Description Data Mining Techniques For Marketing_10 pptx

It starts with an overview of different types of customer relationships, then goes into the details of the customer life cycle as it relates to data mining.. Levels of the Customer Relat

has largely replaced human-to-human interactions is allowing companies to treat their customers more personally

This brings us back to the customer and to the customer life cycle This chap­ter strives to put data mining into focus with the customer at the center It starts with an overview of different types of customer relationships, then goes into the details of the customer life cycle as it relates to data mining The chap­ter provides examples of how customers are defined in various industries and some of the issues in deciding when the customer relationship begins and when it ends The focal point is the customer and the ongoing relationship that customers have with companies

Levels of the Customer Relationship

One of the major goals of data mining is to understand customers and the rela­tionships that customers have with an organization A good place to start understanding them better is by using the different levels of customer rela­tionships and what customers are telling us through their behavior

Customers generate a wealth of behavioral information Every payment made, every call to customer service, every click on the Web, every transaction provides information about what each customer does, and when, and which interventions are working and which are not The Web is a particularly rich source of information CNN does not know who is viewing or paying attention

to their cable news program The New York Times does not know which parts of

the paper each subscriber reads On the Web, though, cnn.com and nytimes.com have a much better indication of readers’ interests Connecting this source of information back to individuals over time is challenging (not to mention the challenge of connecting readers interests to advertising over time)

Customers are not all created equal Nor should all customers be treated equally, since some are clearly more valuable than others Figure 14.1 shows a continuum of customer relationships, from the perspective of the amount of investment worthy of each relationship Some customers merit very deep and intimate relationships centered around people Other customers are too numerous and, individually, not valuable enough to maintain individual rela­tionships For this group, we need technology to help make the relationship more intimate The third group is perhaps the most challenging, because they are in between those who merit real intimacy and those who merit feigned intimacy This group often includes small businesses as well as indirect rela­tionships The sidebar “No Customer Relationship” talks about another situa­tion, companies that do not know about their end users and do not need to

(deep intimacy) Small and medium

businesses

Very small businesses (low intimacy)

Few customers Many customers

Each small contribution to profit Very important in aggregate IntimacyTechnologies:

Mass intimacy Customer relationship management

Each large contribution to profit Important individual and in aggregate Technologies:

Sales force automation Account management support

Figure 14.1 Intimacy in customer relationships generally increases as the size of the

account increases

Deep Intimacy

Customers who are worth a deep intimate relationship are usually large organizations—business customers These customers are big enough to devote dedicated resources, in the form of account managers and account teams The relationship is usually some sort of business-to-business relationship One-off products and services characterize these relationships, making it difficult

to compare different customers, because each customer has a set of unique products

An example is the branding triumvirate of McDonald’s, Coca-Cola, and Disney McDonald’s is the largest retailer of Coke products worldwide When Disney has special promotions in fast food restaurants for children’s movies, McDonald’s gets first dibs at distributing the toys inside their Happy Meals And when Disney characters (at least the good guys!) drink soda or open the refrigerator—Coke products are likely to be there Coke also has exclusive arrangements with Disney, so Disney serves Coke products at its theme parks,

in its hotels, and on its cruises There are hundreds of people working together

to make this branding triumvirate work Data mining, with even the most advanced algorithms on even the fastest computers, is not going to replace these people—nor will this process be automated in the conceivable future

On the other hand, even large account teams and individual managers can benefit from analysis, particularly around sales force automation tools Data mining analysis can help such groups work better, by providing an under­

standing of what is really going on Data can still help find some useful answers: which McDonald’s are particularly good at selling which soft drinks? Where are product placements resulting in higher sales? What is the relation­

ship between weather and drink consumption at theme parks versus hotels? And so on

million, this means that, on average, every Japanese person purchases

Dive a bit more deeply into the business About the only thing these companies know about their customers is that almost everyone who lives in based, so the companies have no way to tie a customer to a series of transactions over time and in different stores

the distribution side, they are able to make three deliveries each day to the

Japanese convenience stores are an extreme example of businesses that another example, because they do not own the retailing relationship

Manufacturers only know when they have shipped goods to warehouses

End-◆ Use industry-wide panels of customers to see how products are used

◆ Use surveys to find out about customers and when and how they use the products

◆ Build relationships with retailers to get access to the point-of-sale data

transactions each day Given that the population of Japan is a bit over 120 something from one of these stores every other day That is a phenomenal amount of consumer interaction

Japan is at least an occasional buyer Transactions are almost exclusively

cash-The strength of these companies is really in distribution and payments On stores, guaranteeing that lunchtime sushi is fresh and the produce hasn’t wilted Many people also use the stores near their homes to pay their bills with cash, something that is very convenient in a cash-dominated society Combining these two businesses, some of the stores are becoming staging points for orders, made through catalogs or over the Web Customers can pay for and pick

up goods in their friendly, neighborhood convenience store

know very little about their end users Packaged good manufacturers are

user information is still important, but the behavior is not sitting in their databases, it is in the database of disparate retailers To find out about customer behavior, they might:

Listen to the data they are collecting, via complaints and compliments on the Web, in call centers, and through the mail

Distribution data does still have tremendous value, giving an idea of what is being sold when and where Inside lurks information about which advertising

On the business-to-business side, even large financial institutions can bene­

fit from understanding customers One of the largest banks in the world wanted to analyze foreign exchange transactions to determine which clients would benefit from taking out a loan in one currency and repaying it in another rather than taking out the loan in one currency and exchanging the proceeds up front The goal was to provide better products for the clients and

a longer-term relationship However, people are then needed to interpret and act on these results

Although the deep relationship is often associated with large businesses, this is not always the case Private banking groups in retail banks work with high net-worth individuals, and give them highly personalized service— usually with a named banker managing their relationship When a private banking customer wants a loan or to make an investment, that person simply calls his or her private banker Private banking groups have traditionally been highly profitable, so profitable that they can get away with almost anything The private banking group at one large bank was able to violate corporate information technology standards, bringing in Macintosh computers and AS400s, when the standards for the rest of the bank were Windows and Unix The private bank could get away with it; they were that profitable

Also, just having large businesses as customers does not mean that each cus­tomers necessarily merits such close attention Directories, whether on the Web or on yellow pages, have many business customers, but almost all are treated equally Although the customers include many large businesses, each listing brings in a small amount of revenue so few are worth additional effort

Mass Intimacy

At the other extreme is the mass intimacy relationship Companies that are serving a mass market typically have hundreds of thousands, or millions, or tens of millions of customers Although most customers would love to have the attention of dedicated staff for all their needs, this is simply not economi­

cally feasible Companies would have to employ armies of people to work with customers, and the incremental benefit would not make up for the cost

This is where data mining fits in particularly well with customer relation­ship management Many customer interactions are fully automated, especially

on the Web This has the advantage of being highly scalable; however, it comes

at a loss of intelligence and warmth in the customer relationship Using tech­

nology to make the relationship stronger is a multipronged effort:

■■ Staff who work directly with customers (whether face-to-face, through call centers, or via Web-enabled interfaces) must be trained to treat cus­

tomers respectfully, while at the same time trying to expand the rela­

tionship using enhanced information about customers

452 Chapter 14

■■ Automated systems need to be flexible, so different messages can be directed to different customers This clearly applies on the Web, but it also applies to billing inserts, cashier receipts, background scripts read while customers are on hold, and so on

■■ Both staff and automated systems that work with customers need to be able to respond to new practices and new messages Sometimes, these new approaches come from the good ideas of staff Sometimes, they come from careful analysis and data mining Sometimes, from a combi­nation of the two

This is an extension of the virtuous cycle of data mining Learning— whether accomplished through algorithms or through people—needs to be acted upon Rolling out results is as necessary as getting them in the first place Success involves working with call centers and training personnel who come

in contact with customers Customer interactions over the Web have the advantage that they are already automated, making it possible to complete the virtuous cycle electronically People are still involved in the process to manage and validate the results However, the Web makes it possible to obtain data, analyze it, act on the results, and measure the effects without ever leaving the electronic medium

The goal of customer understanding can conflict with the goal of efficient channel operation One large mobile telephone company in the United States, for instance, tried asking customers for their email addresses when they called

in with service related questions Having the email address has many benefits For one thing, future service questions could be handled over the Web at a lower cost than through the call center It also opens the possibility for occa­sional marketing messages, cross-sell, and retention opportunities However, because the questions added several seconds to the average call length, the call center stopped asking For the call center, getting on to the next call was more important than enhancing the relationship with each customer

WA R N I N G Privacy is a major concern, particularly for individual customers However, it is peripheral to data mining itself To a large extent, the concern is more about companies sharing data with each other rather than about a single company using data mining on its own to understand customer behavior In some jurisdictions, it may be illegal to use information collected for operational purposes for another purpose such as marketing or improving customer relationships

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Mass intimacy also brings up the issue of privacy, which has become a major concern with the growth of the Web To the extent that we are studying cus­

tomer behavior, the data sources are the transactions between the customer and the company—data that companies typically can use for business pur­

poses such as CRM (although there are some legal exceptions even to this) The larger concern is when companies sell information about individuals Although such data may be useful when purchased, or may be a valuable source of revenue, it is not a necessary part of data mining

In-between Relationships

The in-between relationship is perhaps the most challenging These are the customers who are not big enough to warrant their own account teams, but are big enough to require specialized products and services These may be small and medium-sized businesses However, there are other groups, such as so-called “mass affluent” banking customers, who do not have quite enough assets to merit private banking yet who still do want special attention

These customers often have a wider array of products, or at least of pricing mechanisms—discounts for volume purchases, and so on—than mass inti­

macy customers They also have more intense customer service demands, hav­

ing dedicated call centers and Web sites There are often account specialists who are responsible for dozens or hundreds of these relationships at the same time These specialists do not always give equal attention to all customers One use of data mining is in spreading best practices—finding what has been working and has not been working and spreading this information

When there are tens of thousands of customers, it is also possible to use data mining directly to find patterns that distinguish good customers from bad, and for determining the next product to sell to a particular customer This use

is very similar to the mass intimacy case

Indirect Relationships

Indirect relationships are another type of customer relationship, where inter­

mediate agents broker the relationship with end users For instance, insurance companies sell their products through agents, and it is often the agent that builds the relationship with the customer Some are captive agents that only sell one company’s policies; others offer an assortment of products from dif­

ferent companies

Such agent relationships pose a business challenge For instance, an insur­ance company once approached Data Miners, Inc to build a model to deter­mine which policyholders were likely to cancel their policies Before starting the project, the company realized what would happen if such a model were put in place Armed with this information, agents would switch high-risk policyholders to other carriers—accelerating the loss of these accounts rather than preventing it This company did not go ahead with the project Perhaps part of the problem was a lack of imagination in figuring out appropriate inter­ventions The company could have provided special incentives to agents to keep customers who were at risk—a win-win situation for everyone involved

In such agent-based relationships, data mining can be used not only to under­stand customers but also to understand agents

Indirection occurs in other areas as well For instance, mutual fund compa­nies sell retirement plans through employers The first challenge is getting the employer to include the funds in the plan The second is getting employees to sign up for the right funds Ditto for many health care plans at large companies

in the United States

Product manufacturers have a similar problem Telephone handset manu­facturers such as Motorola, Nokia, and Ericsson, would like to develop a loyal customer base, so customers continue to return to them handset after handset Automobile manufacturers have similar goals Pharmaceutical companies have traditionally marketed to the doctors who prescribe drugs rather then the people who use them, although drugs such as Viagra are now also being mar­keted to consumers Another good example of a campaign for a product sold indirectly is the “Intel Inside” campaign on personal computers—a mark of quality meant to build brand loyalty for a chip that few computer users ever actually see However, Intel has precious little information on the people and companies whose desktops are adorned with their logo

Customer Life Cycle

When thinking about customers, it is easy to think of them as static, unchang­ing entities that compose “the market.” However, this is not really accurate Customers are people (or organizations of people), and they change over time Understanding these changes is an important part of the value of data mining These changes are called the customer life cycle In fact, there are two cus­tomer life cycles of interest, as shown in Figure 14.2 The first are life stages For an individual, this refers to life events, such as graduating from high school, having kids, getting a job, and so on For a business customer, the life cycle often refers to the size or maturity of the business The second customer life cycle is the life cycle of the relationship itself These two life cycles are fairly independent of each other, and both are very important for business

High School

Customer's Life Cycle

(phases in the lifetimes of customers)

Established

New Customer

Figure 14.2 There are two customer life cycles

The Customer’s Life Cycle: Life Stages

The customer’s life cycle consists of events external to the customer relation­

ship that represent milestones in the life of each individual customer These milestones consist of events large and small, familiar to everyone

The perspective of the customer’s life stages is useful because people—even business people—understand these events and how they affect individual cus­

tomers For instance, moving is a significant event When people move, they often purchase new furniture, subscribe to the local paper, open a new bank account, and so on Knowing who is moving is useful for targeting such indi­

viduals, especially for furniture dealers, newspapers, and banks (among others) This is true for many other life events as well, from graduating from high school and college, to getting married, having children, changing jobs, retiring, and so on Understanding these life stages enables companies to define products and messages that resonate with particular groups of people For a small business, this is not a problem A wedding gown shop special­

izes in wedding gowns; such a business grows not because women get mar­

ried more often, but through recommendations Similarly, moving companies

do not need to encourage their recent customers to relocate; they need to bring

in new customers

Larger businesses, on the other hand, rarely have business plans that focus exclusively on one life stage They want to use life stage information to develop products and enhance marketing messages, but there are some com­plications The first is that customers’ particular circumstances are usually not readily available in corporate databases One solution is to augment databases with purchased information Of course, such appended data elements are never available for every customer, and, although such appended data is read­ily available in the United States, it may not be available in jurisdictions with different privacy laws And, such external sources of data indicate events that have occurred in the past, making the customer’s current life stage a matter of inference

Even when customers go out of their way to provide useful information, companies often simply forget it For instance, when customers move, they provide the new address to replace the old How many companies keep both addresses? And how many of these companies then determine whether the customer is moving up or moving down, by using appended demographics or census data to measure the wealth of the neighborhood? The answer is very few, if any

Similarly, many women change their names when they get married and pro­vide such information to the companies they do business with At some point after two people wed, the couple starts to combine their finances, for instance

by having one checking account instead of two Most companies do not record when a customer changes her name, losing the opportunity to provide tar­geted messaging for changing financial circumstances

In practice, managing customer relationships based on life stages is difficult:

■■ It is difficult to identify events in a timely manner

■■ Many events are one-time, or very rare

■■ Life stage events are generally unpredictable and out of your control These shortcomings do not render them useless, by any means, because life stages provide a critical understanding of how to reach customers with a par­ticular message Advertisers, for instance, are likely to include different mes­sages, depending on the target audience of the medium However, in the interest of developing long-term relationships with customers, we want to ask

if there is a way to improve on the use of the customer’s life cycle

Customer Life Cycle

The customer life cycle provides another dimension to understanding cus­tomers This focuses specifically on the business relationship, based on the observation that the customer relationship evolves over time Although each

business is different, the customer relationship places customers into five major phases, as shown in Figure 14.3:

■■ Prospects are people in the target market who are not yet customers

■■ Responders are prospects who have exhibited some interest, for instance,

by filling out an application or registering on a Web site

■■ New customers are responders who have made a commitment, usually

an agreement to pay, such as having made a first purchase, having signed a contract, or having registered at a site with some personal information

■■ Established customers are those new customers who return, for whom the

relationship is hopefully broadening or deepening

■■ Former customers are those who have left, either as a result of voluntary

attrition (because they have defected to a competitor or no longer see value in the product), forced attrition (because they have not paid their bills), or expected attrition (because they are no longer in the target market, for instance, because they have moved)

The precise definition of the phases depends on each particular business For an e-media site, for instance, a prospect may be anyone on the Web; a responder, someone who has visited the site; a new customer, someone who has registered; and an established customer a repeat visitor Former customers are those who have not returned within some length of time that depends on the nature of the site For other businesses, the definitions might be quite dif­

ferent Life insurance companies, for instance, have a target market Respon­

ders are those who fill out an application—and then often have their blood taken for blood tests New customers are those applicants who are accepted, and established customers are those who pay their premiums for insurance payments

Subscription Relationships versus Event-Based Relationships

Another dimension of the customer life-cycle relationship is the commitment inherent in a transaction Consider the following ways of being a telephone customer:

■■ Making a call at a payphone

■■ Purchasing a prepaid telephone card for a set number of minutes

■■ Buying a prepaid mobile telephone

■■ Choosing a long distance carrier

■■ Buying a postpay mobile phone with no fixed term contract

■■ Buying a mobile phone with a contract The first three are examples of event-based relationships The last three are examples of subscription-based relationships The next two sections explore the characteristics of these relationships in more detail

An ongoing billing relationship is a good sign of an ongoing subscription

T I P

Event-Based Relationships

Event-based relationships are one-time commitments on the part of the cus­tomer The customer may or may not return In the above examples, the tele­phone company may not have much information at all about the customer, especially if the customer paid in cash Such anonymous transactions still have information; however, there is clearly little opportunity for providing direct messages to customers who have provided no contact information

When event-based relationships predominate, companies usually commu­nicate with prospects by broadcasting messages widely (for instance in media advertising, free standing inserts, Web ads, and the like) rather than targeting messages at individuals In these cases, analytic work is very focused on prod­uct, geography, and time, because these are three things known about cus­tomers’ transactions

Of course, broadcast advertising is not the only way to reach prospects Couponing through the mail or on the Web is another way Pharmaceutical companies in the United States have become adept at encouraging prospective customers to call in to get more information—while the company gathers a bit

of information about the caller

Sometimes, event-based relationships imply a business-to-business rela­

tionship with an intermediary Once again, pharmaceutical companies pro­

vide an example, since much of their marketing budget is spent on medical providers, encouraging them to prescribe certain drugs

Subscription-Based Relationships

Subscription-based relationships provide more natural opportunities to understand customers In the list given earlier, the last three examples all have ongoing billing relationships where customers have agreed to pay for a service over time A subscription relationship offers the opportunity for future cash flow (the stream of future customer payments) and many opportunities for interacting with each customer

For the purposes of this discussion, subscription-based relationships are those where there is a continuous relationship with a customer over time This may take the form of a billing relationship, but it also might take the form of a retailing affinity card or a registration at a Web site

In some cases, the billing relationship is a subscription of some sort, which leaves little room to up-sell or cross-sell So, a customer who has subscribed to

a magazine may have little opportunity for an expanded relationship Of course, there is some opportunity The magazine customer could purchase a gift subscription or buy branded products However, the future cash flow is pretty much determined by the current composition of products

In other cases, the ongoing relationship is just a beginning A credit card may send a bill every month; however, nothing charged, nothing owed A long-distance provider may charge a customer every month, but it may only

be for the monthly minimum A cataloger sends catalogs to customers, but most will not make a purchase In such cases, usage stimulation is an impor­

tant part of the relationship

Subscription-based relationships have two key events—the beginning and end of the relationship When these events are well defined, then survival analysis (Chapter 12) is a good candidate for understanding the duration of the relationship However, sometimes defining the end of the relationship is difficult:

■■ A credit card relationship may end when a customer has no balance and has made no transactions for a specified period of time (such as 3 months or 6 months)

■■ A catalog relationship may end when a customer has not purchased from the catalog in a specified period of time (such as 18 months)

■■ An affinity card relationship may end when a customer has not used the card for a specified period of time (such as 12 months)

Even when the relationship is quite well understood, there may be some tricky situations Should the end date of the relationship be the date of cus­tomer contact or the date the account is closed? Should customers who fail to pay their last bill be considered the same as customers who were stopped for nonpayment?

These situations are meant as guidelines for understanding the customer relationship It is worthwhile to map out the different stages of customer inter­actions Figure 14.4 shows different elements of customer experience for news­paper subscription customers These customers basically have the following types of interactions:

■■ Starting the subscription via some channel

■■ Changing the product (weekday to 7-day, weekend to 7-day, 7-day to weekday, 7-day to weekend)

■■ Suspending delivery (typically for a vacation)

■■ Complaining

■■ Stopping the subscription (either voluntarily or forced)

In a subscription-based relationship, it is possible to understand the cus­tomer over time, gathering all these disparate types of events into a single pic­ture of the customer relationship

SALE

Voluntary Churn

Forced Churn

SUBSCRIBER paying

SUBSCRIBER late paying

START

PayBill

ORDER

Create Account

Deliver Paper

No

t Pay

Stop Paying

Stop for Other Reason

Temporarily

Complain Stop

Temporarily

Figure 14.4 (Simplified) customer experience for newspaper subscribers includes several

different types of interactions

Business Processes Are Organized around the Customer Life Cycle

The customer life cycle describes customers in terms of the length and depth

of their relationship Business processes move customers from one phase of the life cycle to the next, as shown in Figure 14.5 Looking at these business processes is valuable, because this is precisely what businesses want to do: make customers more valuable over time In this section, we look at these dif­

ferent processes and the role that data mining plays in them

to spread through different regions

There are three important questions with regards to acquisition, which are investigated in this section: Who are the prospects? When is a customer acquired? What is the role of data mining?

462 Chapter 14

Who Are the Prospects?

Understanding who prospects are is quite important because messages should

be targeted to an audience of prospects From the perspective of data mining, one of the challenges is using historical data when the prospect base changes Here are three typical reasons why care must be used when doing prospecting:

■■ Geographic expansion brings in prospects, who may or may not be sim­ilar to customers in the original areas

■■ Changes to products, services, and pricing may bring in different target audiences

■■ Competition may change the prospecting mix

These are the types of situations that bring up the question: Will the past be

a good predictor of the future? In most cases, the answer is “yes,” but the past has to be used intelligently

The following story is an example of the care that needs to be taken One company in the New York area had a large customer base in Manhattan and was looking to expand into the suburbs They had done direct mail campaigns focused on Manhattan, and built a model set derived from responders to these campaigns What is important for this story is that Manhattan has a high con­centration of very expensive neighborhoods, so the model set was biased toward the wealthy That is, both the responders and nonresponders were much wealthier than the average inhabitant of the New York area

When the model was extended to areas outside Manhattan, what areas did the model choose? It chose a handful of the wealthiest neighborhoods in the surrounding areas, because these areas looked most like the historical respon­ders in Manhattan Although there were good prospects in these areas, the model missed many other pockets of potential customers By the way, these other pockets were discovered through the use of control groups in the mailing—essentially a random sampling of names from surrounding areas Some areas in the control groups had quite high response rates; these were wealthy areas, but not as wealthy as the Manhattan neighborhoods used to build the model

WA R N I N G Be careful when extending response models from one

When Is a Customer Acquired?

There is usually an underlying process in the acquisition of customers; the details of the process depend on the particular industry, but there are some general steps:

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■■ Customers respond in some way and on some date This is the “sale”

date

■■ In an account-based relationship, the account is created This is the

“account open date.”

■■ The account is used in some fashion

Sometimes, all these things happen at the same time However, there are invariably complications—bad credit card numbers, misspelled addresses, buyer’s remorse, and so on The result is that there may be several dates that correspond to the acquisition date

Assuming that all relevant dates are available, which is the best to use? That depends on the particular purpose For instance, after a direct mail drop or an email drop, it might be interesting to see the response curve to know when responses are expected to come in, as shown in Figure 14.6 For this purpose, the sale date is most important date, because it indicates customer behavior and the question is about customer behavior Whatever might cause the account open date to be delayed is not of interest

A different question would have a different answer For comparing the response of different groups, for instance, the account open date might be more important Prospects who register a “sale” but whose account never opens should be excluded from such an analysis This is also true in applica­

tions where the goal is forecasting the number of customers who are going to open accounts

What Is the Role of Data Mining?

Available data limits the role that predictive modeling can play Predictive modeling is used for channels such as direct mail and telemarketing, where the cost of contact is relatively high The goal is to limit the contacts to prospects that are more likely respond and become good customers Data available for such endeavors falls into three categories:

■■ Source of prospect

■■ Appended individual/household data

■■ Appended demographic data at a geographic level (typical census block or census block group)

The purpose here is to discuss prospecting from the perspective of data min­ing A good place to begin is with an outline of a typical acquisition strategy Companies that use direct mail or outbound telemarketing purchase lists Some lists are historically very good, so they would be used in their entirety For names from less expensive lists, one set of models is based on appended demographics, when such demographics are available at the household level When such demographics are not available, neighborhood demographics are used instead in a different set of models

One of the challenges in direct marketing is the echo effect—prospects may

be reached by one channel but come in through another For instance, a com­pany might send a group of prospects an email message Instead of respond­ing to the email on the Web, some respondents might call a call center Or customers may receive an advertising message or direct mail, yet respond through the Web site Or an advertising campaign may encourage responses through several different channels at the same time Figure 14.7 shows an example of the echo effect, as shown by the correlation between two channels, inbound calls and direct mail Another challenge is the funneling effect during customer activation described in the next section

WA R N I N G The echo effect may artificially under- or overestimate the performance of channels, because customers inspired by one channel may be attributed to another