Citrix Relies on Data Science to Improve Customer Experience and Operation Efficiency.
Data science is one of the fastest growing fields in the world, and for good reason. It offers significant advantages to businesses and organizations of all types, in any industry. Moreover, it can be applied to virtually any need within an organization – something that Citrix realized and capitalized on.
Who Is Citrix?
Citrix is based in the US, and focuses on software and services in the virtualization and remote access sphere. The company was founded in 1989, and has gone on to achieve success on a global scale. The company’s vision is to, “Measure the value of technology by how it benefits people. It’s about what they need to do and what they need to achieve.”
Citrix’s products range from the Citrix Workplace Suite for mobile workplaces to enterprise apps, the XenMobile management system, Worx Mobile App suite and a great deal more. The company’s customers range from Fortune 500 companies to freelancers.
While Citrix is a technology company, their goal is to improve work and life for real people. Part of that requires handling customer support needs. In short, the company needed to find a way to identify cases that would escalate into serious problems if not attended to immediately.
In most companies, customer support is handled on a call-by-call or first come, first served basis. It’s a logical approach, at least superficially. However, Citrix faced the same challenges that any other business does in this area – some of the support calls were relatively minor, while others were critical. By following a first come, first served support model, customers with serious issues were being forced to wait for answers. The issue here is that serious problems demand an immediate response, or the customer’s situation deteriorates rapidly, forcing them to escalate the situation.
Obviously, complaint escalation means that the problem will now involve more people at Citrix, reducing the staff’s ability to handle other customers. It also increases stress, aggravation and hassle to the client. In a worst-case scenario, this could see the customer abandoning Citrix in favour of a company he or she felt could better serve their needs in a timely manner.
Not only did these instances adversely affect the customer’s overall experience, but they highlighted inefficiency within Citrix’s operation. By identifying cases that would escalate quickly if not addressed in a timely manner, Citrix hoped to improve customer satisfaction, sales and worker experience as well. However, the company’s in-house data science team needed assistance.
To assist with its challenge, Citrix chose to work with Wise.io, Inc. Founded in 2012, Wise.io has significant experience in machine learning, statistics, computer science and related areas. The Wise.io data science team collaborated with Citrix’s own in-house team via Wise’s proprietary Wise Enterprise Platform. The Citrix team gathered data from a wide range of different sources within the company. This data was then prepared for modelling, with appropriate attributes selected.
The Wise.io team collaborated to help build a model with unsurpassed accuracy, with the results of the modelling sent to a mobile application usable directly by Citrix’s sales and tech support representatives.
The data science project was able to deliver significant value to both Citrix and the company’s customers. The team was able to put advanced machine learning technology to use without the need for extensive, expensive in-house development, which shortened the duration of the project to just weeks, rather than months.
The most important value here, though, was the creation of an application that had an immediate impact on customer experience, turning what could have become negative experiences into positive ones by allowing sales and support staff to immediately take action where the problems were most serious, rather than simply handling support issues on an as-they-come basis. In addition, the project benefited Citrix by reducing inefficiency, streamlining the support staff experience, and reducing churn.
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