Understanding the customer behavior and their actions is one of the key challenges that businesses of all sizes face today. What drives the customer to a specific action or otherwise is the million-dollar question that all businesses are keen to know and make necessary adjustments to their strategies to get closer to the customer.
The best part of today’s business is that they are not short of data to help them in understanding the customer insights. In fact, their problem is the other way around. They are flooded with volumes of data that needs to be processed in quick time, make a sensible analysis of data and take swift action based on the analysis.
While manual process is one option that is available for the business to process the data, the challenge comes when large and many datasets needs to be processed in a short span of time. Adding the other challenges like human errors, repeatability issues, structure of the data etc. makes the whole process more complicated. Some of the data that are available for the businesses are not structured for easy interpretation and analysis.
Key Challenges
To summarize, some of the key challenges associated with the manual process are:
- Accuracy and consistency of the process
- Time delay in processing large volume of data
- Handling unstructured data from social media platform
While manual processing is the best option for processing unstructured data, the challenges lies in the process time and the human errors while processing the data. Robotic process automation built on top of ML model is a one of the best and possible solution for businesses to process the large volume of data in quick time to get some meaningful insights from the data.
AWS provides an easy to use service, AWS Comprehend that can ingests the unstructured from any data sources like Twitter or Facebook and the structured data from databases and intelligently classify it using a training dataset and then do a sentiment analysis of the data.
“We built an RPA system for one of our customer to process and classify their unstructured website feedback data and perform a sentiment analysis to better understand customer sentiments.”
The system was seamlessly integrated from end-2-end using multiple AWS services like AWS S3, AWS Comprehend and AWS Lambda. The raw data from the website was ingested to the system which then triggers series of automated processes to clean the data, process and classify and upload the output to another AWS S3 bucket.
The processed and structured data with the classifications and sentiments was then integrated with AWS Quick sight for in-depth analysis, insights and for generating drill-down reports. From the user perspective, the raw unstructured data which took hours for manually processing, can be transformed to an insightful and actionable report, in short span of time. The best feature of the system is that AWS Comprehend can continuously trained to learn and improve the accuracy of classification.
Business benefits delivered
- Cuts down in the processing time and effort by more than 95%
- The accuracy of the process gets better with time
- Allows building customizable reports for better customer insights and quicker action
- Reduces the overall cost of the business process
A thoughtful and strategic usage of RPA using AWS services is the most flexible, adaptable and cost-effective way to go. For more information about Robotic process automation using AWS Comprehend and migration solutions please write to us!
Written by,
Delivery Head
1CloudHub
