Enhancing Process Mining with Customer Experience Data: A Banking Perspective

By blending process mining with customer experience data, banks have a fantastic opportunity to elevate their services and truly connect with their customers. We’re excited to share some practical insights on how to align process mining with CX management. It includes key strategies for bridging the gap between process analysis and customer expectations, along with actionable recommendations that can genuinely enhance the banking experience. Let’s explore how these approaches can lead to more meaningful interactions and improved satisfaction for customers!

In today’s competitive banking landscape, delivering an outstanding customer experience is more important than ever. As customers seek seamless and personalized interactions, banks are increasingly adopting customer experience management (CXM) strategies that incorporate process mining to enhance their services. This blog post will delve into how process intelligence can significantly improve CX management in banking, providing practical insights and addressing common questions along the way.

To truly harness the power of process mining, analyses must align with strategic goals. One of the key objectives in banking is to elevate the customer experience (CX). By integrating process mining with CX data, banks can identify where their processes may be falling and make informed adjustments.

As we move forward, we’ll outline a practical approach to enhance process mining analysis with CX data, helping you achieve your goals and create a more satisfying experience for your customers.

The Challenge: Bridging Process and Customer Perspectives

Today’s customers expect a seamless, Amazon-like experience when they engage with their banks. They want convenience, speed, and personalization. To meet these expectations, banks need to identify and address any shortcomings in their processes. While process mining provides valuable insights into system data, it often overlooks the vital customer perspective.

This gap can lead to missed opportunities for improvement and a disconnect between what customers want and what banks deliver. It’s essential for banks to not only analyze their processes and understand the customer journey deeply. By bridging this divide, banks can create a more holistic approach that truly enhances the customer experience.

Pinpointing the Customer Journey

To truly enhance the customer experience, the first step is to select a critical customer-facing process—like account opening, security verification, or incident resolution. It’s essential to collaborate with your CX teams, those dedicated individuals who understand the nuances of customer interactions. Together, you can identify those pivotal “moments of truth”—the interactions that significantly impact customer satisfaction.

While Process Mining provides valuable insights, it often focuses primarily on system data, missing out on the crucial customer perspective. Most banks already have task forces that have done amazing work mapping customer journeys and identifying pain points. Through direct feedback, they’ve pinpointed the critical touchpoints that determine whether a customer’s experience is deemed “good” or “bad.” By integrating these insights with process mining, banks can create a more holistic view that truly reflects customer needs and expectations.

Customer journey mapping in ARIS.

These pivotal interactions are dubbed “moments of truth” in CX lingo – instances where banks can either wow or worry their customers. As process miners, we aim to integrate these critical touchpoints into our analytical framework. By doing so, we’ll gain invaluable insight into the customer experience and pinpoint areas ripe for enhancement. It’s about turning data-driven discoveries into heartfelt connections that truly resonate with our patrons.

Many process mining projects focus solely on system data. While this is a necessary starting point, it shouldn’t be the final destination. Alongside analyzing event data, it’s vital to gather insights that exist beyond system records—what we can call CX-enhancement data. It means we model our processes, mine the data, and then enhance our understanding with these valuable insights. By doing so, we can create a richer, more meaningful picture of the customer experience that truly reflects their needs and feelings.

To know about Business Process, Read the Blog: What is a Business Process – Definition, Types, Characteristics, Importance, and Lifecycle.

Defining and Combining CX and Process Data

CX-enhancement data takes qualitative customer feedback and turns it into measurable metrics. For example, in fraud reporting, we can define what constitutes a “good” or “bad” experience by looking at response times and resolution outcomes. This approach enables us to assign meaningful CX scores to each process instance, helping us understand how well we’re meeting our customers’ needs. By blending these insights, we can create a clearer picture of the customer experience, allowing us to make improvements that truly resonate with our clients.

By layering these CX scores onto our process execution records, we create a rich dataset that shows how well customer expectations match up with actual process performance.

Take fraud reporting as an example. Customers often express dissatisfaction when:

• They don’t receive an initial response within an hour.
• There’s yet to be a resolution within one business day.
• If applicable, they have yet to receive a refund within three business days.

It’s essential to incorporate these expectations into our process mining analytical model. We need to ask ourselves: Were these expectations met? By doing this, we can better understand where we’re falling short and make the necessary adjustments to improve the customer experience.

The model might look like this:

By incorporating these criteria into our data model, we uncover insights that were previously hidden. It’s clear that longer response times lead to a frustrating experience, but our data still needs to capture or evaluate this. We create a dataset of critical moments of truth layered over the process execution records. If required, we can “weight” these customer experience ratings to reflect the significance of these key moments.

CX analysis with process mining 

With our process dataset now enhanced by customer experience (CX) metrics, we’re ready to start unveiling valuable insights.

We begin with an exploratory analysis of the execution data, leveraging the CX scores integrated into our model to form case cohorts.

At this stage, our aim is to establish CX service-level agreements (SLAs). Through root-cause analysis, we want to identify when cases start showing signs that a poor experience is likely. By examining the process stages, we can pinpoint instances where the calculated “CX score” falls short of expectations.

This analysis can guide operational teams in identifying “remediation windows.” This crucial time frame exists between when a case first shows signs of underperformance and when it reaches a critical moment of truth. In simple terms, there’s still an opportunity to address potential issues.

By using our findings to flag cases within the remediation window, we can alert operational teams to proactively escalate these situations before customers feel compelled to do so. In this way, process mining is actively helping the bank achieve its CX objectives, but our journey continues.

Read Also: Business Process Governance and its Importance: Complete Guide.

Proving the value of process mining

There’s even more to explore! We can also develop targeted improvement ideas and quantify their potential impact.

Using our analysis model, we can generate reports for specific case cohorts based on the CX scores we’ve established:

• Did-not-breach
• Trended-saved
• Trended-breach
• Intermediate breach

With these cohorts in hand, we can examine the data more thoroughly and conduct further analysis to uncover actionable recommendations for improving the process.

Here are some thoughtful suggestions for enhancing customer experience (CX):

• Are there particular case types that frequently fall short? Could we offer additional training upstream to help prevent this?
• Do cases with low CX scores require extra support? Let’s suggest shifting team capacity to address underperforming activities.
• Can we identify best practices from cases that excel in our CX evaluations? How can we share these insights with other execution teams?

It is promising, as process mining can be a valuable tool in achieving our strategic goal of enhancing customer experience (CX). However, we will likely face pressure to demonstrate the financial value that process mining creates, especially in the financial services sector.

While we’ve helped operational teams focus on critical CX inflection points, the question remains: can we quantify this value?

Gather your CX-focused stakeholders and discuss the costs associated with underperformance.

To showcase the effectiveness of our detection, intervention, and improvement initiatives, we can track “cost avoidance” for instances where we prevent closed accounts, escalated complaints, and negative reviews.

Moreover, the model we’ve developed can drive change and illustrate its effects over time. With our insights, we can recommend adjustments to relevant processes and update the documented “live” process. By harnessing the power of process mining within a comprehensive process intelligence suite like ARIS, we can continuously enhance customer experiences and quantify those improvements.

Like process mining, enhancing customer experience (CX) relies heavily on teamwork.

It’s clear from every stage of this journey that cross-functional collaboration is vital for successful transformation initiatives. This teamwork is essential not only for creating impactful process mining analyses but also for achieving tangible improvements in customer experience. Unsurprisingly, reaching strategic goals is challenging, and collaboration is key to success.

Process mining can significantly contribute to CX goals in banking by providing a comprehensive view of the customer’s journey, moving beyond mere reporting of process execution records. By integrating real-time data on how customers interact with process outputs, we can build a detailed case-by-case history of typical customer responses to performance.

The key lies in capturing crucial CX data points by engaging the right teams within the bank. Achieving CX objectives is directly tied to our process mining analysis; we must expect performance indicators to reflect customer sentiments with this essential data.

As we’ve seen, process mining can unravel complex CX challenges and bring financial services closer to the exceptional experiences that customers expect, akin to what they find with Amazon.