How to use Big Data in the Auto Industry
LILY thinks that it is not what you know that matters, it is what you do with what you know.
Customer satisfaction is the key to maintaining any business now. In this article, I’ll be focusing into the auto aftermarket service industry. In this era of “Big Data”, corporations have better tools to find out what can “satisfy” their customers by knowing what the customers want. Edd Dumbill explained Big Data to Forbes as “data that exceeds the processing capacity of conventional database system. The data is too big, moves too fast, or doesn’t fit the structure of your database architectures.” The computing world describe Big Data as “extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations especially relating to human behavior and interactions.”
Whether you like it or not, this new era has enabled the capture and analysis of all our behaviors and interactions; you and I will live a live a pendulum-like life, swinging from convenience to frustration. We experience convenience because the products and services become easily accessible but dissatisfaction become increasingly intense. What is the implication of this?
Big organisations that can access big data technology will have the upper hand, but if you are a small business owner with limited funds, this is not the end for you if you can understand the implication of this trend and position your business well. Big Data essentially means that businesses are becoming “Customer Centric”. Gone are the days of Henry Ford’s “any color so long as it is black”. If your competitors are meeting what the customers want and you are still maintaining the big “I am”, that will be the end of your business.
You just need to adopt the habit of learning what will satisfy your customers and plan your subsequent action strategically. In one of its studies, the Massachusetts Institute of Technology found that 38% of the executives cite a lack of understanding on how to use analytics to improve the business. Therefore, knowing what your customers want and do not want does not mean anything until you can strategically use the data to achieve customer satisfaction.
I have a real case study here from a company that I want to keep anonymous – let’s call it ABC. ABC is in the automotive after-service industry. The data that they have gathered revealed that consumers are looking for value-for-money services (generally, all consumers want that). The obvious solution to me is to differentiate myself from my competitors by:
1. Bringing down my servicing/material cost and transferring the cost-savings to the customer, but ensuring that the quality of service remains unchanged.
2. Provide additional services/product that can add value to the customer and maintain my service charges.
Should I choose alternative number one, I am competing with the mass market on price, while the alternative number two sets my service center apart as having a better quality and better image, and attracts the group of customers that appreciate quality. There’s no right or wrong, it’s just a choice.
ABC Company tried to solve the symptom of the problem and not the root. They have the “Big Data” but did not strategically solve the problem. The solution they had was straightforward:
1. Extend the service interval of the car by 100%; use the cheapest mineral lubricant and thus reducing cost drastically.
2. Remove all other optional products and services.
This was effective to reduce the numbers on the customer’s bill but will not solve the actual problem; rather, it may create new problems. Back in 1980, Professor Noriaki Kano illustrated customer satisfaction using the Kano Model (below). I believe other older models preach the same idea.
Let reexamine the ABC case study by referring to the Kano Model. Mineral engine lubricant at every 5,000km interval is a basic need of a car (the green line). However, by extending the 5,000km service interval to 10,000km using the same category of engine lubricant, customer satisfaction will drop lower on the “dissatisfied” axis, though not immediately but eventually. The proposed solution cannot sustain the original purpose of the product; the quality decline will find the customer’s satisfaction level somewhere in the bottom left section of the graph.
Next, did ABC Company really need to remove the optional products from the shelves? As they were “optional”, customers only procure it if they wanted it and feel “satisfied” using it. That “delighter” product over time will transform into a basic need, and become a necessary “customer satisfaction inducer”. What happens when you remove them? We can already predict the outcome of ABC’s move: increased warranty claims, fewer customers, reduction of revenue and profits, tarnished image, etc.
The auto service industry should not compete on price but on value. Work with your suppliers to add value to services rendered without adding cost. Adopt only the optional products that increases performance, then shout about the value that you have so that everyone knows it. To excel, it is not what you have, but it is how strategically you use what you have to deliver what your customers want.