Customer value modelling

CUSTOMER VALUE MODELLING -‐ A Business Intelligence Approach

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CUSTOMER VALUE MODELLING

Business Intelligence Approach A "Customer Value Model" (CVM) is a data-driven representation of the worth, in monetary terms, of what a company is doing or could do for its customers. Customer Value Models are tools used primarily in B2B markets where the choice of a given product, service, or offering is based primarily upon the amount customer value created. Customer value is defined as Value = Benefits - Price. Thus, customer benefits are quantified in a CVM - product features and capabilities are translated into dollars. Business Intelligence have been very much instrumental in CVM.

2012

Submitted by: Anit Kumar Roy – 11202008

Group Number: 03 (DM&BI) MBA Batch (2011-13)

SCHOOL OF MANAGEMENT, KIIT UNIVERSITY

BHUBANESWAR - 751024

15-Oct-12


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ACKNOWLEDGEMENT

“Success is an endeavor of co-operations & guidance from all, especially dear ones, seniors,

colleagues and environment.”

Hereby, I express my sincere thanks to all who have contributed to this work either directly

or indirectly and hope this project will be beneficial for its users. This assignment wouldn’t

have been possible without the endeavor efforts provided by the following people for guiding

me throughout.

I would like to owe my gratitude to, Prof. J R Hota (School of Management, KIIT

University, Bhubaneswar), who was always beside me guiding and directing me to prepare

and develop the project in a most efficient way so that I gain insight of the importance of

Business Intelligence.

Extending my sincere thanks towards all, my friends, I would like to thank everyone for their

co-operation, valuable information and feedback without which I would not have been able to

complete the project.

Last but not the least, would like to thank, management of ‘School of Management, KIIT

University’ for including this Project training in our curriculum. This will definitely help us

and given us Opportunity to express our talents and provide a valuable support and a

lucrative opportunity to expand our professional business skills and knowledge.


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TABLE OF CONTENTS

ACKNOWLEDGEMENT ................................................................................................................ 1

EXECUTIVE SUMMARY .............................................................................................................. 3

INTRODUCTION ............................................................................................................................. 4

Business Intelligence .................................................................................................................... 4

CUSTOMER VALUE MODELING ............................................................................................. 6

Firms Using Customer Value Models ............................................................................................ 6

Define and Quantify Customer Value ............................................................................................ 6

What Is Customer Value? ............................................................................................................... 7

Uses of Customer Value Models .................................................................................................... 7

Are You the Customer of Your Products/Services? ........................................................................ 7

Customer Value Model Methods .................................................................................................... 8

Models of Customer Value ............................................................................................................. 8

Customer Value Framework ........................................................................................................ 10

Using the Customer Value Framework ........................................................................................ 12

Key Insights .................................................................................................................................. 12

ROLE OF BI IN CVM ................................................................................................................... 13

CONCLUSIONS .............................................................................................................................. 16

REFERENCES ................................................................................................................................. 17


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EXECUTIVE SUMMARY

This report speaks about the scope ahead of Business Intelligence in “Customer Value

Modeling”. It clearly depicts the need and possibilities for any business organization. This

report also includes detailing about how the BI market has emerged and expanded along the

period of time, with integration along with latest technologies and innovative products over

decades.

The buzzwords of the moment in business intelligence seem to be predictive analytics. These

words encompass two notions: all the data we've been gathering should be used to predict

customer actions (in addition to reporting on them), and these predictions should be used in

operational systems to help guide interactions. In other words, we need headlights in addition

to a dashboard, and the headlights should be placed so the driver can see what they show.

Finally it reports the impact & current trends along with assistance of business intelligence.


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INTRODUCTION

“Sustainable development cannot be achieved by a single enterprise or even by the entire business

community in isolation. It is a pervasive philosophy to which every stakeholder in society and participant in

the global economy must willingly subscribe…”

A word, stronger than the will of a million. “VALUES” when looked up closely is not a word; it’s a way of

life. This is a tribute to men who championed the values. Values which lived through generations.

With these valuable words let’s observe the stands of ‘Business Intelligence’ and ‘Customer

Value Modeling’. Hence, the very first questions which arise are ‘What is Business

Intelligence and what is Customer Value Modeling?’

Business Intelligence

Business intelligence (BI) is defined as the ability for an organization to take all its

capabilities and convert them into knowledge. This produces large amounts of information

that can lead to the development of new opportunities. Identifying these opportunities, and

implementing an effective strategy, can provide a competitive market advantage and long-

term stability within the organization's industry.

BI technologies provide historical, current and predictive views of business operations.

Common functions of business intelligence technologies are reporting, online analytical

processing, analytics, data mining, process mining, complex event processing, business

performance management, benchmarking, text mining, predictive analytics and prescriptive

analytics.

The goal of modern business intelligence deployments is to support better business decision-

making. Thus a BI system can be called a decision support system (DSS). Although the term

business intelligence is sometimes used as a synonym for competitive intelligence (because

they both support decision making), BI uses technologies, processes, and applications to

analyze mostly internal, structured data and business processes while competitive intelligence

gathers, analyzes and disseminates information with a topical focus on company competitors.

If understood broadly, business intelligence can include the subset of competitive

intelligence.


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Often BI applications use data gathered from a data warehouse or a data mart. However, not

all data warehouses are used for business intelligence, nor do all business intelligence

applications require a data warehouse.

In order to distinguish between concepts of business intelligence and data warehouses,

Forrester Research often defines business intelligence in one of two ways:

Using a broad definition: "Business Intelligence is a set of methodologies, processes,

architectures, and technologies that transform raw data into meaningful and useful

information used to enable more effective strategic, tactical, and operational insights and

decision-making." When using this definition, business intelligence also includes

technologies such as data integration, data quality, data warehousing, master data

management, text and content analytics, and many others that the market sometimes lumps

into the Information Management segment. Therefore, Forrester refers to data preparation

and data usage as two separate, but closely linked segments of the business intelligence

architectural stack.

Forrester defines the latter, narrower business intelligence market as "referring to just the top

layers of the BI architectural stack such as reporting, analytics and dashboards."


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CUSTOMER VALUE MODELING

A "Customer Value Model" (CVM) is a data-driven representation of the worth, in monetary

terms, of what a company is doing or could do for its customers. Customer Value Models are

tools used primarily in B2B markets where the choice of a given product, service, or offering

is based primarily upon the amount customer value created. Customer value is defined as

Value = Benefits - Price. Thus, customer benefits are quantified in a CVM - product features

and capabilities are translated into dollars. Customer Value Models are different from

Customer lifetime value models, which seek to quantify the value of a customer to its

suppliers.

Firms Using Customer Value Models

Many firms have been reported to use Customer Value models, including General Electric,

Alcoa, WW Grainger, Qualcomm, Sonoco, BT Industries Group, Rockwell Automation,

Akzo Nobel, and Quaker Chemical.

Define and Quantify Customer Value

Recently while having an evening coffee with Dr. Raju Konduru, a business acquaintance

and a dear friend, the topic of customer value came up. In the early morning Raju went to get

his bike repaired. As is usual in India, he was asked to wait during the repairs; he went to a

nearby tea stall for his morning tea. While sipping his hot morning tea, he started observing a

cockroach lying on its back struggling to get up. It was obviously injured and needed some

support. Raju also observed a group of ants frantically searching for food. He saw that the

ants were about to move to a direction away from the cockroach. He was itching to tell the

ants that the cockroach was nearby. The food these ants were desperately searching for was

so near to them, yet they could not see it and were about to lose it. The role of an innovation

champion is much the same – connect the searching ants to the cockroach, connect various

needs and their solutions so that synergy is created faster, cheaper and without failure.

To do this, a champion not only needs to be at a higher vantage point (where he can see both

the ants and the cockroach), but also needs a deeper understanding of the behavior of ants –

that they are searching for food, that the cockroach is a possible food option for the ants and

that an injured or dying cockroach definitely is an easily available food item. The champion

needs to know the customer's need.


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"Innovation can create value at different levels..."

In this example, the ants are Raju's customers. But suppose Raju's customer is the cockroach?

In this case, Raju needs not only to indicate to the cockroach the clear and present danger of

ants eating it up, but also help the cockroach get up on its legs and move away from the ants.

What Is Customer Value?

Customer value is so fundamental to businesses that it is sometimes completely ignored. It

gets hidden under layers of actions and decisions. Companies may believe that they know

what value they are delivering, but they may not be able to easily define it. So what is value?

How does one know whether value is being created in the work one does? Is it created

optimally? At what cost? For whom? Does the customer know what is valuable? Is value the

same as what the customer demands? How can value be defined and measured?

Uses of Customer Value Models

Customer Value Models appear to have two major uses:

1. New Product & Service Development and Refinement. The dialog and customer

immersion that is part of a CVM is used to discover and determine which potential

product features and functionality would create the most value for customers. This on-

site interaction can be used to frame and define those features and functionality. Often

a key is to focus on product or service capabilities rather than on features. Successful

CVM efforts change the basis of the customer-supplier product conversation away

from features and functions and toward problems, benefits, and value.

2. Sales Tools. CVMs can serve as a quantified statement of value and benefits for a

customer that is used by the vendor sales staff to both sell into a new account, as well

as to reaffirm and validate value created for current customers as a means to retain

and grow current customer.

Are You the Customer of Your Products/Services?

It is imperative for a company to understand and empathize with its customers before

defining value – and remember that there will always be a trade-off between total benefit

versus total cost. The customer will continually evaluate that trade-off. A salesman's role is to

convince the customer that the benefits the customer perceives are the best that money can


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buy. The salesman may not keep the relationship going if the value remains a perception

only. This calls for an end-to end customer value model which runs, adapts and is refined as

the customer relationship progresses.

Customer Value Model Methods

There are several methods and approaches used to create Customer Value Models. All of

these approaches appear to depend on substantial customer interaction and on-site interviews

and observations of customers challenges related to the product or service being valued. The

CVMs are of varying complexity. One consulting firm has found it useful to reverse-engineer

customer P&Ls (profit and loss statements) to establish a clear connection between the

product benefits and the customer bottom-line.

Models of Customer Value

Marketing and innovation expert professor Mohanbir Sawhney, has described customer value

as, "The perceived worth of the set of benefits received by a customer in exchange for the

total cost of the offering, taking into consideration available competitive offerings and

pricings." This definition encompasses seven fundamental lessons of customer value shown

in Figure 1.

Figure 1: Fundamentals of Customer Value


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Tyson Browning, assistant professor of enterprise operations at Texas Christian University,

has reviewed some logical and mathematical expressions for customer value. Figure 2

summarizes the value models.

Figure 2: Models of Customer Value

In the March 2006 article "Customer Value Proposition in Business Markets" in Harvard

Business Review, James Anderson, James A. Narus and Wouter Van Rossum claim, "….there

is no agreement as to what constitutes a customer value proposition – or what makes one

persuasive." They classified value propositions into three types – all benefits, favorable

points of difference and resonating focus.

1. All benefits: The suppliers list every perceived benefit delivered by their product or

service. This method requires a standardized list to be prepared for all customers in all

scenarios; however, this leads to what the authors call benefit assertion without any

actual benefit to the target customers.

2. Favorable points of difference: Based on the customer's awareness of alternatives,

this requires the supplier to have knowledge of alternatives to his own offerings. The

proposition is for the supplier to articulate the ways in which his offering is different

(and better) to the alternatives. This leads to what is called the value presumption – an

assumption that points of difference articulated by the supplier are beneficial to the

customers.


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3. Resonating focus: The suppliers need to make their offerings superior on key

elements of value that are most relevant to the customers. The supplier's offerings

must demonstrate and document their superior performance. In addition, the offerings

must clearly display the supplier's sophisticated understanding of their customers'

business problems.

It is clear from these few views that the terms "value," "customer value" and "customer value

proposition" tend to be overused, as companies and customers incorrectly assume that the

terms are easily understood by one and all. In fact, value may be the least understood concept

in business parlance.

Customer Value Framework

There are two key dimensions of any customer-supplier scenario: 1) how well the customers

know what they need (customer needs are hidden and not clearly articulated) and 2) how well

the suppliers know what the customers need. (See Figure 3)

Figure 3: Elements of Customer Value Framework

These two dimensions create four scenarios/boxes:

1. Known-known: The first box represents the deterministic world – the focus is on

delivering quality. The gold standard in customer value propositions is creating a

resonating focus with the key needs of the customers. When these needs are clear and

known to suppliers and customers, the customer value is measured and reflected in

delivery quality – how efficient, how robust and how timely the service is. Parameters

such as system availability, reliability and robustness become more relevant and


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contribute more to customer value. This is mostly the case in established,

commoditized products and services, where typically the certainty of service and

determinism of the product are a given. In this case, however, customer value can be

enhanced by building a congenial relationship with the customer through all customer

touch points. Innovation or improvement in this case is typically in the way the

delivery happens or in otherwise creating a unique customer experience.

2. Known-unknown: The supplier has to discover what clients need by following the

path of customer intimacy – getting to know the customer better. This requires

scanning, observing, seeing, detecting, examining and recognizing the client's needs

through a deep intimate process should be based on trust and confidence. The delivery

quality strategy of Known-known fails in this box. Here, the insights that the client

has need to be captured through multiple interactions and touch points.

3. Unknown-known: The supplier has to let the customer learn through a process of

orchestrated customer learning. Elements of known solutions or needs are highlighted

through exploration workshops, interactions and designed experiences so that a

customer's hidden needs are revealed. The finesse and diplomatic skills of suppliers

besides the stickiness of their solution becomes an important component of this

strategy as the customer is guided through the process.

4. Unknown-unknown: Where no player knows the needs and where maximum

synergy and value can be co-created. In this scenario, value net deep dive is a model

for discovering and creating value. The first problem is fundamentally accepting that

unknown, but not knowing can be perceived as a weakness. Accepting ignorance is

the first step toward learning and creating.

The supplier and the customer can accept that they do not know the "value," but have faith

that their mutually distinct capabilities will help create what in military parlance is called a

common relevant operating picture (CROP). The value net framework is a starting point to

create the CROP for both sides. Once the needs are understood, then targeted solutions for

specific needs can be created.

The value net, as described in the book Co-Opetition: A Revolution Mindset That Combines

Competition and Cooperation by Adam Brandenburger and Barry Nalebuff, is a complete

map of business relationships and a shared template for discussions of strategy. The value net

framework helps in understanding the connections among stakeholders. A deep-dive into the


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value net makes oft-hidden dependencies, constraints and complexities visible to everyone

concerned.

Using the Customer Value Framework

To use this framework, it is necessary to begin by understanding how much the supplier

knows about its customer needs and how much the customer knows about its own needs.

Consider an asymmetry in perceptions of what the supplier and customer know – if the

supplier and customer think they both are in Box 1 (both know what the customer needs), but

they are actually in Box 4 (neither knows the needs). In this case they will both be focused on

delivery quality – and the best quality solution may be developed, delivered and

implemented. This, however, does not solve the problem leading to the potential of rework –

a significant transaction failure.

Given the challenge of starting off by knowing the needs of the customer, a supplier should

always start in Box 4. By diving into the value net, the need will emerge for both sides, taken

them to Box 3 (if the supplier understands the need) or to Box 2 (if the client understands the

need) – ultimately reaching Box 1 where delivering quality becomes the focused strategy.

In a nutshell, the framework has four steps:

1. Value net deep dive

2. Customer intimacy

3. Orchestrated customer learning

4. Delivering quality

Key Insights

Customer value is one of the least understood business concepts, yet it is a fundamental part

of every business. It is important to start with a value net system of the business – before

locking onto key customer needs. The customer value framework helps suppliers understand

their clients and leads to success for both.


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ROLE OF BI IN CVM

The buzzwords of the moment in business intelligence seem to be predictive analytics.

These words encompass two notions: all the data we've been gathering should be used to

predict customer actions (in addition to reporting on them), and these predictions should be

used in operational systems to help guide interactions. In other words, we need headlights

in addition to a dashboard, and the headlights should be placed so the driver can see what

they show.

This makes sense, but driving safely is one thing, and knowing where to go is quite

something else. Most predictive analytic applications are strictly tactical: which offer is the

customer more likely to accept or, at best, which will yield the most profit? Yet maximizing

profit on a single interaction may actually reduce the value of the long-term relationship.

An inappropriate offer might annoy many customers or preempt another offer with less

immediate return but greater ultimate value.

Good businesspeople understand this intuitively. It's why they offer special discounts to

retain new customers and bend the rules when their most profitable customers have a

problem. The challenge is translating this intuition into corporate policies that optimize

results.

Such policies must be based on empirical analysis, but the proper data is rarely available.

Unless a formal test of alternative treatments is in place, companies treat all customers

according to the same business rules. Thus, the best an analyst can do is look at how

customers behaved after being given certain treatments in certain situations.

This may identify obvious problems, such as high attrition rates when an offer is made

following a service problem. The inference is that something other than an offer would

reduce attrition in that situation. However, it's a tenuous link at best because the data can't

show what would have happened had the company done something else. Only a formal test

- randomly making different offers to customers in similar situations and tracking their

subsequent behavior - can really isolate the effect of that single decision. Such tests are

often difficult to set up and take a long time to evaluate. Managers may also be reluctant to

treat some customers differently than others, fearing that some will feel discriminated

against.


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These difficulties should not be overstated. In practice, some interactions are clearly more

important than others. First-time buyers, customers who have had problems and customers

approaching a renewal date are at obvious inflection points. Companies can test alternative

treatments in these situations with reasonable assurance that differences in near-term results

will correlate with differences in long-term value. Once the obvious candidates have been

optimized, the company can work on more subtle options such as changes in contact

frequency or different loyalty incentives.

If marketing treatments were the only choices that companies had to make, offer testing

would suffice to optimize results. However, customers are affected by many things: product

design, manufacturing processes, logistics, customer service and even personnel policies.

Many of these decisions compete for financial resources; all compete for management

attention. Each needs to be assessed in terms of its impact on customer relationships.

Although it's a bit of a cliche, the aggregate value of these relationships truly is the value of

the company itself. Thus, impact on customer value can be used as a standard metric to

compare investment opportunities in all business areas.

Why use customer value instead of a traditional measure such as return on investment? The

reason is that calculating customer value requires modeling the major interactions between

the company and its customers; that is, it forces the company to simulate the customer

experience. This leads to an understanding of the interconnections of different business

decisions, which traditional financial analysis does not. Thus, the focus on customer value

helps to prevent shortsighted decisions that meet an immediate goal but harm the

organization as a whole.

For example, one part of a simulation model will track how many customers interact with

customer service and how they behave afterwards. This means that assessment of any

proposal to reduce customer service costs will include the impact on later customer

purchases. A return on investment calculation can incorporate such factors, but only if the

analyst is clever enough to identify them. With a customer value analysis, these factors are

included automatically. Similarly, the customer value model will highlight the downstream

impact of business plans such as new acquisition programs, avoiding unexpected

bottlenecks in distribution or service should volume suddenly increase.

Looked at another way, the customer value approach means the company is using the same

framework to assess marketing treatments as staffing levels or business policies. This


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simplifies the task of the analysts and managers, who can work with a single tool. More

importantly, it ensures that all opportunities are evaluated thoroughly and consistently.

The simulation model also provides a comprehensive inventory of customer interactions,

which can be used as a checklist of opportunities for business improvement. Having a

comprehensive list is more important than it may seem because managers cannot otherwise

be certain they are considering all of their options. Today's businesses are so complex that

even experienced managers can no longer be confident that they are intuitively focusing on

the most important choices, or that they correctly understand how these choices interact.

Thus, a proper customer value model provides immense value for corporate planning.

A customer value model supplements, rather than replaces, tactical devices such as

predictive analytics. More precisely, it provides a context to ensure that tactical decisions

take into account their strategic consequences. Of course, finding the correct strategy itself

is still a challenge; the customer value model cannot ensure that a company gives the right

answers. However, it does help the company to ask the right questions, and that is an

excellent start.


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CONCLUSIONS

The analysis, development and deployment involved rationalization of data for new and

existing customers, contact information, demographic information, recharge information

and usage from various data sources to create a strong information or knowledge base to

create an ideal customer value model to create efficient value proposition for the

deliverables.

The process flow can be summarized as:

1. Defining the value model

2. Setup parameters

3. Execution of the test environment

4. Monitoring the performance

5. Evaluate and analyze the performance


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REFERENCES

Fallowing reference materials were very helpful during our project work:

• Anderson, James C; and Narus, James A, (1998), "Business Marketing:

Understanding What Customers Value", Harvard Business Review, March

• Dupuie, Jeff: Using Customer Value Models to Improve B2B New Product

Development, OakStone Partners

• Anderson, James C; Narus, James A; and van Rossum, Wouter, (2006), "Customer

Value Propositions in Business Markets", Harvard Business Review, March

• Lindstedt, Per and Berenius, Jan, (2003), "The Value Model: How to Master Product

Development and Create Unrivaled Customer Value", Nimba Publishers

• http://en.wikipedia.org/w/index.php?title=Customer_Value_Models&oldid=48529617

5

• http://customerexperiencematrix.blogspot.com/

Education

Customer value modelling