Support is naturally a data-intensive business. Leaders have access to tons of operational data, customer sentiment, and experience data. It was not until process mining technology and Journey-to-Process approach was available that we were finally able to connect the data sets and understand the cause-and-effect relationship. The process mining insights uncover opportunities to rapidly introduce improvement across the organization, making process optimization easier than ever and taking data-driven decisions to an entirely new level.
Join me in this blog as I share with you how Product Support leverages SAP Signavio Solutions to better identify and address experience gaps, to bring an effortless support experience to customers.
What is Journey-to-Process Analytics?
Journey-to-Process Analytics helps organizations improve their processes, by analyzing experience journeys and operational processes in conjunction. In a previous blog post, we laid out a 10-step plan as a guide to this kind of joint analysis; today we want to show you an example of real implementation. Read on to discover how SAP Signavio solutions help Product Support to find actionable insights from the combination of their web analytics [1] and customer survey data.
Introducing Product Support
Product Support provides customers with an award-winning support experience. With its Real-Time Support offerings, SAP is taking the enterprise support experience above and beyond industry standards with SAP’s one-stop shop with SAP for Me. Product Support strives to resolve cases [2] fast, conveniently and to the customer’s fullest satisfaction.
Motivated by a relentless pursuit of excellence, SAP consistently works to find new ways to pinpoint areas of success and unearth untapped potential for further enhancement. With this spirit in mind, in late 2022, Product Support and SAP Signavio joined forces to tap into Journey-to-Process Analytics, guided by the 10-step plan. This is how that adventure began…
Following the 10-step plan – Starting from the business and experience and adding the process
Having gathered insights into operational and customer experience metrics separately, Product Support acknowledged the gap (#1) in analyzing exactly how these areas interacted and affected each other.
The O without the X (‘operational data without experience data’): For example, Product Support may notice a high usage rate of a newly added feature on the website. If we were to observe a feature such as our support assistant [3], we would review the operational, process side of things, as seen in traditional web analytics. But it does not immediately inform the team of whether this feature helps customers in accomplishing the overall task of resolving their issues – that is the experience or journey side. This can help us answer how the support assistant feature is helpful to customers.
In this example, a follow up inquiry may be to determine the effectiveness of the placement of the feature in its current case creation process, which is now possible with SAP Signavio giving us insight into a customer’s journey.
The X without the O (‘experience data without operational data’): Another gap in our understanding could be that customers report their issue as being resolved, but we can’t identify whether that was through self-service content. With process data and analysis of sessions, however, insight can be gathered on whether support assistant, together with self-service content, helped to drive customer satisfaction.
Objectives
Next, the team identified the focus of the journey, and its processes and owners (#3). The process to be analyzed is the creation of cases, from the moment a user logs into SAP for Me till they leave the website. The resulting journey is the sum of experiences the user has, when navigating through each individual touchpoint on the website. At this stage, the main objective can be formulated:
“Deflect cases, resolve issues or (if this is not possible) reduce customer effort when they need to create a case.”
This is then divided into three sub-goals:
A: Deflect Cases & Resolve Issues
Successful outcomes can be defined as users report on their experience via Qualtrics “issue resolved before case opened, but after SAP for Me was opened.” This means helping customers to solve their issues on their own, for example by pointing out relevant Knowledge Base Articles (KBAs), even before the customer has to open a case.
B: Deflect Cases and Resolve Issues with Self-service by SAP
This goal constitutes a desired version of ‘issue resolved,’ as it indicates high-quality and easily accessible content.
C: Make it Easier to Submit a Case
For issues where a case cannot be avoided, the objective is to make it easy for users to submit a case.
In each instance, the overall goal for customers is to open fewer cases saving operational costs via reduced manual support effort, while also saving customers time and therefore increasing customer satisfaction.
Stay tuned to learn how these scenarios were implemented and which findings could be generated
in the second part of this blog post.
Glossary:
[1] Web Analytics: Web Analytics is used to analyze website data. Here it’s used to merely collect anonymized web data.
[2] Case(s): A case is an issue in a system that needs to be resolved. A ticket is the documentation of a case. For more information, see here.
[3] Support Assistant: The support assistant is a web assistant within SAP for Me, to guide users to relevant knowledge base articles, to help solve their issue before having to create a case.
More about Signavio
Discover the story behind SAP Signavio or visit the SAP Signavio website to learn more about the SAP Signavio portfolio and its powerful and integrated platform.
