Presented by: Wessel Roose, Research Manager based in Rotterdam As a service provider, for example if your brand is a financial institution or telecom company, you may try to increase your revenue by cross-selling additional services to your current customers. One way to achieve this is offering multiple services in a “bundle” and making the bundle attractive to customers by offering it at a discount. At our Research & Results workshop, we shared how to define bundles and optimize revenue by bundling. This is a new approach using our Menu Based Conjoint (MBC), our innovative method specifically designed for markets where the purchase choice is based on a menu. Find out more at http://skimgroup.com/menu-based-choice-modeling.

1. 1
Getting clear bundling insights and
recommendations from a MBC study
Pilot with cable-internet-phone bundling
October 26, 2016
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Introduction

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What is a
bundle?
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$
%

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Why do we care about bundling?
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Holiday gift
baskets
Cell phone
bundles
Travel
packages
Phone/Internet/
TV bundles
Bundles in CPG
can be things
like toothpaste/
toothbrush
bundle
How can we make the
best bundle?

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Let’s back-solve the problem. How can we
calculate the utility for any given bundle?
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Existing approaches in quantitative market
research
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Natural grouping, cluster
analysis, Kano analysis
Incorporating options,
measurement of
willingness to buy

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Existing approaches in quantitative market
research – let’s use conjoint?
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Fixed bundles as an
attribute
Separate items as
attributes

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Short introduction to Menu Based Conjoint (MBC)
A B C
AND AND
With MBC, respondents make from zero to multiple
selections of options to build their preferred choice.

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• Price elasticity per item
• Price cross effects
• Price elasticity per segment
• Cannibalization/complementarity
• Revenue optimization
• Most chosen combinations
Key learning from
traditional MBC studies

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What is MBC still missing?
!
%

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To answer the
most generic
bundling question,
we thought of
combining a CBC
exercise and an
MBC exercise
CBC MBC
configure
any bundle
individual
elasticities for
the ‘a-la-carte’
menu

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Case Study

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For this project,
the Cable-
Internet-Phone
bundling offers
in the US were
explored
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Survey
Methodology

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An MBC exercise with two components was used
to collect data from respondents

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Analysis

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Analysis approach
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2
Counts analysis to extract
interaction effects
Models selected
perform
a counts analysis
#
3
Independent variables
Dependent variables
Which variables can
we now use to link all
the models together?
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a-la-carte choice Model
Bundle Choice Model
Variables
Determine
which models and
variables we need

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Sawtooth Software’s MBC analyzing tool was used
to generate utilities and the simulatorPRICE
SERVICE OPTIONS DISCOUNT APPLIED TO
BUNDLE?
BUNDLE 1
BUNDLE 2
RESULTS

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Resulting market simulator shows the preference
for any two bundles a consumer might want
COMPOSITION
PRICE
DISCOUNT
BUNDLE SETTINGS
PREFERENCE SHARE REVENUE CALCULATION
$
%

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Content of the two optimal bundles
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Internet 100 MB/s
TV 150 Channels
Phone
Extra Options
US & Canada
HBO and streaming
20% discount relative to offering items separately
Internet 150 MB/s
TV 200Channels
Phone
Extra Options
US & Canada
Sports and streaming
20% discount relative to offering items separately

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Summary

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Summary
QUESTION APPROACH ≠ BUNDLE NOT
PRE-DEFINED
COUNTS
# VARIABLESSAWTOOTH
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Contact us
skimgroup.com
@SKIMgroup
SKIMgroup
SKIMgroup
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Marco Hoogerbrugge
Research Director
m.hoogerbrugge@skimgroup.com
+31 10 282 3544
Kevin Lattery
VP, Methodology and Innovation
k.lattery@skimgroup.com
+1 646 645 7646
Wessel Roose
Research Manager
w.roose@skimgroup.com
+31 10 282 3520

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Thank You!
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