Beyond Customer Churn

13 Pages·2011·0.24 MB·English

Journal of Intelligent Learning Systems and Applications, 2011, 3, 90-102
doi:10.4236/jilsa.2011.32011 Published Online May 2011 (http://www.SciRP.org/journal/jilsa)
Beyond Customer Churn: Generating Personalized
Actions to Retain Customers in a Retail Bank by a
Recommender System Approach
Michele Gorgoglione, Umberto Panniello

Department of Mechanical and Management Engineering, Polytechnic of Bari, Bari, Italy.
Email: [email protected], [email protected]

Received July 10th, 2010; revised October 1st, 2010; accepted December 1st, 2010.

ABSTRACT
Customer churn may be a critical issue for banks. The extant literature on statistical and machine learning for customer
churn focuses on the problem of correctly predicting that a customer is about to switch bank, while very rarely consid-
ers the problem of generating personalized actions to improve the customer retention rate. However, these decisions
are at least as critical as the correct identification of customers at risk. The decision of what actions to deliver to what
customers is normally left to managers who can only rely upon their knowledge. By looking at the scientific literature
on CRM and personalization, this research proposes a number of models which can be used to generate marketing ac-
tions, and shows how to integrate them into a model embracing both the analytical prediction of customer churn and
the generation of retention actions. The benefits and risks associated with each approach are discussed. The paper also
describes a case of application of a predictive model of customer churn in a retail bank where the analysts have also
generated a set of personalized actions to retain customers by using one of the approaches presented in the paper,
namely by adapting a recommender system approach to the retention problem.

Keywords: Customer Churn, Customer Retention, Personalization, Predictive Models, Recommender Systems
1. Introduction knowledge. However, these decisions are at least as cri-
tical as the correct identification of customers at risk. A
Retail banks often deal with customer churn. Among the
customer churn prediction model should be integrated
several issues addressed by Customer Relationship Ma-
with a model to decide what personalized marketing ac-
nagement (CRM), identifying the customers who are
tion to deliver to customers; otherwise the benefits of
about to quit the relationship with a company is one of
using accurate predictive models would be lost.
the most important in the financial services industry.
The issue addressed by this research is to identify a set
When competition becomes tougher, when laws decrease
of approaches to generating actions to retain customers,
either the barriers to entry or the customer’s switching
once customers at risk are identified and profiled. We
costs, or when a company aims at strengthening its posi-
believe that this issue is important for both businesses
tion in a new market, the issue of retaining customers and
launching customer retention campaigns and researchers
avoiding customer churn becomes even more crucial.
aiming at building models of customer churn. From a
In the last decade several computer science scholars
business perspective, the problem is to support managers
have tackled the problem of building accurate models to
identify customers at risk in a bank by using statistical, in making the right decision on how to define personal-
machine learning and data mining approaches [1]. How- ized retention actions. In fact, this decision has to con-
ever, much literature only focuses on the problem of sider both organizational aspects, such as control and
correctly predicting that a customer is about to switch. efficiency, and technological aspects, such as the accu-
Very rarely the problem of generating personalized ac- racy of the algorithms. The challenge for managers is to
tions to improve customer retention rates is considered. integrate the results of a predictive model of customer
The decision of what actions to deliver to what customers churn with the decision of delivering certain marketing
is normally left to managers who can only rely upon their actions. The problem is how to associate the right action
Copyright © 2011 SciRes. JILSA
Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a 91
Retail Bank by a Recommender System Approach
with the right customer. From a scientific perspective, a to review the approaches to deliver actions to individual
comprehensive model of how to generate actions might customers. A review of studies on personalization can be
be helpful to develop more effective data mining and found in [17]. The authors consider three main areas,
statistical models to support CRM processes. namely computer science and information systems (CS/
By looking at both the scientific literature on CRM IS), marketing, management science and economics. It is
and personalization, this paper proposes a number of possible to extract from that review the works directly
approaches which can be used to generate personalized related to the problem of generating personalized actions.
marketing actions, and discusses benefits and risks asso- In the CS/IS area the main streams dealing with ac-
ciated with each model. Finally, a case of application is tions are Recommender Systems and Web Contents Per-
described. A retail bank developed a predictive model of sonalization. In both cases the problem of generating
customer churn. The model was integrated to a method to personalized actions is solved by processing individual
generate personalized actions aimed at customer reten- customer profiles which describe personal preferences. In
tion. The method is based to the approach followed by a recommender system the profile describes a customer’s
Recommender Systems. The results of this model are preference by including either a set of ratings to certain
compared to those obtained by the same company in a products or information on customers’ previous pur-
formerly launched retention campaign. chases [18]. The action is the recommendation of a prod-
uct and it is generated by analyzing the similarities be-
2. Prior Research
tween the profiles of customers who bought or rated a
The problem of customer churn has been analyzed by product and those of customers who did not. These sys-
scholars in the fields of marketing and analytical CRM, tems are the most common approaches to personalization
by analyzing several aspects. For instance, [2] analyzes in real industrial settings ever since Amazon adopted a
the possible payoffs of retention and acquisition strate- Recommender System [19]. In the case of Web Content
gies depending on the market structure. An analysis of Personalization the user’s profile includes statistics on
the switching costs is provided by [3]. Customer churn the usage of the Web and the action is generated by asso-
has also been studied by analyzing CRM-related prob- ciating a content with similarities with the user’s profile
lems such as the effectiveness of loyalty programs [4] [20].
and customer satisfaction [5]. Scholars in marketing have dealt with the problem of
Analytical models to predict customer churn have been generating personalized actions by studying competitive
developed in several areas and industries as well. Mar- personalized promotions [21], customization [22], the
keting models have been deployed to predict customers personalization of services [23], and recommender sys-
at risk [6,7] as well as data mining models [8], especially tems. In all cases, except that of recommender systems
in telecommunication-related industries [9-11]. Specific which was reviewed above, the problem is solved by
prediction models have developed also in banking and using optimization models that allow the analyst to find
finance [12,13]. the best price and/or product based on individual cus-
This body of literature have mainly focused on the tomers response models. The response models are built
problem of correctly predicting customer churn. Very by collecting data on customers’ previous behavior.
rarely the problem of how to generate personalized ac- The research in management science [24] and eco-
tions, once customer churn has been modeled, is consid- nomics has mainly faced the problem of generating per-
ered. For instance, [14] states that a good customer churn sonalized actions by studying personalized pricing and
model should also provide managers with a way to gen- mass customization [25]. The first stream falls in the
erate personalized actions, although the research focuses marketing approaches reviewed above. The second str-
only on predicting customer churn. The need to integrate eam mainly analyzes the benefits and costs of producing
the analysis of customer behavior with the generation of as many products as the number of different customers’
actions in a comprehensive model is maintained by [15], individual preferences a company can identify in the
while [16] suggests to include customers found at risk of market [26]. In these models the action is offering of a
leaving the company in the organization of events in or- vast variety of products. Actions are generated by pro-
der to strengthen their relationships with the company. cessing customers’ preferences in an aggregated way, not
As the literature on customer churn does not directly individually, while the choice of the specific product is
face the problem of how to generate actions, and in order left to customers.
to achieve a more general understanding of the problem, A few research streams can be added to the review
we have broadened the focus of the literature analysis to taken from [17] which do not directly deal with person-
the research on personalization-related areas. The goal is alization, namely Direct Marketing and Knowledge Dis-
Copyright © 2011 SciRes. JILSA
92 Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a
Retail Bank by a Recommender System Approach
covery in Databases (KDD). An extensive literature ex- decided a-priori or a-posteriori. In the first case the front
ists in the area of direct marketing dealing with targeting office managers select the right customers to whom a
problems. The largest body of knowledge is related to the certain action can be offered, while in the second actions
process of segmentation, targeting and positioning. It are decided by the front office managers, who have to be
includes several methods to define and select a target given the necessary degree of autonomy, based on their
market and position an offer, i.e. define a specific mar- understanding of customers’ needs.
keting mix (or any other marketing action) to that spe-
3. Approaches to the Definition of
cific market. Several scholars in marketing have warned
Personalized Actions
about the fact that a segmentation should be done only
aiming at a clear marketing goal [27], i.e. segments should By reviewing the literature, five main categories can be
be built in order to deliver targeted actions. The research identified to classify the approaches to the generation of
on direct marketing has produced models to define and personalized actions. Each category represents a set of
deliver actions to more granular targets, even single indi- homogeneous approaches which can be used to decide
viduals. For instance, “RFM models” represent custom- what action should be delivered to what customers in a
ers by their Recency (i.e., the time since they have made CRM program. The approaches are reported in Table 1
the last purchase), Frequency (i.e., how often they pur- and discussed below.
chase), and Monetary value (i.e., the average money they Computational approaches include all those approaches
have spent) in order to compute the probability to which that build a complete model of customers’ behavior, ac-
a customer will make the next purchase by a certain pe- tions, and customers’ reactions based on information
riod of time. RFM models have quickly become common stored in a data set. These approaches can use both data
in direct marketing and have been extended to give rise mining [32] and optimization models [21-23]. An exam-
to more sophisticated models such as Automatic Interac- ple of applications in banking and finance is a retail bank
tion Detection Models and Regression Scoring models which stores the data related to promotion of stocks, and
[28]. In the industrial applications of these models, man- the reactions of customers who might have purchased
agers often define a very small set of actions, even one those stocks or not. The fundamental condition that en-
action (e.g. proposing a discounted subscription to a new ables the adoption of these approaches is the complete-
service), and run the model in order to select a subset of ness of data. If the data sets do not include, for instance,
individual customers who represent the optimal target. the reaction of each customer to a certain marketing
Knowledge Discovery in Databases (KDD) is a pro- campaign, then neither a mining algorithm nor an opti-
cess to find valid, new, understandable and useful pat- mization model can be run to generate personalized ac-
terns in data [29]. This process can be seen as a possible tions. Computational approaches allow a company to
way to target actions to customers. If the rules derivable fully automate the generation of actions based on cus-
from KDD are “actionable”, i.e. present information on a tomers’ profiles, as no human decision is needed. A li-
customer useful to decide “what to do” in order to mod- mitation is that only marketing actions already launched
ify that customer’s behavior, then a manager can define before can be considered in such approach. The full cov-
marketing actions tailored to that customer [30,31]. The erage of customers may be another problem because
main limitation of these approaches is that either they are some customers’ reactions may remain unknown (for in-
not efficient, due to the big amount of discovered rules stance, when a customer does not respond to a survey).
which need human supervision, or actions are not per- Similarity-based approaches are those used by Re-
sonal, because when rules are aggregated to improve effi- commender Systems [19] and Web content personaliza-
ciency the target becomes aggregated as well. Starting tion methods [20]. This kind of approach assumes that
from KDD, some attempts were done to define a general actions are related to customer preferences, preferences
method to mining actionable patterns in databases and may be inferred by customer profiles, and that either
thus generating personalized actions based on data de- similar customers behave similarly or similar actions
scribing customers’ reactions to the company’s previous cause similar reactions. A “similarity-based” approach
offers [32]. does not require to store as much information as a com-
A last remark is useful to highlight that many compa- putational approach. Recording customers’ preferences is
nies, especially in banking and finance, make use of front enough, because it is assumed that the unknown prefer-
office personnel to generate actions tailored to the per- ences of a customer can be derived by identifying the
sonal characteristics of customers. In this approaches, similarity with other customers. However, the twofold
managers have to talk to customers, understand their pre- condition of applicability of such approaches is that cus-
ferences and propose them specific offers. Actions can be tomers’ profiles have to represent preferences, and only
Copyright © 2011 SciRes. JILSA
Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a 93
Retail Bank by a Recommender System Approach
actions associated with those preferences can be gener- rather from customers’ choices. This makes customiza-
ated. For instance, a customer who owns multiple credit tion different from other personalization approaches [26].
cards can be classified as a customer who “prefers” using The offer has to be granular enough in order to adopt this
credit cards, whereas the fact that a customer has a mort- approach. For instance, a retail bank can propose cus-
gage does not necessarily represent a “preference”. A “si- tomers to choose one among many discounted options
milarity-based” approach is useful to automate the per- (e.g., a discount on bank transfers, credit cards, cash
sonalization process. An example of this approach in cards, e-transactions, etc.). The association between ac-
banking is given in Section 5. tions and profiles is performed by the customers instead
Bottom-up approaches include the knowledge discov- of the company. For this reason, the control over the
ery methods [31,33] and the use of front office personnel. process is low and the resulting actions can turn out to be
These approaches consist of two separate steps: 1) pro- quite expensive. However, the targeting is expected to be
filing customers, 2) deciding proper actions, where the
quite effective.
first step has to precede the second step (i.e., actions de-
pend on profiles). They cannot be fully made automatic 4. Which Approach is Best? Discussion of
because only the first step is performed by an algorithm. Benefits and Risks
For this reason these approaches are typically not very
The approaches identified above have characteristics that
efficient. The condition of applicability is that the deci-
make them beneficial in certain settings while risky in
sion-making effort has not to exceed the company’s re-
sources: either the number of customers is low or the others. The suitability of each approach should be thor-
number of decision-makers is high. The advantage is that oughly discussed by managers based on the observation
targeting can be very effective because each profile is of their benefits and risks compared to the company
thoroughly analyzed before generating a proper action. goals and business conditions. Typically, managers have
An example of bottom-up approach in banking is the to deal with trade-offs. The main characteristics which
work of a financial advisor who manages a portfolio of can differentiate the five approaches are the following:
customers. The advisor has periodic conversations with a
 control on the decision-making process;
customer, analyzes her needs and proposes tailored fi-
 automation of the personalization process;
nancial solutions.
 effectiveness of targeting;
Top-down approaches include the direct marketing
 process efficiency;
approaches [28]. They consist of the same two separate
 scope of actions;
steps typical in bottom-up approaches. However, in this
 coverage of customers.
case, the decision of what actions to deliver is made be-
fore the definition of customers’ profiles and, hence, pro- 4.1. Control on the Decision-Making Process
files depend on actions. For instance, a retail bank man-
Companies normally want to keep as much control as
agers may first decide to offer customers a discount on
possible on the definitions of the actions to deliver. Pos-
bank transfers, and then select the target customers by
sible reasons are the need of controlling the costs related
building appropriate profiles. For this reason top-down
to marketing actions, and avoiding to leave too much
approaches do not cover the whole customer base: only power to front office personnel. In this case, the decision
customers falling in the target profiles are delivered the of what offering to customers should be centralized. As
actions. In this approach, the control on the decision- an example, a retail bank may want to avoid that the front
making process is very high, as the definition of actions office personnel makes the decision of what to offer to
is made centrally (it is not delegated to front office per- the customers in their portfolio, because both costs may
sonnel as in a bottom-up approach). Profiles can be de- increase and the bank could be exposed to opportunistic
termined by either running algorithms or asking the front behavior. In this case, the suitable approaches to be used
office personnel to identify suitable target customers. are “computational”, “similarity-based” and “top-down”.
The condition of applicability is that actions can be de- In all these approaches, the decision of what action to
fined before customers’ profiles. In some CRM problems, deliver is made centrally, by the support of either an al-
such as reducing customer churn, this condition can be gorithm or the strategic orientation. The “bottom-up” and
questionable. “customization” approaches may be risky because the
Customization approaches are those which offer cus- first may involve the use of front office personnel, the
tomers many different options and let the customers second requires the intervention of customers.
choose the suitable one. This approach is typically adopt-
4.2. Automation of the Personalization Process
ed in mass customization [25]. In this approach actions
do not derive from processing customers’ profiles but The need of automating the process of profiling, defining
Copyright © 2011 SciRes. JILSA
94 Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a
Retail Bank by a Recommender System Approach
personalized offers and delivering actions can come from involved in the process. The efficiency of a “customiza-
several reasons, such as the need of keeping the cost low tion” approach seems to depend on the business settings,
and the promptness of CRM processes high. For instance, as it can be easy to implement but the results can turn out
companies in the financial services industry may want to to be expensive to the company. Efficiency can be meas-
be prompt and fast in starting a retention program when ured by the number of people involved and/or by time
the churn rate suddenly increases. In this case, automat- spent on the process.
ing the process is critical. “Computational” and “similar-
4.5. Scope of Actions
ity-based” approaches are the most suitable when the
need of automation is high. In fact, they can be imple- Companies may want to be creative in the definition of
mented into an algorithm which can be integrated in the the actions to deliver to customers. The proposed approa-
company information system. The remaining approaches ches have different impacts on the capability of the com-
(“bottom-up”, “top-down” and “customization”) can only pany to define a wide spectrum of actions. The only ap-
be partially automated, as it was observed in the previous proaches that look suitable to this aim are those in the
section. “bottom-up” category. In fact, it always requires the hu-
man supervision, to either infer the needs from the pro-
4.3. Effectiveness of Targeting
files and, in turn, the actions, or talk to each customer,
Companies involved in CRM programs are often con- dig into his/her preferences and needs, and finally find
cerned in maximizing targeting, i.e. to maximize the out the best offer to deliver. The scope of actions in
probability that a customer receives an offer tailored to “computational” and “similarity-based” approaches are
her own needs. For instance, a retail bank may want to limited by the nature of the data. In the former, the only
make sure that each customer is offered exactly the pro- actions which can be offered to customers are those re-
duct she needs. In these case, the best approaches to de- corded in the databases, for which customers’ reactions
fine personalized actions are the “computational”, “bot- are already recorded and codified. In the latter, actions
tom-up” and “customization” ones. In the first approach have to be related to the variables used to profile cus-
actions are derived by knowing the customers’ response. tomers. In the “top-down” approach, the scope of the
In the second, actions are defined by analyzing each pro- actions is usually small because companies define a
file. In the third, customers themselves decide what ac- small set of offers. In the “customization” approach the
tion they prefer. Targeting is less effective in the “simi- scope of actions depends on the spectrum of different
larity-based” and “top-down” approaches because the choices customers are provided with. A potential meas-
customers’ need are inferred by similarity or simply not ure for this variable can be the number of different ac-
considered when the decision of actions is made. The tions.
effectiveness of targeting can be evaluated by the “re-
4.6. Coverage of Customers
demption rate” which measures how many customers
react the expected way to a marketing campaign. Not all approaches allow a company to fully cover the
customer base with personalized actions. The “similar-
4.4. Process Efficiency
ity-based” and “bottom-up” approaches guarantee a full
Whatever the goal of a CRM program, companies have coverage. In the first, actions can be defined by similarity.
to keep the costs of CRM as low as possible. Companies Even customers for whom the amount of data is very
may choose the best approach to define personalized little (e.g., new customers), actions can be derived by the
actions by considering the level of resources they can preferences of similar customers. In the second, human
allocate to that process. Some approaches requires less decision makers can rely on all their knowledge to define
resources than others. Particularly, “similarity-based” actions starting by profiles. On the contrary, in a “com-
and “top-down” approaches look suitable to this aim. The putational” approach the coverage is limited to customers
algorithms required by the first approach are relatively for whom relevant information on actions and reactions
simple, as it is witnessed by their quick diffusion in in- have been recorded. In a “top-down” approach, actions
dustrial settings. The second approach makes the integra- are defined independently of profiles, with the conse-
tion between the decision of actions and definition of quence that not all profiles can be associated with actions.
profiles simpler than other. On the contrary, the algo- In a “customization” approach only customers who make
rithms required by a “computational” approach are more an explicit choice are then offered the actions, those who
complex. A “bottom-up” approach requires either a huge do not want to express any preference remain outside the
effort in terms of supervision of the knowledge discovery coverage of the CRM program. The coverage of custom-
process or a high level of resources in terms of people ers can be measured by the percentage of customers for
Copyright © 2011 SciRes. JILSA
Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a 95
Retail Bank by a Recommender System Approach
whom actions can be derived from profiles. They were ordered by the number of signals of attrition
Table 1 represents each approach in terms of its char- in a ranking from high to medium risk.
acteristic, conditions that must be true to adopt that ap- Once the customers were identified, the team had to
proach, the benefits and risk that should be discussed in carefully face the problem of what actions should be part
order to select the most appropriate approach. of a marketing campaign. A fairly straightforward choice
would be leaving the decision to the personal financial
5. A Case of Application
advisors, who are sales reps with a portfolio of about 150
Born as a small local bank, the company went through a (on average) customers to manage. Since they know each
significant M&A process over the last few years. It ac- customer personally, they could decided the best offer to
quired many new customers as a result of this process, retain each customer. However, the bank top manage-
currently having about 300 000 customers in total and ment decided that the actions to deliver to customer had
being a medium-sized Italian retail bank operating at a to be identified centrally. The reason was threefold. Fir-
national level. In 2008 the bank managers had observed a stly, not all customers had a direct and strong relation-
growing churn rate. Although the churn rate was much ship with their personal advisors. Some preferred to in-
smaller than the acquisition rate, the cost for acquiring a teract with the bank via the home banking system or
new customer was much higher than the cost of retaining other virtual channels. Secondly, the high number of
a customer. Moreover, the cost for the re-acquisition of a personal advisors, about 2000, would make the process
lost customer was even higher. These observations made quite expensive. The auditing process would be slow and
the need of reducing customer churn one of the bank’s complex. Thirdly, the top managers wanted to keep a
strategic priorities. strong control over the whole customer relationship
At the end of 2008 a team of managers was involved management process. The problem was too crucial to be
in a project aimed at both identifying customers at risk of left to the initiative of front office personnel.
leaving the bank and launching a marketing campaign to Therefore, in order to generate appropriate retention
retain those customers. By talking to financial advisors actions, the managers had several meetings with senior
and senior managers, the team identified a number of managers and sales/marketing managers. A list of few
events which normally signal that a customer is about to marketing actions was generated, such as offering the
leave the company. The most important signals were a customer a credit card for free or a discount on the an-
relevant decrease in the assets, the sale of more than one nual fee. The list was communicated to all the personal
financial products formerly owned, the cancellation of all financial advisors. They were told to pick one or more
the automatic outgoing payments and incoming credits. actions among those in the list for each customer identi-
By analyzing the customer data set, the managers identi- fied at risk of leaving the bank, depending on the cus-
fied the customers at risk by selecting those customers tomer’s profile. The actions had to be suitable to each
who presented one or more signals of imminent attrition. specific customer and picked based on the advisors’

Table 1. Approaches to the definition of personalized retention actions.
Approach Characteristics Conditions Benefits Risks
The data set includes information on • Control
Complete model of preferences, • Limited scope of actions
Computational prior marketing actions and • Automation
actions and reactions • Customer coverage
customers’ responses • Targeting
• Control
Actions are related to customer
Comparison between customer • Automation
Similarity-based preferences, and preferences may be • Limited scope of actions
profiles • Efficiency
inferred by customer profiles
• Coverage
The definition of customers’ The supervision effort has to be • Targeting
• Low efficiency
Bottom-up profiles precedes the definition affordable (in terms of number of • Scope of actions
• Lack of control
of actions profiles and resources) • Coverage
The definition of customers’ • Lack of targeting
Actions can be defined independently • Control
Top-down profiles follows the definition • Limited coverage of
of customers’ profiles • Efficiency
of actions customers
Customers are free to choose • Low efficiency
Customization The offer has to be granular enough • Targeting
the appropriate action • Lack of control

Copyright © 2011 SciRes. JILSA
96 Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a
Retail Bank by a Recommender System Approach

knowledge of the customer’s behavior. Secondly, the group decided not to include the data re-
As a result, a redemption rate of 78.5% was observed, lated to customers in the branch offices most recently
meaning that 1062 customers out of 1353 contacted cus- acquired from other banks, in order to keep the data ho-
tomers responded positively to the marketing action mogeneous. Thirdly, the data would be referred to the
proposed and did not leave the bank, thus reducing the last two years. The reason was that the bank had older
overall churn rate. data only for some customers. The data available on cus-
Although the results were not impressive, they were tomers coming from other banks recently acquired by
fairly aligned with the managers’ expectations. In fact, M&A processes was not older than two years. Moreover,
the manager themselves were aware of some flaws in the a technical constraint in the storage service made the
first project. First of all, actions were independent of retrieval of data referred to years preceding the last two
customers’ profiles. The decision of what to offer to cus- very slow. Finally, the data would include demographical
tomers at risk was made upstream, based on previous information on customers, such as age and job, the his-
experiences rather than a deep knowledge of each custo- tory of the relationship between customers and bank (e.g.,
mer. Therefore, a very small number of customers would when an account was open or when a financial product
receive an offer tailored to their actual needs. Another was purchased and/or sold), the products owned by cus-
flaw of the method was the very limited possibility to tomers (e.g., credit cards, cash cards, stocks, insurances,
automate the process. While the identification of custo- etc.), the operations made on the account. Operations
mers at risk could be implemented on an information were classified and divided into subcategories: 1) pay-
system, the decision-making process through which to ments by card, bank transfers and cheques (incoming and
identify retention actions could not. outgoing), 2) payments to utility services, 3) salaries and
Encouraged by the results and the commitment of the similar incomes credited on the account, 4) other opera-
bank executives, the team decided to launch a new, im- tions. Other information for each customer were also
proved project of customer retention. The main goal included in the data set, such as the name of the cus-
would be improving targeting and the possibility of au- tomer’s personal financial advisor, the branch office the
tomating the process. customer belongs.
Once the group was provided with a data set, a pre-
5.1. Predictive Model of Customer Churn
liminary analysis was run in order to check the correct-
The managers decided to collaborate with a group of ness of the data. The acquisition and analysis proceeded
analysts, including this paper’s authors. The project fol- in an iterative way, until a complete and correct set of
lowed the Cross Industry Standard Process for Data Mi- data was provided. However, the data on operations on
ning (CrispDM) methodology [34]. The first step was customers’ accounts was available only for the last year.
aimed to codify the managerial knowledge and beliefs, to For this reason that specific data was not used in the
define organizational and business constraints and to subsequent modeling phase. The initial dataset included
identify the sources of useful information. This was help- around 200 000 customer, 300 000 records and 100 va-
ful to guide the analysts in the definition of the variables riables.
that would be included in the model. For instance, in the The next step was aimed to clean and modify the data
managers’ experience the more the products owned by a according to the analysis’ goals and method. Some re-
customer the higher the probability that she would be cords were deleted while others were re-coded, and seve-
loyal, while customers owning only one product had the ral variables were transformed starting from the initial
highest probability of leaving the company. The products data set. The records referred to users older than 70 years
which made customers loyal were typically financial were dropped from the database (customers older than 70
products such as loans, stocks and bonds. could leave the bank for “natural” reasons more than for
In the next step, the analysts discussed with the man- a real choice). The records referred to customers with
agers the nature of the data sets to use based on the first more than one account were merged into one overall re-
step output. The data warehouse service was outsourced cord, in order to obtain a data set where each customer
by the bank. Therefore, any data set had to be explicitly corresponded to one record. Several variables were
asked to the outsourcer which managed the data storage. transformed. Some were discretized (e.g., length of the
Firstly, the team decided to focus only on customers in relationship between customer and bank). For some va-
the “basic” segment with at least a charged bank account. riables the analysts computed statistics such as average,
The “basic” segment was the one with the highest num- maximum and minimum value (e.g., operations made on
ber of customers and the highest churn rate. Customers in the account). Mistakes were cleaned (e.g., double codes
higher segments shall not be included in the analysis. for the same value of a variable) and some records with
Copyright © 2011 SciRes. JILSA
Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a 97
Retail Bank by a Recommender System Approach
too many missing values were deleted. Table 2. Variables used in the modeling phase.
The final number of customers in the data set was
Variable Type
203 196. More than 200 variables were included in the
new data set, although only a small subset would be used Length of customer-bank relationship numerical1
in the subsequent modeling phase. The depth of historical
Place of residence nominal
data owned by the bank was two years, namely 2007 and
2008. During the analysis which was performed in the Type of card owned nominal2
first months of 2009, the bank continued to gather similar
Maximum value of a customer’s transaction numerical
data in order to apply the model to a new set of data.
Type of transaction corresponding to the maximum
The core step of the project was aimed to build a pre- nominal
value
dictive model of customer churn and test it on actual data.
After several experiments, a relatively small subset of 27 Maximum quantity of a product purchased numerical
variables, taken from the initial set of 200, was selected Type of product corresponding to the maximum
nominal
to build the model. The set included the length of the quantity purchased
relationship between customer and bank, the customer’s Type of account owned by the customer nominal3
place of residence, the type of card owned (e.g., cash,
People sharing the same account with the customer binary
credit or pre-paid card), the type of financial products
and services owned (e.g., stocks, loans, insurances) and Products owned by the customer nominal4
their quantity and value, the type of account owned (cor-
Automatic payment of utility services binary
responding to a certain set of discounted options on the
account), the amount of payments and incomes auto- Automatic credit of incomes on the account binary
matically debited and credited, respectively, on the ac-
1All the numerical variables were discretized; 2split in 4 binary variables;
count (see Table 2). 3split in 3 binary variables; 4split in 11 binary variables.
After several trials, the algorithm used to build the
predictive model was J4.8. The training and validation tive rate equal to 0.916. The second model selected 32 938
sets included all data referred to 2007 and 2008. Each potential churners among the active customers with an
point was represented by the figure at the end of each accuracy equal to 67.34% and a true positive rate equal
semester in 2007 and 2008, therefore 4 vectors of 27 va- to 0.994. The potential “churners” identified by the first
riables for each customer were used at most to train the model were a subset of those selected by the second
model. A ten-fold cross-validation method was used. model, except 282 customers (0.14% of the whole cus-
This data included information about 11,000 customers tomer base).
who had abandoned the bank between 2007 and 2008 The bank’s managers decided to select the second
and about 180,000 customers who had remained loyal in model. The decision was based on the fact that the cost
the same period. An additional test was performed by of a false negative was considered substantially higher
applying the model, once trained and validated, to the than the cost of a false positive. The managers preferred
data related to the first semester of 2009. This data set to contact more customers, even loyal customers mis-
was gathered while the analysts were building the model classified as potential “churners”, than risking not to
and the test consisted of applying the selected model, identify actual customers at risk. Moreover, the second
with the selected settings, to the actual data in order to model performed significantly better than the first, once
predict the churner users. tested on the first semester of 2009, as the second identi-
Two predictive models were built by the analysts and fied 96.89% of customer who actually left the bank,
provided to the bank managers who would select one, whereas the percentage of re-identification in the first
based on some managerial implications. The models pre- model was 79.33%.
dicted whether a customer would leave the company or
5.2. Selection of a Suitable Approach to the
not, based on the data. In fact, because of the limited
Generation of Personalized Actions
temporal depth, it was not possible to train the model to
predict customer churn within a specific period of time. The next problem was to integrate churn prediction with
The output of the models were two lists of customers the decision of which marketing actions should be deliv-
with a predicted binary value (“will leave” or “will be ered to which customer. The analysts proposed all the
loyal”) and a corresponding score. The first model se- five approaches presented in the section above to gener-
lected 11,070 potential churners among the active cus- ate personalized actions for those customers predicted as
tomers with an accuracy equal to 87.35% and a true posi- potential “churners”. Firstly, the team discussed the con-
Copyright © 2011 SciRes. JILSA
98 Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a
Retail Bank by a Recommender System Approach
ditions of applicability of each approach. Secondly, the paigns had been carried out by defining non-personalized
main managerial issues were ranked by priority in order actions, and the goal was just to improve targeting. A
to discuss benefits and risks. “top-down” approach would represent too small a step
The only condition to discard, among those listed in towards personalization, while a “similarity-based” ap-
Table 1, was the one related to the “computational” ap- proach would allow the bank to achieve a better targeting.
proach. Prior actions and customers’ response data had Figure 1 reports a graphical representation of the process
never been stored in the dataset by the bank. Given the followed to select the most suitable approach to the defi-
absence of this kind of data, a “computational” approach
nition of personalized actions.
could not be adopted. All the other approaches could be
5.3. Generating Retention Actions by a
potentially adopted. Actions could be inferred by cus-
Recommender System
tomer profiles by a “similarity-based” approach, at least
partially, because they included some behavioral data
The most common practical applications of the “similar-
(products owned). The bank could afford the supervision
ity-based” approach are Recommender Systems (RS).
of effort in a “bottom-up” approach if the process was
Given a set of customers and a set of products or items, a
delegated to front office personnel. Actions could be de-
RS predicts the unknown utility of an item for a customer.
fined independently of customers’ profiles, in a “top-
If item j is predicted to be of a high utility for customer i,
down” approach, because managers had some experience
then the system delivers the marketing action of recom-
on prior campaigns. Finally, the offer was granular
mending that customer to purchase that item. The action
enough to let the customers choose in a relatively wide
is personalized because the set of items recommended to
set of alternatives, thus adopting a “customization” ap-
customers is different for each customer.
proach.
More formally, a RS deals with two types of entities:
The discussion of benefits and risks was crucial to se-
users (e.g., customers) and items (e.g., products). In the
lect the most appropriate approach. The two main issues
transaction-based RSs, the utility of an item for a custo-
raised by the managers were ranked as (1) keeping high
mer is measured by a Boolean variable indicating if the
control on the marketing process, (2) achieving better
user owns a particular item or not, or with the purchasing
targeting with respect to prior marketing campaigns.
According to the first issue, the team excluded the adop- frequency of an item, or with the usage frequency of that
tion of both a “bottom-up” and a “customization” ap- item [35,36]. Based on the known values of utility, a RS
proaches. In fact, given the high number of customers tries to estimate the utility of the yet unseen items for
predicted at risk, a “bottom-up” approach would be fea- each customer. In other words, a RS can be viewed as the
sible only by delegating the task of revising each profile rating function R that maps each user/item pair to a par-
and deciding the corresponding actions to the front office ticular utility value [18]:
personnel. This would entail a substantial loss in control.
R: Users × Items  Utilities (1)
The same would happen by letting customers choose
among different optional actions in a “customization” One of the main tasks of a RS is to make this function
approach. The choice between a “similarity-based” and a total by estimating the unknown utilities. The estimation is
“top-down” approach was eventually made based on the done by using a similarity function, which can be designed
second issue. In fact, the bank’s prior marketing cam- by following one of the following three approaches [18]:

Wh at No data on Exclude the
conditions are actions and “computational”
not true? reactions approach
Exclude the Exclude the Select the
What are the Control “bottom-up” and Targeting “top-down” “similarity-based”
main issues? “customization” approach approach
approaches

Figure 1. Representation of the decision-making process.
Copyright © 2011 SciRes. JILSA
Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a 99
Retail Bank by a Recommender System Approach

 Content-based recommendations: the user is rec- bank, place of residence).
ommended items similar to the ones the user pre- According to the standard collaborative filtering ap-
ferred in the past; proach, the “neighborhood”, i.e. the set of customers z
 Collaborative-filtering recommendations: the user with tastes and preferences similar to customer i, was
is recommended items that people with similar formed by using the cosine similarity, which is given by
tastes and preferences liked in the past; the following formula:
 Hybrid approaches: they combine collaborative-
 r r
filtering and content-based methods. iz i,s z,s
simi,zcosi,z  sSiz (2)
This general approach had to be adapted to the prob-
i  z  r2  r2
lem of generating retention actions to customers at risk of 2 2 i,s z,s
sSiz sSiz
churning. Three hypotheses were identified in order to
consider the approach feasible: 1) recommending certain where r and r are the ratings of item s assigned by
i,s z,s
products to customers can improve retention; 2) the pro- user i and user z respectively. S ={s  Items|r ≠  
iz i,s
ducts able to improve customer retention are not the r ≠ } is the set of all items co-rated by both user i and
z,s
same for all customers and can be identified by looking user z, and iz denotes the dot-product between the
at the behavior of loyal customers; 3) similar customers vectors i and z. In this specific case, the customer i be-
behave similarly and have similar preferences. longs to the set of customers predicted as churner,
While the third hypothesis has much support in the whereas the customer z belongs to the set of loyal cus-
marketing literature, the first two were significantly tomers Z. Only the customers predicted as loyal with a
supported by the management expertise and partially probability higher than 90% were considered in Z. The
by empirical evidences. In fact, the bank’s managers strong hypothesis of this approach is that similar cus-
were confident that certain products, such as bank’s tomers behave similarly, so a customer predicted to leave
stocks, insurance products and loans, directly contrib- the company can be retained if she is recommended
ute to improve customers’ loyalty. These beliefs had products characterizing the loyal customers with similar
emerged in the first project step in the statements of tastes and preferences.
several managers interviewed by the analysts. The Therefore the main difference between a standard re-
managers were also persuaded that the products which commender system approach and the system used in this
can improve retention are not the same for all custom- case is the way the neighborhood is formed. While in the
ers, as each customer may have a different sensitivity former, the neighborhood include all the customers simi-
to different products. These differences come from lar to the customer to whom an action has to be delivered,
several causes, such has different personality traits and in the latter the customers included in the neighborhood
different life cycle stages. Because of this variety of were taken from a subset of all customers, namely the
behavior, the statistical analyses only partially vali- most loyal customers. The final output of the recom-
dated these beliefs. Customers had been split into loyal mendation algorithm was a list of products that should be
and those who had abandoned the bank, although some suggested to each customer at risk.
differences could be observed between the two groups As a result, a redemption rate of 81.6% was observed,
in the distribution of the products owned by customers, meaning that 1313 customers out of 1609 contacted cus-
they were statistically significant only at a low prob- tomers, responded positively to the marketing action
ability. proposed and did not leave the bank, thus reducing the
As a consequence of these observations, the team of overall churn rate.
analysts and managers decided to adopt a collaborative
5.4. Discussion of Results
filtering approach to recommendations, where each
customer at risk would be recommended products that The method used in the first project can be classified as a
loyal customers with similar tastes and preferences hybrid of a “top-down” and a “bottom-up” approaches.
liked in the past. In fact, on the one hand the team of managers decided
Each user was defined as a vector containing both de- what actions should be delivered to customers independ-
mographic and transactional data. That kind of informa- ently of the profiles of customers at risk. The decision
tion was used because two users can be defined as simi- was made by talking to senior managers and was based
lar only if they are similar in terms of preferences (e.g., on a general knowledge of the way customer reacted to
products and services owned by customer, quantity and marketing campaigns in the past. On the other hand, the
amount of transactions) and other behavioral and demo- team asked the front office personnel (the personal fi-
graphical variables (e.g., length of relationship with the nancial advisors) to select the most appropriate action
Copyright © 2011 SciRes. JILSA