BGSCustomer Relationship

Management

BGSCustomer Relationship

ManagementChapter 11

CRM Program Measurement and Tools

Chapter 11CRM Program Measurement

and Tools

Thomson Publishing 2007 All Rights Reserved

CRM Program MeasurementSurprising Findings

• 90 percent of the fifty largest CRM users were unable to quantify a return on their CRM investment

• Only 30 percent of companies measured benefits of CRM

• Only 37 percent knew if they shared a customer with another division in their company

• Only 20 percent knew if a customer had ever visited the company web site

Top Ten Mistakes Companies Make When Measuring Their

CRM Effectiveness• Off in a corner• Lack of leadership support• Too much data• No clearly defined strategy• Knowing without acting• Stale as seven-day old bread• Death by 1,000 dashboards• Failure to benchmark• Culture of inertia• Failure to measure outcomes

Program Measurement

Measurement Background

1. Measuring satisfaction– Differentiate among first time, short-

term, long-term, those about to defect, and desirable vs. undesirable customers

– Use direct measure of satisfaction and not “gap” measures

– Invest in moving customers to top score as opposed to middle-value scores

Measurement Background

2. Service quality• Customers use five dimensions to assess

service quality• Tangibles• Reliability• Responsiveness• Assurance• Empathy

Measurement Background

3. Retention– Retention curves are one way to see how

responsive profits are to changes in defection rates. One credit card company’s experience

– Defection rate cut from 20 percent to 10 percent

– Average customer life doubled from five to ten years

– Customer value went from $134 to $300

Measurement Background

3. Retention rates – “As the defection rate dropped another 5%

the average life span doubled again and profits rose 75% from $300 to $525.”

Reicheld and Sasser

CRM Effectiveness

CRM Effectiveness Measures Can be Grouped into Four

Distinct Areas1. CRM customer cycle measures

− Acquisition/bonding/effecting behavior Change/retaining/preventing downward Migration/winback

2. Company 3E measures− Efficiency, effectiveness, employee behavior

3. Customer and company worth measures4. Customer knowledge measures

CRM Customer Cycle Measures: Acquisition

• Acquisition rate/yield rate/capture rate• Conversion rate• Acquisition cost or conversion efficiency• Other acquisition effectiveness measures

CRM Customer Cycle Measures: Developing Closer Bonds

• Maintenance ratio• Continuity of most profitable customers• Measures of overall customer satisfaction,

loyalty, and commitment• Emotional affinity• Service/transaction satisfaction• Measures of service quality• Perceptions of value• Numbers of customer

complains/referrals

CRM Customer Cycle Measures: Effecting Behavior Change by

Cross-Selling and Up-Selling

• Size of wallet• Share of wallet• Share of category• Other behavioral change effectiveness

measures– Number of company products held per

customer– Type of channel usage– RFM measures

CRM Customer Cycle Measures

Retaining Customers• Average retention rate• Average defection rate• Average lifetime duration• Survival rate

– Number from customer cohort remaining at point of time in the future

Survival Rate: Example

Of the 2,500 new customers acquired in 2001, how many will survive to 2004?

Year Retention Rate Survival Rate # Of Survivors 2001 90.00% 90.00% 22502002 93.00% 83.70% 20932003 95.00% 75.50% 1889

Defection Indicators Used To Prevent Defection and Downward Migration

• Measures of downward migration– Days since last purchase– Buying cycle changes– Share of wallet changes– Change in number of product categories

bought– Change in purchase of ancillaries– Changes in order size– Changes in number of contacts and touch

points

CRM Customer Cycle Measures: Winback

• SCLV: second customer lifetime value• Average cost per customer reactivated• Percent of customer reactivated per

campaign

Company 3E Measures: Efficiency/Effectiveness/Employee

Behavior• More effective marketing campaigns• Better campaign management• Report generation time efficiencies • First contact resolution of problem• Successful resolution and time to resolve• Reduced customer attrition• Revenue per CRM $• Improved customer response rates across TPs• Time spent on developing promotions, reports, etc.• Reduced campaign development cycle and product

development• Cost reduction measures

– Reduced transaction costs– Staff efficiency gains– Cost savings through improved customer selection

• Employee changes– General job satisfaction/satisfaction toward specific

aspects of job/turnover/employee referrals/percent of calls leading to cross or up- sell

Measures of Customer and Company Worth

• The importance of CLV and customer equity

• In computing CLV, should costs be considered or simply revenue?

• How is trading-up factored in?• How is the emergence of new

competitors factored in?

Customer Equity

Customer equity equals the profit from first-time customers minus the cost of acquiring the customers plus profits from future sales divided by the discount rate, summed across all customer segments.

based on Blattberg and Thomas

Customer Equity (CE) “According to Rust, Lemon, and

Narayandas”• CE is driven by three things:

1.Value Equity: customers’ objective evaluation of firm’s offering compared with others based on price/quality/convenience

2.Brand Equity: customers’ subjective view of firm and offerings based on affective components

3.Relationship Equity: feelings of loyalty often based on loyalty programs

Customer Knowledge

CRM Effectiveness Measures: How Well do You Know Your

Customers?• Longitudinal behavior• Ethnological data• Human-based observational data• Customer buying cycle data• Customer experience cycle data• Interaction scores• Critical incident scores• Acquisition/defection data• Customer-brand relationship data• RFM vs. event history model for

predicting response rates

Customer Knowledge:Customer Behavior over Time—

Longitudinal Data

Formerly done through use of panels and diaries, it now can be done via the use of database analysis.

Customer Knowledge:Ethnological Data

• Used by Hallmark Cards before entering England.

• Used by Gillette to improve products.• Used by supermarkets to understand the

mental state of customer segments before they go grocery shopping.

Customer Knowledge:Human-Based Observational

Data• Used by Four Seasons Hotels• Used by McDonald’s• Used by Harley-Davidson• Used by Jeep • Used by Apple• Used by General Electric

Customer Knowledge:Customer Buying Cycle

Analysis• Why does a customer first purchase your

offering?• In what situation does a customer

purchase your offering?• What is the progression of

product/service orders for a typical customer? High-value customer?

• How do customer lifestyle or life-stage changes affect their relationship with you?

Customer Knowledge:Customer Experience Cycle

Data•Nycamp recommends that companies

evaluate their performance in the following areas– Field sales– Point-of-sale service– CCC– Web– Marketing and corporate communications– Public relations– Advertising

Customer Knowledge:Interaction Scores

• These scores represent a customer’s assessment of the quality of interaction in each of the eight experience cycle areas. Analyze scores for inconsistencies.

Customer Knowledge:Critical Incident Analysis

Have customers rate specifics—not global generalities. For example, have banking customers rate

– Wait time for a teller– Errors/mistakes– Parking– Teller treatment– Teller competence– Hours of service– Service environment

Customer Knowledge:Acquisition/Defection Matrix

(Partially Completed)

TOCINB Ist Harris

LaSalleFROM CINB .7 .25 .03

.02 Ist .02 Harris .2 LaSalle .03

Customer Knowledge:Customer Brand Relationship

Data• The hierarchy of effects model is useful

–Awareness–Knowledge–Liking–Preference–Purchase intentions–Purchase behavior

Customer Knowledge

• RFM scores vs. event-history modelsof customer response rates

– RFM: Customer given points based on recency, frequency, and monetary value of purchases

– Event-History Model: Takes purchase pacing into accountn = number of purchases made in time periodt = fraction of period between first and last

purchase

Event-History Model

• Used to predict the probability a customer will still be active at some future point in time–Fred made four purchases from L. L. Bean, the first in January 2006 and the last in July (month seven)–JoAnne made two purchases from L. L. Bean, the first in January 2006 and the last in July • Probability each will be active in January 2007? Fred: (7/12)4 = .116 JoAnne: (7/12)2nd = .34 JoAnne is three times more likely to be an active

customer vs. Fred

Questions?