2023-24
Word
Introduction and Background
‘Imagine 2030,’ is the vision for the revolutionary future of The University of Manchester’s (UoM) library to maintain its position as the UK’s one of the top libraries, where one of its key goals is to provide high-quality services and exceptional spaces (UoM, 2022) to create a remarkable experience for researchers, students, and academics. With digital transformation, it is also important to emphasize the evolution of physical spaces.
As part of UoM’s main library, the lounge café is central to the library’s ecosystem as a key amenity that provides a comfortable space to relax and choose from a wide range of menu from beverages to snacks and even meals. However, it is also important to keep this frequently utilized space active, as it reflects the overall usage and visitors’ satisfaction with the library. This study aims to examine the relationship between the library’s foot traffic and the lounge’s sales through quantitative data to propose strategies to enhance its performance by identifying patterns and opportunities for improvement. Through an in-depth analysis, this report will provide insights as to how targeted improvements will enhance not just the lounge café, but also the library and help them align with the institutional goal to foster a creative and active environment.
Problem Statement
Research Aim
In many libraries, the overall user experience is enhanced through supplementary services such as private study carrels and cafés (Liang & Zhang, 2009). However, operating and regulating these services is challenging without considering the factors that influence their performances. To identify these challenges and create opportunities, this study aims to follow a quantitative approach for the library Lounge café, Main Library at the University of Manchester (UoM).
On continuous visits and through discussion the researcher understood that the café expects about 150-300 purchases daily during their operating time between 8:00 am – 4 pm This large indifference led the researcher to comprehend the visitor’s numbers and compare them with their sales. Therefore, this study seeks to address, how is the café’s sales performance influenced by the number of visitors to the library, and how sales can be improved
Research Objective
- To analyse the correlation between the sales of the café through a series of periods against the daily library visitor counts.
- To develop an accurate predictive model through historical data to forecast future sales.
- To provide recommendations on potential future business strategies to the café’s management.
Methodology
The researcher has used two tools for analysis, as the primary data was provided in Excel, organising, and enhancing was done in Excel, saving time and RStudio for deriving correlations and creating predictive models as RStudio achieves accurate and more sophisticated analysis (Hair et al., 2021)
Data Collection and Preparation
The researcher collected primary data by observing the number of entries made at the main library between the period of 2 pm – 3 pm for five consecutive days, while also observing the purchasing behaviour of visitors at the café between 1 pm – 2 pm. With the collected data, estimates were run through the graphs provided on Google (Figure 1 and Appendix 1).
However, these observed data seemed inaccurate as the average entry of the five days (from 6 pm – 12 pm) was 1584 students (Appendix 1). Upon re-checking with the library, librarians provided the research paper with the secondary information on the count of entries and exits for the year 2024 (Appendix 2) for which two different periods of data were extracted (Figure 2).
Figure 1: An estimated summary of visitors at the Main Library.
Additionally, for the café sales, both primary and secondary data were collected. Through primary acquisition, the researcher identified the number of buyers during peak hours, most selling items and the average transaction value based on the item they collected Secondary data sourced the breakdown of each day in terms of date, value, count and average transaction value which will be used to investigate the correlation between visitors and sales at the café (Figures 3 and 4) (Appendix 6).
Data Preprocessing
Data preprocessing is a transformation of raw data which is noisy, incomplete, and inconsistent to clean, fusion and accurate which can be used as inputs for machine learning (Ramírez-Gallego et al., 2017). In this paper, excel was used to reduce complexity by decreasing the variable which did not add value and filtering out the null values.
Figure 2: A summary of Entry and Exit count for February and April.
Figure 3: Sale transactions for February
Figure 4: Sale transactions for April
Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a statistical process with a “flexible attitude” allowing analysts to explore data and model decisions. These explorations are done in the form of visualizations through bars and graphs and can be used to examine the correlation between variables (Wongsuphasawat et al., 2019). EDA also assists in detecting data clearing issues as the outcome profiles through by giving strong correlations. Based on the calculations from EDA, the strongest correlation between the variables will be suggested and taken further into consideration and any irrelevant variables will be discarded.
Data Visualization
One of the key components of EDA is Data Visualization due to its supporting nature in uncovering patterns, distributions and outliers. At UoM, the second semester tends to be busier as students get familiarized with the campus and the facilities around it. This was one of the main reasons why the researcher chose to analyze this period. However, to grasp a better relationship between the café’s sales and footfall at the library, a comparison will be made between the two periods. The first being 5th February 2024 to 9th February 2024 and the second being 22nd April 2024 to 26th April 2024.
The Figures 5 and 6 below provide a clear comparison of entries at different times and days. It provides insights into traffic patterns, for example for February the average peak entry floats around 1 pm and quickly tumbles down While for April the busiest hour is around 1 pm as well however the peak slightly drops until 4 pm. Therefore, from this analysis we can derive that student’s capacity at the library remains elevated during the month of exam preparations. For a complete numerical breakdown refer to Appendix 3.
Similarly, the following Figures 7 and 8 illustrate the pattern of exits over the week of February and April. During the February period, we can see a spike in the exits from 1 pm to 4 pm while for the April period, we can see the fluctuations amongst the data. The value remains relatively constant between the period 1 pm to 5 pm. Thereby, reflecting that the period of April is relatively busier, and students tend to stay longer at the university library which might have a positive impact on the sales of the café.
Considering the café’s sales, the subsequent analysis will examine the secondary sales data of the library lounge café. Figures 9 and 10 present a summary of items sold by category, quantity, gross value and percentage contribution of each category to sale. The below two tables show that Hot drinks and Tiffin items are the most sold items contributing 72% and 75% of the total sales respectively. However, researchers’ primary observation noticed that tiffin items usually had a high chance of being unsold due to excess quantities availability thereby adding to the overall cost of operations.
Figure 5: Line graph of entries in February
Figure 6: Line graph of entries for April
Figure 7: Line graph of exits in April
Figure 8: Line graph of exits in February
Pearson Correlation Analysis
The Pearson correlation coefficient (PCC) measures the linear relationship between two variables through a statistical metric (Zhou et al., 2016). Figure 11 represents a guide which describes the degree of correlation based on the correlation coefficient values where +1 means very strong relation and -1 means inverse correlation. By conducting a successful correlation analysis, the researcher can predict an estimate of future sales based on the number of visitors.
Figure 9: Summary of items sold by categories and their contributions in percentage in February
Figure 10: Summary of items sold by categories and their contributions in percentage in April
Figure 11: Interpretation of Pearson's correlation coefficients (Joo et al., 2022)
The researcher has performed correlation analysis to show the relationship between sales and the number of visitors via RStudio. As the café’s operating hours are from 8 am – 4 pm, we have considered all the entries during the same period and excluded all the others. Figure 12 illustrates a scatter plot which depicts a high correlation coefficient of +0.789, indicating a strong positive correlation.
The strong positive correlation between the number of library entries and the café sales indicates that any strategy implemented to attract visitors to the library will have a positive effect on the sales of the café. The scatter plot also suggests that as the daily entries increase the sale will also increase.
However, to identify the accuracy of the relationship between the two variables, the researcher studied the data for April month by running the same model. The image also depicts a high correlation coefficient of +0.754. Additionally, the Figure 13, scatter plot also portrays the strong relationship between the two variables. Therefore, it implies that daily sales and daily entries are highly correlated.
Figure 12: Scatter Plot of Daily entries vs Daily Sales for February
Predictive Model
Linear Regression Model
The simple linear regression aims to find the best-fitted straight line for a dependent variable by estimating the values of the slope and interception (James et al., 202). As the secondary data for sales was limited, the researcher will run the model with two different data sets to derive the best predictive model by analyzing the R-squared value as it indicates the variability (Figure 14).
14: Interpretation of Determination Coefficient of R-square
et al., 2023)
To conduct a linear regression model, firstly the set of variables needs to be formatted correctly. As the café’s operating hours are from 8 am – 4 pm, we have considered all the entries during the same period and excluded all the others (Appendix 4 and 5) For this specific model we used sales as the dependent variable and the daily entry as an independent variable. This model is generated for the period 5th February to 9th February. From the regression results, we obtained the R-squared as 0.6221 and the probability value as 0.113 (Figures 15 and 16). This means that as the daily entry
Figure 13: Scatter Plot of Daily entries vs Daily Sales for April
Figure
(Sarjana
increases, the gross sales will also increase. To gather more insight, the following formula can be used to predict future sales just by evaluating the independent variable.
i.e., Predicted sale = -38.80 + [0.1471 (slope) x Number of visitors]
Based on the available secondary data for April, the researcher developed the predictive model and determined the R-squared as 0.568 and the probability of the model as 0.1412 (Figures 17 and 18). As a result, from the guidelines, we can interpret that the effectiveness of this model is moderate
i.e., Predicted sale = -10.937 + [0.1061 (slope) x Number of visitors]
Figure 16: RStudio developing the model for February
Figure 15: Regression of Gross Value on Daily Entry
Figure 18: RStudio developing the model for April
Figure 17: Regression of Gross Value on Daily Entry
Evaluations
The study was conducted to analyse the sales performance of the Library Lounge Café. Upon observation, the lounge was highly occupied by students with 37 out of 40 seats occupied upon arrival. This made the researcher curious to observe the sales, and upon collection of data, the sales seemed below average as compared to the activity in the lounge. Therefore, through this study, the researcher wanted to know how the café’s sale is affected and how can the sales be improved.
Following through primary and secondary research, this research reveals a strong positive correlation between the number of visitors and the sales at the café. The Correlations Coefficient of February and April was +0.789 and +0.754. This indicates that the café’s performance is influenced by the library’s foot traffic. With this, as the café operates for limited hours, there is a lost sale of approximately 20% during the busiest period of the term which is determined by the past trends. Similarly, with the help of line graphs, the research was able to depict the trend of foot traffic in the library which acts as a guide for the café operating hours. Additionally, predictive modelling was developed to estimate future sales. This was done by running a linear regression model for which the R-squared values were 0.6221 and 0.568. However, due to the limited variables and data, this model is a moderate fit suggesting that it can predict trends to a certain extent and has many areas for improvement.
Recommendations
1. Subscription Models have been recently successful, and many businesses have adopted them to foster their revenue and customer loyalty (Şimşek et al., 2022) For the Library Lounge Café, beverages account for 50% of their sales, implementing this model specifically will encourage students to frequently visit while also increasing engagement around the café. For the café, there would be a substantial increase in earnings as membership models are associated with contract-based earnings.
2. The positive correlation of 0.789 and 0.754 suggests that the larger the number of visitors, the higher the sales. Therefore, this could be done by promoting and attracting visitors through promotions, events, and attractive deals. With this, during the researcher’s observation of the busy periods, a suggestion of the online pre-ordering system could enhance the customer journey experience by reducing the wait times, increasing the café’s managing capacity and facilitating quicker transactions. Thereby enhancing the overall operational efficiency
3. Introduce a pilot program where subscripted users could get rewards or discounts if the consumption of the beverage is lower than their break-even. This would not only drive the sales of other category items but also aid in quickly selling perishable items and avoiding perishable loss. Overall, introducing the subscription model on a trial basis would enable the cafe to develop a regression model and allow gathering data on the shifted purchasing behaviour for future enhancements
4. As studied, the cafe could benefit by optimizing operating hours based on the traffic data. For example, as we saw the sales were up by 465 pounds in April during the working hours itself (i.e., the busiest period of the month), working extra hours could have resulted in greater sales Also, as observed from the line graph, the rush on Friday is limited so the café could run normal hours on Fridays and could potentially save on the staffing and operational costs
References
Hair, J.F. et al. (2021) ‘Overview of R and rstudio’, Classroom Companion: Business, pp. 31–47. doi:10.1007/978-3-030-80519-7_2.
James, G. et al. (2022) An introduction to statistical learning: With applications in R. Boston: Springer.
Joo, H.Y. et al. (2022) ‘Age or environmental radiation dose rate: Which is more correlated with cancer incidence rates in the Republic of Korea?’, Nuclear Engineering and Technology, 54(9), pp. 3452–3458. doi:10.1016/j.net.2022.03.032.
Liang, X. and Zhang, S. (2009) ‘Investigation of customer satisfaction in Student Food Service’, International Journal of Quality and Service Sciences, 1(1), pp. 113–124. doi:10.1108/17566690910945903.
Ramírez-Gallego, S. et al. (2017) ‘A survey on data preprocessing for Data Stream Mining: Current status and Future Directions’, Neurocomputing, 239, pp. 39–57. doi:10.1016/j.neucom.2017.01.078.
Sarjana, K. et al. (2023) ‘Analysis of pre-service teacher’s performance viewed by creativity and self-regulated learning’, Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran, 9(1), p. 234. doi:10.33394/jk.v9i1.6467.
Wongsuphasawat, K., Liu, Y. and Heer, J. (2019) Goals, process, and challenges of Exploratory Data Analysis: An interview study, arXiv.org. Available at: https://doi.org/10.48550/arXiv.1911.00568 (Accessed: 01 May 2024).
Zhou, H. et al. (2016) ‘A new sampling method in particle filter based on Pearson correlation coefficient’, Neurocomputing, 216, pp. 208–215. doi:10.1016/j.neucom.2016.07.036.
Şimşek, T. et al. (2022) ‘A journey towards a digital platform business model: A case study in a global tech-company’, Technological Forecasting and Social Change, 175, p. 121372. doi:10.1016/j.techfore.2021.121372.
UoM (2022) IMAGINE2030. Available at: https://documents.manchester.ac.uk/display.aspx?DocID=51825 (Accessed: 02 May 2024).
Appendix
8am-9am
11am-12pm
1pm-2pm
2pm-3pm
3pm-4pm
4pm-5pm
5pm-6pm
6pm-7pm
7pm-8pm
Appendix 1: Manually collect data (primary data)
Appendix 2: Proof and Count of Entry and Exit of Main Library
Appendix 3: A complete count of the entry and exit data for Feb and April 2024
Appendix 4: The number of visitors at the library during the cafés operating time (Feb 2024)
Appendix 5: The number of visitors at the library during the cafés operating time (April 2024)
Appendix 6: Phone and email conversation with the manager of The Library Lounge Cafe