The Problem
A leading 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 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 experiencing rapid growth through acquisitions. The organization previously tried traditional process mining to shed light on its business challenges and to understand process flows better. However, this attempt failed because of a 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 responded to 7,500 requests monthly, 10% of which had 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 was unsuccessful 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, as well as where the time is spent in each of the process steps. Every customer request was to be mapped to each workflow 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.
With ProcessMaker Process Intelligence, they analyzed all customer service operations in one country, covering 3 teams and 65 customer service agents. The focus was to:
