Electronic copy available at: http://ssrn.com/abstract=1441302Electronic copy available at: http://ssrn.com/abstract=1441302

Product Customization and Customer Service Costs: An Empirical Analysis

Anuj Kumar, Rahul Telang (akumar1, rtelang@andrew.cmu.edu)

The Heinz College

Carnegie Mellon University

Abstract We conduct a field study with a US health insurance firm to examine how product customization affects firm’s cost to serve the customers through its call center. In our setting, the product is a complex health insurance plan. Firm incurs substantial cost in serving the customers through its call center, and adjudicating the claims using its information systems. Firm sells either standard plans, or in some instances allows customer groups to customize their plan by including, modifying certain aspects of the plan with active collaboration with firm – product co-creation. We show that the process of co-creation is such that it increases users’ familiarity with their coverage and improves the fit with their medical needs. This, in turn, reduces their incentives to call the firm’s call center for clarifications regarding their product coverage. In particular, we show that users with customized plan call 20% less frequently than users with standard plan. Our results account for any possible self selection of customers to customized plan. We also show that there is no difference in claim adjudication cost between a standard vs. customized plan. Overall, our results suggest that, customized products may be operationally cheaper to serve than standard products. Thus our paper provides a link between a growing business concern (customer support cost via call center) and a prevalent business strategy (product customization via co-creation). Keywords: product customization, product co-creation, health insurance product, field study, customer service, product familiarity.

Electronic copy available at: http://ssrn.com/abstract=1441302Electronic copy available at: http://ssrn.com/abstract=1441302

1. Introduction In today’s competitive business world, firms are increasingly trying to differentiate by offering

customized goods, services and experience to their customers. Product customization requires

designs of new products, which begins with eliciting what customers need and then accordingly

selecting appropriate design parameters to create new product. Traditionally, this function was

performed by the experts who had professional training and the experience to accurately match

customers need to the product design parameters. However, with advent of internet and web, user

design of customized product or product co-creation with active customer collaboration has

emerged as an effective method of product customization or personalization. The basic

philosophy is to shift a part of product design towards customer because (i) customers have the

best incentive to choose exactly what they need, and (ii) gathering information on what customers

need is costly for firm (Von Hippel 1998, 2002). For complex multi-attribute products, the

process of co-creation is likely to result in customize product where product attributes closely

match the customer needs, and increased customer understanding of product attributes and thus

more realistic expectations.

On one hand such product customization has demand side benefits like higher demand, user

loyalty, lower churn, higher willingness to pay, etc. (Murthi and Sarkar 2004, Dewan et al 2003,

Ansari and Mela 2003, Chellapa 2005, Wattal et. al. 2008). But on other hand it also creates

supply side problems like complex logistics, distribution and customer support. Customization

could lead to proliferation of product variety which is harder to manage, and thus can result in

higher operation cost and lower operational productivity (Macduffie 1996, Fisher 1995 &1999

and Ittner 1995, Zipkin 2001). In fact, one major reason firms want to standardize its products

and processes is to reduce operational costs. In case of service products, being intangible and

produced and delivered via computer information systems, the production related supply side

challenges like logistics and distribution may be less relevant but the customer support is a major

operational challenge (Chesbrough and Spohrer 2006). Firms spend significant amount of money

on customer support costs. One large component of customer service costs is via firms’ call

centers. There were more than 50,000 call centers in US alone with almost 2.65 million workers.1

In fact, call centers constitute a major part of the entire day- to-day operations for a category of

continuously delivered services like insurance, banking & financial services, IT and Telecom

related services etc. A large academic literature is devoted to studying and analyzing customer

service and call centers (see Gans, Koole and Mandelbaum 2003).                                                             1 McDaniel Executive Recruiters’ 2004 North American Call Center Report, 9‐23‐2004 

In this paper, we focus on the supply side of challenge of product customization. In particular we

examine how product customization affects demand for customer service, specifically, the

customer demand for call center. We study health insurance customization. A health insurance

plan is complex and elaborate product2. Unlike many other traditional products, creating,

delivering and especially serving a healthcare plan is a costly and challenging exercise.

Technology is playing a prominent role in this value chain. In particular, because a health

insurance is an intricate product, the firms spend considerable resources in serving its customers

via its call center. In our setting, the firm spends about $47 million per year in customer support,

most of it in the call centers. Unlike personalizing an online newspaper, or a search result, where

customers are usually passive participants, customizing a health policy requires significant

customer participation and thus it has a flavor of product co-creation. In our field study, the firm

customizes its product for variety of group of customers. The focus of this paper is how

customizing health product has an impact on firms’ customer support cost; in particular on how

product customization affects consumer demand for the call center.

The literature, by and large, is silent on the link between product customization and customer

service costs. However, researchers generally agree that customization leads to higher customer

satisfaction (Anderson, Fornell, and Rust 1997) and a satisfied customer is less costly to serve. In

our paper, we provide a link between customization and customer calling behavior. We argue that

product customization for a complex health insurance plan (a typical plan booklet runs into 60-70

pages) requires significant customer participation. In our setting, a group of consumers can create

a customized plan by repeatedly interacting with the firm. This process is akin to product co-

creation. During the co-creation process, the customers and the firm go over the product details

to include or exclude features that fit with the customers’ needs and are amenable to the firm.

This, in turn, leads to a better customer fit and familiarity with the product. In our data, many

calls to the call center are product coverage related calls (questions regarding plan features,

networks etc.). Many of these calls stem from customers’ lack of familiarity and fit with their

insurance plan. We argue that the process of product co-creation, and a better fit and familiarity

with the customized product should reduce product coverage uncertainty. This, in turn, should

also reduce the numbers of calls related to product characteristics and coverage.

To test our hypothesis, we collect a rich individual level data set from a large health insurance

firm. 3 The firm offers variety of health insurance plans to different organizations in the US. In

                                                            2 We will use term health insurance plan and health insurance product interchangeably  3 Due to disclosure agreements, the firm name will remain anonymous.  

the dataset, users (or a group of users) select either standard plans or customized plans

(customized based on the group requests). In the customized plans, users make explicit changes to

the standard plan to fit their needs. To control for various unobserved effects, we follow groups

over a period of time such that one set of randomly selected groups make a switch from a

standard plan to a customized plan, while the other groups continues to remain on the same health

plan. We then capture the detailed call volume data and show that on average, when users move

to a customized plan, their calls pertaining to product related queries reduce by about 20%. We

see no such evidence of call reduction when users stay on the same plan or when they switch

from one standard plan to the other. We also see no such reduction in non-product related calls on

moving to the customized plan, in support of our theory. We find that our results are not merely

driven by specific trends in call volumes of the groups changing plans but that the results are

consistent over the entire period. Our results are also robust to any possible selection issues.

The firm also incurs claims related customer service costs when the claims filed by doctors and

medical facilities get suspended by their computer information systems and requires manual

intervention. We find that suspension rates are not affected by their shifting from standard plans

to the customized ones. Overall, this indicates that the customized plans are less costly to serve.

Our study is significant in many ways. First, there is no work which has examined the link

between product co-creation and operation costs (especially customer support costs). Technology

is making it easier and cheaper for the firms to customize their products and offer a wide menu of

products to customers of different taste. However product information, features, designs can have

consequences later on customer service costs. Our study provides an evidence of operational

benefit of customization via co-creation on service operations and customer support. Second, our

study focuses on health insurance plans. Health insurance plans are unique and the literature has

not investigated the customization process of these plans let alone the impact of customization on

various outcomes. Third, even for the empirical work on customization, our study is unique in

that we conduct a field study and collect rich group level actual usage data. The panel nature of

the data allows us to control for various unobserved effects providing robust estimates on the

impact of customization.

This paper is organized as follows. In section 2, we provide literature review of the relevant

papers in this domain. We describe our study setting in Section 3. Section 4 outlines our

theoretical framework. We describe our data in section 5. Econometric specifications and results

are presented in section 6. Finally, in section 7, we conclude and outline future research

possibilities and limitations.

2. Related Literature

Our research draws from the literature on product design, development and innovation, literature

on customization, and literature on production management.

The information processing view of product design states that the design process begins with

sensing of a gap in user experience, leading to plan for a new artifact (product) and resulting in

the production of the new artifact (product). How well the product satisfies user needs and

thereby closes the perceptual gap between the current state and goal state, determines the quality

of product design (Ulrich 2007). For high quality design, the designer should first understand the

users’ experience gap clearly, accordingly define the problem precisely, search for the high

quality solution by selecting the solution space appropriately, and then deliver the product

consistent with the design. Although users experience the perceived gap between the current state

and the goal state and thus understand their exact needs, yet they don’t necessarily understand the

causal structure linking their needs to product design parameters. Some time users are also not

able to articulate their actual needs well. In contrast, the expert designer in firms has the

professional training and experience to help users articulate their exact needs and then search for

the appropriate design parameters of product that fulfills user needs (Terwiesch et.al. 2007). For

this reason, traditionally the expert designer in the firms designed products through interaction

with lead users. However, with easy availability of internet and web, firms can easily and

economically gather each user needs and the design experts (human or computer) can then

configure the product for user by selecting appropriate design parameters of the product. This has

led to the advent of the user design of customized product or in general co-creation of product

with active participation of actual user of products. Von Hippel (1998) suggests to shift the

application specific portion of custom product and service design to users if (i) agency related

costs are high – users, being the direct beneficiaries, are motivated to create a solution that best

serves their needs. In contrast the agent firm may have the incentive to create the solutions that

are good enough for a wider range of potential customers. (ii) Eliciting user specific needs are

costly for the firm. Von Hippel (2002) further proposed the concept user toolkits for design of

products where the user put in his specific need information and then the system automatically

calculates the appropriate design parameters at the backend to offer complete product design.

Mass customization and marketing literature talks about “collaborative customization” where

experts in firm conducts dialogue with users to help them articulate their needs, and accordingly

provide a customized product (Pine et.al.1997, Zipkin 2001, Kahn 1998). The approach of

product co-creation is attractive especially in complex multi- attribute products with

heterogeneous demand for different attributes. Thus product co-creation process leads to product

customization and since the customer is involved in the product development she understands the

product well and thus has more realistic expectation from product.

Product customization leads to positive demand side outcomes like enhanced customer perceived

value, customer satisfaction and thus retain them by winning their loyalty. Ansari and Mela

(2003) show that the customization of e-mail content to the customer interest results in increased

click-through rates. Wattal et al. (2008) empirically validate that using customer information to

offer targeted product generate a significantly positive response from customers. Tam et al.

(2003) found that subjects who received personalized recommendation downloaded the music

significantly more than others. Srinivasan et al. (2002) conducted online survey on a sample of

online customers maintained by a prominent market research agency. They found customization

as a significant factor that impact customer loyalty in an online B2C context. Fornell et al. (1996)

conducted nation- wide surveys on customers of major firms from different sectors of the

economy. They find that customization is more important than reliability in determining customer

satisfaction. It must be pointed that much of the empirical work on personalization is survey

based, experimental and usually assumes the customer is a passive partner. Our study is a field

study where customer is an active participant in customization process.

Product customization and consequent proliferation of product variety leads to supply side cost

like operational complexity and productivity decrease. Some studies in production management

literature suggest that the product variety leads to loss of operational productivity (Macduffie

1996, Fisher 1995 &1999 and Ittner 1995). However, some other studies have shown the absence

of association between the product variety and productivity (Kekre 1990, Foster 1990). Producing

larger product variety requires sourcing larger variety of raw materials and parts, complex

production scheduling, higher inventory, higher machine down time, stock out situations etc. The

operations literature highlights the strategies to mitigate the consequences of product proliferation

via flexible manufacturing, product architecture and process standardization (modular product

structure, vanilla box method) etc. (Ramdas 2003, Ulrich 1995, Silveira 1998). Studies also

suggest that firms resorting to product customization would achieve higher customer satisfaction

and therefore need to allocate lesser resources for handling returns, reworks, warranties,

complaints etc. which may result in lower cost and higher productivity (Crosby 1979, Juran 1988,

Anderson, Fornell and Rust 1997). This view is consistent with our paper.

However, the literature linking product information or design to customer support is fairly sparse.

Goffin and New (2001) provide case studies on how some firms explicitly take into account the

cost of customer service when they design the product. Similarly, an article in Wall Street Journal

(WSJ 2007) highlights consumers returning products back to retailers not because the products

are defective but because they do not understand product features. Many retailers are taking

action (educating consumers, providing better information) to alleviate this problem. Our study

also highlights how lack of product information can sometimes lead to calls into the call center

requesting clarification.

The healthcare literature has widely documented that consumers do not understand their health

coverage. Marquis (1981) and Garnick et al (1993) documents how many families are

uninformed about their coverage and argue that educating consumers regarding their coverage is

essential for effective policy making. Researchers have shown how lack of knowledge regarding

their health coverage leads to users choosing inefficient health plans which leads to significant

welfare loss (Parente et al 2001, Davidson et al. 1992). These inefficiencies have prompted the

government to actively use technology in the form of decision support system to help patients

make right decision in choosing appropriate Medicare plans.4

Our paper not only outlines the process of customization of a healthcare plan but also highlights

the beneficial impact of customization on customer service costs.

3. Research Site

Before we outline our theoretical framework, we first provide details of our field study and the

research site.

Our study setting is a large health insurance firm in the US. The firm sells several different

health insurance plans (herein after interchangeably referred to as product and plan) to a customer

base of millions. After the plans are sold, it serves its customers through its operational unit. The

operational unit performs three broad activities

1. Initial setting up and routine periodic activities - coding customers and plan details in the

computer system maintaining customer accounts and issuing regular invoices.

2. Call Center Services - Resolving customer’s queries through the call center (through

telephones calls).

                                                            4 Based on Jonathan Gruber’s presentation at the Carnegie Mellon University.

3. Claims Processing – Automatic processing of claims through computer systems (where

claims processing logic for different products are coded). Only claims suspended or

wrongly processed from computer system are processed / adjusted manually.

Activities 1 and 3 are predominantly enabled by extensive information systems set up the firm by

coding the benefits and claims processing logic. Activity 2 requires customer service

representative (CSR) to resolve customer’s queries on telephone. CSRs are aided by the

information system (customer and product benefit database, computer telephone integration

software etc) which provides customers’ insurance plans related information directly on their

computer screen. Activity 2 accounts for about 70% of the total running operational cost which

was about $47 million in 2007. The firm received about 3 million calls in 2007.

The firm sells health insurance plans to different organizations (referred to as clients) through the

designated client administrator in the organization. Within an organization, there are various

member groups which purchase different plans. Members in the organization, either through their

union or through other bodies, apprise the client administrator of their specific needs and

accordingly the client administrator negotiates the appropriate plans and prices from the firm. The

firm thus identifies an individual member with his unique member ID number under a group

number and a client number. Therefore, all members under a group within an organization have

subscribed to the same product and usually have similar demographic profiles.

A typical health insurance plan (products) is a bundle of descriptive coverage with quantitative

specifications. Descriptive coverage includes eligible medical procedures, network of providers,

pharmacy, drugs and the explicit exclusion in each one of these. Quantitative specifications

include extent of coverage against each category of descriptive coverage e.g. coinsurance,

copayments, deductibles etc. As a result, a typical product is quite comprehensive and

complicated (a typical product benefit booklet runs between 60-70 pages). Such complex

products are not only difficult for the customers to understand but also are difficult for the firm to

administer. Over the years, the firm has created hundreds of different products. To overcome

this, in recent years, the firm developed an elaborate matrix of standard product coverage

components through which a large variety of existing final products can be built (modular product

structure). Such final products are termed as standard products. Since these are existing products,

their benefits and claims processing logic are coded in the relevant computer system and these

have been stabilized. However, in order to attract new customers and retain existing customers,

oftentimes the firm makes deviations from these standard coverage components to accommodate

the specific needs of a group of customers. Such products are termed by the firm as the non-

standard products. These products are essentially customized products where a group of

customers request specific changes to be made in a standard product.5

The firm management was of the opinion that the non-standard products are operationally more

costly, as these not only require additional upfront cost of coding but also believed to result in

higher call volumes, and higher claim suspension rate. As a result, the management took a

strategic decision to start a new integrated service operation environment where only standard

products were offered. The management had set up a target of 30% higher productivity for this

new environment (30% less employee to service per 10,000 customers). The firm initially gave

2% reduction in premium as an incentive for customers to migrate to this new service

environment. This new environment was introduced in July 2005 with the objective to gradually

migrating the entire general customer base to this new environment in 3-4 years. Initially the firm

had been successful in persuading the customers to shift from their earlier non-standard products

at old environment to the standard product at the new environment. However, over time, the firm

had to introduce new non-standard products at the new environment to accommodate specific

needs of customer groups to shift them into the new environment.

4. Theoretical Framework and Hypotheses

In the present research setting, we examine whether there is any significant difference in

operational cost in administering non-standard (customized) products vis-à-vis the standard

products. We first identify key operational cost drivers in present set up as given in Figure 1

Figure 1: Key Operational Cost Drivers

                                                            5 We will continue to use the term non-standard and customized interchangeably. 

These operational cost drivers were identified by examining the impact of the product category on

each of the three operational activities as below –

• Initial Setting up Activity - One time coding cost for a new customized product in the

computer systems.

• Call Center Activity – Call volumes received for each category of product and the

average call handling time for responding to such queries by the CSR.

• Claims Processing Activity - Claim suspension (auto-adjudication failure) rate and

the claims adjustment rate for each product category. In the event of either failure of

claims auto-adjudication or correct adjudication on computer system, additional time

(cost) of manual claims adjudication / adjustment is required.

The additional coding cost for a new product is fixed, it is incurred once, and is relatively

straight-forward to estimate. However, the other cost drivers are the result of complex

interactions among people (both customers and CSRs), products, processes and technology

(computer systems). In the present work, we face the challenge of controlling for customer

heterogeneity, and the process differences in the old and new environment.

We argue that controlling for other things, the identified productivity drivers are manifestation of

interaction of product with the different entities involved in the service delivery operation as

represented in Figure 2.

Figure 2: Product Entity Interaction

Initial Setting Up 

Call Center 

Claims Processing 

‐ One time coding

- Claims  suspension rate 

- Claims  adjustment rate 

- Call Volumes  - Average  Call  handle 

time  

BROAD OPERATIONAL ACTIVITIES  KEY DRIVERS

In this paper, we will focus on call volume (A) and, on claim adjudication rate (B) and how they

are affected by product customization. To see whether call handling times may be different, we

had detail conversations with the CSRs regarding call handling time and they believe that there is

no difference in the time taken to respond to a standard product related call as opposed to

customized product related call. CSRs are aided by the product related information provided on

their screen by computer system which does not change with whether the product is customized

or not. Moreover, the CSRs, a priori, do not know whether the call is coming from a customer

signed up for a customized product. Nonetheless, in this paper, we cannot verify their conjecture

that the average call handling time remains the same for standard vs. a non-standard product due

to non availability of call handling time data.

Claim adjudication rate (B) depends on how correctly the information system is coded. Computer

Systems are useful in efficient administration of a health insurance product, as it not only reduces

CSRs’ average call handle time by displaying the requisite product related information to CSRs

on their computer screen readily but also automates the standard repetitive activities and thus save

precious man hours to boost operational productivity. In the present setup, the firm achieves this

by coding the product benefit and claims processing logic in the computer system. Claims

processing operation specifically requires the collation of product benefit related information

from customer, facility (health provider), and drug information from several other databases.

Since the non-standard product requires adding new code for the product related benefit and the

claims processing logic, the probability of claims suspension in case of non-standard products is

considered to be higher than the already developed and in use standard products.

The key focus of this paper is customer call volume (A). Customers call for many reasons

including inquiries related to product benefits /coverage, claims rejection, issue of inaccurate

P2 P5

P6

P4

P3

P7

P9

P1MEMBER  

Call Volume (A)

 

CSR

 IT SYSTEMS

Avg. Call Handle Time (C)

Claim Auto-Adjudication Rate (B)

P1P 1 P 2 P 3 P 4

P 5 P 6 P 7 P 8

FIRM’S PORTFOLIO OF PRODUCTS

invoice or ID card etc. For the analysis in this paper, we only include the calls categorized as

product related calls. Calls received at the call center are categorized on the basis of its reasons –

coded into a total of 164 reason codes. The CSRs allocate reason codes to each received call.

Simple analysis of call volumes on reason codes suggested that about 48% of the total calls

belong to product coverage related enquiries i.e. enquiries regarding coverage of medical

procedure, facility, providers, pharmacy or drugs. The other reasons for calls are fragmented and

are generally the failure or delay in the delivery of services by the firm e.g. failure in timely

claims processing, ID card dispatch etc.

We now formally model the product coverage related calls generation process.

4.1 Call Generation Process

We held extensive discussions with the CSRs, operational managers at the call center, and some

client administrators to understand what triggers product coverage related calls from customers.

We also randomly listened to a large number of live calls to understand the contents of product

coverage related calls. Most of the product coverage related calls were namely “My doctor has

prescribed ----- and I was told that my plan does not cover it / Is it covered under my plan?”; “I

thought my plan allowed for --- specialist visits but I was told otherwise / How many specialist

visits do I have in my plan?”; “What are my co-pay for out of network --- treatment?”;” What

are my generic drug coinsurance rate / co-pay?”. We observe that these calls are usually

triggered during customer interaction with their doctor / facility (consumption of insurance

product). At this time, customers face their medical needs and then they assess whether their

insurance products covers their medical needs or not. If such medical needs are satisfactorily met

by their product, customers usually do not need to call. When medical needs are not met by their

product adequately, then customers’ understanding about their relevant product coverage comes

into question. If the customer is clear about her product coverage, she has little reason to call and

inquire about the product related coverage. However, if the customer is uncertain about her

product coverage, she may call the call-center for clarification. The failure of the product to

provide desired coverage can be attributed to the lack of fit between the product coverage and

customer’s medical needs. The uncertainty in customers about their relevant product coverage

can be attributed to customers’ lack of familiarity of their product coverage.

Both lack of fit and familiarity with the health insurance coverage has been documented by other

researchers. Issacs (1996) notes from a national survey that many users are poorly informed about

the range of services offered (or excluded) by their health plan. He also outlines that more than

one third of the consumers would like to get more information and education regarding their

policy and choices. For example the paper quotes - “Half of insured respondents merely skim or

do not read at all the materials about their health plan. More than four in ten said that it would be

very useful to have more opportunity to talk with health plan representatives in person, to ask

questions on nights and weekends, and to ask questions anonymously” (p 39). This highlights the

general lack of familiarity on the part of consumers regarding their health policies.

Lack of knowledge and information also leads to consumers making suboptimal plan choices.

Lubalin and Harris-Kojetin (1999) provide a review of how lack of information, complexity of a

health policy, and hence bounded rationality on the part of consumers can lead to suboptimal

choices. Hanoch and Rice (2006) highlight how too many choices and complex information needs

force elderly to make suboptimal choices in supplemental Medicare plans leading to significant

welfare losses. Parente, Salkever and Dvanzo (2003) estimate the impact of information on

consumer choices and document that better information leads to significant cost savings for the

society. Based on their study, they suggest that Medicare should spend $3 per year per

beneficiary in consumer education alone. The role of information systems is also highlighted in

prior research. Sainfort and Booske (1996) provides results of an experiment where a computer

systems aids users in making healthcare plan choices.

In summary, a significant body of research highlights how lack of information and awareness

leads to suboptimal choices and poor fit between customers need and selected plan. Firms also

recognize the need to provide better information to consumers. Many insurance firms are actively

using web to provide detailed information to end users so they have better understanding of their

coverage and can make more informed decisions. Thus lack of fit and familiarity are widely

documented constructs in healthcare plan choices. From our earlier discussion, it is also evident

that consumers are more likely to call when a medical need arises and the consumers are either

unclear regarding their plan coverage and features and/or the plan is not a good fit leading to poor

coverage and hence the need to make a call to the call center.

4.2 Product Customization:

Before we discuss how customization would impact a consumer’s decision to place a phone call,

we provide details on insurance selection process at the firm.

Insurance Choice

To gain insight in the process of product sales and specifically the process of customized product

creation, we conducted interviews with several sales and operational managers of the firm. We

also interviewed a few client organizations to understand the process of creating customized plans

for certain member groups. At the time of contract renewal or a new contract, the firm’s sales

managers offer a set of standard products at tentative prices to the client administrator of the

organization. Normally the client administrator negotiates on the price and by-and-large accepts

the standard products as offered or accepts it with minor changes which still fit the standard

product coverage matrix of the firm. However, when offered standard products do not provide for

certain common medical needs of a group of members, such member groups push on the client

administrator through their member unions/pressure groups/representatives for its inclusion. This

results in a prolonged negotiation between the firm’s sales managers and the members through

client administrator. The proposed product prototype reached at each step of negotiation is then

discussed internally by the client administrator with the members. The firm’s sales manager in

turn consults the operational managers and product development managers at back end to discuss

the feasibility of creating such products. After several such iterations, the agreement on final

product configuration is reached, which often requires the firm to make deviations from the

standard product coverage matrix to accommodate the specific requests of member groups. As we

mentioned, such negotiated products are called the non-standard products. Some examples of

such non-standard product creation are - (i) a consortia of school teachers negotiated to

incorporate sterilization reversal procedures to be incorporated in their health plan, (ii) a

university graduate student association got additional mental health and substance abuse

procedures incorporated in their health product etc. (iii) A group of auto-mechanics modified

their policies to include more robust preventive vision care in their health plan.

This process of non-standard (customized) product creation has a flavor of collaborative

customization (Pine 1993) and collaborative prototyping (Terwiesch 2004) where firm goes over

several iterations of product prototypes to help end users group clearly articulate their needs. In

each of these iterations, the product design experts in firm make appropriate changes in the

parameters (configuration) of the product in line with end users needs and then discuss it with the

end users. This way the firm elicits costly users need related information by shifting the product

prototype to users (Von Hippel 1998, 2002). The final product configuration is therefore co-

created by end users where the firm first help users clearly express their needs and then match

these needs with appropriate product configuration to maximize end user utility (Terwiesch

2007).

 Kahn (1998) suggests that the personalized / user designed / co-created products should match

users need better and thus it should lead to higher satisfaction, higher customer loyalty and lesser

occasions of required reworks, returns and warranty cost. Anderson, Fallon and Rust (1997) also

show that customization leads to higher customer satisfaction. We have not seen any paper in the

literature which examines customization of health policies. Parente, Feldman and Christianson

(2003) study the role of consumer driven health plan (CDHP) on expenses and utilization rate and

find that the CDHP leads to lower expenditure and better utilization. While the CDHP are not

necessarily customized plans, they do require significant participation and active management on

the part of the users.

4.3: Linkage between Call Generation and Product Customization:

An accepted concept in marketing literature is to view product as a bundle of attributes

(components), and that a customer’s utility form the product can be modeled as function of these

components (Xi). Conversely, customers feel disutility (DU) when the product components do not

match (fulfill) customers’ needs.

DU = ∑gi (Xi)

Common model for gi is to indicate the relative significance of each component (Xi) in the

product for the customer. Typically more relevant product components for the customer will be

used more often e.g. a heart related product component will be used more often by a heart patient

etc. As discussed in the foregoing section, the two main causes of disutility for customer with a

product component are (i) lack of fit between product component and customer medical need

(fiti), and (ii) lack of familiarity with the product component (fami). Naturally, customers make

product related calls to mitigate this disutility. So overall model for calls by a customer is

Tel = f (DU) = f {∑gi (Xi)} = f { ∑gi ( h[fiti , fami] ) }

Where f and h are any general monotonic functions. Thus we see that the call generation form

product component is determined by (i) the relevance of the product component for the customer,

(ii) fit between the product component and the customer need, and (iii) familiarity of customer

with the product component.

In our setting, customization process involves a group of customers modifying the health

insurance policy to suit their salient medical needs through a multi step negotiations process,

which involves significant interaction between the firm and the customer group. This improved fit

and familiarity of the product should reduce product related uncertainty and therefore, also reduce

the need to make product related calls. Moreover, customization is usually done with features or

services which are highly relevant to the members’ medical needs (which are more likely to lead

to a call). Thus we see that the customization process results in increase in factor of fit and fam

for components with high g value. So we hypothesize that

H1: Customers migrating from standard product to customized product reduce their product

coverage related calls.

5. Data and Methodology

To test our hypothesis, we take advantage of a quasi-natural experiment that occurred at the firm.

As we mentioned earlier, the firm started a new environment for providing customer support.6

The new environment went operational in July 2005. In the trial phase, the firm provided

incentives to some select customers to move them to the new environment. All these customers

were in standard plan. After about 6 months, it had moved more than 250,000 such customers. By

July 2006, the new environment was stabilized and all groups (not some select few) were

encouraged to migrate to the new environment. While the goal of the new environment was to

promote standardization of plans, by July 06, the firm was allowing users to move into the new

environment even if they were choosing into the non-standard plans. Thus, July 06 time-frame

was appropriate for our study. We could find a large number of users switching to new

environment along with their plans.7 Some customers switched to customized plans from standard

plans when they migrated while the others remained on the same plan. We should note that the

customized plans were specifically created for these customer groups.8

Our goal is to examine how migration of a user from a standard plan to a customized plan affects

her calling behavior. Since our data is such that change in plan is also associated with change in

environment, we need control groups as well. Users who do not change their plan after July 2006

period provides us with the control groups. While we have the data at individual level, since users

typically make few calls in a year, we aggregated the data at the group level. As noted earlier, a

group is a collection of similar individuals within an organization that sign up for the same plan.

                                                            6 The new environment saved money to the firm as some of the operations were off-loaded to the client organizations. 7 We could potentially go back to earlier times to collect a sample where users changed the plans but the environment was unchanged. Unfortunately, at that time, the definition of standard and non-standard was fairly vague within the firm. 8 Sometimes, the firm converts a personalized product into a standard product after some time.

We captured migration of customer groups to new environment with all possible change in broad

product categories namely standard (S) → non-standard (NS), one type of standard → another

type of standard, and customers who did not change their product at all. Out of all these

migrations, we randomly selected customer groups in each of the categories listed above but

maintained the same product for each contract periods July05-June06 (pre) and July06-June07

(post) –

• S→NS Category (Treatment Group) – 170 separate customer groups, who migrated

from standard product at old environment to non-standard product at new

environment.

• No product change (Standard) Category (Sim S→S) – 66 separate customer groups,

who have remained on the same standard product on migration from old to new

environment.

• One S→ another S – 34 separate customer groups, who have migrated from one type

of standard product at old environment to another at new environment.

• S→S Category but no environment change (Old S_S) – 458 separate customer

groups, who have remained on the same standard product in the old environment for

the entire period.

Sometimes the group size changes somewhat over the year and a few new users join and/or leave

the groups in the middle of the year. Thus the membership count of each group varies somewhat

during the period of study. We ensured that all the selected groups did not change more than 10%

in size over the period of the study. The summary statistics for the category wise member counts

for groups is shown in Table 1.

Table 1: Category wise member counts for groups

Category Number of Groups

Mean number of

members/group

Std. Dev. Min Max

S_NS 170 25.14 30.80 1 233

Sim S_S 66 49.79 71.67 1 371

Dis S_S 34 54.84 73.70 1 381

Old S_S 458 19.44 33.36 1 377

We do not have demographic data for the groups. However, as will show later that since we

mostly estimate fixed effect, difference-in-difference model, lack of demographic information is

irrelevant.

We collected weekly call data for each group of customers from the Automatic Call Distributor

(ACD) of the call center. As noted earlier, we only focus on product coverage related calls by the

customers. In the new environment, the firm widened the definition of product related calls.

Therefore, in general, the number of product related calls increased in the new environment due

to recoding. Since the group size varies, as well as the number of groups per category, we present

our statistics for a normalized group size of 100 members. In Table 2, we present mean number of

calls per group per month in pre July 06 and post July 06 period.

Table 2: Mean number of calls per group

Category/ Environment Number of obs Mean Std.

Dev. Min Max

S_NS Pre 12 2.750 0.64 1.79 4.28 Post 12 2.751 0.49 2.05 3.75

Sim S_S Pre 12 2.458 0.52 1.77 3.56 Post 12 2.855 0.52 2.09 3.62

Dis S_S Pre 12 2.315 0.58 1.13 3.22 Post 12 3.013 0.50 2.01 3.96

Old S_S Pre 12 2.340 0.40 0.77 3.12 Post 12 2.061 0.45 1.40 2.88

Despite general increase in number of product related calls with change in environment (mostly

due to recoding of product related calls in new environment) for all other categories, it is evident

from Table 2 that the mean product coverage related calls per group are decreasing for migration

from standard to non-standard products. Thus the data provides preliminary evidence of reduction

in calls with product customization. Note that the mean product related calls have decreased

somewhat for the Old S_S category but recall that the product related calls were being recoded

only in the new environment. So it is not directly comparable to the old environment.

Selection Problem: Even though we are using difference-in-difference method to control for

various unobserved issues, there may be still some worries as to whether groups who switch from

standard to customized plans are somehow different. Notice that our most obvious control group

is Sim S_S (who change environment but remain on the same standard plan). The mean number

of monthly calls made in the old environment by this group is about 2.46 while mean number of

monthly calls made by S_NS group (treatment group) in the old environment is 2.75. Thus the

calling propensity of both the groups in the pre period is not much different. The second worry

could be that the treatment group somehow anticipates the medical problems in the coming year

and selectively chooses to customize its plans. In short, their level of medical sickness may be

different than the control group that may lead to a different calling pattern. To control for this, we

also include number of medical claims filed by the group. Claims are filed by medical facilities

(doctors, hospitals etc) for a medical treatment provided to an individual in a group. Usually a

user visit to a doctor or a hospital generates claims which are processed by the firm.

In the Table below, we provide summary statistics on number of claims filed by various

categories.

Table 5: Yearly number of claims filed by different category of groups

Categories Obs Mean Std Dev Min Max

S_NS Pre 170 266 304.97 2 2222

S_NS Post 170 265 332.91 1 2700

Sim S_S Pre 66 489 695 2 3317

Sim S_S Post 66 553 763.49 9 3735

Dis S_S Pre 34 575 741.5 5 3676

Dis S_S Post 34 594 773.6 2 4052

We see that there is not much difference in the number of claims filed by the S_NS groups after

change in product but all other category of migration groups have increased their number of

claims filed. Thus number of claims would be a potentially useful control in case of an

unobserved selection issue.

We provide more evidence below to convince the readers that selection does not seem to be a

problem in our data.

6. Empirical Model and Results

Our goal is to identify how change in product choice affects the customer call volume. However,

in our case, change in product is also associated with the change in environment. There is also

customer related heterogeneity. Thus, we need to weed out the effect of customer related

heterogeneity, environment related heterogeneity and any time trends or seasonality on call

volumes. Given rich dataset we have, there are various ways to estimate the effect of product

customization on call volume. We now explore these alternatives in detail to document that all

alternatives provide consistent and robust estimate.

Model A: It is evident that we can difference out the effect of environment and time easily by a

difference in difference estimator. In Figure 5 below, notice that we have a treatment group

(S_NS), category of customer groups changing their product from standard to customized with

the change in the environment, and a control group (S_S), category of customer groups having

the same standard product with the change in the environment.9

Figure 5: Model A

We employ a diff-in-diff strategy to estimate the desired effect. In particular, we estimate

0 1 2 3 4 5* * * *it i t it i t it itCvol TG Post claims TG Post Mnctβ β β β β β ε= + + + + × + + (A)

Where

i indexes group and t indexes period (two periods are used here, before July 06 and after July 06).

Cvolit = Total calls by group i in period t.

TGi = 1 for the treatment group and 0 for the control group.

Postt = Dummy for the Post period (0 before July 06 and 1 after)

Claimsit = number of claims filed by the group i at time t.

Mnctit = size of group i at time t.

The estimate of interest is β4 which captures the net effect of product change on the product

related call volume in this model.

                                                            9  In the Appendix B, we show how we can even eliminate the time effect by running diff-in-diff-in-diff estimation. The results, as shown in Table B1,are unchanged.

Call Vol (S)

Call Vol (S) Call Vol (S)

Call Vol (NS)

OldEnvironment (Pre)

NewEnvironment (Post)

Treatment Group 

Control Group 

Product Effect + Time EffectEnvironment Effect  

Environment Effect + Time Effect 

 

Model B: Alternative specification would be to use group level fixed effects. Notice that in

model A, we pool all the data without worrying about group level heterogeneity. We next take

the disaggregated call volumes for each group of customers and run a fixed effect model. Fixed

effect estimation is a logical choice in present context since members in a group may have some

unobserved similarities. Moreover it is reasonable to assume that these unobserved similarity will

remain constant over the period of study. Fixed effect model would account for these unobserved

effects. The fixed effect model would also control for the estimated effect if only a few groups are

driving this result in the pooled regression in model (A).

Thus we run a diff-in diff regression design with fixed effect. The model we estimate is

(B1)

Where

βi = Group fixed effects for group i. The remaining variables are same as in model (A) and

omitted for brevity.

Again, β4, coefficient of interaction term TG*Post is of interest which highlights the net effect of

product change on the product related call volume in this model.

Notice that in model (A) and (B1), we use total number of calls in the pre and post period.

Aggregating call volumes for the entire contract year helps avoid the serial correlation problem in

the idiosyncratic error term for the group over period of study (24 months) (Bertrand et.al. 2004).

However, in this process, we lose the variations in call volumes over time for each group. We

also can not include time trend when we aggregate the data. So in model B2, we disaggregate call

volumes for each group in time dimension as well. Thus now we take the monthly call volumes

per group as a dependant variable. Besides fixed effects, we also include time dummies for each

month to control for time effects. So the model we estimate is

1 2 3 4 5

51,24

* * * * _

it i i t it i t it

t itt

Cvol TG Post claims TG Post McntTime dummies

β β β β β β

β ε+=

= + + + + × + +

+∑    (B2) 

Here, t indexes month where t =1,2,….24 where t = 1 is the month of July05. We include 24

monthly time dummies and their associated estimated. These time dummies should capture any

seasonality in the call volumes. Instead of including time dummies, one can potentially also

include a continuous time variable which captures the time trend. For example, it may be that the

1 2 3 4 5* * * * *it i i t it i t it itCvol TG Post claims TG Post Mcntβ β β β β β ε= + + + + × + +

calling trend for the treatment group may be declining. This alternative specification was also

tested and leads to the same results.

Before we estimate this model, to confirm that the selection issues are not driving our results and

change to customized plans is really the driver for change in number of phone call, we estimate

model B1 for only pre July-06 data. We split pre July 06 data in two periods, first six months

(July 05 to Dec 05) and last six months (Jan 06 to June 06). Both groups (control and treatment)

are selected into a standard plan in this period. Therefore, if treatment and control groups are

consistent then we should not expect a significant estimate on β4. We present these results in

Appendix A. The results in table A1 confirm that indeed β4 is insignificant (both economically

and statistically) for this time period suggesting that the calling behavior of the treatment group is

similar to that of the control group.

6.1 Results

We present the results of all three models in the Table 3 below. Since our dependant variable is a

count data, we run a Poisson model to estimate these parameters. We also tested the model with

negative binomial (to overcome same mean and variance restriction of Poisson regression) and

we also estimated the model with linear regression. The results are similar. In all three models,

S_NS group is the treatment group and Sim S_S is the control group.

Table 3: Estimation for model A, B1 and B2 (standard errors in parentheses)

Dependant variable = Call Volume

Model A Model B1 Model B2

TG 0.47*** (0.048) dropped dropped

Post 0.105** (0.044) 0.138*** (0.045) 0.148*** (0.044)

TG*Post -0.171*** (0.065) -0.215*** (0.066) -0.190*** (0.058)

Log (Mcnt) 0.359*** (0.047) 0.342** (0.146) 0.006*** (0.0015) ¥

Log (Claims) 0.521*** (0.048) 0.221* (0.114) 0.001* (0.0005) ¥

Time dummies Not Applied Not applied Applied

Constant -1.87*** (0.149)

N 472 2 observations each for 236 groups

24 observations each for 236 groups

R2 0.66

Note - ***, **, * = statistically significant at the 1%, 5% and 10% levels (two-sided test) respectively

¥ - In Model B2 we have monthly observations for variables and thus variables Mcnt and Claims were taken as such without log values – At monthly level Claims variables had many 0 values and taking its log would result in its omission from data.

First, notice that the Post dummy is positive and significant in all models. This is consistent with

our observation that higher numbers of product related calls are being observed (mostly due the

way the firm recoded this category).

Estimates on member count (Mcnt) is positive. Recall that we estimate group level fixed effects

in B1 and B2. This suggests that within changes in the group size is consistent with our

expectation that if group size increases somewhat, it also leads to more calls. Estimate on claims

(Claims) is also positive and significant. More number of claims signals more medical needs of a

group and consistent with our theory model, this leads to higher number of product related calls.

All three models show a negative and highly significant coefficient for the interaction term (β4).

The interaction term captures the net effect of customized plans on call volume. A significant

estimate for β4 signifies that controlling for other things; customers going from standard to

customized (non-standard) products make statistically fewer calls. Moreover this reduction in

product call volumes is also economically significant. The reduction amounts to approximately

20% (17 – 22%) reduction in the number of calls for the users who migrate to customized plans.

These estimates are consistent across three models thus robust to the aggregation problem and

any group level unobserved effects. Thus we find support of our hypothesis.

To understand the economics significance of this estimate, the firm received about 1 million

product related phone calls from April 2006 to March 2007. The average number of members it

supported was about 3 million. Even if 20% of the members selected personalized policies, this

translates to about 40,000 fewer calls per year.

The implications of these findings go beyond this sample. The widespread use of technology and

Internet is enabling firms to provide significant choices to users. Firms are aggressively

encouraging users to manage their health outcomes and plans and self serve themselves via Web

portals. The firm under study is considering offering user toolkits/product configurators to end

users over the Internet. These configurators would allow users (or clients) to pick and choose

various modular features to create a healthcare plan for their own needs. However, much of the

empirical work has ignored how these offerings impact the customer service costs. Our results

suggest that there is little reason to believe that they will increase the customer service cost. If

anything, our results point out that it will reduce the demand for call center services as customers

are more involved in creation of plans that suit their needs and increase their awareness regarding

plan feature.

Customized plans may still increase other costs, which we discuss in subsequent sections.

Robustness:

While our theoretical framework that customization of a product with customer involvement

reduces misfit and increases awareness and hence it should lead to fewer product related calls

seems to borne out in our data, we may still have some concern because we do not directly

measure product fit or product familiarity. In the following, we rule out other potential

explanations.

1. One concern is that simply change of product induces these effects. To account for this, we

include in our treatment group the users who change from one standard plan to another standard

plan and compare them to the users who stayed on the same plan. We report the results below and

find that mere change in the product from one standard product to another does not lead to any

reduction in call volume. To avoid clutter, we only report the estimate on interaction dummy. We

also compare the users who migrate to Non-standard plans with the users who migrate to a

different standard plan. We find that migration to non-standard plan indeed is highly significant.

Table 4: Estimates for different Treatment and control groups

Coeff of TG*Post Model B1

Treatment group different S_S, Control Group same S_S 0.096 (0.075)

Treatment Group S_NS, Control group different S_S -0.378*** (0.071)

Note - ***, **, * = statistically significant at the 1%, 5% and 10% levels (two-sided test) respectively

2. Another strong support to our theory comes from non-product related calls. Our theory

highlights that product co-creation process should reduce the number of product related calls.

However, we do not expect the other calls to reduce (for example claim related calls, or other

transactions related calls like sending the insurance card, change in address etc). We now re-

estimate our model with the non-product related calls. The results are shown below in Table 5. It

is evident that there is no reduction in number of non-product related calls due to product

customization. This result is reassuring for it negates the possibility that the customer groups

moving from standard to non-standard products are somehow making fewer calls independent of

product customization.

Table 5: Estimates for Non-product related calls (standard errors in parentheses)

dependant variable=Call volume Model B1

TG Dropped

Post 0.149** (.068)

TG*Post 0.07 (0.09)

Log (Mcnt) 0.766*** (0.239)

Log (Claims) 0.244 (0.178)

N 2 observations each for 236 groups

Note - ***, **, * = statistically significant at the 1%, 5% and 10% levels (two-sided test) respectively

3. To test whether the reduction in call may be a temporary shift, we split the post period into four

quarters and estimate the impact for all four quarters. To avoid clutter, we only report the

estimates for interaction with post quarter dummies. As can be seen from the results below in

Table 6, all the estimates are negative and significant (except Q2). Notice that quarterly split in

the data increases standard error. Thus, the effect of customization persists for the whole year.

Table 6: Estimates when Post period is split into 4 quarters (standard errors in parentheses)

dependant variable=Call volume Model B1

TG*Q1 -0.25** (0.12)

TG*Q2 -0.12 (0.17)

TG*Q3 -0.22** (0.11)

TG*Q4 -0.21*(0.12)

N 6 observations each for 236 groups

Note - ***, **, * = statistically significant at the 1%, 5% and 10% levels (two-sided test) respectively

Impact on Claim suspension rate:

Recall that another aspect of customer service cost is claim suspension rate (see Figure 2). Firm

tries to automate the claim processing as much as possible by investing in its IT infra-structure. If

the claim does not get adjudicated automatically, it requires manual intervention which is costly.

One of the challenges of non-standard plans is that they are not widely distributed and need to be

coded in the system properly. So the firm worries that the claim suspension rates on such plans

may be higher.

We collected data on claim suspension rates and estimated the impact of migration from standard

product to customized product on the claim suspension rates of customer groups. We essentially

estimate model A here with treatment groups changing from standard to customized products in

July 06 (with change in environment) and the control group remaining on the same standard

product with change in environment in July 06. We use fixed effect OLS regression here (since

the claim suspension rate is not a count data) with claim suspension rate for a group before and

after July 06 as dependent variable. The results are given in Table 7.

Table 7: Estimates on claims suspension rate due to product customization (standard errors given in parentheses)

Dependant Var = Claim Suspension Rate

Model B1

TG dropped

Post 0.01 (0.014)

TG*Post -0.0056 (0.0171)

Constant 0.85*** (0.003)

N 2 observations each for 236 groups

Note - ***, **, * = statistically significant at the 1%, 5% and 10% levels (two-sided test) respectively

We see an insignificant coefficient of Tg*Post, which indicates that the claim suspension rate for

the customer groups does not change statistically significantly by the change in their product from

standard to customized. This indicates that once the customized products are coded in the relevant

computer systems of the firm, the computer systems and processes at the firm are robust enough

to handle both standard and the customized product equally well.

7. Conclusions, Managerial Implication, Limitations and Future work –

We show using actual usage data in a field study that customizing a complex product like a health

insurance has a significant impact on customers demand on call center and thus cost to serve the

customer. We provide a theoretical framework for the same by proposing that the factors of fit

and familiarity determine the product related call volumes to the call center. Lack of fit and

familiarity leads to product uncertainty and misfit that cause product related calls. Customization

of such a product is usually an interactive exercise and acts as an educational tool to familiarize

the customers with their plan coverage. This, in turn, reduces product uncertainty and misfit

reducing the need to call the call center for product coverage clarifications. In the present

empirical setting, we find that customers migrating from a standard to a customized product on an

average make 20% fewer product related calls due to this change. With the annual customer

service outlay of US $47 million, this call volumes reduction may result in a substantial cost

reduction. We also find that the results are not a short term blip but also that the reduction in call

volume persists for the year. We also provide various robustness checks to test the validity of our

results under alternative specifications.

While our setting is a healthcare product, we believe that our results would be applicable to any

such setting where the product involved is a complex product. Product complexity can lead to

customer dissatisfaction and customer service costs (WSJ 2006). Educating customers is one

effective process to reduce product uncertainties and customer service costs. For example, Dell

Computer deliberately avoided selling to low end customers who need a lot of hand-holding

(Magretta 1998). Our results show that customization via co-creation involves significant

interaction and thus has an indirect effect of lower customer service cost as well. Our study also

contributed to understanding of healthcare policies. Lack of awareness and lack of understanding

regarding healthcare policies lead to significant welfare losses. Many researchers explicitly argue

to educational efforts on the part of policy makers to improve user knowledge. Our results

highlight that while better product understanding may lead to many direct benefits, it generates

some substantial indirect benefits in terms of fewer costs to both customers and the firm in terms

of customer service costs.

We believe our paper is one of the few (if any) that has examined the link between customization

via co-creation and operation costs (especially customer support costs). As the firms offer

increasing variety and more complexity in their product offerings, the customer service costs of

these strategies cannot be ignored (Knowledge@Wharton, 2006). Our study provides a direct

evidence of operational benefit of customization via co-creation on service operations and

customer support. Our study also contributes the growing empirical literature on personalization.

While many direct benefits of customization have been documented, we provide evidence of less

obvious benefit of customization in terms of lower customer service costs.

Despite rich dataset and robust empirical tests, our study is not without limitations. One obvious

limitation of the present work is our inability to measure the latent factors of fit and familiarity.

While we document the benefit of customization, and we believe that in our setting customization

provides direct benefits as well (like higher customer retention or higher pricing), the interactive

nature of customization may increase firm’s costs. It may take the firm longer to sell such plans

or the plan may increase the complexity of its operations. Our study does not allow us to measure

these issues more directly. We also do not observe call handling times.

Despite limitations, we believe our study takes a step forward in examining a link between a

complex healthcare plan customization process, and customer service costs. We believe this line

of investigation is under-investigated and highly promising, and will benefit from future research.

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APPENDIX A– We show that the groups in the two categories (S_NS and Sim S_S) are quite similar in their

calling patterns in the Pre July 06 period (before the products were customized for the S_NS

group). To show this we split the yearly data in pre period into two 6 month period and estimated

model (A). We estimate the model by running a fixed effect poisson regression with same diff in

diff design on our treatment group (S_NS) and control group (Sim S_S).

Table A1: Estimate for model on calls in the Pre period (standard errors in parentheses)

Call volume is the dependant variable Model B1

TG dropped

En -0.142** (.066)

TG*Post 0.038 (0.085)

Log (Mcnt) 0.836*** (0.169)

Log (Claims) 0.226*** (0.058)

N 2 observations each for 236 groups

Note - ***, **, * = statistically significant at the 1%, 5% and 10% levels (two-sided test) respectively.

Notice the estimate of interest (the interaction estimate) is both economically and statistically

zero. This suggests that in the Pre period, our treatment group behaves similar to the control

group.

APPENDIX B–

We also ran the diff in diff in diff design on our data to control for any time related effect on call

volumes. We used members in S_NS category as treatment group, members in Sim S_S as

control group1 and members in Old S_S category as control group2. Here the dependant variable

is weekly call volumes for 5000 members in each of these categories for 52 weeks in Pre July 06

period and 53 weeks in Post July 06 period. The results of OLS regressions are shown in Table

B1.

Table A1: Estimate for model with Diff-in-Diff-in-Diff design (standard errors in parentheses)

S_NS Gr AND Sim S_S Gr Model A

Tg -0.104 (1.64)

En 6.38*** (1.65)

Tg*En -4.92** (2.34)

Constant 4.18*** (1.16)

N 210

R Squared 0.09

Note that the coefficient of interaction term is negative and highly significant. This indicates that

the members product related calls reduces when they move from a standard to a customized even

after controlling for any effect of time and change in environment.