Problem Solving Interview Questions: A Feedback-Data Case

Read the case before choosing a solution

A community booking team receives feedback about two service windows. Morning has eight complaints; afternoon has twelve. Someone proposes moving staff away from the morning because the afternoon has more complaints. No transaction totals are initially provided.

Your task is to assess that recommendation. The counts show that afternoon has four more recorded complaints. They do not show the complaint rate, workload, cause or staffing requirement. A larger number can come from a larger number of interactions rather than a worse experience.

Two plausible hypotheses are: afternoon has more interactions, or afternoon has a process problem that produces more complaints per interaction. Both might be true. Do not pick the second because it makes a more dramatic story.

Ask for the comparison denominator

For a rate comparison, ask for the number of relevant interactions in each period, counted consistently, and how complaints are recorded. Check that both periods cover comparable dates and that duplicate reports are handled on the same basis. These are questions about the fictional data, not a universal survey design.

The case now supplies 80 morning interactions and 240 afternoon interactions for the same week. Each complaint count refers to one interaction with a complaint, using the same definition. No interaction has been counted twice. Those supplied assumptions allow the following arithmetic.

Update the recommendation without overclaiming

Practice answer: “I would not move staff solely from the initial complaint counts. With the supplied interaction totals, morning has a 10% complaint rate and afternoon 5%. I would review the complaint categories and process conditions in both windows before recommending a change. The data changes the comparison, but it still does not tell us whether staffing caused the complaints.”

This is stronger than declaring that morning is definitely understaffed. Staffing levels, interaction complexity and complaint categories remain unknown. A rate can identify a question worth examining without proving the explanation.

If asked for a next step, propose a bounded review of representative records using the organization’s approved access and privacy arrangements. Compare the recorded reasons and check whether the same issue appears repeatedly. Do not bring real customer records to an interview.

Design a small test of one hypothesis

The fictional record review finds that several morning complaints mention unclear confirmation wording. A possible test is to have the responsible team approve a clearer confirmation message for a limited period, then compare the relevant feedback and completion evidence using consistent definitions.

State the limits: a different mix of interactions or another process change could affect the result. The exercise does not provide test outcomes, so you cannot report an improvement. You can say what you would observe and how the findings would inform a next recommendation.

If later evidence shows the wording was already clear and complaints concern waiting instead, revise the hypothesis. Problem solving is not defending your first explanation after its basis changes.

Turn the reasoning into a behavioral answer

For a past-experience question, choose your own event and explain the decision you personally influenced. Harvard’s interview guidance supports preparing specific experience examples. Keep the sequence clear: initial observation, missing comparison, check, revised action and observed result.

Ask a practice partner to challenge the denominator, cause and decision separately. If your answer uses a percentage, confirm its numerator and denominator. If it attributes a team outcome to you alone, correct the scope. Useful reasoning can end with a justified next check rather than an invented dramatic success.