Filtering by
Workers in sales roles are often faced with a large number of time management decisions on a daily basis. Sales people must choose where they should be spending their time in order to create revenue while also maintaining a healthy work-life balance. In this thesis project, a sales process is analyzed to see if there is an opportunity to increase both revenue and work-life balance. This paper investigates a wholesale insurance brokerage company, Risk Placement Services, and their sales force of brokers. A significant portion of these brokers’ workday consists of the backend task of marketing accounts to insurance carriers to find coverage. This is necessary for the completion of the sales cycle but either limits the amount of time brokers can be out on the road or on calls trying to bring in new business or makes them work longer off the clock hours to get these accounts out to insurance carriers. The more business a broker is bringing in, the more time they have to spend marketing these new accounts to carriers, which puts them into a constant snowball of increasing tasks and goals. The main model for the analysis of this problem will be Reframing Organizations by Bolman & Deal which focuses on using their four-frame model to analyze and gain more insight into organizations. Being able to understand this problem from multiple perspectives will allow a more holistic solution to be reached. Following this analysis multiple potential solutions are discussed towards the end of this thesis project.
The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand what the students need. One of those tools is an online course ratings predictor. Using the predictor, online course instructors can learn the qualities that majority course takers deem as important, and thus they can adjust their lesson plans to fit those qualities. Meanwhile, students will be able to use it to help them in choosing the course to take by comparing the ratings. This research aims to find the best way to predict the rating of online courses using machine learning (ML). To create the ML model, different combinations of the length of the course, the number of materials it contains, the price of the course, the number of students taking the course, the course’s difficulty level, the usage of jargons or technical terms in the course description, the course’s instructors’ rating, the number of reviews the instructors got, and the number of classes the instructors have created on the same platform are used as the inputs. Meanwhile, the output of the model would be the average rating of a course. Data from 350 courses are used for this model, where 280 of them are used for training, 35 for testing, and the last 35 for validation. After trying out different machine learning models, wide neural networks model constantly gives the best training results while the medium tree model gives the best testing results. However, further research needs to be conducted as none of the results are not accurate, with 0.51 R-squared test result for the tree model.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
I conducted a study on the effective design, implementation, motivational factors, and takeaways upon completion of such contests. The purpose of this study is to find out whether or not sales contests are an effective way of motivating a diverse workforce. The results suggest that sales contests are a hyper-efficient tool to increase employee motivation but must be prepared for and implemented correctly in order to achieve efficient results. I recommend that sales managers use contests as a tool to gauge the motivational and behavioral changes in their employees resulting from such contests, instead of just trying to gain more revenue. Also, to combat the growing threat of unethical behaviors as a result of running sales contests, leaders need to implement appropriate measures, like unethical behavior diversion courses.