Customer Insight

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In order to be successful at CRM, you simply have to know your customers. In fact some people define CRM as precisely that—knowing your customers. Actually it is much more than that, but if you don't know your customers, then you cannot be successful at CRM.

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It's all tied in with the notion of customer insight—knowing about our customers. Whereas our knowledge regarding our friends is mostly in our heads, our knowledge about customers is generally held in stored records. Every order, payment, inquiry, and complaint is a piece of a jigsaw puzzle that, collectively, describes the customer. If we can properly manage this information, then we can build a pretty good picture of our customers, their preferences and dislikes, their behavioral traits, and their personal circumstances. Once we have that picture, we can begin to develop customer insight.

There are three main types of segmentation that we can apply to customers: the customer's circumstances, their behavior, and derived information.

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Circumstances

This is the term that I use to describe those aspects of the customer that relate to their personal details. Circumstances are the information that define who the customer is. Generally speaking, this type of information is customer specific and independent and has nothing to do with our relationship with the customer. It is the sort of information that any organization might wish to hold. Some obvious elements included in customer circumstances are:

1. Name

2. Date of birth

3. Sex

4. Marital status

5. Address

6. Telephone number

7. Occupation

A characteristic of circumstances is that they are relatively fixed. Some IT people might refer to this type of information as reference data and they would be right. It is just that circumstances is a more accurate layperson's description.

Some less obvious examples of circumstances are:

1. Hobbies

2. Ages of children

3. Club memberships

4. Political affiliations

Theoretically, each of the data elements that we record about a customer could be used for segmentation purposes. It is very common to create segments of people by:

1. Sex

2. Age group

3. Income group

4. Geography

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Behavioral Segmentation

Whereas a customer's circumstances tend not to relate to the relationship between us, a customer's behavior relates to their interaction with our organization. Behavior encompasses all types of interaction such as:

1. Purchases—the products or services that the customer has bought from us.

2. Payments—payments made by the customer.

3. Contacts—where the customer has written, telephoned, or communicated in some other way. This might be an inquiry about product, a complaint about service, perhaps a request for assistance, etc.

The kind of segmentation that could be applied to this aspect of the relationship could be:

1. Products purchased or groups of products. For instance, an insurance company might segment customers into major product groups such as pensions, motor insurance, household insurance, etc.

2. Spending category. Organizations sometimes segment customers by spending bands.

3. Category of complaint.

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Derived Segmentation

Very often, however, we need to segment our customers in ways that require significant manipulation and interpretation of information. Such segmentation classifications may be derived from the customer's circumstances or behavior or, indeed, a combination of both.

some examples of derived segmentation are:

1. Lifetime value. Once we have recorded a representative amount of circumstantial and behavioral information about a customer, we can use it in models to assist in predicting future behavior and future value. For instance, there is a group of young people that have been given the label “young plastics” (YP). The profile of a YP is someone who has recently graduated from college and is now embarking on the first few years of their working life. They have often just landed their first job and are setting about securing the trappings of life in earnest. Their adopted lifestyle usually does not quite match their current earnings, and they are often debt laden, becoming quite dependent on credit. At first glance, these people do not look like a good proposition upon which to build a business relationship. However, their long-term prospects are, statistically, very good. Therefore, it may be more appropriate to consider the potential lifetime value of the relationship and “cut them some slack,” which means developing products and services that are designed for this particular segment of customers.

2. Propensity to churn. We have already talked about the problem of churn earlier in this chapter. If we can assess our customers in such a way as to grade them on a scale of, say, 1 to 10 where 1 is a safe customer and 10 is a customer who we are at risk of losing, then we would be able to modify our own behavior in our relationship with the customer so as to manage the risk.

3. Up-sell and cross-sell potential. By carefully analyzing customers' behavior, and possibly combining segments together, it is possible to derive models of future behavior and even potential behavior. Potential behavior is a characteristic we all have; the term describes behavior we might engage in, given the right stimulus. Advertising companies stake their very existence on this. It enables us to identify opportunities to sell more products and services (up-selling) and different products and services (cross selling) to our customers.

4. Entanglement potential. This is related to up-sell and cross-sell and it applies to customers who might be encouraged to purchase a whole array of products from us. Over time, it can become increasingly difficult and bothersome for the customer to disentangle their relationship and go elsewhere.

The retail financial services industry is good at this. The bank that manages our checking account encourages us to pay all our bills out of the account automatically each month. This is good because it means we don't have to remember to write the checks. Also, if the bank can persuade us to buy our house insurance, contents insurance, and maybe even our mortgage from them too, then it becomes a real big deal if we want to transfer, say, the checking account to another bank.

“Householding” is another example of this and, again, it's popular with the banks. It works by enticing all the members of a family to participate in a particular product or service so that, collectively, they all benefit from special deals such as a reduced interest rate on overdrawn accounts. If any of the family members withdraws from the service, then the deal can be revoked. This of course has an effect on the remainder of the family, and so there is an inducement to remain loyal to the bank.

Sometimes there are relationships between different behavioral components that would not be spotted by analysts and knowledge workers. In order to try to uncover these relationships we have to employ different analytic techniques. The best way to do this is to employ the services of a data mining product. The technical aspects of data mining will be described later in this book, but it is important to recognize that there are other ways of defining segments. As a rather obvious example, we can all comprehend the relationship between, say, soft fruit and ice cream, but how many supermarkets place soft fruit and ice cream next to each other? If there is a statistically significant relationship such that customers who purchase soft fruit also purchase ice cream, then a data mining product would be able to detect such a relationship. As I said, this is an obvious example, but there will be others. For instance, is there a relationship between:

1. Vacuum cleaner bags and dog food?

2. Toothpaste and garlic?

3. Diapers and beer? (Surely not!)

As we have seen, there are many ways in which we can classify our customers into segments. Each segment provides us with opportunities that might be exploited. Of course, it is the business people that must decide whether the relationships are real or merely coincidental.

Once we have identified potential opportunities, we can devise a campaign to help us persuade our target customers of the benefits of our proposal. In order to do this, it might be helpful to employ another facet of CRM, campaign management.