Imagine someone trying to buy a laptop. They have shortlisted three models with similar prices, designs and technical specifications. The more they research, the more information they collect, yet they still cannot make a decision.
Now imagine you are the Product Manager of the online shop. Should you show fewer options, recommend one model as the best choice, or build a more sophisticated comparison tool?
None of these solutions is necessarily sufficient on its own. The problem may not be a lack of information. The customer may simply struggle to understand which information matters for their particular needs.
That distinction is at the heart of a challenging product-sense interview question.
The Interview Question: If a User Is Confused Between Several Similar Purchase Options, How Would You Help Them Decide?
The interviewer is not simply looking for an idea for a product comparison page.
They want to understand whether you can distinguish the underlying user problem from its visible symptoms, prioritise a specific customer segment, and propose a solution whose effectiveness can be tested.
A strong answer demonstrates how you frame the problem, which assumptions you make, how you evaluate competing approaches, and what evidence would persuade you to change your mind.
First, Understand Why the User Cannot Decide
Difficulty choosing a product does not always mean there are too many options.
Products may look similar while having meaningful differences that customers struggle to recognise. Important specifications might be scattered across separate product pages. Technical terminology may be unfamiliar. Information about warranties, total costs or return policies may be missing.
A customer might even understand every specification but remain unsure which attributes matter most for their intended use.
It is therefore useful to distinguish three problems.
Product discovery: The customer has not yet eliminated unsuitable options.
Product comparison: They have identified several plausible products but cannot see the differences that matter.
Decision confidence: They understand the differences but remain concerned about making the wrong choice.
These problems require different interventions.
Relevant filters can address discovery. Structured comparisons can address difficulty evaluating alternatives. Clear warranties, return policies and trustworthy product information can help customers assess purchase risk.
Research published by Nielsen Norman Group distinguishes between decisions based on non-negotiable criteria, such as a maximum budget, and decisions involving trade-offs across multiple attributes.
That distinction gives us a useful starting point.
Define the Problem for a Specific Customer Segment
A weak interview response might begin with: “I want to make choosing easier for everyone.”
But a technically experienced customer and a first-time buyer do not necessarily need the same assistance.
An experienced laptop buyer may want to compare processors, memory configurations or connectivity. A first-time buyer may simply want to know which device is suitable for everyday work.
For this exercise, let us assume that our primary segment consists of people buying their first mid-range laptop for work and general use.
This is an analytical assumption, not a finding from actual research. If evidence shows that another segment experiences the more serious problem, we should reconsider our priorities.
Before designing anything, I would investigate several questions.
What is the customer trying to accomplish with the purchase? What is their budget? Which requirements are essential? Are they most concerned about performance, durability, price or after-sales support? How do they currently compare alternatives?
We could investigate these questions through search and browsing analytics, navigation between product pages, short customer interviews, support enquiries and reasons for product returns.
However, repeatedly visiting product pages does not prove that someone is confused.
They might be waiting for a discount, discussing the purchase with another person or researching the market without intending to buy immediately.
Behavioural data provides clues. Qualitative research helps us interpret those clues without assuming they reveal the customer's motivation.
Start With Essential Requirements, Not Product Scores
Before ranking products, we need to understand how customers evaluate them.
For a laptop buyer, budget, software compatibility and an acceptable warranty might be non-negotiable requirements.
Weight, battery life and display quality might instead be preferences involving trade-offs.
These should not be treated identically.
A laptop that exceeds the customer's budget should not automatically become the preferred choice merely because it has a faster processor.
Conversely, someone who carries their laptop every day may value lower weight more than a marginal performance improvement.
A sensible decision process is therefore to eliminate unsuitable products first, then clarify the meaningful differences between the remaining alternatives according to the customer's priorities.
There is no need to declare a universal winner.
Sometimes the most useful outcome is allowing customers to understand why two similar products differ and which trade-offs they would be making.
Three Possible Solutions and Their Trade-offs
Option One: Direct Product Comparison
Customers select two or three products and view their most important attributes side by side.
Total price, weight, battery life, warranty and relevant technical features appear in a consistent format.
A “Show Differences” option removes repetitive information and draws attention to attributes that distinguish the products.
The main advantage is transparency and customer control. People decide which products to compare and which factors they consider important.
The limitation is that a technically accurate table may still be difficult to understand.
If customers cannot interpret the specifications, we may simply have organised their confusion into a neater layout.
Option Two: Needs-Based Buying Guidance
Instead of immediately presenting technical specifications, we ask a few short questions.
What will the laptop mainly be used for? How important is portability? Is the budget flexible?
Based on the answers, the service narrows the options and explains why particular products may be suitable.
This approach could offer greater value to less experienced customers.
However, it also requires more design work, maintenance and validation. Poorly chosen questions or incorrect interpretations of customer priorities could produce misleading recommendations.
Importantly, customers should be free to bypass the guided process.
Option Three: An Intelligent Product Recommendation
A recommendation system could use customer behaviour, product attributes and potentially a scoring model to suggest a particular item.
Such a feature may eventually be useful, but it would not be my first choice under our current assumptions.
Reliable recommendations depend on complete product information, appropriate decision criteria and the ability to explain why a recommendation was made.
Without those foundations, a system may present an unsuitable suggestion with unwarranted confidence.
For relatively expensive purchases, describing one item as the “best choice” without sufficiently understanding the customer's circumstances could undermine trust.
Which Solution Would I Choose?
For our assumed segment of first-time laptop buyers, I would initially prioritise essential filters and a straightforward comparison of two or three products, rather than developing a sophisticated recommendation engine.
Customers should be able to exclude unsuitable alternatives, select a manageable shortlist and examine the differences in plain language.
The initial comparison should emphasise attributes relevant to the purchasing decision while keeping full technical specifications available for customers who want more detail.
This is consistent with recommendations from Nielsen Norman Group about limiting the number of compared items, presenting consistent attributes and enabling users to identify differences.
Baymard Institute's research also highlights the importance of displaying both general product information and attributes specific to a particular category. Missing information can prevent customers from recognising products that might suit them.
Nevertheless, this recommendation is conditional.
If research shows that the primary difficulty is understanding technical concepts rather than comparing products, a short needs-based buying guide may be more effective.
Practical Example: An Online Laptop Shop
Hypothetical scenario
Suppose a Product Manager at an online electronics retailer notices that some customers repeatedly move between similar laptop product pages before leaving without making a purchase.
The commercial team suggests highlighting the best-selling models.
The design team proposes a comparison table.
The engineering team suggests building an intelligent recommendation feature.
The Product Manager should not choose purely according to which proposal sounds most attractive.
Working with a data analyst, they first investigate where the behaviour occurs and which categories or customer segments are affected.
They then collaborate with a product designer or researcher to interview customers and identify the obstacles preventing a decision.
The research might reveal that customers cannot understand the practical difference between two laptops.
Alternatively, customers may discover delivery charges only at checkout, making it difficult to compare total prices. Warranty information might also be inconsistent across listings.
In those circumstances, building an intelligent recommendation engine would not address the fundamental problem.
The Product Manager therefore proposes a small experimental comparison experience.
Customers can select up to three laptops, inspect relevant attributes side by side and choose to display only the differences.
Unfamiliar technical terms are accompanied by brief explanations. On mobile devices, the number of products displayed simultaneously is reduced to preserve readability.
Engineering and catalogue operations must ensure that specifications, prices, availability and warranty information are sufficiently accurate and current.
Missing information should be labelled as unknown, rather than being presented as evidence that a feature is absent.
The Product Manager is responsible for framing the product problem, identifying the intended customer and business outcomes, and evaluating whether the solution creates value.
A Project Manager, where the organisation has that role, may coordinate delivery, timelines and dependencies.
Within Scrum, the Product Owner is accountable for maximising the value resulting from the Scrum Team's work and for effective Product Backlog management, as described in the Scrum Guide.
These responsibilities may overlap in practice, but the roles are not conceptually interchangeable.
What Must Not Be Sacrificed for Simplicity?
There is an important distinction between helping customers choose and covertly steering them towards products the business prefers to sell.
Imagine a retailer promoting a laptop because it generates a higher margin, while labelling it “Recommended for You”.
If the ranking is not genuinely based on customer needs, that presentation may be misleading.
The UK Competition and Markets Authority's evidence review discusses how online choice architecture, including rankings, defaults and visual presentation, can influence consumers and sometimes work against their interests.
Commercial placements and sponsored recommendations should therefore be distinguishable from personalised suitability recommendations.
Total purchasing costs, important limitations and essential product information should remain clear.
The objective is to reduce the effort required to make a decision, not to remove the customer's control over that decision.
How Would We Measure Success?
An increase in clicks on the “Compare” button would not, by itself, demonstrate success.
Customers might use the tool more frequently because the purchasing journey has become harder, rather than easier.
I would evaluate the intervention across three levels.
1. Successful Task Completion
During moderated usability testing, we provide participants with a purchasing scenario containing defined needs and constraints.
We then observe whether they can select a product consistent with those requirements and explain the reasoning behind their choice.
The task-success rate would be the number of participants making a suitable selection divided by all participants attempting the task.
We would also examine decision time, requests for assistance and whether participants correctly understand the differences between products.
Self-reported confidence alone is insufficient. A customer may feel confident about a decision that does not actually satisfy their requirements.
2. Behaviour in the Live Product
We could examine purchase conversion among eligible shopping sessions.
For example, eligibility might be defined as sessions in which customers view at least two comparable products.
The conversion rate would be the number of eligible sessions resulting in a purchase divided by the total number of eligible sessions.
We would define session boundaries, purchase-attribution windows and the relevant customer population before conducting the experiment.
With sufficient traffic, a controlled experiment comparing the new experience against the existing one could help establish whether the change affects purchasing behaviour.
However, improved conversion alone would not prove that customers are making better decisions.
3. Guardrail Metrics
We should monitor product return rates, order cancellations, support enquiries about incorrect specifications, complaints about unexpected costs and post-purchase satisfaction.
Where appropriate to the retailer's policies, we might use a 30-day observation window for returns, comparing orders only after they have completed that window.
If conversion increases while returns and complaints rise, the apparent improvement should be questioned.
Results should also be segmented by device, customer group and product category.
Otherwise, an improvement in the overall average could conceal a deterioration for the users we originally intended to help.
What If You Had Only Two Weeks?
This is one of the two follow-up questions included in the original interview-question reference.
With only two weeks available, I would not attempt to deliver a complete decision-support platform.
During the first week, I would analyse existing customer behaviour, conduct a small number of targeted interviews and work with a designer to create a simple comparison prototype.
During the second week, I would test that prototype with people from the target segment.
The objective would be to establish whether customers understand the relevant differences more clearly and can select products that satisfy the scenario's requirements.
If the prototype proves understandable and the underlying product data is dependable, I would plan a limited live experiment.
Otherwise, I would revise the problem hypothesis or the design before investing in further development.
Two weeks can provide meaningful early learning. It will not necessarily establish long-term commercial effects or changes in return rates.
What Evidence Would Make You Reject Your Proposed Solution?
This is the second follow-up question from the source material, and it tests whether the candidate can respond intelligently to contradictory evidence.
Several findings could change my recommendation.
If users still cannot explain their choices after viewing the comparison table, we may have reorganised information without improving understanding.
If customer interviews reveal that the main concern is authenticity or after-sales support, the priority should shift towards trust and clearer purchasing information.
If product attributes are incomplete or inconsistent, the underlying data should be improved before relying on comparisons. A comparison based on incorrect information can be worse than having no comparison tool.
If purchase conversion improves but returns or unsuitable purchases increase, I would question whether the intervention creates genuine customer value.
Finally, if the target audience needs guidance about intended use more than a comparison of specifications, I would shift investment towards a needs-based buying guide.
Good product judgement involves being willing to abandon an appealing idea when the evidence no longer supports it.
A Natural Interview Answer
Here is how I might answer the original question in an interview:
“First, I'd want to understand exactly why customers are struggling to choose. Is it because there are too many options, because the differences aren't clear, or because they don't trust the information?
“To keep the problem manageable, I'd initially focus on people buying a relatively expensive product, such as a laptop, for the first time.
“I'd combine behavioural data with a few short customer interviews to understand the source of the hesitation. If comparing products is the main difficulty, I'd start with a simple tool that lets customers compare two or three options, see the meaningful differences and assess them against their own needs.
“I wouldn't begin with an intelligent recommendation system because I'd first want to establish that the product data and decision criteria are reliable.
“I'd validate the initial solution through usability testing and then, if appropriate, measure its effect on purchasing behaviour and guardrails such as product returns. If people still can't make suitable choices, or if the quality of purchases deteriorates, I'd reconsider the approach.”
This demonstrates a defensible decision process rather than simply promising to build a collection of features.
What Makes an Interview Answer Weak?
“I'd use AI to recommend the best product.”
This jumps to a solution without establishing what the customer needs, whether the product information is reliable or how the recommendation would be justified.
“I'd put every specification into a comparison table.”
More information does not necessarily create greater understanding. A useful comparison should emphasise relevant differences rather than overwhelm customers with detail.
“I'd recommend the best-selling product to everyone.”
Popularity does not establish suitability. This approach risks substituting the retailer's commercial preferences for the customer's requirements.
“If conversion goes up, we've succeeded.”
That overlooks the quality of the decision, post-purchase satisfaction and the possibility of increased returns.
“All customers need a comparison tool.”
This ignores differences in experience, purchasing goals and the consequences of a poor choice. A simple, inexpensive item may not warrant a detailed comparison experience at all.
As Reforge's discussion of Product Manager interviews illustrates, strong candidates explain their assumptions, articulate their decision-making logic and demonstrate professional judgement.
Knowing the names of popular frameworks is not a substitute for those abilities.
Conclusion
When a customer struggles to choose between similar products, the Product Manager's first responsibility is not to select an item on the customer's behalf.
It is to discover what prevents the decision: too many unsuitable options, unclear differences or insufficient confidence in the available information.
For relatively complex purchases, my initial recommendation is to eliminate unsuitable choices and provide a simple, transparent comparison based on the customer's own priorities. This can improve the information available for a decision without prescribing a particular outcome.
But if evidence shows that the actual difficulty lies in understanding purchasing criteria, unreliable product information or perceived purchasing risk, the approach should change.
The goal is not merely to make customers purchase more quickly. It is to help them make choices that genuinely suit their needs and are less likely to result in regret.
Sources
Nielsen Norman Group — Comparison Tables for Products, Services, and Features
Baymard Institute — Product Listing UX: What Information to Display
UK Competition and Markets Authority — Evidence Review of Online Choice Architecture
Reforge — 7 Product Manager Interview Questions From Top Product Leaders
