Process Intelligence for Customer Service

Nordic Real Estate company solved the visibility challenge of their customer service operations.

Process Intelligence for Customer Service
Sector: Customer Service

Highlighted Processes

  • Customer identified 5+ customer request-triggered workflows that they can automate with a robotic process automation
  • Increased the automation ratio by 10% and decreased time to resolution from days to minutes

The Problem

In this case study, we share how a leading Nordic real estate company increased the end-to-end visibility of its customer service operations and internal workflows. Due to the rapid growth and acquisitions, the case company ended up with a complex IT landscape with dozens of legacy CRMs. The main goal was to increase the visibility of workflows in order to improve automation levels, make better business decisions, and ensure accurate billing of out-of-scope requests.

The client is a leading real estate management company that has been growing fast through acquisitions in the market. The organization has already tried traditional process mining tools to shed light on its business challenges and to understand process flows better. However, it failed because of the complex mixed IT landscape with legacy CRMs brought over by acquired companies. This resulted in highly variant workflows over dozens of IT systems, portals, and documents.

The Solution

The customer service unit handles 7500 requests monthly, 10% of which has already been automated. Unfortunately, the customer couldn’t map any more workflows beyond that manually, and thus the visibility across processes was highly limited.

The event log extraction-based method used previously didn’t succeed primarily because it overlooked the majority of the workflows happening across different systems. Moreover, it failed to bring business insights into the time usage per customer.

The client wanted to understand how the process flows overall and where the time goes in each of the process steps and for each 
 of the customers. They needed to map the workflows per customer request to identify and prioritize the 
 most impactful automation targets.

The issue was further complicated by the following characteristics of the process:

  • Work happens over 10+ different legacy IT systems and supporting applications.
  • Customer requests are of such diverse nature and require such different actions across business applications that it’s impossible to identify repetitive actions manually.
  • Management fears that employees aren’t billing out-of-the-scope requests that should be billed separately.

This is when they found out about ProcessMaker's Process Intelligence. Together with the client, we analyzed the full customer service operations in one country covering 3 teams and 65 customer service agents. Our focus was to:

  1. Identify the time used per customer and separately billable work to bring better business decisions and verify the accuracy of invoicing.
  2. Find repetitive and automatable workflows in prioritized order for quicker customer request resolution to improve customer experience.

The Value Created

Already within 4 weeks of using ProcessMaker's Process Intelligence software, the client was able to successfully identify the gaps in invoicing that caused them 5-figure losses in the tracking period? Customer also identified 5+ customer request-triggered workflows that they can automate with a robotic process automation (RPA) solution to cut manual efforts, increasing the automation ratio by 10% and decreasing time to resolution from days to minutes.

In addition to the project scope findings, the customer learned that:

  1. The share of the customer request-related work was below 50%, compared to the expected 85%, meaning that the share of “other” nonimportant work was higher than the planned capacity level.
  2. 10% of customer requests were opened/touched, but not taken action upon. These were probably seen as problematic/hard cases to be solved, causing delays in customer responses.
  3. 30% of employees were still using their old work practices, which were replaced already 4 months ago, instead of the new prioritize → solve → report approach
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