“Time kills all deals.” We’ve all heard the expression. It’s folksy. It’s colloquial. But, it’s not quite actionable. How do you correct your time-wasting mistakes if you don’t know you’re making them? There’s a simple answer that also happens to be a favorite saying of Developer Evangelist Rob Spectre — look at the data.
Splunk helps companies organize and synthesise data of all sorts so they can do anything from streamlining their sales calls to making sure their website is running efficiently. David Greenwood, self professed “Data Geek” and Splunk employee, built a Twilio and Splunk integration to help you quantify all your call metrics.
How long is too long for a sales call? How can one second of extra load time increase support tickets? We talked with David about how he built his app and the importance of data below.
What made you build the Twilio and Splunk app?
Splunk customers rely heavily on telecommunications to run their businesses; sales, support, and operations are just a few that come to mind. Many are using Twilio to power their communication systems and it was only a matter of time before an app started to take shape.
How was the build process?
I had some head start being a Splunk employee, but the build process was super-simple. I once heard Splunk described as Google for Big Data – this is a nice description to summarise app development process too. Apps can be easily created using a series of Splunk searches to ask questions and visualise data.
How does having call data on hand affect your customers’ business?
Comparing call length against sales success, looking at call costs vs customer value, or understanding the most effective time to call prospects. Those are just a few examples that I’ve seen Splunk customers implement in tele-sales environments. The use-case for this data reaches much further than just call centres though. I’ve heard from finance teams using Twilio billing data to help with forecasting and rate negotiations, for example.
Any parting thoughts?
Splunk is a platform that makes it easy to collect and analyse machine data from almost any source, Twilio being just one. When customers can look at all data being generated across their environments in real-time it often leads to some exciting discoveries. One example of this was when I looked at web server data alongside Twilio call data. A 1 second slowdown in page load time led to a 10% increase in calls to the support desk!