The whole point of automation is to make tasks and processes more efficient. Nothing throws a left-handed monkey wrench in the gears of automation faster than when your users bail out of your application to talk to an agent.

Granted, sometimes it’s necessary for a customer to talk to an actual person, but for the most part if you have something that can be easily automated, like making payments, your self-service application should be more than enough for the vast majority of your customers.

Know Your Metrics

You can tell how well your self-service automation application is functioning by tracking your containment rate. If we’re talking payments, then this is the percentage of people who call into, and complete a payment over the phone without transferring out of the application to speak to an agent.

For a payment application, it’s better to have a higher containment rate. So, if a company had a containment rate of 83% that means 83% of users complete their transaction using the self-service application and 17% of users may have transferred out to an agent.

Why Visualizing Transfers?

One of the nice things about VoiceTrends is that it provides users with a whole wealth of data and analytics on how callers use their voice applications. But when we designed VoiceTrends it became clear that looking at tables and graphs didn’t always tell the whole story. That’s why we created diagnostic flow.

Diagnostic flow shows how people use your voice application; the path they follow from one menu to another. So, if your transfer rate is high and you want to lower it, looking at the diagnostic flow can help you to figure out where callers are encountering issues. Once you know where the bottleneck is it’s much easier to focus on that section of the application and test out fixes.

Diagnostic Flow Options

When you first login to VoiceTrends, Diagnostic Flow is the light green button in the menu on the left. Once you click on that you’re presented with five different flows to visualize.

  • Common Call Path: This shows that actual path that your callers followed when using your application.
  • Disconnects: Tells you where in the application that callers hung up.
  • No Matches: A ‘no match’ occurs when the callers’ input does not match what the system is expecting. For example, if someone enters 11 digits for a phone number when the application only expected 10, that would be a ‘no match.’ A ‘no match’ can also occur if the value entered cannot be found in the backend database.
  • No Inputs: This occurs when a caller fails to input anything and, typically, the menu/prompt repeats.
  • Transfers: This is when a caller abandons the automated/self-service application for an agent.

How To Interpret Diagnostic Flow?

Let’s get back to the question of transfers. When you first open the diagnostic flow for transfers, you’ll see several colored boxes of various sizes. These boxes, or nodes, represent different menus in your application. The larger the node is, the more callers that visited that page in the application.

The flows that connect one box to another gives you three pieces of information. First, is the page info, which details that page the callers started on and where they went from there. Second, is the form info, which specifies the form callers filled out to move forward in the call-flow. The final piece is the field info, which specific field callers filled out in the form to initiate the transfer. The total number of transfers at that point in the call is given in parenthesis.

The advantage to the diagnostic flow is that it shows, proportionally, where the most common transfers occur in your application. This makes it quick and easy to identify trouble spots without having to do a deep dive into your data. Of course, you’ll still want to do that when it comes to fixing the problem, but the point here is that the diagnostic flow lets you pinpoint the trouble area faster so you don’t waste time sifting through unneeded data.

Here’s an illustrated example:

Screen Shot 2016-11-23 at 10.08.40 AM

If we hover our cursor the light green node to the darker green node it tells us that 22 callers transferred out of the application when it came time to entering their payment amount. By contrast, the path below it, which leads to a payment success form, has zero transfers.

Now let’s compare these transfer numbers with the total number of callers as seen in the common call path diagnostic flow.

VT Common Call Path

A quick look shows that more people are completing payments (55), as denoted by the yellow box here, than are transferring out. If 23 people transferred out and 55 successfully completed payments, then that’s a 70% containment rate. That’s pretty good!

Once you have this information, what can you do with it to improve your process and reduce the number of transfers?

One thing you can do is look within your voice app itself to see if there is anything that might be confusing or unclear to callers. You can always A/B test changes to see what impact your changes have.

Another strategy is to consider things outside of your IVR. For example, if your payment app requires customers to enter their account number, then you want to make sure they know what that number is. You may need to look at your billing paperwork and make sure the account number is clearly marked.

If it’s buried in the customers’ bill, or easily confused with transaction or invoice numbers, then try making the account number more prominent. You can update the prompt on your app to tell customers where, exactly, to look on their bill to find the number as well.

Final Thoughts

The diagnostic flow is a really useful tool, and it’s designed to help users quickly identify trends in the way callers use their voice applications. Once you know where the trouble areas are it’s still up to you to figure out how to best fix them, but VoiceTrends and diagnostic flow make that process go a lot faster than it ever was before.

VoiceTrends CTA - A


Jason Myers stitches together letters and words into cogent thoughts as the Copywriter at Plum Voice.

    Also find me at: 
  • linkedin
  • twitter