November 1, 2012
Many auto recyclers are beginning to understand the value of collecting customer data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining – technologies and techniques for recognizing and tracking patterns within data – helps businesses sift through layers of seemingly unrelated data for meaningful connections, where they can anticipate, rather than simply react to, customer needs.
The way in which companies interact with customers has changed dramatically over the past few years. A customer’s continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made is shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate the desires of their customers.
In the days of the corner market, auto recyclers had no trouble understanding their customers and responding quickly to their needs. The recycler would simply keep track of all of their customers in their head, and would know what to do when a customer walked up to the counter. But today’s recyclers face a much more complex situation. More customers, more products, more competitors, and less time to react makes understanding your customers a more complex undertaking. A number of forces are working together to increase the complexity of customer relationships:
• Compressed marketing cycle times. The attention span of a customer has decreased dramatically and loyalty is a thing of the past. A successful company needs to reinforce the value it provides to its customers on a continuous basis. In addition, the time between a recycled part need and when you must deliver that part is also shrinking. If you don’t react quickly enough, the customer will find someone who will.
• Increased marketing costs. Everything costs more. Printing, postage, special offers (and if you don’t provide the special offer, your competitors will).
• Streams of new product offerings. Customers want things that meet their exact needs, not things that sort-of fit. This means that the number of products and the number of ways they are offered have risen significantly.
• Niche competitors. Your best customers also look good to your competition. Your competitors will focus on small, profitable segments of your market and try to keep the best for themselves.
Successful companies need to react to each and every one of these demands in a timely fashion. The market will not wait for your response, and customers that you have today could vanish tomorrow. Interacting with your customers is also not as simple as it has been in the past. Customers and prospective customers want to interact on their terms, meaning that you need to look at multiple criteria when evaluating how to proceed.
You will need to automate and build a data playground to better understand your inventory buying, your customer recycled part needs, the value of the recycled part to your customer, the new replacement part price, the location of the customer or repairer and the data information list goes on.
What Is Data Mining?
Data mining, by its simplest definition, automates the detection of relevant patterns in a database. For example, a pattern might indicate that married males with children are twice more likely to drive a particular sports car than married males with no children. If you are a marketing manager for an auto manufacturer, this somewhat surprising pattern might be quite valuable.
However, data mining is not magic. For many years, statisticians have manually “mined” databases, looking for statistically significant patterns.
Data mining uses well-established statistical and machine learning techniques to build models that predict customer behavior. The leading data mining products are now more than just modeling engines employing powerful algorithms. Instead, they address the broader business and technical issues, such as their integration into today’s complex information technology environments.
However, the value that an analyst provides cannot be automated out of existence. Analysts are still needed to assess model results and validate the plausibility of the model predictions. Because data mining software lacks the human experience and intuition to recognize the difference between a relevant and an irrelevant correlation, analysts will remain in demand.
A Look at Customer Retention
Imagine that you are a manager for an automotive recycling company. You are responsible for managing the relationships with the company’s customers. One of your current concerns is customer attention (sometimes known as “churn”), which has been eating severely into your margins. You understand that the cost of keeping customers around is significantly less than the cost of bringing them back after they leave, so you need a cost-effective way of doing this.
The traditional approach to solving this problem is to pick out your good customers (that is, the ones who spend a lot of money with your company) and try to persuade them to buy more recycled parts from you. This might involve some sort of gift (possibly a gift card) or maybe a discount plan. The value of the gift might be based on the amount that a customer spends, with big spenders receiving the best offers.
This approach is probably very wasteful. There are undoubtedly many “good” customers who will stick around without receiving an expensive gift. The customers to concentrate on are the ones that will be leaving. Don’t worry about the ones who will stay.
This solution to the churn problem has been turned around from the way in which it should be perceived. Instead of providing the customer with something that is proportional to their value to your company, you should instead be providing the customer with something proportional to your value to them. Give your customers what they need.
There are differences between your customers, and you need to understand those differences in order to optimize your relationships. One big spending customer might value the relationship because of your high reliability, and thus wouldn’t need a gift in order to continue with it. On the other hand, a customer who takes advantage of all of the latest features and special services might require a gift in order to stick around for another. The key is determining which type of customer you’re dealing with.
It is also important to consider timing in this process. You can’t wait until a week before a customer’s contract is up and then pitch an offer in order to prevent them from churning. By then, you are unlikely to affect their decision at such a late date.
On the other hand, you don’t to start the process immediately upon signing a customer up. It might be months before they have an understanding of your company’s value to them, so any efforts now would also be wasted. The key is finding the correct middle ground, which could very well come from your understanding of your market and the customers in that market. Or, as we will discuss, you might be using data mining to automatically find the optimal point.
Relevance to a Business Process
Data mining is part of a much larger series of steps that takes place between a company and its customers. The way in which data mining impacts a business depends on the business process, not the data mining process. Take product marketing as an example. A marketing manager’s job is to understand their market. With understanding comes the ability to interact with customers in this market, using a number of channels. This involves a number of areas, including direct marketing, print/radio advertising, among others.
The issue that must be addressed is that the results of data mining are different from other data-driven business processes. In most standard interactions with customer data, nearly all of the results presented to the user are things that they knew existed in the database already. A report showing the breakdown of sales by product line and region is straightforward for the user to understand because they intuitively know that this kind of information already exists in the database. If the company sells different products in regions of the county, there is no problem translating a display of this information into a relevant understanding of the business process.
Data mining, on the other hand, extracts information from a database that the user did not know existed. Relationships between variables and customer behaviors that are non-intuitive are the jewels that data mining hopes to find. And because the user does not know beforehand what the data mining process has discovered, it is a much bigger leap to take the output of the system and translate it into a solution to a business problem.
How does someone actually use the output of data mining? The simplest way is to leave the output in the form of a black box. If they take the black box and score a database, they can get a list of customers to target (send them a promotional piece, increase their discount, etc.). Mailing costs can often be effectively reduced without reducing the response rate.
Then there’s the more difficult way to use the results of data mining: getting the user to actually understand what is going on so that they can take action directly. For example, if the user is responsible for ordering a recycled part, understanding customer demographics is critical. A data mining analysis might determine that customers in New York City are now focused in the 30-to-35-year-old age range; whereas previous analyses showed that these customers were primarily aged 22 to 27. This change means that the parts purchase might move from the Ford Focus to the Hydunai Sonata. There’s no automated way to do this. Unless the output of the data mining system can be understood qualitatively, it won’t be of any use.
Both of these cases are inextricably linked. The user needs to view the output of the data mining in a context they understand. If they can understand what has been discovered, they will trust it and put it into use. There are two parts to this problem: 1) presenting the output of the data mining process in a meaningful way, and 2) allowing the user to interact with the output so that simple questions can be answered. Creative solutions to the first part have recently been incorporated into a number of commercial data mining products. Response rates and (most importantly) financial indicators (like profit, cost, and return on investment) give a context that can ground the results in reality.
Data Mining and Customer Management
The first task, identifying market segments, requires significant data about prospective customers and their buying behaviors. In theory, the more data the better. In practice, however, massive data stores often impede marketers, who struggle to sift through the minutiae to find the nuggets of valuable information.
Data mining applications automate the process of searching the mountains of data to find patterns that are good predictors of purchasing behaviors. After mining the data, marketers must feed the results into campaign management software that, as the name implies, manages the campaign directed at the defined market segments. Tightly integrating the two disciplines presents an opportunity for companies to gain competitive advantage.
How Data Mining Helps Marketers
Data mining helps marketing users to target marketing campaigns more accurately; and also to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. If the necessary information exists in a database, the key is to find patterns relevant to current business problems.
In the automotive recycling industry companies, Hollander, a Solera Company, and Car-Part.com are data collectors for automotive recyclers that assign their parts inventory data to their companies. Both companies then market the data to companies like Mitchell and CCC.
Typical questions that data mining addresses include the following: Which customers are most likely to buy recycled auto parts? What is the probability that a customer will purchase a recycled part from a particular car? What are the top vehicles for a particular recycled auto part? Answers to these questions can help retain customers and increase campaign response rates, which, in turn, increase buying, cross-selling, and return on investment (ROI).
Data mining builds models by using inputs from a database to predict customer behavior. This behavior might be attrition for cross-product purchasing, and willingness to use a recycled part in place of a more expensive OE new or aftermarket, and so on. The prediction is usually called a score.
Increasing Customer Lifetime Value
Today’s salvage auctions are using data mining to move salvage vehicles to market based on the best return for their insurance customers and to predict the best return for the four million total-loss salvage vehicles a year. Consider, for example, a salvage buyer who is identified by their buying data history analysis reveals what types of vehicles fit the buyer.
Auctions are answering with data mining these new questions: Where a vehicle should be sold (Internet only, live auction, or assigned buyer)? What class of salvage (high end, middle, low end) fits what auction? Which auction is best to sell to based on transportation and return? Looking for four of the best buyers by the highest value paid for salvage. Assigning low end salvage to “tagged” buyers. This is defining target segment salvage buying. The data drives to a greater return on salvage.
Benefits of Data Mining for Auto Recyclers
For buying salvage, it is helpful for developing:
• A “black book” of actual salvage data values with weekly prices;
• A complete damage assessment (from repair estimate) available to help buyers determine bid or better yet, an assessment of all undamaged parts on a salvage vehicle; and
• More shared data from OE’s, insurance companies, collision repairers, mechanical repairers, part providers and legislative changes in tilting laws.
For recycled parts sellers:
• Data for faultless execution;
• Front knowledge of part condition;
• Easy management data report building tools in management systems to map customers and parts; and
• Good clean data of part quality by grade, images and customer feedback.
Excerpted with permission from Building Data Mining Applications for CRM by Alex Berson, Stephen Smith, Kurt Thearling (McGraw Hill, 2000). Modifications for auto recycling industry provided by Ginny Whelan.
Kurt Thearling has more than fifteen years of experience with analytics and data mining. His background includes work in a variety of areas, including financial services, life sciences, insurance, utilities, and telecommunications. He is currently Head of Decision Sciences for Vertex Data Science, a multi-national business process outsourcer. His extensive data mining and analytics web site can be found at www.thearling.com.