With online reviews becoming increasingly significant for customers exploring new products to buy, services to engage and places to go, business owners are naturally concerned about how to constructively use them to improve their operations and offerings.
However, acting on customer feedback in an effective and productive way is not always straightforward, a task compounded by the fact that many reviews may be ambiguous, incomplete, or even fake. Enter Eagle, a web-based AI platform by NJ Group that helps those in the service industry monitor and manage customer feedback.
Neelendra Jain, Founder of NJ Group shares on his company’s use of AI to address the issues faced by their own Food and Beverage (F&B) establishments in managing customer feedback, and how AI can enhance other aspects of the sector.
What inspired you to harness AI in addressing issues with customer feedback?
AI Enabled problem Identifier identifies the complex patterns of issues which cannot be manually identified with the lot of data being generated during this process. This data is used by our AI-Predict model, where the business owners can have a lot of deep insights about the business like customer footfall pattern, sales prediction, manpower requirement, inventory requirement, menu pricing improvements, staff performance analysis, and so on.
Considering the fact that customer-based data generation within business, along with the system data being clubbed in backend, is going to grow exponentially every day, this gives a great opportunity for AI to play its game and dig out the key insights which were almost impossible with human minds in silos.
What was NJ Group’s experience utilising Eagle among their own F&B establishments?
Before this, we would have used verbal feedback, paper forms, emails and online portals to learn about our customers with incomplete information and not be able to make any corrective actions to identify the real problems.
We used to spend a humongous amount of money on marketing gimmicks to ensure that we retained customers. But that rarely paid off in the long run. We were leaking customers badly and didn’t know where the holes in the bucket were located.
Today we have a real time system that provides us immediate insights into our customers’ thinking. What they liked, what they didn’t like and most of all an opportunity to understand how we can serve our customers better. The business owners can do a lot of deep analysis of their business process and define the actions to solve the problematic areas which were unidentified initially.
Our floor managers immediately figured out where the root cause of certain issues were coming from. And top management were able to dig deeper and discover that we were having quality issues in our supply chain. The ROI has been enormous.
The biggest problem we had was with fake customer reviews. Not that the person is fake, but maliciousness and wrong intent can be seen very easily. However these reviews tend to take your business down a few notches. All it takes is one bad review to turn away ten customers.
With Eagle our engagement with the customer became vastly intimate and in the moment. If there was a genuine issue, we are able to solve it then and there.
Could you tell us how the Eagle platform was developed, and how its infrastructure needs are met?
Eagle was developed In-house from ideation to development. It is available on App store and Google play store and hosted on AWS. We are managing with very lean professional team.
Are there other aspects of the service industry than can be you plan to enhanced with AI?
We have strong plans to bring more power of Artificial Intelligence to solve many direct and indirect related matters including sales prediction, manpower planning, menu pricing optimization, auto-categorization of issues, problematic sector identification, performance analysis and much more.
AI can predict the probable issue in advance, allowing not only real time monitoring but also minimizing the bad experiences of customer and identify the complex patterns of issues which cannot be manually identified easily in a multi factored dependant environment, such as manpower needs, traffic, sales amounts, and number of customers.
Based on feedback data, AI based algorithms will learn and generate business rules like:
- When sale amount increases, kitchen issues increases
- When number of patrons increases, manager to focus on service side
These rules will keep on learning with more and more feedback data generated every day and the algorithm accuracy will increase, ultimately leading to a mature AI enabled system.
These AI and ML defined rules will be the base of a Precaution System before the Prevention System, that is, predicting the probable issue in advance and planning accordingly well in advance.
So this system is not only handling the customer feedback smartly on a real time basis but also trying in the background to minimize the bad experiences of customers.
