Peaks and Valleys or Critical Moments Analysis
How can you gain interesting insights just from looking at descriptive charts based on your data? Select a key metric of interest like Overall Satisfaction (scale 1-5) and using a text analytics software allowing you to plot text data as well as numeric data longitudinally (e.g., OdinText) view your metric averages across time. Next, view the plot using different time intervals (e.g, the plot could display daily, weekly, bi-weekly, or monthly overall satisfaction averages) and look for obvious “peaks” (sudden increases in the average score) or “valleys” (sudden decreases in the average score). Note down the time periods in which you have observed any peaks or valleys and try to identify reasons or events associated with these trends, e.g., changes in management, a new advertising campaign, customer service quality, etc. The next step is to plot average overall satisfaction scores for selected themes and see how they relate to the identified “peaks” or “valleys” as these themes may provide you with potential answers to the identified critical moments in your longitudinal analysis.
In the figure below you can see how the average overall satisfaction of a sample company varied during approximately one month of time (each data point/column represents one day in a given month). Whereas no “peaks” were found in the average overall satisfaction curve, there was one significant “valley” visible at the beginning of the studied month (see plot 1 in Figure 1). It represented a sudden drop from the average satisfaction of 5.0 (day 1) to 3.1 (day 2) and 3.5 (day 3) before again rising up and oscillating around the average satisfaction of 4.3 for the rest of the days that month. So what could be the reason for this sudden and deep drop in customer satisfaction?
Figure 1. Annotated OdinText screenshots showing an example of a exploratory analysis using longitudinal data (Overall Satisfaction).
Whereas a definite answer requires more advanced predictive analyses (also available in OdinText), a quick and very easy way to explore potential answers is possible simply by plotting the average satisfaction scores associated with a few themes identified earlier. In this sample scenario, average satisfaction scores among customers who mentioned “customer service” (green bar; second plot) overlap very well with the overall satisfaction trendline (orange line) suggesting that customer service complaints may have been the reason for lowered satisfaction ratings on days 2 and 3. Another theme plotted, “fast service” (see plot 3), did not at all follow the overall satisfaction trendline as customers mentioning this theme were highly satisfied almost on every day except day 6.
This kind of simple exploratory analysis can be very powerful in showing you what factors might have effects on customer satisfaction and may serve as a crucial step for subsequent quantitative analysis of your text and numeric data.
[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior. Please feel free to request additional information or an OdinText demo here.]