresight research should inform portfolio strategy at the early stages of strategic planning, especially when the company is making long-term investment decisions or innovation bets. Essentially, you want foresight insights to guide what products or services to develop (or phase out) before those decisions are locked in. For example, if trend research and scenario planning indicate that certain consumer behaviors or technologies will surge in the next 5-10 years, that's the time to adjust your R&D and product portfolio accordingly. This typically means involving foresight during annual or multi-year portfolio reviews, when you're deciding which new ideas to greenlight or which areas to diversify into. It's also critical when entering emerging markets or dealing with signs of disruption in your industry — foresight can highlight where growth opportunities or risks lie, informing how you allocate resources across your project portfolio. In short, foresight research should influence portfolio strategy whenever you're looking ahead to shape the business's future offerings, ensuring you invest in the right innovations ahead of the curve.

Spikes

Features and attributes

What is Spikes?

Services and Expertise

Market Research FAQs with Expert Insights

How do confidentiality concerns inhibit employee candor & how do we relieve it?

Will relationship NPS decline as journeys fragment across channels?

How do we robustly validate new co-viewing models?

Is color-coding the key to making CX dashboards understandable and actionable?

When should foresight research influence portfolio strategy in research?