Posted by This post is a continuation of our series of blog posts highlighting presentations from the 2nd Edition of Seville Machine Learning School (MLSEV). You may also check out the previous posts about the 6 Challenges of Machine Learning, Predicting Oil Temperature Anomalies in a Tunnel Boring Machine, or Optimization of Passenger Waiting Time for Elevators.
Today, we will zoom in on an example application of Machine Learning in the labor-intensive enterprise functions of customer service and call center operations. This presentation was given by Andrés González of CleverData.io. Located in Barcelona, CleverData is an IT services and consulting firm delivering Machine Learning solutions for a variety of corporate customers such as Mazda, Penguin Random House, iT Now (CaixaBank), and Sorli.
The project in focus was implemented for the European branch of the Japanese multinational imaging and electronics giant, Ricoh. Among other business activities, Ricoh operates a profitable turn-key ‘Managed Print Services’ business, where they actively manage the entire stock of printers and supplies as well as any scheduled maintenance of those printers on behalf of their customers. This helps businesses lighten the upfront cost hits to their capital structure. At the same time, outsourcing such business services helps them more easily adapt to changes in their operations as they can flexibly dial up or down the variable managed services costs — much to CFOs liking.
As part of their service agreements, Ricoh is also responsible for solving any after-sale service or printer issues with the help of their call center operations. Naturally, Ricoh is very interested in increasing the productivity of their call center operations in general and the associated incident tracking and resolution process in particular. This can be best achieved by avoiding the dispatching of technicians to the customer’s office site by maximizing the likelihood of issue resolution remotely. In this project, CleverData helped Ricoh build a ‘Dispatching Bot’ to automate this critical decision point in their incident management process.
The raw historical incidents dataset CleverData employed accounted for 19 months of incident reports and had around 150,000 records spanning some 61 columns such as printer characteristics, incident description, contract characteristics, and more importantly the labels explaining issue resolution outcomes.
Since the incident descriptions contained rich textual data, they were ideal targets for feature engineering. Consequently, CleverData proceeded to utilize BigML’s unsupervised Topic Models to do exactly that. In the following steps, the enriched datasets were fed to BigML supervised learning models to arrive at the final predictions deployed in production. During the 7 months following initial solution deployment, the ‘Dispatching Bot’ managed incidents were proven to increase the remote incident resolution rate from 61% to 81% on average — a rather dramatic improvement in saved costs and productivity!
Now, let’s hear, in more detail, how CleverData went about implementing both the custom BigML Machine Learning workflow and the eventual production application for this successful project:
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