Starting Up pt. 2
So far, some of the projects that I have been working on using Data Sciences have all been business related. Since I am working with a Start up (I will refer to it as InCahoot, I’m not sure why I chose that name) that has not yet launched, I have to be creative with the research and reports that I present to the CEO. Due to privacy constraints, I am limited in what I can share, but I will try to be as detailed as possible.
My first step was to create, together with the CEO, an objective listing all of the areas where the company can grow using analytics.
- They were using what was described as a shot gun approach to sign new clients — their clients are restaurants, bars, breweries, and pubs. The company has a product that needs these venues. Although they have been able to sign about 15 businesses in the Dallas area, the CEO believes that their approach of randomly choosing a place and emailing them is time consuming and not well thought out. So, my first task was to find a solution that could save them both time and money.
I created a list of things they need: customer data, venue data, and demographic data (according to zip code). My end goal was to create a profile of the average consumer that would fit our ideal profile. I had to get census data in order to achieve this goal and from there use inferential statistics to get a 5 number summary (minimum, first quartile, median, third quartile, and maximum). My 5 number summer included the following categories: age, salary, education, renter or home owner, ethnicity, sex, marital status, and a couple of more categories.
I chose zip codes as my parameters because with my experience in dealing with city data I have seen that it’s a pretty good indicator of the population that reflects a businesses average consumer.
My next step was to start doing some exploratory data analysis, using python, and start putting together some descriptive statistics for a quick review of the data collected.
Below are a couple examples:
The next step is to start seeing the frequencies and getting percentages to get an idea of the averages and figure out who lives in which zip code and how accurately does that reflect the businesses average consumer. My next blog will have more details on the second objective and how I plan to tackle on the next problem.
