Out of the total number of electronic appliances in India, around 8-10% of them go for repair each year. The installed base of electronic appliances in India being approximately about 3 billion, 8-10% of it makes a huge difference, and this percentage alone is responsible for a $4 billion market every year for repairs.
A customer makes use of a home appliance for about 8-10 years after purchasing it, so the need for these appliances to be of good quality and durable is essential. Plus, if an electrical appliance runs for that long, it becomes an important factor the next time customers are making a decision to buy an electric appliance. In India, the home appliance market has seen a perfect blend of foreign and indigenous brands selling appliances in all categories. So, even though there is a healthy mixture of brands, to capitalise on the market, Indian brands have to bet on the after-sales journey so that they can garner a substantial share of the market. One player that plans to leverage the latest technologies like AI and ML, to improve the post-purchase home appliance servicing experience for the customers is 247around.
In today’s era, where every company should focus on integrating more technology in their services, majority of the companies still use primitive ways of repair resulting in poor customer experience with lack of standardisation and one of the biggest mistakes they do is leaving the quality of the service to be decided by the service personnel.
For this week’s Deep Dive column, we asked Anuj Aggarwal, CTO & Co-Founder, 247around to give us some inputs on how they use AI and ML capabilities in their products/services.
247around’s Tech Stack
For the company’s tech stack, it uses LEMP stack- the Linux OS. They use lightweight webserver Nginx and MySQL relational database management system (RDBMS) along with the PHP programming language, the world’s most popular web development open-source platform. All the software are used based on their open-source nature, worldwide adoption by the developer’s community, integration with other frameworks/3rd party APIs, and speed of development.
The company hosts apps using AWS stack and also uses many Google APIs for various needs like showing a route map to engineer for customer location. 247around also uses custom AI/ML algorithms for the development of cloud-based apps. Along with these, it uses third party APIs in its products, which make them robust, efficient and responsive.
During this ongoing pandemic, 247around has integrated its technician app with COVID-19 information and started showing affected hotspots to them so they can reach customers safely.
Use Of AI and ML
247around uses AI to ensure ‘First time Right’ service by leveraging historical data on repairs from millions of services that have been delivered already to predict future repair decisions and prompt probable solutions to its field engineer workforce. This is mainly used to reduce the downtime of electronic appliances and improve customer experience.
Usually, a technician visits the customer and upon diagnosis of the appliance, identifies the problem with it. Then if needed, orders spare parts, once the parts arrive, the technician re-visits the customer and replaces the faulty spare parts. 247around uses AI to reduce the number of times a technician visits and solves the problem quickly. 247around uses algorithms that suggest spare parts to the technician that he/she should carry along with him during his first visit. It suggests parts based on the problem, appliance, model, and many other parameters.
247around also has custom-developed algorithms for auditing purposes and to ensure that all its customers receive the best service experience from its team and service partners.
Once a customer raises a complaint and when it is resolved, 247around’s audit team earlier used to manually check the details provided by the technician and verify the claims. This process in the past involved the tedious work of checking the appliance serial number, purchase invoice, warranty plans, spare parts consumed and examining the pictures clicked by technicians. But, recently, leveraging AI and ML algorithms here, the company can identify serial numbers in uploaded photos, find the irrelevant ones and discard them along with removing duplicate pictures. All this information is reused to tag engineers and train its model. This has improved the efficiency of the audit team, and now, because of the use of AI and ML, the team can audit more calls quickly.
247around’s Hiring Phase And Employee Training Model
247around with their hiring experts collaborate with hiring partners for their hiring needs to scour out the best-suited talent. Currently, the company has 20 members in its engineering team who take care of product development, QA, and partner support. When it comes to adding talent to this pool of engineering team, the hiring specialists expect in-depth technical knowledge and proven past domain experience, besides these, strong communication skills and leadership qualities are especially desirable.
Every employee that joins at any level undergoes a mandatory training program the first month they join. There is also an 8M training model at work, where each employee has to go through 8 hours of training every month, which constitutes soft skills and technical programming.
In future, 247around aims to leverage more cutting-edge technologies to handle their 50,000 transactions per month, which is roughly 1666 transactions per day. The company further looks at partnering with brands to enable remote diagnostics and provide a better customer experience in every customer interaction. 247around will be forever committed to deploying AI methods based on modern techniques and best practices to enhance customer experience and gain the trust of its customers.