Data Science & Business Strategy
Andrew Olton
262

Hi Andrew, this is an excellent first attempt at using data science to shape business strategy. In these types of scenarios with limited data, I look for business value by first eliminating what I’d expect to see from the data, and then discussing things I wouldn’t normally expect.

For instance, I’d expect demand to be high

  • on workdays
  • among loyal customers (read ‘registered users’), and
  • when weather conditions are favorable.

I’d also expect to see reduced demand with

  • high humid days
  • holidays, and
  • days with bad weather conditions.

When there are spikes in demand, I’d expect that things return to normal immediately. (Hint: it doesn’t always work that way)

For this challenge (with its detailed weather information), I think the business strategy comes from the question:

“When should supplies be dynamically adjusted?”
In this context, “supplies” refers to both bikes and types of customers that ride.

In answering this question, I love the data science work that you’ve begun. To find insights I first looked at the Correlation Matrix for Bike Rental Demand. I focused on any unexpected behaviors in the last 3 rows as they represent things the business either can change or reasonably apply effort to change: count, registered, and casual. Here are some of my observations

  1. when temperatures are favorable, less loyal customers (read ‘casual riders’) show better demand [Customer Acquisition Impact]
  2. Casual ridersprefer holidays and avoid workdays, while ‘registered ridersprefer workdays and avoid riding on holidays [Marketing & Promotions Impact]
  3. Casual riders’ give more thought to how humid it is, when they decide to use the service [Marketing & Promotions Impact]

In examining the graph of rental fluctuations around holidays I saw some interesting anomalies:

  1. Before the dip in demand on Independence day, there’s a noticeable surge in demand in both 2011 and 2012 [Marketing & Promotions Impact]
  2. There are two dips in the first weeks of 2012. Could one of these dips have been caused by severe weather conditions? [Inventory Impact]
  3. There’s a noticeable difference between rest-focused holidays and more commercialized holidays [Marketing & Promotions Impact]

So as I said at the start, this is a good first attempt at discovering business strategy from data science.

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