The company wanted to hire more people to manage phone bookings because they had a surge of customers. However, the founder was wary. Was it the right decision to hire more people? Was there a better way?
They provided 10 years of their phone call data. Using Tableau, a simple graph and report were generated. Instead of hiring, what if the shifts was rearranged? Shifts were reworked such that during peak hours, there would be two shifts overlapping allowing two people to man the phones during the period.
At the time, I did not have much information about data science. This data was not cleaned. It was just utilized as a whole. This was 10 years of data. I would first have just used the last 3 years to get a better gauge of their peak period. Having 10 years of data causes the graphs to become a normal curve. Furthermore, these phone calls were all not just for booking. Some of them would have been for customer support or other inquiries. So the data was not true to purely booking slots for the indoor field.
If this was to be done again, data cleaning would be essential. Ensuring to answer the question of changing shifts to help with bookings, the data utilized was purely from those situations. Furthermore, I would have looked into their online booking. Was there something missing? How can I get the majority of people to book and manage their slots online rather than on the phone? This would reduce the load on manning the phones as well.