Quantifying intent and success in the world of digital support

How do you measure something that didn’t happen?

That’s the legitimate challenge many support leaders face when attempting to measure and move the needle on case deflection. What was once an appealing channel to reduce support costs through less call volume has now turned into a business-critical experience that companies simply need to master in order to retain customers. But the benefits are still there: customers largely prefer self-service, it can be more than a hundred times more cost efficient than assisted support depending on your industry, and it takes the repeatable and monotonous cases out of the daily work of support agents.

So why hasn’t every support organization mastered this art yet? Because it’s hard to improve something you can’t measure.

The good news is, you can.

See how Fuze Boosted Case DeflectionConnecting Disparate Data for KCS at Fuze

Let’s start with the basics: Understanding the events of self-service and deflection

Customer self-service success is the rate at which customers are able to complete their desired outcome themselves on your support portal. This behavior does not always mean that a case creation or a call into the contact center was avoided. In short, self-service success is the broader concept from which customers are able to find answers or information on their own.

Case deflection only considers the interactions during which a customer intended to submit a ticket. Case deflection rate is the frequency at which customers are able to find answers on their own to issues that they would have otherwise called support for. Essentially, they were about to submit a case or call you, but resources or content made available to them eliminated the need for live customer service assistance. In other words, the case creation was successfully deflected.

How to measure the different types of case deflection signals

“Not everything that counts can be counted, and not everything that can be counted counts’’ –  A. Einstein

But we’re not mind readers, are we? How do you measure something that was about to happen, but didn’t? The key lies within understanding and measuring the different case creation and case abandonment signals.

Assumed Signals

Looking at our different support channel trends can give us an idea if self-service success is moving in the right direction. We can look at the number of cases coming into the contact center and traffic and content consumption increases to the support site, and somewhat confidently assume that customers are being more successful at self-serving. But that does not give us a reliable and quantifiable view of case deflection, how they were deflected, and more importantly, how much money was saved.

Confirmed Signals

Historically, confirmed signals have been the only way for support organizations to reliably measure case deflection. And the only confirmed signal that was available to them was a post session survey or automated question confirming resolution via self-help. But who answers those? (Hint: very, very few customers)

Explicit Deflection

A customer landing on the contact us form or starting to create a support ticket page indicates an intent to escalate an issue and create a case. One of the ways companies can control and increase those explicit deflections is by providing relevant content recommendations on the case creation page, based on the customer’s intent and context. Basically, use what the customer is telling you about the case they are about to create as the last attempt to solve their issue before they press ‘’submit’’. If they click on one of your content recommendations, and abort the case submission process, you can pretty confidently say you deflected their case.

Time to take out your calculator. 

Once you have technology that allows you to create and measure these explicit deflections, the ROI math on cases avoided becomes pretty straightforward.

(Cost of a case through assisted support – cost of a case through your online channel) X the number of confirmed deflections per time period

$1,500,000 per month. This is how much Tableau Software saved after using Coveo to power their self-service portal and deflecting cases from their contact center.

So how do I move the needle on self-service success and deflection?

  1. All support content, one search to rule them all. Identify all support content across all enterprise sources and service channels, from your Salesforce community, product documentation, YouTube channel. Make all that content available through your support site, and case creation process.
  2. Give your customers confidence in your search box. Provide prominent access to this unified search on your self-service site, portal, or community. Make it front and center so they know you have confidence in your search experience.
  3. Search analytics data is a gold mine. Your customers are using search to tell you what’s wrong with your product, what they’re looking for. Leverage search analytics to identify content gaps and spot emerging support issues.
  4. Relevance at scale takes AI. You’ll need machine learning capabilities to automatically improve the self-service experience over time. Learn from your visitors and what’s making them successful at self-serving. Continually improve search results and predict relevant content.
  5. Seize the deflection opportunity. Use the information customers are giving you about their issue while creating a case to proactively recommend helpful content. It’s your last chance to help them find the answer without having to call you – use all the sources of support content you have, and machine learning to fine-tune content recommendations over time.

Download the Guide to Intelligent Self-Service

Self-service programs are a continuous process, not a project with a beginning and an end. Our latest ebook is the ultimate guide to creating your self-service strategy, with 7 proven best practices and examples from customer service leaders. Download it today!

guide to self-service

About Audrey Patenaude

Audrey Patenaude is the Senior Director of Acquisition Marketing at Coveo. In her role, she oversees sales pipeline generation, leading the team responsible for planning and executing campaigns to create and convert new customers. She joined Coveo in 2014 and previously served as Director of Marketing for our Coveo for Salesforce business. With years of experience in multiple managerial and marketing leadership roles, Audrey has been instrumental in the organization’s growth by reaching and expanding Coveo’s presence in new market segments. She lives in New York City and holds a bachelor’s degree from the famous Quebec City palindrome university.

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