Data Analysis and Customer Relationship Data Analysis and Customer Relationship ManagementManagement

Business Context

• Business topics addressed in this chapter are roughly p p g yin ascending order of complexity of the customer relationship, starting:– Communication with prospects (little knowledge of them)Communication with prospects (little knowledge of them)– On‐going customer relationships involving multiple:

• Products• Communication channels/methodsCommunication channels/methods• Increasingly individualized interactions

ProspectingProspecting

• Prospectp– Noun – someone/something with possibilities– Verb – to explore

• > 6B people worldwide• > 6B people worldwide– Relatively few are prospects for a company– Exclusion based on geography, age, ability to pay, need for 

d / iproduct/service, etc.• Data mining can help in prospecting:

– Identifying good prospectsy g g p p– Choosing appropriate communication channels– Picking suitable messages

Choosing the right place to advertiseChoosing the right place to advertise

• One way of targeting prospects is looking for people who y g g p p g p presemble current customers. Through surveys, one nationwide publication determined that its readers have the following characteristicsfollowing characteristics.

• 59% of the readers are college educated.

• 46% have professional or executive occupations.p p

• 21% have household income in excess of $75 K /year.

• 7% have household income in excess of $100 K/year.

• Understanding this profile helps theUnderstanding this profile helps the publication in 2 ways‐

• By targeting prospects who match the profile,By targeting prospects who match the profile, it can increase the rate of response to its own promotional efforts.

• This well educated, high income readership can be used to sell advertising space in the publication to companies wishing to reach such an audience.

Campaign ManagementCampaign ManagementAn advertising campaign

Who fits the profile for this nationwide publication?

Reader-ship

YESScore

NOScore Mike Nancy

MikeScore

NancyScore

College educated

58% 0.58 0.42 Yes No 0.58 0.42

Prof/Exec 46% 0 46 0 54 Yes No 0 46 0 54Prof/Exec 46% 0.46 0.54 Yes No 0.46 0.54

$ > $75k 21% 0.21 0.79 Yes No 0.21 0.79

$ > $100k 7% 0.07 0.93 No No 0.93 0.93$ $ %

Total 2.18 2.68

AdvertisingAdvertising• But…that might be a bit naïve; compare readership to US 

l ti th Mik d Npopulation, then score Mike and Nancy

Reader- YES Reader- NOship US

PopIndex ship US

PopIndex

BS or > 58% 20.3% 2.86* 42% 79.7% 0.53*BS or 58% 20.3% 2.86 42% 79.7% 0.53

Prof/Exec 46% 19.2% 2.40 54% 80.8% 0.67

$ > $75k 21% 9.5% 2.21 79% 90.5% 0.87

• Mike’s score: 8.42 (2.86 + 2.40 + 2.21 + 0.95)

’ ( )

$ > $100k 7% 2.4% 2.92 93% 97.6% 0.95

* 58% / 20 3%• Nancy’s score: 3.02 (0.53 + 0.67 + 0.87 + 0.95) * 58% / 20.3%* 42% / 79.7%

TIPTIP

• When comparing customer profiles (Mike andWhen comparing customer profiles (Mike and Nancy), it is important to keep in mind the profile of the population as a wholeprofile of the population as a whole.

• For this reason, using indexes (table #2) is often better than using raw values (table #1)often better than using raw values (table #1)

Direct Marketing Campaignsg p g

Direct marketing campaigns have response rates measured inDirect marketing campaigns have response rates measured in single digits. Response models are used to measure response rates by identifying prospects who are more likely to respond in a direct solicitationa direct solicitation.

• Typical mailing of 100,000 pieces costs about $100,000 ($1/piece)

• Typical response rates < 10%• Any list of prospects/customers that can be ranked by 

likelihood of response is goodlikelihood of response is good• Given a ranked list, direct marketers can increase the 

percentage of responders reached by the campaigns by targeting people at the top of the listtargeting people at the top of the list.

As the response rate to acquisition campaigns goes down,the cost of acquiring new customers goes up.q g g p

Consider the following…Consider the following…

• 1 000 000 prospects1,000,000 prospects

• Budget = $300,000

ili 300 000• Mailing to 300,000 prospects

• Rank order list (model) vs no rank order:

100%

Concentra Model

30%

Concentration (%age responders)

66%

Benefit

No Model

Model

0%0% 100%30% List Penetration

(%age prospects)

• The upper curved line plots the ‘çoncentration’, the pp p ç ,percentage of all responders captured as more and more of the prospects are included in the campaign. The straight diagonal line is there for comparison It represents whatdiagonal line is there for comparison. It represents what happens with no model, so the concentration does not vary as a function of penetration.

• Mailing to 30% of the prospects chosen at random would find 30% of the responders. With the model, mailing to the top 30% of prospects finds 65% of responders30% of prospects, finds 65% of responders.

Consider the following…Consider the following…

• Is the benefit worth the cost?• Often, smaller, better‐targeted campaign can be more profitable than a larger and more expensive one.

• Be sure to consider real revenue (for example, 10 people buy = $100 revenue; 20 people buy = $200people buy = $100 revenue; 20 people buy = $200 revenue).

• Campaign profitability depends on many variables p g p y p ythat can only be estimated, hence the need for an actual market test.

Numeric Weighting Technique

It is a statistical adjustment to the data in which each case orrespondent in the data base is assigned a weight to reflect itsimportance, relative to the other cases or respondents.

The value 1 0 represents the unweighted case The effect of weightingThe value 1.0 represents the unweighted case. The effect of weightingis to increase or decrease the number of cases in the sample thatpossess certain characteristics.

It is most widely used to make the sample data more representativeof a target population on specific characteristics. For example, it may beused to give greater importance to cases or respondents with higherg g p p gquality data. Yet another use of weighting is to adjust the sample sothat greater importance is attached to respondents with certaincharacteristics.

Campaign ManagementCampaign Management• The goal of marketing should be to reach the prospects who are more 

likely to make purchases because of having been contacted This islikely to make purchases because of having been contacted. This is known as differential response analysis.

• Goal is to change behavior (to help drive revenue)

• How do we know if we did?How do we know if we did?– Control Group – randomly receives mailing– Test Group – model selected to get mailing

Holdout Group model selected not get mailing– Holdout Group – model selected not get mailing– Compare responses of the groups

If h h h d i d ff ll illIf the treatment has the desired effect, overall response will be higher in the treated group than in the untreated group.

Response Modelling for CRMResponse Modelling for CRM

A response model is usually the first typep y ypof targeting model that a company seeks todevelop. A response model can provide ap p phuge boost to the efficiency of a marketingcampaign by increasing responses and/orp g y g preducing campaign expenses.

Differential Response AnalysisDifferential Response Analysis• How do we know if the responders actually responded because of our 

campaign or would have anyway?campaign or would have anyway?

• Answer: Differential Response Analysis (DRA)

• DRA starts with Control & Treated groups

• Control gro p no “mailing”• Control group = no “mailing”

• Treated group = receive “mailing”

• Compare results…see if there is any “uplift”

Control Group Treated Group

Young Old Young OldYoung Old Young Old

Women 0.8% 0.4% 4.1% 4.6%

M 2 8% 3 3% 6 2% 5 2%Men 2.8% 3.3% 6.2% 5.2%

Objective: To find segments where the difference in response between the treated and untreated groups isresponse between the treated and untreated groups is greatest.

Group with the highest response rate is young men who received the mailing.

Does this mean that a campaign for this service should be aimed i il ?primarily at men?

Not if the goal is to sign up maximum no. of new customers who would not have signed without prompting.

Men included in the campaign do sign up for the service in greater numbers than women, but men are more likely to purchase the service in any case.

Differential response analysis tree

The differential response tree clearly says that the group most affected in the case is old women. The group is not likely to purchase the service without prompting, but with prompting, they experience a more than tenfold increase in purchasing

UsesUses

• Matching campaigns to customersMatching campaigns to customers

• Segmenting the customer base

• Reducing exposure to credit riskReducing exposure to credit risk

• Determining customer value

• Cross selling and Up selling• Cross‐selling and Up‐selling

• Retention and Churn ([in]voluntary attrition)

• Different kinds of churn models predicting who will• Different kinds of churn models – predicting who will leave; predicting how long one will stay

*