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In 2016, Pointwest aimed to showcase to one of its US-Based Healthcare Information clients the benefits of Robotic Process Automation in reducing costs. A Proof of Concept (PoC) project was initiated and the details of the case are detailed below.
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In 2016, Pointwest aimed to showcase to one of its US-Based Healthcare Information clients the benefits of Robotic Process Automation in reducing costs. A Proof of Concept (PoC) project was initiated and the details of the case are detailed below.
In 2016, Pointwest aimed to showcase to one of its US-Based Healthcare Information clients the benefits of Robotic Process Automation (RPA) in reducing costs. A Proof of Concept (PoC) project was initiated and the details of the case are detailed below.
To say that the results of the 2016 US Presidential Elections elicited a strong reaction from a large number of people is the understatement of the year, if not the decade. Euphoric supporters from the Trump campaign reveled at their surprise victory while silent shock could be seen in their counterparts at the Clinton HQ.
To say that the results of the 2016 US Elections elicited a strong reaction from a large number of people is the understatement of the year, if not the decade. Euphoric supporters from the Trump campaign reveled at their surprise victory while silent shock could be seen in their counterparts at the Clinton HQ.
A leading airline carrier from the United States of America* needed help in making sure that their passenger-facing channels abide by Air Carrier Access Act (ACAA) Accessibility Amendment.
A leading airline carrier from the United States of America* needed help in making sure that their passenger-facing channels abide by Air Carrier Access Act (ACAA) Accessibility Amendment.
The amendment was made by the Department of Transportation in 2013, and required that all pages and functions of airline carrier public channels be compliant to WCAG 2.0 Level A and Level AA guidelines.
Other identifiers of the project are as follows:
A compliance schedule was also set to give airline carriers time to implement the changes to their channels:
Phase 1: All “Core functions” must be compliant by December 12, 2015. This was later extended to June 30 2016, and the core functions were defined as:
Phase 2: All remaining pages must be made compliant by December 16, 2016.
For Project Ravenala, the functions to be modified for compliance were the post-booking phase for the airline carrier’s customer – the passenger. This included features related to the passenger’s ability to manage their itinerary (viewing and modifying flights), selecting their preferred seats and even upgrading them when eligible.
The project started in April 2015.
The team assigned to Project Ravenala performed both Application Development and Maintenance services combined with Software Testing to apply the needed changes for Accessibility compliance.
The team was made up of 5 software engineers, but it was scaled up based on the workload when necessary.
The team used the following tools in testing the pages and functions for Web Accessibility issues:
For the implementation of the changes, the core skills needed were:
Certain challenges were experienced by the team as they applied changes to make the site accessibility compliant:
Given that there were branding standards to be followed, the challenges were mostly related to verbiage and colors, which required the team to verify with the client prior to implementation.
As in most cases, back and forth interactions add to the schedule.
The team mitigated this challenge by listing down all the verbiage that needed to be changed, and also provided suggestions. For the colors, the changes were kept to within the airline’s shades of their brand colors.
This eventually urged the client to modify standards for the entire website.
Being one of the leading airline carriers from the US, the client had to be an example for their airline industry. It helps with branding the airline as an industry leader.
Avoiding the risks of being penalized for non-compliance was the most evident benefit on the side of the airline carrier. Fines were witnessed to be around the value of $50,000.00 for some carriers, and some have even been penalized for up to USD$2-Million due to violations of ACAA regulations related to disability and complaints from the sector.
Reducing the risks of litigation, and increasing the chances of retaining their customer base were also considered to be benefits critical to the airline.
Delighting customers has always led to an increase in sales. Making the customer’s experience seamless across all channels for all the functions that they expect to be working for them has shown evidence of this. For customers with disabilities, booking a flight, accessing information, and managing your itinerary were usual challenges. By making the functions accessible for them, the chances of them going to another airline become slimmer.
Thereby, the most obvious benefit will be felt by the passengers.
In the US alone, there are more than 22.5 million adults with visual impairments. Globally, the estimated number of visually impaired people has reached 285 million, 39M are blind and 246M have low vision.
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Founded in 2003 by pioneers of the Philippine Global Sourcing industry, Pointwest creates value for its list of satisfied clients — including top Fortune 100 and local companies — with world-class IT and BPM services backed by international-standards methodologies and innovative practices.
Passengers who regularly travel recognize the struggles of airline companies in providing consistent and positive customer experience. To understand what passengers say about their services, airline companies have been trying to engage with passengers through social media.
Passengers who regularly travel recognize the struggles of airline companies in providing consistent and positive customer experience. To understand what passengers say about their services, airline companies have been trying to engage with passengers through social media.
Social media sites are useful sources of unstructured data that customer experience and social marketing professionals can use to determine certain issues on facilities and services airlines provide, and what can be added to improve customer experience.
Text analytics is one way to give decision makers information on what people say about their brands on social media. As an example, analyses were done on tweets directed to five major airline companies in the US: American Airlines (@AmericanAir), Delta (@Delta and @DeltaAssist), Southwest Airlines (@SouthwestAir), United Airlines (@United), and Virgin America (@VirginAmerica). Sentiment analytics was performed to classify these tweets into three categories – positive, negative, and neutral.
Furthermore, visualizations and descriptive statistics were included to give us more insights about passenger sentiments. The count of negatively tagged tweets increases with the flow of customer complaints. We drilled down to the cause of this through inspecting what aspects or services of the airline companies the complaints are about.
All tweets were extracted from April 17, 2016 to April 27, 2016 through a Twitter API called ‘Search’. We can indicate keyword(s) to be “searched” by the API. It returns tweets containing the indicated keyword(s). Although the API doesn’t return 100% of the tweets with the keyword(s), it returns recent and popular tweets. As mentioned above, we used the official Twitter handles of the five airlines as keywords in this example.
Word clouds are visual representations of word frequency. The size of the font correlates with how frequent the word is being used. The larger the font of the word, the more frequent it appeared on tweets. Word clouds can help communicate the most salient themes through effectively highlighting the most frequently used terms. In this example, we used R’s ‘wordcloud’ library to make a wordcloud.
Sentiment analytics is a text analytics technique that is used to determine whether a piece of writing (can be a phrase, sentence or a document) is positive, neutral or negative in nature. It can also derive the opinion of the author. The analysis can be done by establishing a set of rules that will enable the sentiment analytics model to examine the nature of the text.
Polarity of tweets
Through Natural Language Processing (NLP) techniques, words tagged with polarity, and a set of language rules translated into Python codes, we were able to classify the tweets into the following categories: positive, neutral and negative tweets.
Classification of negatively scored tweets
To further understand what customers complain about on Twitter, we classified negative tweets according to reasons (bad flight, cancelled flight, customer service, damaged luggage, flight attendant complaints, flight booking problems, late flight, lost luggage, and long lines). This analysis utilized a mixture of machine learning algorithms and NLP techniques. It involved tagging thousands of negative tweets with reasons similar to those mentioned above and training a classification model using the tagged tweets. To improve the performance of the classification model, the context of the tweet was considered by following a set of language rules.
The bar chart above shows that American Airlines garnered the most number of mentions in tweets from their passengers for the given time period. What can be derived further is how Virgin America garnered the fewest mentions. Further analysis may show how the mentions relate to positive or negative sentiments about the airlines.
Word clouds somehow give us an idea of what people are talking about. In this case, word cloud for positively scored tweets is separated from that of negatively scored ones. The word cloud on the left shows words that are most frequently used on tweets having a positive sentiment score. We can see that “thanks”, “great”, “love”, “customer”, “service”, “security”, and “time” are among the included words. What does it say about the airline companies’ services? We can infer that there are passengers who were satisfied with the airlines’ customer service. On the other hand, the word cloud on the right shows words that are used on tweets that are negatively scored. Words such as “rude”, “missed”, “lost”, “stuck”, “late”, “delay”, and “cancelled” are included. From this, we can infer that the complaints of the passengers who tweeted are mostly about their flights being cancelled or delayed, their lost baggage, and the airline’s customer service. Deeper analysis will be made on the latter part of this study.
In general, the positive tweets outnumbered the negative ones, although by only a small fraction. Airline companies would naturally want to lessen negative responses from their passengers. With this amount of bad comments relative to the amount of positive comments, they might want to check possible reasons why their customers give them negative feedback. The next figure shows the number of positive, neutral, and negative tweets across the five aforementioned airline companies.
Even though American Airlines accumulated the most mentions, it also got the smallest gap between its number of positive and negative tweets. The polarity chart above similarly shows that Southwest and United are at par not only in terms of tweet count, but also in terms of positive to negative tweets ratio. While being the least mentioned airline, the number of positive tweets for Virgin America is twice the count of its negative.
In this analysis, however, Delta can be considered the leading airline having both a strong Twitter share of voice, and relatively more positive tweets than negative ones. Overall, there is not much difference between the number of negative and positive tweets for all five airline companies, which indicates that their customers are not satisfied with their services. Further analysis will be done to dissect what could be the possible reasons why these companies receive too much negative responses.
The figure below shows a graph of reasons for negatively scored tweets directed to Delta. We can see that a large chunk of tweets are about delayed flights and bad customer service. (Similar analysis can be done for the other airlines, but to just show an example, only those tweets directed to Delta are analyzed.)
Monitoring what customers say about a particular brand is quite tedious when done manually considering the amount of data we can get from social media sites. We learned from this study that airline companies receive thousands of feedback from customers just on Twitter. With the help of machine learning algorithms and natural language processing techniques on the field of text analytics, there is a lot less effort in analyzing customer sentiments now as compared to how it is done traditionally (having a team monitoring social media sites manually, poring over tweets on Twitter and Facebook comments).
In this study, we have learned that while it is true that the amount of feedback airline companies receive reflects how popular their brand is, it is not always true that strong share of voice on social media sites means positive image in general, as in the case of American Airlines. We have seen that overall, there is a lot of negative feedback being voiced out on social networking sites (SNS). Therefore, it is advisable for airline companies to not just monitor a single value metric such as share of voice but learn the process that generates this number.
More important than knowing the proportion of negative tweets is being cognizant of the reasons why airlines receive such feedback. This is what we tried to do by sorting out the tweets according to the aspect of airline service they pertain to. By doing so, we have broken down this bulk of complaints into more manageable chunks.
Users of the study results could utilize this information in different ways. Airline companies may want to deal with the most common issues – delayed flights and customer service – that trigger passengers to express their frustrations on these sites, because these complaints posted publicly put brand reputation at risk of being tarnished. One could review the compensations offered for passengers with delayed flights, or go as far as the root of the problem, if it can be solved within the airlines. Customer service could be improved by administering refresher trainings to both ground staff and flight attendants.
While these findings could not be set aside, the airlines could also capitalize on its strengths, which are rarely long lines during the onboarding process and almost zero percent damaged luggages of passengers.
Aside from branding, such sentiments are gathered and utilized by Customer Experience Designers to improve their services and products. By further analyzing such information into actionable insights, airlines can decide to make changes to feature, functions, or people to correct errors and enhance the experience of their patrons and target market.
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Carry on a conversation with us through the comments below, or you can send a message or inquiry about our Data Management and Analytics, among other services we specialize in for pertinent industries.
Founded in 2003 by pioneers of the Philippine Global Sourcing industry, Pointwest creates value for its list of satisfied clients — including top Fortune 100 and local companies — with world-class IT and BPM services backed by international-standards methodologies and innovative practices.
For part 2 of the concluding study on the social media data analysis on the Philippine Presidential Debates, we considered comparing the emojis to be a crucial part in understanding the reactions and behavior of people during the event.
For part 2 of the concluding study on the social media data analysis on the Philippine Presidential Debates, we considered comparing the emojis to be a crucial part in understanding the reactions and behavior of people during the event.
Check out Part 1 of this study (Read: Comparing the Data Analysis for the Presidential Debates – Part 1) and see how the change in Share of Voice and the Polarity of Tweets may have contributed to the results of the elections.
Recognizing that tweets aren’t just made up of words, we recorded per candidate, the top 3 most used emojis in the second and in the final stretch of #PiliPinasDebates2016 tweets.
The most reoccurring emoji in the 2016 Pilipinas Debate series went to the face with tears of joy, as it topped the list for the last two legs for all candidates except for one time. This could mean that Filipino netizens found so much humor in the words of the candidates that they had to insert the emoji to express their amusement through their tweets.
For the second leg of the debates held in Cebu, the fire emoji made it to the trending list for 4 out of 5 candidates. Unlike in the first format, the presidential candidates were given the opportunity to ask each other questions on key issues, resulting in a heated battle of words and emotions.
The clapping hands emoji replaced the fire emoji in the third and final installment of the debate series, as it also joined the list of top 3 most used emojis for all candidates but one. This debate followed a town hall format, and nearly all of the candidates were applauded for the way they answered the people’s questions and delivered their opening and closing speeches.
It was also important to note the emojis that were consistent and those that changed between the two debates we covered. The fisted hand sign appeared on Duterte’s list for both Cebu and Pangasinan leg, as this has been constantly used as a symbol of identification for his supporters in social media. For Roxas, perhaps we could say that he redeemed himself in the third debate, as the unamused face emoji from the second leg got replaced by the black heart suit (♥ – Twitter displays the symbol in red), a symbol of love. And finally for Santiago, the loudly crying face emoji due to her absence in the second debate turned to the heavy black heart (❤︎ – Twitter displays this in pink) during the final leg, with tweets expressing solid support to the senator.
As the transmission of the Philippine elections nears its conclusion, the presidential race appears to have been decided with Mayor Duterte leading by a large margin, followed by Sec. Roxas. Sen. Poe is poised to finish in third place, VP Binay in fourth, and Sen. Santiago last.
In general, the results of the presidential election came out in agreement with the netizens’ response during the 2016 Pilipinas Debates as shown in our analyses. However, Twitter only represents a fraction of the population who are active in social media, so discrepancies from actual results can be expected.
Sen. Poe was last in the Share of Voice but ended up 3rd in the actual results. On the other hand, VP Binay had the 2nd highest number of mentions in Twitter but finished 4th in the elections. The sentiments regarding these two candidates could have been a foretelling of the outcome of their bid for the presidency. While Sen. Poe had a small number of tweets mentioning her, she had the largest percentage of positive tweets. On the other hand, Vice-President Binay had the highest percentage of negative tweets. On the average, Sen. Santiago ranked fourth in Twitter Share of Voice. Moreover, she got the highest number of sad tweets, a possible indicator of her disappointing finish.
Sec. Roxas finishing as a runner-up in the presidential elections matches with his presence in social media. Along with Mayor Duterte, he was the only other candidate that experienced minimal change across the 2 debates. He was generally found in the middle of the ranks in terms of share of voice and sentiments. His performance in the debates however was mired with the exceedingly large number of tweets expressing Anger and Disgust, more than his competitors did.
The three types of analysis could provide insights on how and why Mayor Duterte won the presidential race by a landslide. He was consistently visible in social media during the two debates, while garnering the 2nd highest percentage of positive tweets among candidates. Netizens showcased their love and happiness for Mayor Duterte in social media, placing him first in those two emotions in both debates.
While it is possible to analyze the sentiments and opinions of people in the social media, it remains to be seen whether social media does influence the collective consciousness of the voters as to who it is they deem worthy to vote for. The data at hand is insufficient to support this, as the demography and location fields are not fully reliable since it is the user’s prerogative to supply the said information. There is also no definite way of knowing if there are votes casted merely based on what has been seen from social media.
However, in this ever-evolving world, it is not a stretch to think that a candidate’s social media identity will shape and decide future election outcomes.