Webcast Video: Learn How Directly's App Works Like Uber & Facebook


Posted by Andrew Ciaccia

Last week, the Directly data science team presented the results from an in-depth analysis of 1 million customer support tickets resolved using an on-demand model. Data Scientist Eugene Mandel and Data Analyst Spencer Cross did some serious number crunching on a huge amount of data, which, as CEO Antony Brydon explained, is leveraged by algorithms that work in similar ways to Uber and Facebook.

If you weren’t on the webcast, here are some key insights from the data that was shared. And if you prefer to watch the recording, you can find the link at the bottom of this post. 

Insight #1: Attract Highly Educated Support Staff, With $0 Recruitment Cost

Recruiting great talent is a serious challenge.

According to the Bureau of Labor Statistics, the typical call center rep has a high-school diploma or equivalent. By contrast, 71% of Directly experts have a college degree or higher.

What's more, the way the experts are acquired ensures that virtually all of the experts are experienced and passionate about the products they support. And recruiting cost is zero, compared with $15,000 on average.

Insight #2:  Response Times Are Measured in Minutes, Not Hours

CSAT Response Time

Over the million tickets analyzed, the median response time is 3.9 minutes and CSAT is 92.5%.

The key takeaways here, are that on-demand experts answer quickly, speed correlates to satisfaction and explicit satisfaction is very high with on-demand experts.

How do your numbers match up? Are your measuring response time in hours or days? How about minutes? 

Insight #3: Average Savings Per Ticket Across On-Demand Networks is 46%

Remarkably, in our analysis, the cost-per-ticket ranged from 45% to 86% lower - with 46% average savings - using on-demand agents than their in-house or outsourced agents.

This major savings can be attributed to two major factors: overhead and utilization.

On-demand agents don't require buildings, technology, middle managers and salaried wages, which can add up to 50% of the overhead in traditional contact centers.

Insight #4:  During Surges, CSAT Remains Constant And Even Rises up to 6% In Some Cases

Under traditional models, there are two primary strategies in handling surges.  You either overstaff before (this gets expensive and leads to low utilization), or call in more staff after the surge and watch CSAT and response times plummet.

The analysis showed that during a surge across most networks, response times and CSAT remained constant and in some cases even increased by up to 6%! This data throws out traditional assumptions and represents a radical change in the way customer service can operate.

The above insights represent just a snapshot of what was covered during the first ever analysis of on-demand customer service data.

You can view the entire webcast recording here: info.directly.com/data-science