Priya Marie Ross

Summary

Situation: The company needed data rather than assumptions to drive business decisions on the number of customers that could be realistically accommodated at our flagship location.

Task: Develop a data-collection project from scratch to gather detailed information on the in-person customer journey.

Action: Created a Google form for interns and temporary staff to enter data over a two-week period, collecting time stamps for each phase of the customer’s visit. Analyzed the data for trends and insights.

Result: Presented findings to the strategic team, recommending a push to digitize forms to reduce customer wait times.

Details

For over half a year, the strategic team made business decisions on visit goals and expectations at the flagship location based on assumptions rather than data. Questions surrounded the customer journey, such as the actual duration of each visit stage, where dead time occurred, potential improvements, and the ease or difficulty of implementation. Ongoing disagreements due to a lack of data led to postponed decisions, which were not in the company’s best interest.

I was selected to lead the data collection project to obtain the missing pieces of the puzzle. I produced a simplified version of a Google form that had been used months earlier during a test run, making it easier for the data-collecting staff. Over two weeks, two interns and two temporary staff covered all shifts at the location, collecting time stamps at every stage of the customer journey using the Google form. My intern then cleaned, analyzed, and reported on the data with our recommendations.

Major insights included:

• Significant gaps between actual and planned visit durations

• Performance differences when key staff worked solo versus with a partner

• Technology recommendations to decrease wait times

• Data suggesting that increasing support staff could significantly reduce overall visit durations

While questions remained about how to define capacity (customer throughput vs. physical capacity), whether our goal times were realistic, and where we could reduce inefficiencies without sacrificing service quality, we were finally able to establish a baseline. This baseline will become increasingly accurate over time as the investigation is replicated.