Repeatedly receiving similar complaints about a device failure will certainly not leave a good impression on the customer.
Often devices get failed due to unknown reasons and the cost these companies incur to identify the reasons is huge In terms of manpower, it can reduce the unnecessary burden and helps in cycle process duration reduction of identification as well.
Device Data Analytics using Artificial Intelligence and Machine Learning has its applications in various industries. One such application is “Battery Failure Root Cause Analysis using AI & ML”.
In this present world, where safety of human beings and pollution free environment is given priority it has become necessary for companies and government to control emissions resulting from vehicles. Hence companies are moving from traditional fuel using cars/trucks to the vehicles running on batteries.
Batteries at this critical point of time are often facing multiple failure issues.
A battery when faces such failures, the battery prediction model accurately predicts the possible reason of failure using the AI/ML algorithm thereby removing the necessity of dissembling a battery to identify the cause of failure.. It also helps to send the report to developers who can act immediately. This cycle process duration can be reduced by 95%, and total transportation cost incurred, domain expert’s dependency can be reduced at a greater level with an accuracy of 85%.
World is moving at a faster rate than expected. Many things are changing right from the way the items are manufactured to the way they are sold. Technology is the key ingredient without which the customer satisfaction and clients’ growth are impossible.
MobiWeb Analytics of Innodatatics provides a sophisticated way of leveraging technology with the current world trend, to draw many actionable insights for the execution of quick and clear decisions. Our state-of-the-art advanced algorithms of MobiWeb Analytics take into account the information pertaining to google analytics, transactional data, data from social media etc., build predictive and descriptive models that amplifies customer satisfaction and improve sales.
These Models will amplify
Service Desk Automation
The reputation of an organization depends upon the time and quality it maintains in dealing with the customer. The service level agreements, quality and responsibility associated with the services, play a significant role. In general, each and every ticket is crucial and requires equal attention and focus. With improper knowledge base and improper diagnosis, there are chances of escalations, which will in-turn dent the reputation of the company/department.
Service Desk Automation, with its sophisticated algorithms and machine learning techniques automates as the below:
Digitization is penetrating into the remote parts of even the third world, in the recent times. With the advent of advanced technology and digitization, the data that is getting generated is very huge and the number of hands asking for queries on customer product/services is increasing at a very rapid pace. Keeping the current and future demand in mind, it will and is becoming a challenging task for the clients to satisfy their customers in responding to their queries.
Chatbot is the product that eases the life of the customer care by
Server Log Analytics
To every organization, servers are the heart of their operations and day to day activities. In this competitive world, even the startups with great ideas are competing with the big giants. Eventually a simple mistake can divert the mind of the customer to switch to another vendor
Service Log Analytics helps