Introduction: My sales team at a brand-new venue is looking to sell personal seat licenses to consumers and to do that we are searching for a demographic who will buy seat licenses that will give our team the most profit. Our objective is to find which demographics are most important and then figuring out a strategy to target those individuals as well as how to make the most profit off them which means we will need to know how many tickets to sell these people as well as how much to sell the tickets for.

 

Methods: The first step in identifying the target group was to get rid of useless data. Any individuals who did not fill out all demographics were considered not useful. Considering that there were thousands of data points, results could not be skewed because the data was still large enough to make an accurate conclusion. Now that the data had only useful data points, I could now run descriptive statistic models in order to get a general idea about what the data will be when it comes to demographics. The next step was to run a correlation model to help identity which demographics influenced the highest sales. I overlaid a heat map to easier identify which demographic correlated most with ticket sale. The highest was personal seat license but I dismissed it because they are both dependent variables. After running the correlation model a regression model was run to see how much change total sales would have on other key statistical data previously identified with the correlation model. Those would be home value and totals sales as well as price per seat license. This allowed me to see if there was a significant change between a change in home value would there be a significant change in price per seat license and total sales. Once that was finished the final step was to create pivot tables to understand which groups within the key performance indicators were contributing to the most sales. Out of those groups I also needed to know how much people were buying tickets to find the optimal price and how many they were buying at that price.

 

 

 

 

 

 

Results: Once all the different models and pivot table analysis was completed, it was revealed that home value was the most closely related to total sale and price per seat license (21.95% and 24.25%) and then it was followed by income (11.30% and 10.72%). Once that was identified the regression model was run which identified that there was a positive correlation between total sales and price per seat license with home value as well as income. The R squared shows that as income and home value changes it will contribute to the change in total sales by 4.8% and the change in price per seat license by 5.9%. This means that as individuals’ income and home value go up so does their total sale numbers and their price per seat license value, but only slightly. This allows me to speculate that as people gain more money, they buy more expensive tickets. This would be later confirmed by the pivot tables. From the pivot tables, I found that 40% of total sales are to those who make between $141,000 to over $500,000 a year. While 20.68% of sales were to people with homes over $1 million. This shows that income is the most important determinant to increase sales. Most of that bracket of income were males between 42-65 which accounted for 24.42% and 40.70% of that income bracket owned homes over $1million. From the 42-65 age group I found that 50.8% of those people were married. Once knowing who bought the most tickets, I wanted to know at what price people were buying tickets at and I found that the most sold tickets were at $6,000 accounting for 13.99% of tickets. More interestingly about the statistic is that out of the 13.99% the pink division accounted for 12.79% of sales. The next thing to find out was how many tickets people were buying and I found that 55.09% of all ticket sales were bought as a pack of two. This confirms that as people increase income, they are not buying more tickets they are just buying more expensive tickets. In the income bracket of $141,000 to over $500,000 the most tickets were sold at a price per seat license of $3000 covering 9.45% out of those tickets 5.05% of those tickets bought were as packs of 2 that groups ticket sales followed by $15,000 at 6.29% with 4.43% of that being bought as packs of two. This can lead us to believe that there is no increase in the amount of tickets bought as income goes up rather that there is only an increase in the value of tickets that are being bought. 

 

Discussion: In conclusion, most of the ticket sales efforts should go towards males between the ages of 42-65 that make between $141,000-$500,000 who own homes over $1 million. This would target male-dominated high paying industries such as software developers, financial analysts, aerospace engineers, and architects that are senior managers or above in high luxury communities. For these people we would be looking to target them to buy for the pink division since it is in higher demand then we can charge more. For these tickets we can either make deals in packs of two to entice more buying or target married couples with no kids since most people in that bracket buy tickets in two and are married. With these strategies, we can increase profits that provide value to these high-income males because they are the largest contributor to sales currently and in the future, I predict they will continue to do so.