Big data is the new buzzword. Business publications are full of stories about big data. A Google search will yield a lot of information about big data — and a lot of vendors trying to sell you on Big Data. But what is Big Data? It depends on whom you ask. But a general rule is probably that it is more data than you know what do with, or how to handle.
There are several problems with the Big Data paradigm that is being reported in the media and marketed by vendors.
1. It is largely driven by consulting and computer companies, who are always eager to jump to on the next bandwagon. In many ways, they are overselling the potential benefit, which inevitably leads to disappointments. Already, one recent media article declared that the era of Big Data is dead.
2. For service contract providers, we’ve always had lots of data. The amount and type of data is not really changing. Most of the explosion in data in other industries is from automated sources, such as radio frequency transmitters, monitoring stations and smartphones. Industries that use these technologies are experiencing the surge in data.
For example, Progressive Insurance has a product that monitors the driving habits of the insured. This would record the speed, braking, length of the drive and the time spent driving (along with other factors). This information is transmitted to the company and calculates a new rate. This is significantly more data than normal insurance company transactions.
3. Another issue is that even if you have an explosion of data, you might need only to take a sample to analyze it. After all, you don’t need to ask every voter to compute a poll. We often use a technique known as “bootstrapping” where we only analyze a sample of the data, but do this process multiple times. The difference between the predictions of different models is often indicative of how predictive the underlying data is. However, since we are only analyzing a sample, the amount of data is usually not an issue and the calculations are much quicker.
4. Most importantly, it is really not about the data, but what you do with it. Data should drive business intelligence, which should then drive actions. The data is only the first step.
Big Data (or maybe just data) – What can you do with it?
One of the most helpful things we like to do with service contract data is to adjust the exposures (an exposure being one month of service contract coverage). First we adjust the exposures for the underlying manufacturer’s warranty. If the manufacturer’s warranty is covering the claims, we want to eliminate these months.
Second, we adjust for average driving patterns. At the end of the contract, some drivers will have “driven out” out of their coverage — we want to eliminate these. Finally, we want to adjust for the age of the contract. Cars may have more repairs as the contract ages, though this is offset some by sales, total losses and the “forget factor.”
Using this type of analysis allows the administrator to build a powerful model, which can predict losses and is not dependent on segregating data into small buckets based on the type of coverage and term. In this way, you can calculate the current exposures for a contract and the future exposures. A base cost is calculated from historical data and adjusted for deductible, coverage, purchase mileage, class and any other factors.
What else can a VSC writer do with their data? Here are some ideas for projects:
• Analyze the characteristics of dealers who terminate their dealerships. Build an early warning system that predicts the probability that a dealer will terminate their business in the next 12 months.
• Build a profitability index for dealers based on volumes and underlying business.
• For a dealer, build a model which predicts the probability of a sale using historical close data. This could vary by salesperson, customer characteristics, vehicle or even time of purchase!
• For an administrator, build a model which predicts whether an inspection will be cost effective.
The Big Data revolution may be oversold — but that doesn’t mean that your data is not the key to increasing customer penetration and profitability.
