Question 1

Besides lowest unit price, what else can win industrial purchase decisions?

Accepted Answer

Industrial purchase decisions often hinge on more than just having the lowest unit price; factors like product quality, reliability, and overall value frequently carry greater weight. For example, a slightly more expensive component that lasts longer or reduces downtime can win on total cost of ownership, saving money in the long run. Buyers also consider a supplier's track record — reliable delivery, strong technical support, and compliance with industry standards can tip the decision toward a vendor that isn't the absolute cheapest. For instance, a manufacturer might choose a pricier supplier known for quick replacement parts and support, since avoiding a costly production line stoppage is worth more than a small unit price difference. Ultimately, procurement teams often choose the partner offering the best combination of durability, service, and risk reduction rather than simply the lowest upfront price.

Question 2

Why do consent prompts still derail setup flows for new devices?

Accepted Answer

Consent prompts still derail setup flows for new devices because they create a jarring interruption — users are forced to make complex privacy decisions in the middle of a supposedly seamless process. Even with better design, these prompts are legally required (for permissions, data sharing, terms of service) and often appear as lengthy text or multiple pop-ups, which can confuse or overwhelm people when all they want is to get their new device working. For example, a smart home gadget's setup might abruptly ask for location access and present a dense privacy policy; if users are unsure or uncomfortable, they hesitate or quit the setup altogether. In short, the need to secure explicit consent (driven by privacy regulations and trust concerns) can clash with smooth UX, causing many new device onboarding flows to stumble at the consent stage.

Question 3

What do AI models need to predict path to purchase journeys?

Accepted Answer

AI (Artificial Intelligence) models can accurately predict path-to-purchase journeys only if they're trained on rich, integrated customer data covering all the key digital and offline touchpoints leading up to a sale. This means feeding the model a large volume of historical journey data — sequences of events like ad views, website clicks, social media interactions, and store visits — each tied to whether they eventually resulted in a purchase. The model also needs relevant context about each customer (such as demographics or past purchase history) and timing (like intervals between touchpoints) to learn which factors influence different paths. With comprehensive, well-organized data and clear purchase outcome labels, an AI system can recognize patterns in how a prospect becomes a buyer; without those inputs, any path-to-purchase predictions will likely be unreliable.

Question 4

What practices ensure path-to-purchase studies capture omnichannel behavior?

Accepted Answer

You have to design the study to follow customers across all touchpoints, not just one channel. In practice, that means using multiple data collection methods and breaking down silos. For example, combine surveys (or mobile diaries) that ask shoppers to recount every step of their buying journey with hard data from different channels (web analytics, app usage, in-store purchase records). This way, if a consumer searches online, clicks an ad, then buys in a physical store, your study captures each link in that chain. Make sure to prompt respondents about various channels ("Which of these did you use to research? Store visit, website, social media, etc.") so they remember less obvious steps. Also, ensure collaboration between teams - e-commerce, retail, and marketing data should be integrated so you're not missing parts of the journey. By triangulating self-reported behavior with actual transaction or click-stream data, and by intentionally looking at interactions between channels, your path-to-purchase study will reflect true omnichannel behavior rather than a siloed view.

Question 5

Why do NPS-defined promoters defect without complaining or contacting support?

Accepted Answer

Even loyal-seeming customers can leave quietly because being a "promoter" in NPS terms doesn't lock in their future behavior. A customer might give you a 9 or 10 on an NPS survey (meaning they'd recommend you) and still defect later without a peep if something changes for them or a better option appears. Often, these promoters don't complain because, in their eyes, nothing dramatic went wrong - they simply found a product that suits them better or their needs evolved. Many people won't contact support unless they hit a serious problem; if their switch was due to convenience, price, or a feature elsewhere, they just move on silently. It highlights that NPS captures sentiment at a moment in time, but loyalty can be fragile. That's why it's important to also monitor behavioral signals (usage drop-off, purchase frequency changes, etc.) and proactively check in with high-value customers. Promoters defecting silently remind us that companies must continuously deliver value and pay attention to subtle signs of waning engagement - not just rely on high survey scores.

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Market Research FAQs with Expert Insights

Besides lowest unit price, what else can win industrial purchase decisions?

Why do consent prompts still derail setup flows for new devices?

What do AI models need to predict path to purchase journeys?

What practices ensure path-to-purchase studies capture omnichannel behavior?

Why do NPS-defined promoters defect without complaining or contacting support?