A Study of Software as a Service Business Model

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Dan Ma

School of Information Systems Singapore Management University

Abraham Seidmann

W. E. Simon Graduate School of Business Administration University of Rochester

A Study of Software as a Service

Business Model

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Software Delivery Model

Data Centre Demand Side Market Segments Supply Side Resources storage Computation Ongoing Maintenance, Support, Delivery Information, Logical data Up Front Cost: Purchasing, & implemention Application Business 1 Business 2 Business 3 Communication Buy my own; Internally support Pay as I go; External Provider Service Agreement Level Contract

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MOTS: The traditional in-house model

•  The modifiable off the shelf (MOTS) solution

(SAP, GE PACS, Siebel CRM,…)

– requires an upfront (software and

hardware) purchase

– located on user’s site

– high degree of customization/integration

– requires in-house on going IT support

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SaaS: the shared IT business model

•  Software as a Service: “software on-demand” –  No upfront purchase required

–  located at centralized cloud/ data center facility of an external provider

–  Limited customization

–  The external provider deals with maintenance, data management, and all necessary IT support

•  From the economic perspective: –  User shared business structure

one-provider-to-many-customers

–  On-demand payment scheme pay as you go

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* Focus on comparative cost

* Typical cost decomposition

In-house MOTS SaaS

Initial license fee Yes No

Modification fee Yes No

Customization fee Yes No

Integration fee Yes No

Initial consultation fee Yes No

On-going support cost

Yes No

Pay per month No Yes

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Initial license fee S$20,000 0

Modification fee S$500 (per doc) 0

Customization fee S$1,000 (per doc) 0

Integration fee S$15,000 0

Initial consultation fee S$50 (per hour; min 5 hours) 0 On-going support cost S$2,000 (per month; per person) 0

Pay per month 0 S$49 / 99/ 199/ 299* (per month)

* The price depends on the customer’s choice. For example, S$49 per month allows up to 10,000 transaction lines per month; and S$299 per month gives unlimited transaction lines.

* Logistic firm with 40 people

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•  The in-house system

The customer chooses to •  modify 5 documents

•  customize 2 documents

•  requests a minimal time of initial consultations •  hires 1 IT person for internal support.

Total cost è S$159,750

•  The SaaS solution

•  No modification or customization

•  The customer chooses the most expensive services (gives unlimited number of use per month)

Total cost è S$11,940

* Here, the total costs are calculated in a 5-year length period. The comparison result always holds, independent of the calculation length.

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Initial license fee Yes No

Modification fee Yes No

Customization fee Yes No

Integration fee Yes No

Initial consultation fee Yes No

On-going support cost Yes No

Pay per month No Yes

Gives a “perfect -fit” system “ One-to-many” system with hidden disutility/cost

The comparative cost analysis ignored

the “lack-of-fit”

What is the value of the right fit?

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The research analysis

End users (firms in need of enterprise software)

The SaaS vendor The MOTS In-house vendor

Competition

The purpose of the research is to understand the following in the setting of software delivery decisions

• Users’ Adoption(yes or no/ how/ when)

• Vendors’ competition (pricing strategy, targeted market segments)

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Modeling End Users

•  The heterogeneous firms

–

  different usage and stochastic demand

User {i} located at : ~ Uniform

[

₋

_θ

₊

_θ

]

i i

d

,

i d 0 1 d_i +θ

θ

−

i

d

i d i D

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Modeling the MOTS Solution

*

q

MOTS

P

The MOTS in-house Vendor

Initial implementation/ customization / installation Upfront purchase payment Stochastic demand Created value: $u / transaction

The red dot indicates the location of the system

Purchases internal IT

infrastructure (capacity) at

unit infrastructure cost {c}; On-going service cost per

transaction {c₁}

MOTS

C

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Modeling the SaaS Solution

The SaaS

The User

Delivers

services Pays a price _{{$ / transaction}}

Stochastic demand

Created value: {$(u-t) / transaction}

service cost {$c₂ / transaction}

a

p

t: the “lack-of-fit” cost <-> the disutility incurred when you do not get what exactly you need;

Higher t means that SaaS option incurs high lack-of-fit cost/ higher disutility for the user

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•  Only one MOTS solution is available

•  MOTS provider sets its price

•  User firms decide whether to purchase and

install the system

– Yes: install in-house IT capacities

bears over- and under-capacity risk

– No: stay out of the market

The MOTS-only Market

(The Benchmark Case)

*

q

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Optimal In-house IT Capacity

(The Benchmark Case)

•  User {i} determines optimal in-house service capacity :

{ }

*

i q Max q_i E (u − c"# ₁)* min D

_{

_i, q_i

_}

− P_MOTS − cq_i$% ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − + = ⇒ u c d q_i* _i θ 1 2 i

D Transaction volume of user {i}. (a random variable)

θ

Expected transaction volume of user {i},

i

d

Volatility range of users’ transaction volume

u

c₁

MOTS

P

Utility/transaction for user under MOTS solution ($/transaction) Unit service cost

One-time price paid to the MOTS vendor

i i d

D

E[ ] ₌

= Pr{D_i > q_i}(u − c₁)q_i + Pr{D_i ≤ q_i}(u − c₁)E D

[

_i / D_i ≤ q_i

]

− P_MOTS − cq_i

Unit capacity cost

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The Marginal MOTS User

(The Benchmark Case)

Users with non-negative utility will purchase and install the system.

The user with expected transaction volume is the marginal user:

Eu_MOTS(d_i) = d_i(u − c₁−c) − P_MOTS − c(u − c)

u θ

At the optimal capacity level , the expected utility for user {i}

is

{ }

*

i

q

The user’s direct loss due to capacity risks Total expected value

created The user’s one-_{time payment}

{ }

_dM

Eu

_MOTS

(d

M

) = 0 ⇒ d

M

=

P

MOTS

u − c − c

₁

+

c(u − c)

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The Optimal MOTS Price

(The Benchmark Case)

•  The threshold policy market coverage

•  The MOTS vendor chooses the optimal price{ }:

(

)

θ

u c c u c c u C P d C P Max MOTS M MOTS M MOTS MOTS MOTS P_MOTS 2 ) ( 2 2 ) 1 )( ( 1 − − − − + = ⇒ − − = ∏

expected transaction volume

0 1

M

d

Users gain negative utility if the

MOTS vendor charges price { }

M MOTS

P

Use MOTS software

MOTS

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The Market Segment

(The Benchmark Case)

M d 0 1 Expected transaction volume 0.5 M d Priced out of the market Users’ expected utility

•  Users and MOTS vendor share the capacity risks

•  Low-transaction-volume users out of market

1

θ

2

>

θ

1

2

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Competition Between MOTS and SaaS

(The Dual Market)

•  Both SaaS and MOTS software solutions

exist in the market.

•  The SaaS and MOTS providers set their

prices simultaneously.

•  Users choose the SaaS, the MOTS, or staying

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The Indifferent User

(The Dual Market)

•  Given prices , the user {i} chooses

between:

{

P

MOTS

,

p

a

}

(

a

)

i i SaaS

d

u

t

p

d

EU

(

)

₌

₋

θ

u c u c P c c u d d Eu_MOTS ( _i) ₌ _i( ₋ ₁₋ )₋ _MOTS ₋ ( − )

The user with expected transaction volume is the indifferent user who gains the same utility from both models:

θ

)

(

)

(

*

c

t

p

u

c

u

c

t

p

P

d

a a MOTS

−

+

−

+

−

+

=

* d

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The Price Competition

(The Dual Market)

•  The MOTS vendor determines the optimal selling price

•  The SaaS determines the optimal price/transaction

•  The equilibrium price pairs are

{

*

}

MOTS P

{ }

* a p

)

1 )(

(

P

C

d

*

Max

_MOTS _MOTS _MOTS

P_MOTS

∏

=

−

(

−

)

_∫

=

∏

* 0 2 d a SaaS p

p

c

xdx

Max

a } } 2 , min{ , max{ ₂ ₂ ₁ * _c _u _t _c _t _c _c p_a ₌ ₋ ₊ ₋ ₋ } 2 ) ( ) ( 2 , { max ₂ ₁ *

θ

u c c u c c t c C C P MOTS MOTS MOTS − − − − + + =

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Four Outcome Regions

(depending on t)

•  Critical values of t :

{

2

}

* 1

t

_<

* t 1

t

₂ t ( lack-of-fit) 1 2 3 4 SaaS

dominates. Both business _{models coexist.}

In-house dominates. Effective Competition from SaaS Ineffective Competition from SaaS high low

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Region 1 &4 : One business model dominance

{

t <t1

}

SaaS dominance requires:

With the very low lack-of-fit cost, SaaS price is reduced to a very low level, and SaaS product becomes very attractive. Together, it could drive the MOTS solution out of the market.

MOTS dominance requires:

With the very high lack-of-fit cost, SaaS price reaches its upper bound, and SaaS product becomes very unattractive. The SaaS vendor therefore is not able to make non-zero profit even when MOTS still charges the monopoly price.

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Four Outcome Regions

(depending on t)

•  Critical values of t :

{

2

}

* 1

t

_<

* t 1

t

₂ t ( lack-of-fit) 1 2 3 4 SaaS

dominates. Both business _{models coexist.}

In-house dominates. Effective Competition from SaaS Ineffective Competition from SaaS high low

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Region 2: Effective Competition

{

*

}

1 t t t ≤ ≤

{

M

}

MOTS P

{

M

}

MOTS MOTS P P* _< Effective competition requires:

0 0.5 1

Use the MOTS software at

*

d

1

0.5

0

Out of the market

M

d

Use the SaaS

Expected

transaction volume

•  MOTS market share, price, and profit are reduced. •  Full market coverage is achieved.

•  Three market segmentation; all users are better off.

MOTS-only market

Dual market

M

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Region 3: Ineffective Competition

{

2

}

* _t _t t ≤ ≤

{

M

}

MOTS P

{

M

}

MOTS MOTS P P* =

Ineffective, infra-marginal competition :

0 0.5 1

*

d

1 0.5

Use the MOTS software at

0

Out of the market

M

d

Use the SaaS

Expected transaction volume

•  MOTS market share, price and profit are not affected. •  Full market coverage is achieved.

•  The SaaS has non zero profit; extracts all potential

utility value from users.

MOTS-only market

Dual market

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The

Complete Competition

Diagram

•  Critical values of t :

The SaaS’s ability to compete in the market

monotonically increases as the parameter {t}

decreases

{

2

}

* 1

t

_<

* t 1

t

₂ _{t ( disutility /} transaction) 1 2 3 4 0

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Additional Market Factors

•  The volatility of transaction volume:

As the transaction volume uncertainty level ( ) increases:

=> more users will opt for SaaS

•  IT transaction volume:

As the transaction volume ( ) increases: => more users will opt for MOTS.

•  IT infrastructure (capacity) costs:

As the capacity cost (c) increases: => more users will opt for SaaS.

θ

i

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Competition with Dynamic Unfit Costs

Unfit costs (

t

) may change over time.

•  Unfit costs increase

eg. software or hardware changes on the users’ side evolving business demand

•  Unfit costs decrease

eg. technology improvements leading to enhanced

application integration ability

development of a uniform platform for software (such as AppExchange by Salesforce.com)

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Competition with Dynamic Unfit Costs

A competitive model in longer time frame

In the first stage, unfit costs = {t

1

}

- the vendors choose their prices simultaneously. - users choose one software vendor

In the second stage, unfit costs increase to {t

H

} or

decrease to {t

L

}

- users consider switching from the current software vendor to the other

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When Unfit Costs (t)

Decrease:

1

d

Expected transaction _volume

1

0

Users choose the SaaS initially and stay with the SaaS afterwards.

Users choose the MOTS software

initially, and switch to the SaaS in the second stage.

Users choose the MOTS software initially and stay with the MOTS afterwards. S

d

1 d S d

Indifferent user between staying with the MOTS or switching to the SaaS, given it has chosen the MOTS in the first stage.

Indifferent user between the SaaS and MOTS at time 0, after considering the possibility of later switch

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When Unfit Costs (t)

Increase:

1

d

Expected transaction _volume

1

0

Users choose the SaaS initially and stay with the ASP afterwards.

Users choose the SaaS initially, and switch to the MOTS software in the second stage.

Users choose the MOTS software initially and stay with the MOTS afterwards.

S

d

Indifferent user between staying with the SaaS or switching to the MOTS, given it has chosen the SaaS in the first stage.

Indifferent user between the SaaS and MOTS at time 0, after considering the possibility of later switch

S

d

1

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Interesting Findings (I)

• When users anticipate a future decrease of lack-of-fit

costs (

t

1

à

t

L

)

:*

–  SaaS will decrease its price; –  MOTS will decrease its price;

–  the MOTS’ price is the same as in the static competition with t=t1;

–  In the second stage, existing MOTS users will switch to the SaaS only if their transaction volatility is large;

*These observations are based on the comparison to the benchmark “static” case without changes in lack-of-fit costs.

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Interesting Findings (II)

• When users anticipate a future increase of

lack-of-fit costs (

t

1

à

t

H

):*

–  SaaS will increase its price; –  MOTS will increase its price;

–  the MOTS’ price is the same as in the static competition with t=tH;

–  In the second stage, existing SaaS users switch to the MOTS;

* These observations are based on the comparison to the benchmark “static” case without changes in lack-of-fit costs.

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Conclusions and Major Contributions

•  We Analyze the SaaS from the competitive standpoint. •  Study the multiple aspects of the value chain

–  Marketing: pricing competition –  Operation: capacity management

–  IT: IT outsourcing & IT in-house (CIO’s perspective)

•  Identify: “market segmentation.”

•  Suggest: “software segmentation.” •  Predict the future of software industry.

•  Show how users beliefs do influence the SaaS in a

significant way.

•  Suggest proper pricing strategies in a long time

window, under users’ certain expectations about the software future quality change.

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Questions?

Thanks a lot

A Study of Software as a Service Business Model