Local Sales and Marketing Reference Guide

The data warehouse helps you gain insight into which customers you're likely to retain, and which ones are likely to go elsewhere. This helps you target the right messages to the right customers.

The web-based business environment is a business accelerator; it will either accelerate your success or it will accelerate your failure. The business done over the internet quickly shifts emphasis from the novelty of operating in a new medium to an emphasis on dollars, sense, and profitability.

What's important for the long-term success of an on-line business is the business model behind the web environment. If the technicians of the world and the venture capitalists had paid more attention to the business model and less attention to the "gee whiz" aspect of internet technology, we likely wouldn't be witnessing today's economic bubble burst. Instead, technicians and venture capitalists got so wrapped up in technology that no one asked the hard question: Does this make business sense? From March 2000 to March 2001, an estimated 90 percent of the venture-backed internet companies disappeared, according to an article in The Wall Street Journal. Undoubtedly, most of them are gone because of this one fundamental flaw.

As we already know, the internet is a wonderful delivery mechanism for managing and distributing messages. But if all that flows across the internet is a flurry of meaningless messages, it simply isn't worth much. As noted previously, it's up to the business to put the "smarts" into messages. To do so, a business must understand its customers. If you don't know anything more about your customers than their names, you're at a severe disadvantage. Knowing who your customers are, what they're worth to you, and how happy they are with your business relationship is a necessary starting point for creating smart messages.

What information does a business need to get to know its customers? How do you tell a good customer from a bad one? A loyal customer from one who is likely to be disloyal? A profitable customer from an unprofitable customer?

Identify loyal customers

One of the most important issues of "smart" messages relies upon understanding customer loyalty. It's much less expensive to sell to an existing customer than to sell to a new one. Therefore, when you send messages across the internet, the more you know about existing customers, the better chance you have to sell something else to them and further extend their loyalty. Protecting customer loyalty is one of the most important things any corporation can do.

Where it starts: The information to gauge customer loyalty begins in the infrastructure that supports your web data -- the data warehouse. The data warehouse is a collection of detailed, integrated, and historical data. Typically, a data warehouse contains information about past transactions with a customer. When built properly, the data warehouse offers such insights as:

• When a customer first signed up for service
• How often a customer partakes in a service
• What services a customer uses
• How much money a customer spends
• Where a customer uses a service
• When a customer uses a service

A fully developed data warehouse will likely also contain much more information about a customer, including:

• Age, gender, personal information
• Economic information
• Home location
• Family
• Occupation

The data warehouse carries this integrated data for a lengthy period of time. Over time, variables change values. As they change, a new record -- a snapshot -- is written to reflect the change. As such, data in a warehouse is never updated. Instead, a series of snapshots creates a historical record of all changes, thus forming a complete historical record inside the data warehouse. Depending on the environment, detailed customer activity may go back five or ten years. It's this integrated, detailed, historical data that's worth its weight in gold when determining the loyalty and disloyalty of customers. The real value of historical data becomes obvious when using data in a real-time mode. The historical data is analyzed, synthesized, and used as a basis for the creation of a profile record. The profile record is then ready for real-time processing.

Analyze degrees of loyalty

Step one

The first step in creating a loyalty analysis environment -- sometimes called churn analysis -- is to find out which customers have and haven't been loyal. Because the first date of service and the last date of service are a natural part of the data warehouse, it's easy enough to find the top 10 percent of customers who have been with the company the longest time, and the bottom 10 percent of former customers who have been with the company the shortest amount of time. This is done by a simple analysis of the service dates in the data warehouse. Select those customer names and put them aside.

Step two

Next, gather and analyze the records for those customers to determine what common characteristics they have. Some of the analysis might include:

• Do loyal customers live in a particular area?
• Are disloyal customers mostly one gender?
• Do loyal customers sign up for first service on weekends?
• Do disloyal customers use services infrequently?
• What age are loyal customers?
• Do disloyal customers own their own home?

Step three

You'll want to closely study the characteristics of both loyal and disloyal customers in a variety of ways. Perhaps women are more loyal than men, older people are more loyal than younger people, college graduates are more loyal than non-college graduates, and so forth.

Reality check: some perspectives will be fruitful and others won't be. For some categories, there will be the same or nearly the same set of characteristics for both loyal and disloyal customers. But for other categories, you'll identify sharp differences. The result is that a profile -- however imperfect -- is created. The profile is the basis of starting to get to know who your customer base really is.

Step four

After you collect and analyze the categories for loyal and disloyal customers, the next step is to create a profile. The profile is based on the correlation of different characteristics associated with loyalty and disloyalty. For example, a profile might be created that looks like:

Loyal customer

• female
• from 35 to 45
• salaried
• owns home

Disloyal customer

• male
• from 18 to 26, from 45 to 60
• unemployed
• rents

The profile for a loyal customer and a disloyal customer has now been established. What can a company do with this information?

Take action

One obvious usage of profile information is to go back into the existing customer file and determine how existing records match up to the profiles. For each existing record, create a data element called CLASSIFICATION. Then, pass the profile against each record and an assertion is made about the existing customer based on how that customer's records compare to the profile.

Beware: the matching process is hardly perfect. Few customers' records will perfectly match the profile. For example, suppose the match finds a male that is salaried, owns his own home, and is between the ages of 35 and 45. Those criteria fit a loyal customer profile, except the customer is male, not female. The program that does the matching and qualification must be able to handle less than perfect matches.

Each record in the database is assigned a value in the CLASSIFICATION data element of a "d" for disloyal, "l" for loyal, or "-" for undecided. Now you can tell at a glance exactly how many customers are at risk and how many are not. In addition, you can take action to keep the service of disloyal customers. For example, you can:

• Offer new programs with special and unique services
• Discount services
• Offer individual attention

Knowing exactly who is at risk lets your company do whatever is necessary to keep marginally loyal customers in the fold.

There's another way you can use profile information. It's one thing to classify existing customers based on what you know about the customer. It's another thing to predict a customer's behavior. Armed with profiles of what a loyal customer and a disloyal customer look like, you can statistically make an educated guess regarding a new customer's long term viability when he first signs up for service. All it takes is comparing the new customer's traits to the loyalty profiles.

Beware: the approach is admittedly imperfect. Some customers will simply be pegged incorrectly. But, on average, the odds are good that a company can predict who will and will not be loyal upon entry into the system.

Match the message to the customer

Based on predictions, your company can take actions to turn the marginal and likely disloyal customers into loyal customers. Other companies have a hard time trying to take a customer away from a company that understands who's likely to be loyal and who's unlikely to be loyal.

After you create profiles, it's not an understatement to say you can creatively use that information in a thousand ways. The limitations are only in the minds of management and users.

Note: profile activity must be periodically revisited, depending on the subject area being profiled. For a slow changing subject area, such as customer data, revisiting the profile annually makes sense. For a fast changing profile such as a sale or promotion, revisiting the profile quarterly makes sense. Over time, the profile for loyalty and disloyalty changes, and your company must be sensitive to those changes.

Retaining loyal customers means you reduce churn. You'll find that customers are likely to be entering the system but not leaving with any degree of regularity. And once you establish and defend your market share, your business is on a firm footing.

Once you have knowledge about customer loyalty, you can vary the messages you send over the internet. Loyal customers can be sent one kind of message and potentially disloyal customers can be sent a different kind of message. Each type of message is tailored to the projected proclivities of each customer. Now that's getting smart. There are many dimensions to smart messages, but perhaps the most important is to know your customers' attitudes and behaviors. With such business intelligence, all sorts of possibilities enter the picture.

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