Question 1

When should insights teams play a role in strategy communication?

Accepted Answer

Insights teams should be involved in strategy communication as early as possible — both during strategy development and when the strategy is being rolled out — whenever data is needed to back up the message. In practice, this means when leaders are communicating a major strategic decision or shift, the insights team plays a role by presenting the research and evidence that supports it. For example, if a company is shifting focus to a new customer segment, an insights professional might share key survey results or market trends during the announcement to explain why that segment was chosen. By contributing to strategy communication (through presentations, reports, or Q&A sessions), the insights team helps translate data into a clear narrative, making the strategy more credible and understandable to stakeholders. In short, any time strategic decisions are communicated to the broader organization or clients, involving the insights team ensures those communications are grounded in evidence and resonate with the audience's need for rationale.

Question 2

Which decisions benefit most from shop-alongs or in-store observation?

Accepted Answer

Shop-alongs and in-store observation are most useful for decisions that hinge on real-world shopper behavior — especially things like store layout, shelf placement, packaging design, and point-of-sale marketing. For example, if a product is underperforming, observing shoppers in the aisle can reveal whether the item is hard to find, poorly merchandised, or losing attention to a competitor's display — insights that surveys or sales data alone might miss. These methods excel at uncovering the "why" behind shopping choices, informing decisions on everything from signage and store navigation to assortment mix and in-store promotions. In short, any decision aimed at improving the in-store customer experience or shopper journey benefits from the firsthand perspective that shop-alongs provide.

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

What cultural differences complicate change programs across countries?

Accepted Answer

Cultural differences can significantly complicate corporate change programs when rolling them out globally, because management styles and employee expectations vary by country. For example, a change initiative that encourages open feedback might flourish in a low power-distance culture but stall in a high power-distance one where employees are reluctant to question leadership openly. Communication norms differ too: direct, candid messaging might be valued in one culture and seen as rude or alarming in another that prefers a more indirect approach. Even attitudes toward uncertainty and risk (part of national culture) affect how people respond to new initiatives--some may embrace innovation, while others fear it. Successful international change programs recognize these differences and adapt their approach for each culture, often involving local leaders to interpret and tailor the message and methods, avoiding a one-size-fits-all rollout that could backfire.

Question 5

How do we document model assumptions to meet governance and audit requirements?

Accepted Answer

Meeting model governance and audit requirements starts with thoroughly documenting all the assumptions behind your analytics or AI model. Create a formal model documentation file (sometimes called a model card or model memo) that lists the model's purpose, data sources used, and every key assumption made during development. This includes assumptions about the data (for example, assuming certain fields are accurate and representative), about the methodology (like assuming a linear relationship between two variables), and any parameters or thresholds chosen. Each assumption should be explained in plain language so that non-technical stakeholders and auditors can understand the logic. Also document the rationale or evidence supporting each assumption, and note any limitations that result. Keep this document version-controlled and updated whenever the model changes. By maintaining a clear, detailed record of assumptions, you make it easy for governance teams to review the model and for auditors to trace its compliance with policies.

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