AN EXL WHITE PAPER
Customer Engagement: A Driving Force of Business Growth
A Conceptual Framework and Empirical Study
Nupur VermaDecision Analytics, EXL
Rahul LathDecision Analytics, EXL
Swati Jain, Ph.DDecision Analytics, [email protected]
Developing a reliable metric of customer
engagement is as important as having
a loyal base of customers because
effective marketing intervention can be
made only when the organization knows
how engaged the customers are with
its brand. Development of this metric
has always been a challenge because it
involves combining various behaviors of
the customer into one scale. This paper
discusses the importance of engaging
customers, strategies to increase
customer engagement and the use of
factor analysis to measure customer
engagement. Hence, this paper is not
just a conceptual discussion but also an
empirical study of customer engagement.
The developed measure of customer
engagement has been empirically
tested for an online retailer and has
been implemented to design marketing
strategies.
IntroductionIn today’s competitive world, revenue and
margins are not the only way to study
the impact of marketing interventions.
Sustained growth of the organization calls
for investments in developing a robust
customer relationship which is nurtured
through an intelligent marketing program.
The benefits of these marketing efforts
may not be reaped in the short term
but definitely have a powerful impact
in the long term, which is reflected in
a customer’s lifetime value. Customer
engagement becomes a strong measure
of the effectiveness of a marketing
program. Engaged customers are loyal
customers who promote the brand image
of the business. They trust the quality of
products offered and spread the good
message around. These customers are
less sensitive to price changes because
the relationship which they have with the
business is based on satisfaction over a
period of time. Organizations with a loyal
Abstract: With unlimited information available, customers today are highly sensitive to price. An
organization can counter this situation of high price sensitivity if it has a large base of highly engaged
and loyal customers. Such customers are associated with the organization because they are happy
with the services and have an emotional connection with the brand.
Customer Engagement: A Driving Force of Business Growth A Conceptual Framework and Empirical Study
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base of customers are less worried about
customer retention and are able to focus on
improving their services. Their resources are
less diverted to resolving customer issues
and more towards innovative solutions to
customer requirements. According to a
2014 study by Rosetta Consulting, engaged
customers spend three times more each
year than other customers and are five
times more likely to prefer the brand over
others.
Given that the cost of acquiring a new
customer is ever increasing and is higher
than reactivating an existing customer, it
becomes imperative for organizations to
ensure that existing customers are satisfied
and remain associated with the brand.
The recent digital disruption has given
enormous opportunity for customers
to communicate with organizations and
also communicate with each other. A bad
customer experience is not just limited
to one person, but is spread to all their
contacts through social media. Due to
this multi-dimensional interaction, an
organization must become customer
centric. The digital marketplace sees a large
number of new entrants every year, which
makes it difficult to create a differentiated
product. Competition of these types can
be checked though an ongoing customer
engagement program. Engaged customers
are less likely to be swayed away by these
competitive threats.
Literature Review:Dr. Pankaj Gupta in ‘Enhancing
Organizational Effectiveness Through
Customer Engagement’, January 2012
highlights effective methods which online
and offline companies are using to reinforce
customer engagement. ‘A Generalized
Multidimensional Scale for Measuring
Customer Engagement’, December 2014
by Shiri D. Vivek, Sharon E. Beatty, Vivek
Dalela & Robert M. Morgan, explains
the usage of factor analysis to measure
engagement. ‘Measuring and Influencing
Consumer Engagement, February 2010
uses a combination of Cronbach’s Alpha,
Guttman Split-Half Coefficient, Intraclass
correlation and factor analysis to develop
an engagement scale in healthcare
domain. Justina Malciute, August 2012
discusses partial least squares method
to estimate engagement in ‘Customer
Brand Engagement on Online Social Media
Platforms’.
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Strategies to Increase Online Customer Engagement:Engaging customers does not mean a
heavy investment on advertisements to
create an all-pervasive presence through all
communication channels. It needs a well-
defined approach to achieve this objective,
as discussed below:
i. Long-term engagement of the customer
can be boosted by using the principle
of the Four I’s: Interaction, Involvement,
Influence and Intimacy. Regular
interactions with a customer lead them
to get him involved with the brand. This
will influence them in making a purchase.
If the services are delivered well, this
goes a long way in establishing a sound
relationship with the brand.
ii. It is very important to integrate various
channels of communication to maximize
the gains of each contact with the
customer. It ensures that the customer is
contacted at the right time with relevant
content. A communication which is not of
interest to the consumer is of no value to
the website.
iii. The visit to the website should be a
memorable experience to the consumer.
The navigation path to reach the
desired products should be seamless
and hassle free. According to Kate
Leggett of Forrester Research, 92% of
companies view customer experience
as one of their top priorities; 60% use
customer experiences as a competitive
differentiator.
iv. Communications to the customer should
be linked to the customer’s lifecycle,
their lifecycle with the business and their
lifecycle of product repurchase. This
strategy has tremendous opportunity
in establishing a robust brand
association and relationship. Product
recommendations for a customer who is
a senior citizen who has been associated
with the business for five years and
buys a specific product after every
three months should be different than
those for a 25 year old who became a
customer six months back and has made
a purchase just a week ago.
v. Social media should be used as means
to improvise the products and not just
a platform to build a brand image.
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Data on social media can be utilized to
understand latest trends in customer
preferences and requirements. This can
be leveraged to anticipate the demand
and offer it to the customer before the
competition does.
Measuring Customer Engagement:The importance of having a strong base
of loyal customers cannot be negated,
and hence it becomes important to have a
holistic measure of the same. A challenge
faced by all organizations is to define the
metrics on which customers’ engagement
with its product can be measured. Several
parameters can be used to define and
measure engagement like activity onsite,
frequency of visits and customers referring
the website to a friend or colleague. A rule-
based approach can be used to define if a
customer is engaged or not. For example,
if the customer has made a purchase in
last four weeks or has browsed on the
website more than ten times in last six
weeks then they are engaged, or else the
customer is not engaged. Rule-based
methods are simple to implement but given
their nature they cannot be used to rank
order the customers. A set of engaged/
disengaged customers may vary in their
degree of engagement/disengagement, so
marketing interventions must be designed
according to the degree of engagement/
disengagement. Statistical methods provide
a meaningful solution to this problem. This
paper discusses the use of factor analysis
to define the engagement score. The
score was developed using weekly data
for an online retail company wherein the
population size was 0.5 million. The results
were validated on a predefined hypothesis
using a profiling exercise.
Methodology:Factor analysis was used to measure
engagement because it enables combining
different dimensions of customer behavior
to get a measurable scale of engagement.
Factor analysis identifies latent factors
summarizing the characteristics of the
observed variables. Factors or the latent
variables are a linear combination of
correlated variables. Different factors are
independent of each other. It is important to
use only the variables impacting customer
engagement because factor analysis gives
junk results if junk variables are inputted.
The steps outlined below explain the
methodology used to define engagement
score.
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1. All possible variables which reflect
customer engagement were identified.
Some of these variables were frequency
of visit, depth of visit, email open rate,
clicks rate, etc. The hypothesis was set
on the direction of relationship of the
variable with customer engagement.
2. Factor analysis typically groups similar
variables into one factor. However,
when factor analysis was done on the
exhaustive set of variables it showed
that dissimilar variables had higher factor
loading in the first factor. Since factor
analysis was not able to create the
factors in desired manner, the exhaustive
list of the variables were grouped into
different variable categories to manually
create factors of different categories of
variables as below:
i. Browse Behavior: Gallery page
views, product views, etc.
ii. Purchase Behavior: Credit/cash
purchase, average value of order
placed, etc.
iii. Device Mix: Interaction with
the website through laptop,
smartphone, tablet, etc.
iv. Channel Mix: Responsiveness
through different channels of
communication
3. Within each variable category, the
objective was to select the most
important variable. Hence, factor
analysis was done for each factor
separately with number of factor=1
because a variable category captures a
particular characteristic of the customer.
Within a category, a variable was
selected on the basis of the conditions
below:
i. Variables with higher absolute
factor loading than other variables
were selected because such
variables capture high variability
within that variable group
ii. Variables with VIF<5
iii. Variable which made very good
intuitive sense were retained
even if the variable had low factor
loading or high VIF
4. Factor analysis was done on all the
selected variables. Variables were
selected on the same logic as in the
above step. The intent was to have
diversity in the selected variables so
that the engagement score captures all
facets of customer engagement.
5. On the above set of selected variables,
a final factor analysis was done to get
the factor loading of variables, a linear
combination of which explains customer
engagement.
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6. Factor loading of the variable is the
weight for the variable. A sum-product
of the factor loading and the value of
the variable is the engagement score.
Result:The engagement score derived using
the linear combination of the shortlisted
variables was tested against hypothesis
for the variable. This was done through a
profiling exercise to check for the direction
of relationship of the variable with the
engagement score. The validation results
of some hypotheses are presented below.
Validation 1: Hypothesis: More engaged customers
drive up the sales. The chart below
shows that as engagement score
increases, the amount of sales also
increases.
Validation 2:Hypothesis: Customers who have placed
an order in last 3 months are more engaged
than customers who have not placed
an order. The table below validates this
hypothesis.
Customer’s Action
% PopulationAverage
Engagement Score
Did not place an order
75% -0.56
Placed an order 25% 3.01
Engagement Score for orderers vs non-orderers
Engagement Score
Ave
rag
e S
ale
s
900
800
700
600
500
400
300
200
100
0-0.8 -0.7 -0.2 -0.1 -0.2 2.0 2.6 2.9 3.3 4.2
Customer Engagement: A Driving Force of Business Growth A Conceptual Framework and Empirical Study
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Validation 3: Hypothesis: Customers who have placed
an order in last 3 months and have not
returned the order are more engaged than
others. This is validated in the table below.
Validation 4: Hypothesis: Customers who have
opened the email are more engaged
than customers who did not open the
email.
Conclusion:The study shows that factor analysis is able
to create a measurable score of customers’
engagement with the online retailer and
can give expected results if it is tweaked
as per business requirement. This scale
can be used to design loyalty programs,
reactivation programs and offer optimization
strategy.
Organizations can be tempted to make
heavy investments in engaging customers,
but it is equally important to track this
metric against a financial metric which has
direct impact on bottom line. The discussed
metric of engagement is positively
related to sales, which show that higher
Customer’s Action
% PopulationAverage
Engagement Score
Did not open the email sent
62% -0.29
Opened the email sent
38% 1.89
Engagement Score for customers who opened the email communication vs those who did not open the email
Engagement Score for customers who ordered but did not return their order vs others
Customer’s Action
% PopulationAverage
Engagement Score
Did not order or returned
85% 0.13
Ordered but not returned
15% 2.91
Customer Engagement: A Driving Force of Business Growth A Conceptual Framework and Empirical Study
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engagement improves the performance of
the business.
The engagement scale discussed here
was developed using behavioral data of
the customer which was available in-house.
A further enhancement to this scale can
be done by using data available through
social media and survey data on degree
capturing connect of the customer with the
business. This will help to build a 360° view
of customer engagement.
Acknowledgement:We would like to thank Divya Chowdhary,
Chhavi Gupta, Anshul Goyal and the
entire team at EXL whose feedback and
support helped in making this paper more
informative.
References:1 Rosetta Consulting, ‘How Technology Marketers
Can Better Engage Customers’
2 Magento.com, ‘The Rules (and Tools) for Successful Customer Engagement’
3 https://www.teamsupport.com/blog/customer-loyalty-benefits
4 http://www.forbes.com/sites/forbesinsights/2015/01/29/6-strategies-to-drive-customer-engagement-in-2015/#283917a82f09
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