The Preference Gap and what clicks leave unanswered

A customer visits your running shoe category three times, has bought Nike before, and regularly opens emails about running.

You can see what they do. But do you know how often they run, whether they value comfort over speed, or whether the shoes are even for them?

This is what Roccai calls The Preference Gap: the distance between what a business observes about its customers and what it understands about their needs, preferences, and intentions.

The gap matters because businesses use customer knowledge to decide what to recommend, what to say, and when to follow up. When important needs remain unknown, those decisions rest partly on assumptions.

A click can have several explanations

Behavioural data is useful evidence. A product view tells you what someone looked at. A purchase tells you what they bought.

The reason behind the action is less certain.

Someone might buy premium running shoes for performance, comfort, a discount, or a gift. A previous Nike purchase does not necessarily establish brand loyalty. Repeated visits might reflect interest, confusion, or a comparison that ends elsewhere.

The risk appears when a plausible interpretation becomes an unquestioned fact. If a business treats someone as a performance-focused runner, that assumption can shape every recommendation they receive.

Asking relevant questions adds another layer of knowledge: what the customer says matters to them.

These signals work together. Answers help explain behaviour, while later interactions can help a business assess whether its guidance was useful.

When customers intentionally share their preferences and purchase intentions directly with a brand, those answers are known as zero-party data. [1] They add context to clicks and purchases, although preferences can change and an intention to buy is not a commitment.

Four questions to uncover The Preference Gap

Roccai's framework helps teams separate evidence from assumptions and decide which questions are worth asking. Here is how it works, using an illustrative running shoe example.

1. What do we know?

Start with information you can support. The customer visited running shoes three times, bought Nike before, and opens emails about running.

Keep the wording precise. “Bought Nike before” describes evidence. “Prefers Nike” adds an interpretation.

Include what customers have already told you through enquiries or previous interactions. You may know more than your analytics alone can reveal.

2. What do we assume?

Write down the conclusions you draw from those facts.

You might assume the customer runs frequently, prefers Nike and prioritises performance. Each is possible, but none has been confirmed by the behaviour above.

Focus on assumptions that affect a decision. Ask yourself: If this assumption is wrong, what would we do differently?

If a belief about performance determines which shoes you recommend, finding out the customer's actual priority could change the experience.

3. What should we ask?

Identify the missing information that would help the customer make a choice.

For someone exploring running shoes, this might include how often they run, where they run and whether comfort or speed matters most.

These questions become part of a guided shopping experience. The customer shares what matters to them and receives recommendations informed by those preferences and the available product information.

Imagine they answer: once or twice a week, comfort first, mainly on roads. Those answers can help narrow the options and explain why particular shoes may suit their routine.

The customer gets help choosing. The business gains context it could not reliably infer from a category visit alone.

4. What will we do with the answer?

Plan how the information will support both the current choice and the next interaction.

In the running shoe example, the same preferences could inform a recommendation, add context to a lead, and help the business tailor a follow-up email. Across participants, the responses could also reveal recurring priorities worth considering in campaigns and product communication.

To make this useful beyond the initial recommendation, answers need to be organised into structured information that teams can interpret and apply.

Decide which answers should inform product matching, which should enrich customer profiles and which could support audience segmentation. Each question should have a clear role in the experience or a subsequent business decision.

Make answering useful for the customer

Customers need a reason to share. Helping them choose among unfamiliar products creates a clear exchange: they explain what matters and get more relevant guidance.

Roccai supports this through interactive guides that collect preferences and stated intentions, turning responses into structured insight businesses can use.

Customers need a reason to share. Helping them choose among unfamiliar products creates a clear exchange: they explain what matters and get more relevant guidance.

Roccai supports this through interactive guides that use customers’ preferences to help them explore relevant options. Their responses become structured preference and intent data that businesses can use in recommendations, communication and follow-up.

Roccai Growth Intelligence helps teams understand those responses and interactions, so the insight can create value beyond the initial product match.

Choose one customer journey. What do you know, what are you assuming, and which answer would change your next decision?

Source

[1] Forrester, Ask, Don’t Interrogate: Best Practices For Collecting Zero-Party Data. https://www.forrester.com/blogs/ask-dont-interrogate-best-practices-for-collecting-zero-party-data/

Related reading: How brands can stay close to customers beyond the website