avinash kumar

Optimizing Client Expectations in Delivering Certainty

Abstract This paper presents a framework for analyzing, measuring, managing and

optimizing client expectations that can be applied across diverse project and client

types, in delivering certainty and best quality to the project,.

Client expectations are a critical component of the diversity experienced across projects

and clients. An absence of a framework has resulted in ad-hoc practices to record and

manage client expectation, often devoid of well defined methodology or even a “cheat

sheet” to guide the service provider. This gap assumes greater significance considering

that exceeding client expectations is central to client retention in current times, across

industries.

This paper provides a framework to identify core determinants of client expectations and

defines the metrics to measure the same. The framework builds upon the tenets of

consumer behavior to qualify the zone of tolerance for a given client type, as measured

by the relationship between client perception and expectations. It then defines a matrix

for the service provider to discover its positioning to meet the client’s requirement given

its capability (relative to the industry). It finally quantifies the execution quality that not

only defines the client satisfaction, but also influences client perception that defines the

expectation in future.

The framework then quantifies the above three determinants, assigning weights to each,

as per nature of client, project, provider or execution. The guidance score on the client

expectation is then calibrated for the qualitative and macro environmental factors to

accurately reflect the client expectation.

Key words Client Expectation, Provider Positioning, Execution Quality, Expectation Framework

Author Avinash Kumar heads the Business Solutions team for Banking and Financial Services

clients in the North America geography for Tata Consultancy Services Ltd. In his over 20

years of experience, he has worked across several critical engagements for leading Wall

Street firms across their global locations. He has been instrumental in establishing

several new relationships for TCS thereby providing him deep insight into managing

clients' behavior and expectations and setting up the winning teams.

Avinash lives in Toronto with his wife and two children. He can be contacted at:

Tata Consultancy Services

Optimizing Client Expectations in Delivering Certainty 2013

Page 1

Introduction

Client expectations are a critical component of the diversity experienced across projects and clients. Yet,

it remains to be one of the most neglected domains where project management frameworks have been

designed or applied. This has resulted in ad-hoc practices to record and manage client expectation, often

devoid of well defined methodology or even a “cheat sheet” to guide the service provider. This gap

assumes greater significance considering that exceeding client expectations is central to client retention

in current times, across industries.

Client expectation could vary for the same service provider with a long standing relationship, across a

variety of opportunities, and could remain static across a variety of service providers. The expectation is

driven by the underlying problem statement, diversity in industry practices, choices in technology, impact

of implementation risks, opportunity costs, regulatory implications, and the provider’s capability relative to

its peers.

This paper provides a framework to identify core determinants of client expectations and defines the

metrics to measure the same. In doing so, it draws upon the experience of the author from several project

executions, published data on managing client expectations, research findings and tools deployed in

enhancing the same.

Expectation is “Belief”

Client expectation is often interchangeably used with client satisfaction. While the latter is a post facto

measurement of the outcome itself, client expectation is the belief about service delivery and tolerances

around variance in the outcome (Fig 1 – Source: Poiesz and Bloemer1). For this reason, quantification of

client expectation lies beyond the conventional Key Performance Indicators (KPIs). Most of the KPIs in

project management measure the performance or the outcome leaving out measurement and

management of client expectation to the softer skills of the project lead. When a client has high

expectations from a provider, it expects high resilience from the provider in managing project diversity

and provides little tolerance for the variance.


Page 2

On the contrary, when the

client has low expectations

from the provider, there is

heightened monitoring,

reporting and control - each

time there is a variance on the

outcome, often coupled with a

high tolerance. To measure

and manage client

expectations, therefore, we

need to quantify the degree of

control that the client is willing

to vest in the provider and the

tolerance for variance, amidst

uncertainty.

As outlined by Parasuraman2, the client’s service expectations have two levels, namely, the adequate

service level and the desired service level. The adequate service level is the minimum acceptable service

level, given the problem statement, and the perceived capability of the provider. The desired service level

is the service the customer hopes to receive, including nice to have outcome, and is dependent upon the

provider’s past performance or peer reviews about its performance. The difference between the two

determines the tolerance zone (Fig 2).

Fig 1: Expectations, Performance and Outcome

Expectations Performance Outcome

Zones of

Tolerance

KPIsReliabilityTangibles

ResponsivenessAssurance and

Empathy

Missing?

Client Need

Minimum

Outcome

Nice to have

Outcome

Delightful

Outcome

A

Perceived Capability of the Provider

to Deliver an Outcome Level

B

C

D

Under Performance

Over Performance

A B

C

D

Expected Service Levels

for the Provider

Acceptable Service Level

Desirable Service Level

AB

C D

Zone of Tolerance

Fig 2: Expectation and Zone of Tolerance

Thought

Leader


Page 3

When a provider over performs relative to the perceived capability, the adequate service level is adjusted

to the current need, or the perceived capability, whichever is higher, and the desired service level is

pegged at the nice-to-have outcome level. Similarly, when a provider under performs relative to the

perceived capability, the adequate service level is reset to the current client requirement, completely

disregarding the provider capability, and the desired service level is reset to the past performance or the

current need, whichever is higher. This explains for a shift in the client expectation real time, during a

project, as the client continuously re-calibrates the expectation with respect to the provider’s ability and

the real time performance.

The key to measure client expectation, therefore, is to quantify the perceived capability of the provider

that drives the adequate service level. This is the level below which the client does not expect the

provider to perform. The first step in calibrating client’s expectation, therefore, is to discover the

determinants of the adequate service levels and the client perception of the provider’s capability. The

perception itself is influenced by

The current need of the client and the macro environment influencing the same.

The provider’s positioning in the industry and past performance

Decoding the Client

The client’s perception of the provider can be quantified by developing a Client Outlook Score (COS) that

reflects the client’s ability to delegate control to the provider and vest a larger degree of tolerance to

variance in outcome. COS reflects the tolerance of the client to withstand variance in delivery and

endorse the provider for its contribution, net of the delivery outcome. Greater the COS, higher is the

acceptance by the client for the diversity in project execution and lower the expectations from the provider

for stringent monitoring, reporting and control.

Several factors influence the client’s outlook (Fig 3) such as competitve scenario, regulatory requirement,

degree of operational efficiency, opportunity costs and risks, relationship with the provider and choices

available with the client, to name a few. The key determinants of COS are as follows:

What drives the current requirement

What are the risks for the client

Who gets impacted with the outcome

How is the client engaging with the provider, and

What choices does the client have, for meeting its need


Page 4

For example, the KPIs for a project driven by regulation may be entirely different from the one driven by

efficiency or profitability. Time to Deliver may be more critical than Cost to Deliver for such projects. If

project delays or cost –overruns entail reputation risk, the client will not only closely monitor what has

been delivered, but also review as to how was it delivered. Similarly, projects that impact the client’s client

and public at large influence client expectations altogether differently than those that impact only internal

users.

Another sure shot indicator of the client’s trust is the stage and frequency with which provider is engaged

with the client. A provider perceived as thought leader is often consulted at the conception stage, while

the one seen as a mediocre player gets to perform stereotype executions, even as a follower is often

engaged to complement a shortfall in resources, and often characterized with a “Do-as-Directed” posture

by the client.

Finally, the client’s expectation is driven by the choices it may have on the underlying technology,

solution, providers and deployment (scope and time to market). The client is likely to be more demanding

in a buyer’s market and more susceptible to the vendor in a greenfield domain. For example, it is quite

common for clients to issue Request for Information (RFI) rather than Request for Proposal (RFP) for

domains where client has limited competence or information and is expecting the provider to provide

thought leadership and solution for the underlying problem statement.

Provider’s Positioning

Once the Client Outlook Score is arrived at, it becomes essential for the provider to instill the trust in the

client by positioning itself in the right quadrant of the problem statement (Fig 4). This is the time to

calibrate the pre-performance client expectation by an appropriate posturing by the provider, and drive

the expectation during service delivery.

Drivers Risks Impact

•Compliance

•Competition

•Efficiency

•Excellence

•Reputation

•Legal

•Financial

•Operational

•User

•End-client

•Public at large

•Regulator

Fig 3: Determinants of Client’s Outlook

Involvement

•Early

•Frequent

•Need Based

•Tardy

Choices

•Technology

• Solution

•Provider

•Deployment


Page 5

The Provider Position Matrix (PPM) maps the role undertaken by the provider relative to the current problem statement and the provider’s perceived competence. This model draws upon the theory of zone of tolerance

3 that suggests that the service quality results from customers comparing their expectations

prior to receiving service to their perceptions of the service experience itself. A higher PRR demonstrates a provider in control and in an appropriate role to deliver the solution, as also perceived by the client. This increases the client’s acceptance to diversity of outcome, whereas, a weak PRR implies either an under-play or an ambitious positioning of the provider with respect to the current need and therefore a higher expectation from the client on monitoring and control from the provider. For a Business-As-Usual (BAU) requirement, the client would expect higher maturity and faster on-

boarding of the team. For a next generation project, the provider would be expected to demonstrate

thought leadership and business use cases. For a new compliance that needs to be implemented, the

client may seek faster time to market, low risk and re-use of existing technology or assets. In a multi-

vendor environment, the ask from the client would be a crisp collaboration across the stakeholders. It

therefore becomes imperative for the provider to profile the problem statement with its own capabilities in

communicating the strategy it would adopt in delivering the relevant solution.

Quite often, a provider positions itself in the leadership quadrant in an effort to win the business,

notwithstanding that the KPIs for a leadership role are significantly different from those for a routine

Provider Capability(relative to Industry)

Cli

en

t’s

Ne

ed

BAU

Niche

Complex

Next Gen

Low Average Strong Thought Leader

Own

and

Drive the Solution

Lead the Solution with

Industry Collaboration

Forge Alliance with Industry

Leaders

Invest for future growth

Augment

Resources

/ Fill the

gapCourse

Correct

Co-Invest

with the

client

Fig 4: Provider’ Position Matrix


Page 6

service provider. In such case, a routine delivery as against a state of the art delivery goes against the

provider, even if the entire projects KPIs are met. Similarly, an under posturing for a BAU problem

statement erodes trust of the client, and the client may not perceive value for money if the provider low

balls (See: Case Study)

Execution Quality

Even if there is a judgment error in the pre-sales or pre-performance phase, there is an opportunity for the provider to reset expectations during actual execution. According to Berry and Parasuraman

6 a

performance below the tolerance zone will engender customer frustration and decrease customer loyalty. A performance level above the tolerance zone will pleasantly surprise customers and strengthen their loyalty. The consistency of delivery can significantly influence client expectation and can be measured by the Execution Quality (Fig 5).

Impact of Perception and Expectation – A Ryanair Case study4:

In a survey conducted for Ryanair, the client perception and expectation were measured using the SERVQUAL

5 dimensions (Reliability, Responsiveness, Assurance, Empathy, and Tangibles) and the

client profile (namely age and purpose of travel). Client’s perception of service delivery was higher than their expectation on tangible dimensions such as kiosk check-in, ticket quality, dedicated luggage belts etc and this resulted in a higher satisfaction. The gap between the perception and expectation was wider for the youngsters (18-29 yrs) than the senior citizens. The seniors expected a more comfortable experience, thereby lowering the tolerance zone. Also, their perception was lower than their expectation in responsiveness and empathy, leading to lower satisfaction. For tourists and people visiting family or travelling for personal reasons, the expectations were quite lower than the perception, yielding a higher client satisfaction. People traveling on business had highest expectations with lowest perceptions about the airline, resulting in lowest satisfaction score on Reliability. Being a low cost carrier, people expect little on the service but more on reliability, tangible experience and responsiveness. Their expectation on empathy and assurance is low, primarily driven by Ryan Air’s past performance but the client’s believe that Ryanair has the ability to improve the service delivery on these dimensions, which could reset client expectation and behavior in future.


Page 7

A complex project may be expected to face challenges in the ramp up phase, but slowly transition into

steady state, until delivery. However, varying project management skills and provider competence could

yoyo the project from a red to an amber to a green, and back to an amber state for Provider A, or start

from a green state but degenerate into a red state, by the time it gets completed, for Provider B. A close

monitoring of dependencies, available resources, associated constraints and risk mitigation techniques,

along the life cycle of the project can lend consistency to client expectation from the team, and resultant

support to the project.

A project with a high EQ would be consistent with the variance expected across its life cycle. Whereas, a

project with a low EQ could, for example, start very well, raise the bar for itself, and create avoidable

criticism for pitfalls encountered later in the cycle. Similarly, another project that consistently oscillates

between a red-amber-green status will have a low EQ and demonstrate a lack of control.

Environmental Factors

In addition to the tangible determinants, there are lots of intangible and environmental factors that need to

be considered in managing the client’s expectations. Such factors include, but are not limited to

Competitive landscape of the solution

Advertising and Promotion by the provider

Regulatory Requirements

Fig 5: Execution Quality

Ramp up Steady State Delivery

Expected Execution

Provider A

Provider B

Project Phases

Ea

se

of

Ex

ec

uti

on


Page 8

Opportunity Costs for failure

Operational Risks associated with the solution

Industry benchmarks

Communication with the stakeholders – frequency and channels

It is difficult to prescribe the degree of impact of each of these, but it is a good practice to engage in a

conversation with the client to identify the same and assess their relevance and impact for the underlying

problem statement.

The Framework

The framework for optimizing client expectations brings together the above determinants, by assigning

weights to each, and managing the same. It will use a combination of Quantitative as well as

Qualitative Analysis, while developing the Client Expectation ratio or the CE Ratio (Fig 6).

The quantitative analysis provides us a guidance score for measuring client expectation after assigning

weights to each of the determinants. This could be a good starting point, but needs to be validated for

each client and project type. The qualitative analysis overlays the macro environment around the current

need such as technology available in the industry, performance benchmarks, degree of competition,

regulations around the subject etc to arrive at a measure of client expectation which is more relevant for

the current context.


Page 9

Inputs are collected from the clients through a questionnaire or interview to understand the drivers, risk

and the impact to business for the underlying problem statement. The provider then scans the

environment for competition, industry benchmarks and maturity of the client relationship to capture the

determinants of the COS. Factors that influence COS directly, versus those that influence it inversely, are

weighted accordingly. Based on the inputs, a quantitative score between 1 to 10 is assigned to each

attribute that influences the COS.

Similarly, capabilities of the provider relative to the client’s need are quantified on a scale of 1 to 10, to

reflect the current requirement, provider’s competence and posturing.

Finally, the execution quality of past engagements with the client (either from past relationship, or from

peer review) is awarded a score between 1 to 10 to represent the impact of variance across the project

types and phases.

Depending upon the problem statement, client type and the business model different weights may be

assigned to each determinant, and further to various attributes that roll in to the determinant, so as to

present a fair view of the client expectation. For example, COS may hold a 60% weight, PRR a 30%

weight and Execution Quality a 10% weight in the overall CE Ratio calculation. Similarly, attributes within

these major dimensions such as Risks, Impact, Choices, Provider Role, may be weighted differently.

Some degree of normalization may also be needed across determinants.

A guidancescore that measures

the performance of an affiliate on key dimensions like

Client Outlook Score (COS) Provider Position Matrix (PPM)

Execution Quality (EQ)

List of Environmental Attributes such as

Competition

Advertising and Promotion Regulatory Requirements

Opportunity Costs for failure Operational Risks Industry benchmarks

Communication

Qualitative AnalysisQuantitative Analysis

Fig 6: Developing the CE Ratio


Page 10

A weighted average assessment of the above three dimensions yields a guidance score on the Client

Expectation Ratio which represents the client’s perception, the provider’s positioning and the execution

variance for the underlying problem statement. The weights can be assigned based on the provider’s past

experience with the client and its capability in servicing the current need. It is important to note that some

of the underlying factors will directly influence the client expectation, while others may inversely influence

the same. An appropriate scoring of the underlying factors will generate an enabling or a limiting score on

the client expectation, e.g. high risk in the project will lead to lower client expectation, whereas, use of

cutting edge technology and standard automated tools will increase the expectation from the provider.

A sample calculation for these variables is tabulated in Table 1.

Table 1: Consolidated Data for Determinants of Client Expectation


Page 11

Weight Determinant Client

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

CE-Ratio 4.37 6.46 5.12 4.23 4.78 4.93 4.28 5.35 5.61 5.92

Client Outlook Score (COS) 60% 2.65 3.07 2.25 2.68 2.63 2.88 3.23 3.45 3.68 3.94

Drivers (40%) 20% Compliance 4 2 5 3 4 8 2 9 5 6

30% Competition 3 6 1 2 1 2 4 4 8 9

40% Efficiency 6 2 6 4 3 2 5 4 6 8

10% Excellence 4 2 5 5 4 4 5 4 5 6

Risks (20%) 40% Reputation 5 1 5 5 4 4 4 4 5 6

15% Legal 7 2 6 5 5 5 4 4 5 5

20% Financial 5 2 5 5 5 2 5 5 5 5

25% Operational 4 4 5 5 5 5 5 5 5 5

Impact (15%) 10% User 5 5 1 5 5 5 5 5 5 5

30% End-client 6 8 6 5 5 5 5 5 5 5

35% Public at large 6 6 6 6 5 5 5 5 5 5

25% Regulator 6 5 6 6 6 5 5 5 5 5

Involvement (5%) 50% Early 5 4 5 5 5 5 5 5 5 5

20% Frequent 4 1 4 7 7 6 6 6 5 5

20% Need Based 2 2 1 3 9 2 7 6 6 5

10% Tardy 3 7 6 6 10 1 8 7 6 6

Choices (20%) 20% Technology 9 8 7 2 8 1 9 8 7 6

30% Solution 7 4 9 3 2 6 9 9 8 7

10% Provider 7 7 8 9 9 5 10 9 8 8

40% Deployment 5 3 1 8 8 9 9 9 9 8

Provider Role Ratio (PRR) 30% 1.14 1.50 1.40 1.52 1.72 1.04 1.56 1.61 1.86 1.90

Client Need (25%) 10% Next Gen 7 4 6 6 7 1 8 9 9 9

20% Niche 5 4 6 6 6 7 7 8 8 9

30% Complex 4 3 5 5 6 2 7 7 8 8

40% BAU 5 5 5 5 5 3 6 7 7 8

Provider Capability (15%) 20% Low 4 2 1 5 5 5 6 6 7 7

30% Average 4 4 1 4 6 5 5 6 6 7

30% Strong 5 7 1 5 7 5 5 6 6 6

20% Thought Leader 7 9 6 5 2 5 5 5 6 6

Provider Role (60%) 30% Own 5 2 5 5 3 3 5 6 5 6

15% Lead 3 2 4 5 4 5 4 5 5 5

10% Augment 5 7 5 5 8 5 2 5 7 7

10% Collaborate 5 4 1 5 9 2 7 2 8 5

20% Invest 5 6 1 5 10 3 1 2 5 2

15% Course Correct 8 10 1 6 4 5 8 5 4 9

Execution Quality (EQ) 10% 0.58 0.55 0.59 0.57 0.58 0.37 0.56 0.55 0.38 0.62

Expected (50%) 10% Ramp Up 6 4 6 6 6 6 3 3 2 6

70% Steady State 5 5 6 6 6 6 6 5 1 8

20% Delivery 6 6 6 6 6 6 6 6 5 3

Actual (50%) 10% Ramp Up 4 2 5 5 6 6 6 6 6 5

70% Steady State 6 8 1 5 6 6 6 6 6 6

20% Delivery 5 4 1 5 5 6 6 6 6 6


Page 12

The relative contribution of each determinant to the Overall Client Expectation may be arrived at through

a weighted consolidation of the quantified inputs (Fig 7). The clients with highest CE Ratio will typically

carry a high expectation for the provider. The degree to which this expectation is influenced by their

perception, provider’s posturing and ability to execute can also be measured with this quantitative

framework.

Using the framework, it is also possible to discover the key determinants influencing client expectation,

and their relative influence on the same (Fig 8). For example, being perceived as a Thought Leader,

capable of providing Next Gen Solutions and using state of the art technology for the Solution may

0

2

4

6

8

10Compliance

Competition

Efficiency

Excellence

Reputation

Financial

Operational

End-client

Public at large

Technology Solution

Next Gen

Thought Leader

Lead

Augment

Collaborate

Invest

Ramp Up

Steady State

Delivery

C1

C2

C3

High

Medium

Low

Fig 8: Sample Determinants of Client Expectation

Clients / stakeholders

CE

Ra

tio

Fig 7: Sample CE Ratios

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

C3 C6 C1 C4 C5 C2 C7 C8 C9 C10

Execution Quality (EQ)

Provider Role Ratio (PRR)

Client Outlook Score (COS)


Page 13

influence the client expectation more than the execution quality or efficiency.

Measuring Expectation

Based on study conducted by Irja Hyvari7, there is a strong correlation between critical success factors for

projects of varying type (Fig 9). These correlations can be base lined to arrive at KPIs for managing client

expectations, as the clients would turn to service providers in delivering these success factors, across

project types.

Using the above framework, following metrics could be used to measure and manage client expectations:

Adequate Service Levels – The minimum acceptable service level is a sure indicator of client

expectation, factoring the service provider’s capability and past performance

Zone of Tolerance - The difference between the adequate service level and the desired service

level highlights the client’s expectation on the service provider’s performance in the current bid.

Client perception – The belief that a client holds on the provider’s ability to meet its current

requirements, as manifested in client communications (RFI vs. RFP), early involvement vs. late

and degree of control vested in the provider

Fig 9: Correlation between Project Types and Success Factors

End–User

commitment

Adequate

funds /

Resources

Communication Clear

Organization

Job

Description

Client Sub-Contractor

Company/Organization size

Project Size

Project Density (no of cross

stakeholder activities /

interfaces)

Organization Type - Matrix or

functional

Project Management Experience

Positive

Correlation

Weak

Correlation

Negative

Correlation

KPIs for Managing Client Expectations

Project

Diversity


Page 14

Conclusion

Exceeding client expectation is a pre-requisite to client retention and growth. It can only be done by an

accurate profiling of the client and its current need with respect to the macro environment. An appropriate

positioning and posturing is needed by the service provider to ensure that the client expectations are

calibrated for the provider’s ability in delighting the client. Once a trust has been established, impeccable

execution is needed to retain the same and strengthen the perception for the client. It is time project

management frameworks encapsulated the measurement and management of client expectations by

defining processes, checkpoints and metrics that deliver the same.


Page 15

References:

1 J.M.M., P. T. (1991). “Customer (Dis)Satisfaction with the Performance of Products. Proceedings from the

Euroepan Marketing Academy Conference (pp. 446-462). Dublin: Marketing Thought Around the World 2 A. Parasuraman, L. B. (1991). Understanding Customer Expectations of Service. Sloan Management Review, 39.

3 Robert Johnston. (2002). The Zone of Tolerance: Exploring the relationship between service transactions and

satisfaction with the overall service. Warwick Business School, University of Warwick, UK. 4 Nattaphol Thanataveerat, Z. J. (2007, June 07). School of Business. Retrieved from Malardalens University:

http://www.eki.mdh.se/uppsatser/foretagsekonomi/VT2007-FEK-D-1520.pdf 5 Parasuraman, B. Z. (1990). Delivering Quality Service; Balancing Customer Perceptions and Expectations. Free

Press. 6 A, B. L. (1991). Marketing Services: Competing Through Quality,. New York: Free Press.

7 HYVÄRI, I. (2006). Success of Projects in Different Organizational Conditions. Project Management Journal, 31-41.