Wise Insights

How Our New Implementation Engineer Helped Scale Twitter’s Support (and Wants to do the Same for You)

machine learning companyClint Guerrero recently joined our team at Wise as an Implementation Engineer, and we think he brings an interesting perspective that says a lot about us as an organization and the machine learning space in general.

Clint previously worked with heavy hitters Google and Twitter, so he comes to Wise with a breadth of experience, especially in the support area.  As a Support Engineer at Twitter, he was responsible for improving the company’s high-volume customer support ticketing system.  His efforts saved Twitter a ton of money, and made their system faster and more effective.  The experience also whetted his appetite to bring the benefits of automation to even more support organizations.

We’ll let Clint explain in more detail:

Why did you choose Wise to continue your career?

When I was at Twitter working on their ticket triage and flow systems, I tried a number of different methods for making the support system faster, more robust, and less redundant.  Some experiments worked, others didn’t, but overall I was able to do a lot to improve the speed and efficiency of the Twitter support ticketing system.

One of the biggest projects I put together involved using Machine Learning to automatically answer a large subset of the tickets.  Putting this together was a huge undertaking involving system work, Fortran, and a heck of a lot of time.  But it worked.

After I was done, I was exposed to the work Wise is doing in ML and specifically in support, and I realized they were doing what I had been trying to accomplish at Twitter, but they were doing it better and faster.  So that was a huge draw for me to join Wise.  They have the right set of skills and they’ve built the right system, so it was just a perfect fit.

How does your past experience inform what you’ll be doing at Wise?

One of the key skills I had to develop over my years at Google and Twitter was learning how to effectively scale a support system as the business quickly grows.  It’s not an easy thing to accomplish, especially without the best tools in place.

Often times, a company will hit a point where growth accelerates and unfortunately that’s when they realize that the tools and systems they had set up to handle customer support just aren’t scalable enough to grow with them. In my role here at Wise, one of my main goals is to tackle that issue and help our current and future clients effectively scale their support systems using Wise Support to make the scaling process easy and intuitive.

What excites you most about how Wise works with customers?

Wise approaches the entire concept of practical Machine Learning differently from most companies.  Most companies in this field start with a data science approach.  Their core service is simply manipulating the data.  But in order to get the most out of data science and ML, you need to have a solid systems and tools approach.

Wise definitely has both sides of the equation down.  The product is top-notch and focused on delivering results, the talent on the team is unparalleled, and the solutions we can provide for our clients are ahead of their time.

And that’s exciting.

What is the implementation timeline for clients looking to get the most out of what Wise technology has to offer?

Most companies that start working with Wise already have some sort of established support system in place. The technology itself is designed to integrate seamlessly with the existing system and processes.  Of course, no client is a perfect carbon copy of another, but the predictive application immediately learns from each company’s specific ticket and agent data to get really good results right out of the box.

The learning and implementation period typically takes only a few weeks, although we’re constantly working on streamlining that process even further.

If you have any questions for Clint, or if you’re interested in seeing how Wise Support solutions can improve your ticketing and support systems, contact us.

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Topics: Machine Learning, Customer Support