guest lecture | szabist -data mining

14
Leveraging Data Mining for Customer Loyalty SZABIST 105 Campus – Nov 06, 2015

Upload: muzafer-ahmed-malik

Post on 14-Apr-2017

127 views

Category:

Presentations & Public Speaking


1 download

TRANSCRIPT

Page 1: Guest lecture | SZABIST -Data Mining

Leveraging Data Mining for Customer LoyaltySZABIST 105 Campus – Nov 06,

2015

Page 2: Guest lecture | SZABIST -Data Mining

BBA & MBA Class Project Management Customer Loyalty Programs

Venture Partners

Page 3: Guest lecture | SZABIST -Data Mining

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

About Presenters

Born in KHI, 19th Dec 1984Born in KHI, 22nd Sep 1979

Hina Ghufran MasoodNational Manager Customer Service

Muzafer Ahmed MalikManager Service & Quality

MBA in Marketing, 2013

Project Lead, Customer Loyalty ProgramsAvid reader, Music n’ film lover, Football FanPeople person

MBA in MIS, 2001

Chair Person, Customer Loyalty Programs

Avid reader, Music Listener, Coffee Lover

Likes interacting with indifferent people

Page 4: Guest lecture | SZABIST -Data Mining
Page 5: Guest lecture | SZABIST -Data Mining

What is Customer Loyalty?

Page 6: Guest lecture | SZABIST -Data Mining

Customer Loyalty Is….

A result of consistently positive

emotional experience, physical

attribute-based satisfaction and

perceived value of an experience, which

includes the product or services.

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

Page 7: Guest lecture | SZABIST -Data Mining

Howto

DriveCustomer

Loyalty

Page 8: Guest lecture | SZABIST -Data Mining

Introducing

Approach

Page 9: Guest lecture | SZABIST -Data Mining

Net Promoter Approach - Mechanics

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

How NPA Works?

“Based on your recent experience with <touch point>, on a scale from 0 to 10, how likely is it that you would recommend M&P to a friend or colleague?"

Promoter QuestionPromoter Rating

Very likely

Very unlikely

109876543210

Detractors

Promoters

Passives

loyal, enthusiastic customers staying longer, spending more and making referrals

satisfied but unenthusiastic customers being more vulnerable to competitor offerings

complaining often with higher defection rate, spending less, spreading negative news Net Promoter

Score

% Promoters

% Detractors

minus

=

Net Promoter Score

Note: passives are ignored for the NPS calculation

Page 10: Guest lecture | SZABIST -Data Mining

Introduction to Net Promoter Approach

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

Page 11: Guest lecture | SZABIST -Data Mining

Net Promoter Approach –Process flow

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

First call is made to customer (either from business itself or from an outsourced client) to obtain rating on a scale of 0-10

The rating is between 0 – 8; i.e. Detractor or Passive

Second call is made from a designated representative from organization to enquire reason for dissatisfaction or what went wrong.

Page 12: Guest lecture | SZABIST -Data Mining

Winning Customer Loyalty – Data Mining

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

What to do with the available data and how to utilize it fully?

Findings from 2nd call gives customer insight and reason for dissatisfaction, known as Quick Wins

Quick Wins are action items which enables a business to correct what went wrong in the first place

By providing timely appropriate solution you win customer & their Loyalty

by applying

Page 13: Guest lecture | SZABIST -Data Mining

Implementation Examples

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

Page 14: Guest lecture | SZABIST -Data Mining

Implementation Examples

Muller & Phipps Pakistan Pvt. Ltd. | www.mulphico.pk

Thank you