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What aspect of the internship experience added to your personal development?
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I had a short meeting with my mentor about two weeks ago to discuss wrapping up my time here with the organization. He basically just had to ask if there was anything bad about my experience, which there most certainly was not. I explained that life was getting busy for me because of trips and figuring out my situation for next school year when I complete my MBA. I have surpassed the hours that are required for my experience and will most likely find another part-time job that makes me more money. My internship was only minimum wage, but the experience certainly paid off. However, I want to keep myself busy by making sure I am still earning profit while I am spending money on food and trips. Most likely, I am going to attend The Ohio State University, so I have begun the apartment search. My mentor wanted me to come in for a few hours today to wrap things up. I updated a few sheets for the last time and went through every document with him that I had worked on during my time here. Him and I sat down and went through every document and he made note of when I last updated each one. He also collected my badge and had me fill out any paperwork he needed me to do for the accountant team in terms of payroll and clocking in. I am very thankful for my time here at The Toledo Mud Hens and Walleye because of the amount of experience I have gained during my time here. I look forward to seeing where my life takes me and where I can use the experience from my internship in the future.
Today I am only able to be in for a few hours because of how busy my schedule is this week. The first thing I did when I came in is update the text data. There were only two data entries to input for the Walleye side of things, so I went ahead and entered the total sent, total delivered, opens, and opt out for each. Then, I entered those cells into the times and days of the week to find correct averages for each. I went over and asked my mentor about the meeting we are supposed to have to start wrapping up my time here, which he said he will set up for some time tomorrow virtually since his schedule is about as busy as mine. I noticed that there have not been games since I last updated some other sheets, so I did not have to do anything with the ticket dales by days, ticket usages by groups, or postgame surveys. Instead, I updated all the numbers and added new entries in the ROI sheets for both Walleye and Mud Hens. For the last thing I could even do at this point was look back over the weather data I compiled. I thought it would be helpful for me to figure out the average number of tickets for each game based on whether there was rain or no rain on the radar. For example, I started with the 10 days out by separating the days and having a row of with rain and a row of without rain. I entered the number of tickets that were totaled for each particular day and divided those by how many games applied to each. For the 10 days out on Friday with rain there was a total of 12,981 tickets sold and a total of six games fell into that category. I used Excel formulas to find 12,981/2= 2,163.5 tickets bought for every one game.
Today was another day of catching up on files. I started by entering the text data entries that I was missing, but there was only one to add, so I decided to wait to add this to the graphs for when I have more data at a time. I went through all the files I have worked on to see if there was anything else that could be updated. The ROI sheet had some entries to be added and one to mark as complete. Then, I moved on to work on the postgame surveys. I forgot to update these files when I updated the sales by days out file, so I had quite a bit of work with them. I first opened the postgame surveys specifically for food and beverage. In this file, I added in the dates of games I was missing along with the data to complete these. Next, I moved on to catch up on the postgame surveys that were general to all departments of the games. Not only did this include food and beverage, but also merchandise and in-game entertainment. Similar to the food and beverage surveys, this file was not updated when I did the sales by days out file, so there was a bit of data to fill in. Another survey I updated data was for the season ticket holders. Once I got done with this, I believe I am all caught up on everything. Since next week is a busy week for me, I am only going to be in the office for either one or two days. Sometime during those days I will be having a meeting with my mentor about my internship.
Today I continued to work on the ticket profile sheet I started yesterday. However, I worked on the top zip codes areas section that I had not started, yet as of yesterday. In order to complete this, I first went into StellarAlgo to pull up the geo map that shows a heat map of the zip codes that buyers live in. The list of top five on the sheet is sorted by revenue, so I first select the sort by revenue filter on the bottom of the page. This way, I am able to see the order to put the zip codes in as well as the revenue column. Once I fill all of this data in, I go back to the bottom of the page and select the sort by ticket count filter to fill in the next column. After this data is filled in, I move on to open a spreadsheet my mentor sent me that filters out whether the season ticket holder is company or individual as well as full season or partial season. I created a pivot table to sort this information out a little easier for me and pulled the zip codes that I was looking for. I filled in the company and individual numbers for each of the zip codes, which automatically summed up the number of accounts there were for each zip code. I repeated these steps for the partial season section of the ticket profile sheet. Finally, I filled in the data that I had left for the single game section all through StellarAlgo since there was no division between company and individual.
My mentor wanted me to help him do a couple tasks today. The first one was for the ROI sheet I have been working on for both the Mud Hens and Walleye. With this sheet, my mentor wanted me to first divide up the different categories that were there. First, I looked at the 2021 season for Mud Hens and split up the categories like opening night and Columbus buyers. Then, I sorted the entries by revenue from highest to lowest in each of the categories. I did these same steps for the 2021-22 Walleye season as to what numbers were at the time. Once I got done sorting the lists, my mentor sent me a message on Teams to see him about another job he had for me to do. This new job required me to retrieve information through StellarAlgo and CRM for tickets purchased for the 2021 Mud Hens season in each category of full season packages, partial season packages, and single game purchases. For each of those categories, I had to find numbers for age groups, revenue of those individuals, and their distance from the ballpark all through Stellar. After I retrieved all of that data, I went ahead and made graphs comparing the numbers in each category since my mentor had not shown me how to get the rest of the data on the sheet. The remaining data involved the top zip code areas that purchased the different tickets, which I will then do another set of graphs to compare those numbers.
I did a lot of catching up on files that had been put on hold with my weather project going on. I updated the ROI sheets, the text message data, season ticket usages, single ticket usages, group ticket usages, ticket sales by days out, and postgame surveys. Another thing I did was write up a Word document that had all the notes I took from my data analyzing of the weather project. That way, my mentor will be able to quickly see everything I analyzed from breaking down the data. In addition, I also redid the formatting slightly for the heat tables I created by having it be based on the whole section of numbers in that column. I previously had each month heated separately, but I thought it looked better when the numbers were all compared together instead. Then, the viewers are able to clearly see that July has the most ticket sales and it almost disintegrates outwards. By this, I mean that the months around July (June and August) have the next most ticket sales. Following, the months around those (May and October) have the least amount of ticket sales. I asked my mentor if he had any thoughts about another way I could visually show the data I collected, and he said I should maybe focus more on precipitation rather than temperature. Also, he thinks I should focus on the weekend since those are the days we accumulate more revenue. With his advice, I will create several charts that compare the ticket sales for rain predicted and no rain predicted.
When I came in today I got to work on finding the best way to make the data that I analyzed for the weather and ticket sales into a presentable format. To do so, I started with a column of tables that did not take precipitation into consideration and had a table for each month of the season. The rows for the tables were split up in ten degree increments of temperature while the columns for the tables were the days of the week. I entered the ticket sales that applied to each spot of the table, and if there were multiple entries, I took the average of those. Once I had all the numbers entered in the table, I used conditional formatting to create a heat table that had the dark green as the most ticket sales and the yellow as the least amount of ticket sales. The ticket sales that were in the middle of the spectrums were a mix of the two colors. With this kind of formatting, it is easy to see what the approximate ticket sales should look like without taking precipitation into consideration. To then take precipitation into consideration, I made one column of tables that had precipitation the day of the game and another column that did not have precipitation the day of the game. With these new tables, my thought process was that if someone sees that it is supposed to rain the day of the game then they can look at the tables that have precipitation. Then, they would look at the table for that specific month as well.
For my first day back in the office, I first got my notes and everything caught up from my time out of the office. My mentor wrote me in Teams that he noticed the Walleye season ticket holder survey data most likely needed updated from Epifany because there was a lot of results that had been submitted recently. Once I updated all of that data, I saw my mentor made our meeting only a half hour instead of an hour to briefly go over survey data results, more specifically, food and beverage. In preparation for the meeting, I spent the afternoon taking notes from analyzing the food and beverage sections from surveys that were completed so that I could be ready for the meeting. My mentor even came over and mentioned that I should not be afraid to speak up during the meeting at all if I noticed anything in the data that is not brought up in the meeting. My mentor was even able to give me a copy of the notes he has written down for each slide so that I could add notes if I felt there was anything to add as well. The meeting overall went well and there was even discussion on introducing mobile ordering for the Mud Hens season to reduce wait times for lines since this is a common complaint from surveys. I was able to chime in and mention the ticket sales on the comparison of the food and beverage satisfaction to see if the bigger crowds really have a negative impact on food and beverage satisfaction like we expected.
I worked from home again today because the county is on a level three. For the first half of the day, I continued to analyze the weather data that I collected. For the sheets for the ten degree increments, I wrote down notes for each of the months that had multiple data entries. The other half of the day consisted of a meeting that my mentor wanted me to join with him. The purpose of this meeting was to see if there was any data analytics that would be of use to anyone. The meeting basically consisted of another big project I should work on regarding our partnerships. They figured another coworker, Kevin would be the better option to pull the data from CRM because he works a lot more with it than I do. Once he pulls the partnership accounts from CRM, I will be the one to draw conclusions from the data like if there is a certain amount of time a company will partner with our organization before they pull out based on how big the partnership is. For example, maybe a 15,000 dollar partnership typically only stays for two years before they end it. I also need to categorize the partnerships into small, medium, and large based on dollar value. At the end of the project, the most important thing to get out of it is the relationship of number of accounts to revenue to assets. For instance, maybe it would be better for our organization to focus on less accounts with more revenue if they stay longer.
I chose to work from home today because of the blizzard coming, but turns out that it rained almost all day. I used my time today to begin analyzing the weather project I collected data for. I need to find some kind of pattern and eventually maybe come up with a chart showing how many tickets are expected to be purchased. I first created sheets within the Excel file for each day of the week we had games, which was every day other than Monday. In addition, I also made sheets with ten degree increments for another way of analyzing. For the sheets, I would paste every entry that applied to that sheet and at the bottom I wrote down a few notes that I noticed while I was analyzing. That way, if I have to put together a presentation, it will be easy for me to simply paste in the notes I already took. My notes for each of the days of the week were for each of the ten degree increments that were present. For example, on Tuesday, there were multiple games in each the 70s, 80s, and 90s. I have at least one note for each of them below the data. On the other hand, the sheets for the ten degree increments have notes based on the months that the data came from. The sheet that had the most amount of data was the 80-89 sheet since there was multiple entries of data from May, June, July, August, and September.
Today was a day all about catching up on projects. Since I finished entering the data for the weather turnstile project, all I need to do with it yet is analyze the data and come to results. I caught up on everything I had been missing data for while I was occupied with the main project at hand. I first did the ticket sales by days out data since there were four games that had passed since I last updated the list. Next, I updated the text message data that was sent out, which ended up being a handful of data for each Mud Hens and Walleye lists. Another set of lists I updated is the ticket usage sheets that are separated by season ticket, group, or single. I ran reports through TDC to pull the numbers that were needed for the data sheets. The last thing I did was update the ROI sheet to keep the numbers as current as possible. I had to go back and finish up some that were done tracking as well as add additional lists that I did not have on the sheet yet. Since I am now caught up on everything so far, I am ready to get started on the analyzing of the weather turnstile project. I wanted to make sure I started analyzing on a day that I had a lot of time ahead of me, so I did not want to start it quite yet. Therefore, I will work on that Wednesday and Thursday when I work from home with the blizzard coming.
Going into today, I wanted to get all the information done that was going to be discussed for the meeting that my mentor had us scheduled to attend later this afternoon. It only took me an hour or so to finish analyzing this data, which I then sent this on to my mentor so he could have a chance to look over it as well. In the meantime, I moved on to keep working on the weather turnstile project that I am working on. After hours of working on this, I finally get about halfway done with the data collection. I then receive an email about a meeting that my mentor scheduled. Turns out, this meeting is a rescheduling of the meeting that was supposed to happen today. I have not seen or heard my mentor in the office all day, so I assume this is the reason the meeting was pushed back once again. Since he is not in the office to give me additional projects, I continue to work on the weather turnstile project for the rest of the day. Hopefully, I will be able to finish the data collection today so that I can start analyzing the data next week. To analyze the data, my mentor hopes I will be able to predict a temperature cutoff that buyers start to not purchase tickets for the upcoming game. Additionally, I am looking at the days out of whether there is supposed to be any precipitation or not, which could also have an effect on the ticket sales.
I am back in the office today and made sure to leave early so that I was here on time because of the bad weather that had been going on. I was under the impression that I was going to have a meeting as soon as I came into the office, but I found out it was cancelled. Last night, I was thinking the meeting my mentor scheduled was an additional meeting, but it turned out that it was the same meeting that needed rescheduled. While I was talking with my mentor, he mentioned several tasks that need done before the meeting Thursday afternoon. I am now thinking that this could be why the meeting got pushed back. To complete everything my mentor wanted done, I had to go through Epiphany and pull the responses for questions that asked about interest in ticket plans, interest in hosting a group outing, or interest in renewing their ticket plan. All of this information could be pulled from the postgame surveys, lapsed buyer surveys, or the season ticket holder surveys. Once I got this done, it was time to move on to the next survey data to pull. This other survey was through Survey Monkey and was sent to single game buyers, group leaders, and season ticket holders for the Mud Hens. The kinds of questions that were asked include themes at future games, special events through Mud Hens, giveaway items at future games, characters or celebrity appearances at future games, zip code, email, age, having any children under 13, and if they would like to receive emails from organizations through the firm.
I was able to work from home today because of the bad weather since it snowed yesterday and is going to snow all day today. I just started a big project the last time I was in the office, so I had plenty to work on for the day. The project I worked on all day was the weather turnstile idea I brought up to my mentor. The main goal of this project is to find out if there is a temperature cutoff that people stop buying tickets. Another analysis I am looking at is how many days out for a chance of rain impacts ticket sales. In the meantime, however, I am simply finding the data and entering it into my Excel file I created. I have sections to include a range of weather prediction data from days before the event. I thought it would be beneficial to include the day of the game, one day before the game, two days before the game, three days before the game, five days before the game, seven days before the game, and ten days before the game. Along with each of these days for temperature, I also include whether there is any chance of precipitation. Precipitation could include rain or snow that can occasionally happen in the early and late season. To find the data, I go on the WTOL11 website and type in the search bar the date I am looking for. Next, I fast forward the video of the weather forecast to view the extended ten day weather forecast. Then, I am able to fill in the chart the days that I am able to fill in.
When I first came into the office today I finished up the new project I was asked to do about the local county sales. My mentor thought of the idea that I could create a heat map to visually show where the most sales come from, by county. To do this I first found a picture of the area separated by county on the internet and pasted it into my Powerpoint presentation I was using. Next, I created a shape by outlining the border and clicking to create dots wherever needed. Once the shape was created, I could change the color and transparency of the shape to match the corresponding data that was found. I finished this relatively quickly and my mentor had me show someone the presentation so that they could approve of the content and later show a client the advantages to become a sponsor of the organization. Once I made all the corrections he gave me, I got caught up on anything that was needed by going through text messages, ROI spreadsheets, and sales by days out. This did not take long to do, so once I finished all of this up, I went over to my mentor and asked if he had anything else for me to do or if I should get started on the big project we have been discussing lately. He approved of the idea that I should get started on the new project of discussion. In this project I will be looking at the weather forecast by days out of a home game and see if this impacts the number of tickets bought or revenue accumulated.
I did not have to work yesterday with it being Martin Luther King, Jr. Day and my mentor being out of the office for the day. Because he was not going to be in the office, he also did not have me work from home either. When I first came in today, I got to work with the new project my mentor introduced to me the last time I was in the office that I had not started quite yet. This project dealt with more of the sales consultant comparisons that I had been working on lately, just a new document to complete with a few more numbers than I already found. The goal of filling out this document is to find the commission for each consultant using the revenues that were accumulated in each section. Since I already found a majority of the numbers I was going to be using, this new project was not hard to do, just took time to complete. Not only did this require the season tickets, group tickets, and catering that I found before, but the document also required me to fill in cells that asked about merchandise, box rentals, and experience packages. In order to find these values, I need to go back into the pivot table I first used to find the previous numbers. In the middle of me completing this project, my mentor gave me a new little project to complete first before finishing this one. The new project was about a client asking to get more information on the attendances, separated by a list of counties in northwest Ohio and southeast Michigan.
When I left Tuesday, I informed my mentor that I thought I had been caught up on everything from before the holiday break. The first thing I do when I come into the office is check my email and Teams chat to see if there is anything I need to respond to before getting to work on a project. My mentor replied back to my message with a list of a few things to check if they are done and updated as well as asked me about the best day and time to send out a text with a certain message. To do this, I went into the text data that had already been collected for the season and made a new sheet of just the texts that relate to the one my mentor wants to send out. I then did all the formulating that I did with the big group of data to determine the day and time combination that made the most sense based on previous data. From the list of things to look at from my mentor I actually did about half of them Tuesday during my day here. Therefore, I went ahead and got the other couple things done. One of them was the ROI list I have been working on the entire time I have been interning here, which I had not updated since the beginning of December, so there was quite a bit of work to do with that file. The other thing he wanted me to get started on was the survey completed by season ticket holders so that I was not crunched for time when he wanted to see the data.
I first started my day by making sure that I could run TDC reports because for some reason it would not work when I was working from home. Thankfully, this worked, so I was able to update the ticket usage files that I have been working on for the Walleye season. There are separate files for each season tickets, group tickets, and single tickets. Within each of these files, I am to distribute the buyer types to include what is needed for each of these categories. The Winterfest games, however, threw me off because there were completely different buyer types used for this since it is a special event that the organization holds. With that being said, I emailed a lady who has previously helped me distribute these and she even got back to me within a few hours. Once I got this reply back, I was able to finish each of these files to have them updated for the Walleye season so far. This took a lot of time out of my day since I had to add rows into the template and change some formulas for some files. The other thing I worked on today was the postgame surveys because I still have not gone in depth for the games that have been hosted so far. I wanted to get caught up on this so that I do not have to go back too far in the future when my mentor needs this data submitted to him. That way, I will only have minimal work if I keep up with it like I should.
I informed my mentor yesterday that my prescription was supposed to come in the mail sometime today and that I wanted to be home in case I had to sign off on it. In addition, I was out of my prescription and needed my refill as soon as possible, so I asked if I could work from home today and he said that would be fine. Since the Walleye had a home game yesterday, I went ahead and updated the ticket sales by days out file to get that all updated. This did not take too long, so I then moved on to update the text data file. It had been about a week since I updated this, so there were certainly going to be some updates to the file since then. There ended up being about four entries to put into the file, which took a few hours to get all graphs updated and squared away. The last thing I did today was getting more updated on the postgame surveys for the Walleye games I missed. This was another file I had previously forgotten about since I had not opened this since before the holiday break. However, there were only three games I had missed. Then, I found out that the postgame surveys were not sent out for the Winterfest games since those were not held in the home arena. Since the typical postgame surveys were not sent out for those, I only had one game to put into the file. This also made me wonder if there were different surveys sent out that correlated directly with Winterfest.
The first thing I did today was finish up the sales consultant comparison file. This task took me to about lunch time, so I did not want to start anything new before taking a lunch break. After lunch, I began catching up on a couple projects. The first one I caught up on was the ticket sales by days out file since I have not worked on this since before the break I had for the holidays. However, there were only two games over the long break. The two games around the holidays are what the organization calls “Winterfest” and actually puts a hockey rink in the baseball field. This allows for more seating to be available for fans since the baseball stadium has much more seating than the hockey arena. While looking at the tickets sales by days out, it showed that there was certainly a big increase in the number of tickets that were sold since the revenue was even more than twice the amount of the previous high of the season. Once I caught up on this file, I went ahead and moved on to finish up another project that my mentor gave to me to finish up. I had been working on the attendances for each team in the league over the past three seasons of the Walleye. I had been waiting on my mentor to get back with me on official numbers to be given to him since the only numbers I could find online were approximate.
I was informed by my mentor yesterday that everyone in the office has the option of working from home for the rest of the week. With that being said, I have chosen to do so. Today, I was able to finish up the sales consultant comparison file. This file had about 40 consultants in it that I had to retrieve data for including season tickets, group tickets, and catering. Every one of these categories had data over the last three Mud Hens seasons that had to be included. The season tickets and group tickets each had to have both revenue and quantity. Catering, on the other hand, only had revenue to compare between the consultants. I had a little delay with this project because the first files my mentor shared with me were not working for the pivot tables. Once I informed him about this issue, he fixed them right away so that I could get back at it. During my time of getting this project done, I kept messing some things up my having more than one consultant selected or did not have the correct filters selected. Because of this, it took me a bit of time to get going on the project. However, once I knew exactly what I was doing, it was smooth sailing from there. On the other hand, I still had a handful of consultants for this last season to do Thursday because I ran out of time today for that. This will be the first thing I do Thursday to say that I am done with the project finally.
Since it is my first day back in the office after the long Christmas break, there was a lot of catching up for me to do. I started with fixing up a few things my mentor noted on my presentations so that those are ready to go whenever my mentor gets around to want to present those. There were issues with the site, StellarAlgo, that I use to retrieve information from to complete these and I had to wait to complete this until the site was working properly again. The site was finally running again yesterday, so I knew I would be good to get this project finished up. Next, I completed the season ticket section of the sales consultant comparisons for the past three years. There were issues with the Excel files and pivot tables in those that I had my mentor fix over break so that I could get right back to work once I came back. I got those finished up, so now I wait for my mentor to give me the files for the rest of the sheets to complete the project. In the meantime, I also worked on the text messages data to get those files updated. There were even more than I expected since the organization promotes sales around Christmas time in attempt to boost merchandise sales for the teams. There are several other files that need updated this week once I get the time to catch up on those, so hopefully everything gets up-to-date by the end of the week.
The interview that was conducted the other day had to be a Zoom call instead of in-person meetings since I was unable to get anything setup due to staff being overwhelmed with outdoor events coming up. The interview was a great learning experience for me to have to see it all happen and take notes along with it. I even learned some interesting facts about the organization I am doing my internship through. I had to search for a lady who works for the marketing department to get one answer that my mentor was unable to answer since he does not deal directly with those kinds of areas. She was able to get me lots of information to cover the question and my mentor asked me to condense the information a little bit so that it is easier to read about answering the question while keeping important information confidential. Another duty I am responsible for is getting paperwork sent over like the written roles of each department and the mission statements about the organization. I also started a new project to wrap up the year that include the sales by each salesperson on staff for the last three seasons of the Mud Hens season. I have to do this for game plans, group tickets, and catering. My mentor has to pull reports in order for me to retrieve the numbers I am to enter in the file. I also had to change the formatting of the whole thing since this is the first time doing this for the Mud Hens season. In the past, this has only been done for the Walleyes season, so my mentor wanted me to clean it up to suit the other season.
When I first came in today I had a few emails to follow up on. One was for an interview that I recently helped out with for a research project to contribute to the writing of a textbook chapter. The email was about needing more clarification on one of the questions that needed answered about social media use in the organization. This included the reach and engagement to see how numbers progressed throughout the span of 12 months. Another email I received was about a survey winner being able to receive a gift card that they were chosen to win. There was a little mix up yesterday to sort out the gift card situation because there was one person who wanted to come pick up the gift card on their lunch break while all other winners wanted theirs delivered to them. The person who was in charge of sending those out thought that the one gift card that was to be picked up was for another person, so the person who was supposed to receive it in the mail did not know what happened to their gift card. I got the situation figured out by getting another gift card sent out to that person and making sure the correct name is for the gift card to pick up. I let the winner know that their gift card was ready to be picked up and they replied about a day they would be able to pick it up within the week.
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