LUCA Artificial Intelligence of Things, how things plan to make our lives simpler Just as in the Grimm Brothers fairy tale where two little elves teamed up to help the cobbler have a better life, Artificial Intelligence and IoT, Big Data technologies...
Patrick Buckley The Hologram Concert – How AI is helping to keep music alive When Whitney Houston passed away in 2012, the world was shocked by the sudden and tragic news of her death. Fans gathered around the Beverly Hills hotel in Los...
Richard Benjamins. Big Data and Elections: We shine a light on Trump and Clinton Twitter is widely used as a tool to understand and predict phenomena in the real world. Today on our blog, we have been using Twitter to understand the US Presidential...
Patrick Buckley How AI & IoT will save the Aviation Industry As we approach Christmas 2020, the success of various COVID-19 vaccines across the world is beginning to fill us all with a new-found sense of optimism, that, ...
LUCA Deep Learning and satellite images to estimate the impact of COVID19 Motivated by the fact that the Coronavirus Disease (COVID-19) pandemic has caused worldwide turmoil in a short period of time since December 2019, we estimate the negative impact of...
LUCA Success Story: LUCA Transit and Highways England The transport industry is very receptive to the application of Big Data and Artificial Intelligence strategies, as there are clear use cases that can maximize a companies’ efficiency and...
LUCA Open Data and API’s in Video Games: League of Legends puts them to practice Written by David Heras an intern at LUCA alongside Javier Carro a LUCA Data Scientist. When we at LUCA became aware of the release of the upcoming Movistar eSports we wanted to give a nod to fans of...
LUCA Can Big Data and IOT prevent motorcycle crashes? Most of us are familiar with the dangers involved in driving motorbikes, with motorcyclists being 27 times more likely than passenger car occupants to die in a crash per...
Improving Effectiveness at a Call Center with Machine LearningVíctor González 19 August, 2019 Understanding our clients and giving the best response to meeting their needs in the shortest amount of time possible is key to improving satisfaction and engagement with the company. The problem begins when the number of clients is high and we are receiving hundreds or thousands of messages per day. In this situation, we have two problems to solve. First, we have to prioritize which messages we are going to respond first and, second, we have to understand what it is that they are saying. It is clear that some messages will be more important or more urgent than others and that it will not always be easy to prioritize. To make things easier, we can use Machine Learning techniques to help with this task, which can do part of the work for us. In this post, we are going to focus on the case of a Call Center for B2B clients of a Telco operator. Part of the communication and management with the clients of the Call Center is done by e-mail. By analyzing these e-mails, it is possible to extract some metrics that can help us to understand the needs of the clients who communicate in this way. This analysis is mainly done through Natural Language Processing (NLP) techniques. In fact, we did the analysis on two levels: Operational level – This level is more basic and implies a level of sending and answering e-mails. This is done simply by analyzing the subject lines of the e-mails.Content level – In other words, processing the content of the messages from the clients in order to automatically classify them according to their content. We can understand this to be rules of filtering that we program for our e-mail clients, but much more advanced. The analysis on the operational level allows us to obtain simple metrics such as how many e-mails are received by the Call Center, from which clients, what times the most e-mails are received, how long it takes to respond to each e-mail, etc. But it is also possible to carry out a more complex analysis, extracting data from entire conversations: analyzing e-mails from one conversation (an e-mail thread), we can discover how long we take in solving a client’s problem, how many people from the Call Center were involved or which departments were involved. The content level analysis is more complex as it requires a more sophisticated pre-processing of the data, but it allows us to extract much more interesting insights. E-mail cleaning includes extracting the body of the messages, cleaning them (here the challenge is to eliminate signatures or responses to other e-mails) and using Natural Language Processing (NLP) techniques that allow us to transform the content into characteristics that can be understood by the automatic learning algorithm that we want to use. This step usually includes eliminating stop-words (words without meaning such as prepositions or articles), creating “bags of words” or “n-grams” and vectorizing them with algorithms such as TF-IDF. Once the data is prepared, we can feed an algorithm that will learn to automatically categorize the e-mails. We approach this problem as one of classification as the operator provides us with the categories in which they want the e-mails classified. Thus, we end up with eight categories (activations, line modifications, technical incidents or cancellations among others) that we manually tag together with the data for training. Although this may seem like a laborious process, interesting results can be obtained very quickly. For example, with a sample of just 100 tagged e-mails, it was already possible to distinguish perfectly some of the categories suggested by the client. As follows, we detail some of the KPIs that we obtained from the Call Center: Average response time by the operators Incident resolution time (that is, the time between the first and the last e-mail in the chain of e-mails) Size of the e-mail inbox and list of incidents to be resolved in each moment. That is to say, the number of e-mails to be answered and incidents to be resolved in each moment (we can obtain the sizes of these waiting lists by hour or day of the week), which allows us to know if we are on top of incidents. As we process the content of the e-mails, we can categorize them automatically and with these categories, we can automatically assign categories to the correct person to answer them. Additionally, knowing these e-mail categories, it is possible to have metrics for each category in advance. For example, volumes and response times per category. In this post, we have seen which metrics we can obtain from an analysis of e-mails from the clients of a call center. By analyzing the subject lines and content of the e-mails, it is possible to obtain metrics that provide us with KPIs of the operational and dimensioning capacity of the Call Center as well as data that helps us to profile clients and their needs. To stay up to date with LUCA, visit our Webpage, contact us and follow us on Twitter, LinkedIn o YouTube. Could Artificial Intelligence be used to prevent suicide?Can Big Data help reduce Deforestation in the Amazon?
Patrick Buckley The Hologram Concert – How AI is helping to keep music alive When Whitney Houston passed away in 2012, the world was shocked by the sudden and tragic news of her death. Fans gathered around the Beverly Hills hotel in Los...
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Patrick Buckley Thanks to AI, the future of video-conferencing is in sight. Throughout the COVID-19 pandemic, video-conferencing has become the backbone of both our work and social lives. Today, on #WorldHugDay, we take a look at some of the ways in which...