Analytics for Customer Engagement
Author - Tammo H. A. Bijmolt, Peter S. H. Leeflang, Frank Block, Maik
Eisenbeiss, Bruce G. S. Hardie, Aurélie Lemmens and Peter Saffert
Resource -Journal of Service Research, 2010
Teacher- Daphne Yuan
Presenter – Wayne Yang
Agenda
 Background Info
 Scope
 Big Data Analysis
 Models for Customer Engagement
 Models for Customer Acquisition
 Models for Customer Development
 Models of Customer Retention
 Barriers for Implementation in Practice
 Conclusion
1
Background Info
 A different perspective is now emerging, namely, that
customers can cocreate value, cocreate competitive
strategy, collaborate in the firm’s innovation process, and
become endogenous to the firm.
 we distinguish between three general manifestations of
customer engagement: WOM, customer cocreation, and
complaining behavior; all of which affecting the brand or
firm in ways other than purchase.
2
Background Info
 Customer engagement is connected to customer value
management (Verhoef, van Doorn, and Dorotic 2007)
through its objective, namely, to maximize the value of a
firm’s customer base. .
 For example, von Wangenheim and Bayon (2007) find
that the lack to incorporate WOM in the customer
lifetime value (CLV) calculation could lead to an
underestimation of the CLV by up to 40%.
3
Scope
 1. To review opportunities and organizational aspects
with respect to data collection for customer
engagement;
 2. To give a brief overview of ‘‘traditional’’ models
dealing with customer transactions and to subsequently
discuss how key behavioral manifestations of customer
engagement (WOM, cocreation, and complaining
behavior) can be included in these models;
 3. To discuss problems that are apparent in marketing
practice and constitute barriers for introducing analytics
for customer engagement.
4
Big Data Analysis

As illustrated in Figure (a), the standard model building process starts with the
management problem being studied. The model builder develops his model and
applies it to data using his standard suite of modelling tools. However, the custom
programs, specialist modelling environment and complex data structures serve as
barriers to implementation in the firm.
5
Big Data Analysis
 In sharp contrast, the process of building a ‘simple model’ considers
the modelling environment and data management constraints at
the beginning, and these help guide the development of the
formal model, as illustrated in Figure 3(b).
6
Models for Customer Engagement
-Models for Customer Acquisition
 The initial goal of customer acquisition is to select the
‘‘right’’ prospects for the acquisition campaign.
 Once the decision regarding which customers to focus
on has occurred, the next question to be addressed is
how to allocate resources among marketing variables for
leveraging customer acquisition.
 Scoring models ex: RFM, CHAID
 Probability models
7
Models for Customer Engagement
-Models for Customer Acquisition : RFM
 Recency - How recently did the customer purchase?
 Frequency - How often do they purchase?
 Monetary Value - How much do they spend?
8
Models for Customer Engagement
-Models for Customer Acquisition
 The initial goal of customer acquisition is to select the
‘‘right’’ prospects for the acquisition campaign.
 Once the decision regarding which customers to focus
on has occurred, the next question to be addressed is
how to allocate resources among marketing variables for
leveraging customer acquisition.
 Scoring models ex: RFM, CHAID
 Probability models
9
Models for Customer Engagement
-Models for Customer Acquisition : CHAID
10
Models for Customer Engagement
-Models for Customer Acquisition :
The downside of Scoring Model
 1.scoring models predict behavior in the next period
only. However, estimating CLV for example not only
requires information from the next period but also from
periods thereafter
 2. at least two data sets are required for estimation and
validation. When the same data are used for model
calibration as for model validation and comparison, it is
possible that the model explains noise instead of the
underlying relationship, a problem known as overfitting.
11
Models for Customer Engagement
-Models for Customer Acquisition :
Probability model
 1.Probability models overcomes many of these limitations consists of probability models
 2The key underlying idea of the probability modeling
approach is that observed behavior is a function of an
individual’s latent behavioral characteristics. After fitting
a probability model to the data, inferences about an
individual’s latent characteristics can be made, given his
observed behavior, which, in turn, can be used for
predicting future behavior
12
Models for Customer Engagement
-Future Models for Customer Acquisition
 In the context of customer engagement, these rather
purchase-related objectives can readily be replaced by
behavioral manifestations other than purchase and be
included in the ‘‘traditional’’ modeling approaches.
 For example, instead of predicting future purchase levels,
analytical models may be aligned to predict the number
of WOM referrals.
13
Models for Customer Engagement
-Future Models for Customer Acquisition :
WOM (MANAGING CUSTOMER-INITIATED CONTACTS:THE IMPACT ON SHARE OF
CATEGORY REQUIREMENTS AND WORD-OF-MOUTH BEHA VIOR)
 Right censored – above certain value
 Truncated-at-zero – above one
 First, using a logistic regression model, they determine
whether a WOM referral is made.
 Then, applying a truncated- at-zero NBD model, they
estimate the actual number of referrals, given that at least
one referral was made.
14
Models for Customer Engagement
-Future Models for Customer Acquisition :
WOM (MANAGING CUSTOMER-INITIATED CONTACTS:THE IMPACT ON SHARE OF
CATEGORY REQUIREMENTS AND WORD-OF-MOUTH BEHA VIOR)
15
Models for Customer Engagement
-Future Models for Customer Acquisition :
WOM (MANAGING CUSTOMER-INITIATED CONTACTS:THE IMPACT ON SHARE OF
CATEGORY REQUIREMENTS AND WORD-OF-MOUTH BEHA VIOR)
16
Models for Customer Engagement
-Future Models for Customer Acquisition :
WOM (MANAGING CUSTOMER-INITIATED CONTACTS:THE IMPACT ON SHARE OF
CATEGORY REQUIREMENTS AND WORD-OF-MOUTH BEHA VIOR)
17
Models for Customer Engagement
-Models for Customer Development
 In the development stage, a CLV can be stimulated
through many marketing activities. For example, instead of
predicting future purchase levels, analytical models may be
aligned to predict the number of WOM referrals.
 One class of models tries to predict a consumer’s margin
directly using econometric methods.
 For example, Venkatesan and Kumar (2004) use a regression
model based on past transactions and marketing mix variables
to predict the contribution margin.
 Another class of models investigates cross-selling (e.g.,
Kamakura 2008).
18
Models for Customer Engagement
-Models for Customer Development
 Other approaches to cross- selling comprise
recommendation systems. Bodapati (2008) uses a
hierarchical Bayes method with a dual latent class structure to develop a model for firms’ recommendations to
customers.
 Netzer, Lattin, and Srinivasan (2008) propose a hidden
Markov model for the transitions among latent
relationship states and its effects on buying behavior. The
model is intended to guide the firm’s marketing decisions
to alter the long-term buying behavior of its customers.
19
Models for Customer Engagement
-Models for Customer Development : HMM
20
Models for Customer Engagement
-Future Models for Customer Development
 Goldenberg et al employ a VAR modeling approach. They
find that WOM-elasticities are approximately 20 times
higher than that of marketing events and 10 times that of
media appearances.
 Brand communities can create value among networked
firm-facing actors, as such, the active management and
stimulation to cocreate is another important task.
 Ex: LEGO robotistics
21
Models for Customer Engagement
-Future Models for Customer Development
 The degree by which cocreation actually translates into
CLV strongly depends on the trade-off between the costs
for a firm to stimulate consumer participation (e.g.,
financial rewards) and the benefits that firms receive
(Hoyer et al. 2010).
 A further manifestation of customer engagement, which is
likely to affect CLV are customer complaints.
22
Models for Customer Engagement
- Models for Customer Retention
 Customer retention is the third building block of a customer
engagement strategy. A further manifestation of customer
engagement, which is likely to affect CLV are customer
complaints.
 Customer retention focuses on preventing customer
attrition or churn, that is, the termination of the contractual
or noncontractual relationship between the customer and
the company.
23
Models for Customer Engagement
- Models for Customer Retention
 In a noncontractual setting, the challenge is to infer
whether a customer is still active or not. Most models that
have been developed to assess the probability that a
customer is still alive are probability models, such as the
Pareto/NBD model.
 Recent developments in the field of machine learning,
including neural networks (Hung, Yen, and Wang, 2006),
support-vector machine
24
Models for Customer Engagement
- Future Models for Customer Retention
 Numerous tracks for future research open up to
researchers interested in linking customer retention
decisions with customer engagement.
 A promising research area to account for customer
engagement in the context of retention is to incorporate
the potential interactions across customers in churn
prediction models. Ex: eBay buyers and sellers
 In another direction, retention models would also benefit
from accounting for heterogeneity in customer
engagement across the customer base. Customers are
likely to show different levels of engagement to the firm.
25
Barriers for Implementation in
Practice
 Barrier 1: Data Quality, Size of Databases, and New Types
of Data
 Barrier 2: Data Ownership
 Barrier 3: Complexity of the Models
 Barrier 4: Ownership of the Modeling Tools
 Barrier 5: Usability of the Results
 Barrier 6: Integration in Company Processes
26
Conclusion
 We have discussed analytical models for customer
engagement, which goes beyond models for customer
transactions.
 several organizational issues of analytics for customer
engagement remain, which constitute barriers for
implement- ing analytics for customer engagement.
27
Q&A
28
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