Process automation for operational efficiency and better customer experience

Bringing customer-centricity to back office processes
August 17, 2021

By introducing automation, organizations can offer a better customer experience while improving process efficiencies and productivity. To get the full benefit of automation, it’s important to look at processes end to end and often redesign them with a customer-first approach. Process automation can be for customer-facing operations or backend processes.  Examples of automation in the customer-facing process include image recognition, natural language processing, intelligent chatbots, etc. And examples of backend processes could be fraud detection, churn analysis, etc. Some of the ways automation and AI help organizations include: 

  • Increased cost efficiency
  • Improved customer experience
  • Redundant task automation
  • Predictive and prescriptive analytics
  • Insights and decision-making support.

Automation can be broadly classified into three areas.

  1. Automation of routine tasks: These are often rule-based tasks that can be automated; screen scraping, scripts, and Bots often achieve these.
  2. Machine Learning Algorithms: Here self-learning algorithms are used to automate many processes – these can be customer-facing or employee-facing. Machine learning models can also be used to derive insights and help in the decision-making process. Examples include fraud detection, product recommendations, churn predictions, etc.
  3. Deep learning: These involve artificial intelligence functions that closely mimic a human brain in processing and analyzing data. where organizations can deploy artificial neural networks and algorithms. Some examples include image recognition, natural language processing, intelligent assistant, etc.

Some of the key factors driving the adoption of automation include: 

  • Availability of powerful computing platforms to process data.
  • Ability to collect more consumer and transaction data due to digitalization.
  • Availability of talent pool like data scientist etc

Organizations must evaluate the following while deciding about automation:

  • Which algorithm to choose and which model to deploy
  • What data set to use, and what are the controlling attributes of the data
  • What are the training sets for the algorithm
  • How to monitor self-learning algorithms for consistency

For automation to deliver desired results, business and technology must work in close collaboration. A challenge for automation is identifying and selecting the right data set which is often fragmented and resides within multiple functions and silos. Getting a single view of customer data is one of the biggest hurdles. Another challenge is identifying good quality data from a large data set. It’s essential to determine what are the dependent variables and controlling variables for better outcomes. Training the models and finding the suitable training dataset is the final challenge. But the benefits outweigh these constraints, and a proper strategy for automation and execution can ensure that the enterprise benefits from improved customer-facing and backend processes.

Comments are closed.