As a customer, when you buy any home appliances like TV, AC, Refrigerator, Home Theatre or a brand new car, you get a company warranty along with it. This is the commitment from the manufacturer that if any problem arises in the product or spare parts within the warranty period, then company will repair or replace it free of cost.
Industry numbers shows that warranty costs range from 2% to 6% of the company’s revenues. Predicting these warranty costs is an important step for successfully managing the business. If manufacturers reserve too much money, then they lose opportunities to grow the business because they end up with less cash. If they set aside too little money, then they lose opportunities because they have to keep adding to the warranty reserves funds.
Let us see some quick definitions of warranty:
- Base Warranty – original warranty coverage provided by manufacturer at no extra cost, since it is included in the product price.
- Extended warranty – this comes into effect after the base warranty expires.
- Warranty reserves – amount of money set aside by manufacturer for the purpose of servicing the warranty claims. This is based on the forecasted warranty costs.
In automotive industry, warranty generally guaranties free repairs or replacements subject to both age of the car & mileage.
Warranty Analytics is integration of warranty claims data with customer, product, sales and geographic information, so companies can accelerate detection of failures and reduce time to correction.
It can help in significantly improve the early warnings of parts failures based on customer complaints and failure patterns, combining structured data with un-structured data (such as call center records) to give alerts and information about developing trends that would have gone unnoticed earlier.
By identifying warranty-related issues early, companies can save thousands of dollars in both repair costs and customer retention because issues are proactively addressed before they become significant, costly problems.
Root cause identification of parts failures is the biggest challenge in the industry today. 70% of annual warranty expenses are consumed by repetitive and chronic problems. Prioritization of these root causes helps companies calculate how much it will cost if nothing is done. This allows them to determine the best course of action and associated costs, as well as any potential effect on customer satisfaction.
Managing warranty costs is an enterprise wide challenge, impacting multiple departments, including quality, product engineering, customer service, finance & purchase.
Typical areas of applying Analytics on Warranty data involves:
- Data mining to Identify the patterns of claims
- Text mining to identify problem areas and fixing them, instead of technicians trying to select from hundreds of warranty categories
- Predicting the expected number of claims or cost of claims
- Predicting fraudulent claims
- Investigating the association between different types of claims
- Identifying issues before they become showstoppers
- What-if analysis such as if we increase the mileage what will be impact on warranty costs
Some of the warranty analytics benefits:
- Increased customer satisfaction, product quality & brand reputation
- Tremendous impact on bottom line due to early issues identification
- Huge reductions in total manual claims processing costs
- Prevention of fraud on warranty claims
- Optimized warranty policies for maximum financial performance
- Increase efficiency of support logistics such as optimum stocking of replacement parts or deployment of technicians
It helps answers the questions like:
- Our competitors just raised their product warranty from 3 years to 6. If we do adopt the same, how much more warranty costs we will incur? If we don't, how much revenue we will we lose from reduced market share?
- Given a new product with no historical data, should we play it safe and offer only a one year warranty, or can we offer a three year warranty to improve our brand perception?