SUCCESS STORIES / Operational Excellence

Artificial intelligence can direct resources towards serving customers and away from waste

How technology helped predict and prevent expensive, unnecessary field visits. And address genuine customer issues faster

Background

In today’s fast-paced world, businesses should have the ability to deliver proactive support before the customer raises an issue. That scenario isn’t entirely in the future.

One of the ways that telcos, for example, can create happy customers is by optimizing the way they handle customer calls. If they could proactively diagnose problems and resolve issues remotely, they could avoid many customer complaints and a large number of unnecessary field technician visits.

Avoiding unnecessary field visits to customers also means improving the experience for other customers who need a field visit. Human intervention is expensive!

Companies struggle to visualize all the moving parts involved in effectively resolving customer complaints at scale. This opacity leads to wasteful resourcing. Ultimately, the end customer suffers.

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60% reduction in unnecessary technician visits

$61.7 million saved annually

36K technician dispatches saved per month

2X improvement in field technician’s decision efficiency

Client Situation

A leading telecom service provider in North America was facing exactly such an issue. Inadequate assessments of the severity of service requests and unorganized field visits led to redundant technician dispatches. It was a double-edged problem – wasted costs of the technician’s time and longer time to resolve genuine customer problems.

The client had the reputation of being a reliable brand for homes, small businesses, and enterprises, supplying internet, phone, and TV. This reputation was now at stake.

Our solution allowed our client to reform their field service operations. Efficiency is valuable; but when a business thinks about eliminating waste, it saves more than cost; it saves reputation too.

Diagnosis

Our client didn’t realize that their technician dispatch process unnecessarily involved a lot of human interventions. The Customer Service Representatives (CSRs) were manually creating work orders. They did not have adequate information to assess the need for a field visit. There was plenty of scope for improvement, starting from request processing to follow-up activities.

Prodapt used statistical data analysis to learn the causes that didn’t require a technician’s visit, also known as non-productive dispatches (NPDs). We classified NPDs under three categories – no trouble found, remote-fix, and mass outages.

Solving It

Here is how we put our intelligence framework and smart screening team into action: The sheer volume, complexity, and noise in customer complaints generate a staggering amount of data points. Human monitoring is overwhelming, along with a reactive and “swivel-chair” approach, a breaking point is nearby!

Artificial intelligence is an answer to this situation. It can thrive on vast amounts of data, sort complexity and noise into clear signals. Its predictive accuracy rivals the best human judgment with time but at several times the speed and a fraction of the cost.

We implemented an AI/ML-powered intelligent framework to predict non-productive dispatches. The intelligent model classified failures based on the nature of the problem. It then pinpointed failure locations for fast resolutions. It further diagnosed the problem and recommended a set of actions to resolve the issue.

Our smart screening team used a comprehensive qualification tool to identify hidden insights and redundant dispatches proactively. The tool leveraged business KPIs and performance metrics to ease dispatch analysis. It then allowed the operations team to make smarter decisions in real-time..

The customer reduced most of their unnecessary technician dispatches through our AI-driven network & field operations framework. They saved $61.7 millions of dollars annually and doubled the decision efficiency of the field technicians.

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