## 4 Case Study
### BLCC Creation for Hotel A  
#### 4.2.1 Top Level: \(p\)-Chart  
To monitor the attribute importance, \(p\)-charts are drawn for each of the seven attributes, as shown in Figure 5. The fraction of appearance is plotted on a monthly basis; in other words, each point on the charts corresponds to a set of reviews posted in a month. Notably, if the number of reviews in a month does not satisfy the minimum size requirement, the reviews of the month are excluded from the chart. To clarify which months are excluded from the analysis, spaces are left in the charts for the excluded points, although leaving a space is optional. In this case study, Minitab 19, one of the most popular statistics packages for quality management, was used to draw control charts.  
The CL of the \(p\)-charts shows the average fraction of appearance of each attribute, and the UCL and LCL \(\pm 3\) standard deviations off the average represent the range of expected variation. The \(p\)-charts can show the importance of an attribute from the customers' viewpoint and whether any special-cause variations exist in the importance level of the attribute, such as a temporary aberration, a shift or trend in the average, or an increase or a decrease in the standard deviation. The four rules in Table 4 were applied to determine the special-cause variations. In Figure 5, such points of special-cause variations are represented as red squares, the number below which indicates the corresponding rule from 1-4 (i.e., out-of-control, prolonged bias, rising or falling trend, and oscillation).  
For instance, the \(p\)-chart for attribute FA in Figure 5a shows that the mean appearance fraction of FA is 0.148, which implies that 14.8% of customers mentioned FA on average. The appearance fraction was stable for most of the time, except in November 2019, when the fraction was temporarily above the UCL. Such an increased appearance indicates that comments on FA (either compliments, complaints, or neutral comments) increased because of special cause(s) in that month. This phenomenon can be attributed to several potential root causes: quality issues (e.g., breakdown), changes in the attributes themselves (e.g., new furniture and appliances), temporal changes in the proportion of customer types (single, couple, family, etc.), customers having different preferences, and so on. Further investigation of the reviews of the month can help determine specific assignable root cause(s). FA's \(g\)-chart statistics for the month can also be checked to quickly identify whether the special-cause variation relates to quality issues or increased complaints.  
The \(p\)-chart of SS in Figure 5e shows the opposite case, where the appearance fraction dropped below the LCL. This indicates that the SS attribute was not very impressive. However, this can be a good sign of fewer complaints. Alternatively, thus may have been a result of the customers of the month being different from those of other months; the former customers may have assigned a lower importance to SS. The user can identify the root cause(s) through further analysis of the content of the reviews and establish an appropriate strategy.  
The CLs of the \(p\)-charts can be a useful measure of attribute importance. As shown in Figure 5h, the average appearance fraction, \(\overline{p}\), varies significantly between attributes. The chart indicates that RC, FC, and SS are the most frequently mentioned attributes in the market; DF is in the middle, followed by LS, BC, and FA in sequence.