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MVP Explained: A Systematic Mapping Study on the
Definitions of Minimal Viable Product
Valentina Lenarduzzi, Davide Taibi
Faculty of Computer Science
Free University of Bolzano/Bozen
39100 Bolzano/Bozen - Italy
{valentina.lenarduzzi; davide.taibi}@unibz.it
Abstract
Context: One of the most important steps of the Lean Startup
methodology is the definition of Minimum Viable Product (MVP),
needed to start the learning process by integrating the early
adopters’ feedbacks as soon as possible.
Objective: This study aims at identifying the common definitions
of MVP proposed and the key factors identified to help
entrepreneurs efficiently define their MVP, reducing errors due to
unconsidered unknown factors.
Method: We identified the MVP definitions and key factors by
means of a systematic mapping study, defining the research
questions and the protocol to be used. We selected the
bibliographic sources, the keywords, and the selection criteria for
searching the relevant papers.
Results: We found 97 articles and, through inclusion and exclusion
criteria, removed 75 articles, which reduced the total to 22 at the
end of the process. The results are a classification schema for
characterizing the definition of Minimum Viable Product in Lean
Startups and a set of common key factors identified in the MVP
definitions.
Conclusion: The identified key factors are related to technical
characteristics of the product as well as market and customer
aspects. We found a positive improvement of the state of the art of
MVP and the definition of Minimum.
Keywords—Minimum Viable Product, Lean Startup, Startup,
Entrepreneurship
I. INTRODUCTION
The definition of Minimum Viable Product (MVP) is a
fundamental concept of the Lean Startup methodology. The
definition of MVP is an iterative process based on continuous
feedback obtained from the early adopters.
The term MVP was defined by Frank Robinson [S1] in 2001
and then disseminated by Eric Ries from 2009 [S2] and Blank
[S3] from 2010. MVP is a continuously evolving concept,
defined by Eric Ries in 2011 [6] as “a version of a new product,
which allows a team to collect the maximum amount of validated
learning about customers with the least effort”. Starting from
this definition, a lot of different proposals have been made. In
order to understand the differences, we propose a systematic
mapping study of the literature. In this work, we want to identify
the different MVP definitions proposed and the key factors
considered in the definitions itself.
For this reason, the mapping study is intended to answer the
following main research question: What are the common
definitions of Minimum Viable Product (MVP)?
This paper is structured as follows. In Section 2, we present
the background of this work, briefly describing the systematic
mapping process and the Lean Startup methodology. In Section
3, we describe the research questions and the systematic
mapping protocol. In Section 4, we show the systematic
mapping results and in Section 5, we explain the threats to
validity of the study. Finally, in Section 6, we draw conclusions
and sketch future work.
II. BACKGROUND
In this Section, we will briefly introduce the background.
First, we will outline the domain, focusing on the Lean Startup
methodology and the role of Minimum Viable Product (MVP).
Second, we will summarize the systematic mapping procedure
underlying the relevant steps.
A. Lean Startup Methodology
Eric Ries [6] proposed an innovative methodology for
developing businesses and products called Lean Startup.
According to Ries, the product development process can be
reduced by combining business-driven hypothesis
experimentation and iterative product releases. Building a
product iteratively based on the needs of early customers could
lead to reduced market risks such as expensive product launches
and failures.
The Lean Startup methodology is based on the following
five core principles:
• Entrepreneurs are everywhere;
• Entrepreneurship is management;
• Validated learning;
• Build-Measure-Learn;
• Innovation accounting.
Build-Measure-Learn is the fundamental activity where the
entrepreneur must turn ideas into products, then measure how
customers respond, and finally learn whether to give up or
persevere. The fundamental aspect of Build-Measure-Learn is
the Minimum Viable Product (MVP), defined as: “a version of
a new product, which allows a team to collect the maximum
amount of validated learning about customers with the least
effort”. Several others definitions have been proposed since
then, and entrepreneurs and researchers usually face the problem
of selecting the most appropriate definition of MVP.
B. Systematic Mappings
According to Kai Petersen et al. [1], a systematic mapping
study is a useful method for getting an overview of a particular
research area and for identifying the quantity and type of the
research and the results available within it. Unfortunately,
systematic mapping studies are not taken into account by
software engineering researchers often enough, compared with
other research fields [3][4]. In Fig. 1, we show the process
adopted in this work. More details on the systematic mapping
process can be found in [1].
Fig. 1. The Systematic Mapping Process
III. STUDY DESIGN
In this Section, we will describe the study design, defining the
research questions and the systematic mapping protocol. We
selected the bibliographic sources, the keywords, and the
selection criteria so as to retrieve the most relevant papers.
A. Research Question definition
In this step of the systematic mapping process, we
formulated the Research Questions (RQ) so as to define the
protocol to be followed. We structured the RQ based on the
PICO structure [5], PICO being the acronym of Population,
Intervention, Comparison and Outcome. This structure makes it
easier to identify the keywords in the next steps. Our research
question is:
In the field of Lean Startups (P), what are the common
key factors (I) that characterize the Minimum Viable
Product (MVP) (O) compared with the original definition
(C)?
We refined the general RQ into four sub-questions:
RQ1.1: What is the chronological overview of the research
on the definition of Minimum Viable Product (MVP)?
RQ1.2: How has the definition of Minimum Viable Product
(MVP) evolved compared to the original one?
RQ1.3: What are the common definitions of “minimum”
and their related main MVP purposes?
RQ1.4: What are the common key factors that characterize
the definitions of Minimum Viable Product (MVP)?
B. The protocol
In this step of the systematic mapping process, we defined the
bibliographic sources, the keywords used, and the selection
criteria for identifying the relevant papers.
1) Identification of bibliographic sources
The search process can be conducted automatically or manually
among specific journal and conferences. In order to better
address this step, we decided to combine both procedures. As
source engines, we selected:
• ACM digital Library
• IEEEXplore Digital Library
• Springer Link
• Google Scholar
• Science Direct
2) Keywords used
We defined the keywords used based on the PICO [5] terms of
our Research Questions. From the terms Population (P) and
Intervention (I), we identified different acronyms as keywords
as shown in Table I in order to retrieve the relevant papers from
the selected source engines.
TABLE I. THE KEYWORDS USED
P: Lean Startups
P1 terms: “Startup” ,“Lean Startup”,
“Entrepreneurship”, “Entrepreneur”
I: MVP definition
I1 terms: “Minimum Viable Product”
(“Startup” OR “Lean Startup” OR “Entrepreneurship” OR
“Entrepreneur”) AND “Minimum Viable Product”
3) Selection criteria
The search was conducted after defining the selection criteria
in order to identify those articles in the bibliographic sources
that are closest to our research questions. We conducted a
manual search over title, abstract, and keywords. We selected
the papers first by keyword, then by title, and finally by
abstract. After retrieving the results, we applied the selection
criteria to refine the identified papers:
• General selection criteria: We included only papers
published in journals and at conferences. We excluded
not peer-reviewed papers not written in English and
works that are clearly obsolete. We also considered the
contributions of gray literature and blogs so as to
consider possible opinions reported in non-scientific
papers.
• Selection by title and abstract: We removed all papers
that do not provide a potential definition of MVP.
• Selection by full papers: We removed those papers that
did not correctly satisfy our research questions: (1)
showing a definition of MVP in the paper and (2) clearly
defining MVP.
IV. STUDY RESULTS
Starting from the keywords defined in Table I, we retrieved
97 papers including the gray literature. Then we selected the
papers based on the inclusion and exclusion criteria both for
title and abstract. With this selection, we removed 75 papers;
and through a detailed review of each article, we finally reduced
the set of papers to 22 main articles. In Fig. 2, we show the
number of articles remaining after each step of the process.
Bibliographic sources
identification Study results
Research Questions
definition Keywords used Selection criteria
definition
Fig. 2. Exclusion of articles and number of primary
studies [1]
We report the distribution and type of relevant publications
in the source engines used for the search in Table II. Among the
studies selected, only 22 focus on the goal; the other papers are
aimed at analyzing the Lean Startup process underlying the
business aspect and the role of the entrepreneur. We found the
22 papers from the most relevant journals and conferences as
shown in Table III. However, as reported in Section 3, since
there is a lot of work on MVP reported in the gray literature
such as blogs and websites, here we also analyze this and report
it in Table III.
TABLE II. DISTRIBUTION OF RELEVANT PUBLICATIONS PER SOURCE
ENGINE AND PUBLICATION TYPE
Source Engine
Number
ACM
1
Google Scholar
89
Springer Link
4
IEEEXplore Digital Library
9
Science Direct
3
Publication Type
Number
Conferences
21
Journals
31
Workshops
3
Gray Literature
45
TABLE III. PUBLICATIONS CONSIDERED FOR THE SYSTEMATIC MAPPING STUDY
Title Type Study Id.
Year
A Proven Methodology to Maximize Return on Risk
Gray Literature
[S1]
2001
Minimum Viable Product: a guide
Gray Literature
[S2]
2009
Perfection By Subtraction – The Minimum Feature Set
Gray Literature
[S3]
2010
Developing a Minimum Viable Product
Gray Literature
[S4]
2010
Scaling Agile @ Spotify
Gray Literature
[S5]
2012
Minimum Viable Product and the Importance of Experimentation in Technology Startups
Gray Literature
[S6]
2012
Global Mindset: An Entrepreneur’s Perspective on the Born-Global Approach
Gray Literature
[S7]
2012
An Ecosystem-Based Job-Creation Engine Fuelled by Technology Entrepreneurs
Gray Literature
[S8]
2013
Lean Product Development in Early Stage Startups
Conference
[S9]
2013
Now and Later? Mentorship, Investor Ties and New Venture Performance in Entrepreneurial
Seed-Accelerators
Gray Literature [S10] 2013
Course Development and Sequencing for Interdisciplinary Entrepreneurship Education
Conference
[S11]
2013
Customer Development, Innovation and Decision-Making Biases in the Lean Startup
Journal
[S12]
2014
Development of a Mobile Application Using the Lean Startup Methodology
Journal
[S13]
2014
From Agile Software Development to Mercury Business
Journal
[S14]
2014
Developing Entrepreneurial Skills in IT Courses: The Role of Agile Software Development
Practices in Producing Successful Student Initiated Products
Conference [S15] 2014
Creating Minimum Viable Products in Industry-Academia Collaborations
Conference
[S16]
2014
Minimum Viable Product (MVP) for Product startup: A in Indian perspective
Gray Literature
[S17]
2014
Maximizing Product Value: Continuous Maintenance
Conference
[S18]
2014
The Relationship Between Business Model Experimentation and Technical Debt
Journal
[S19]
2015
Lean Startup: Why Large Software Companies Should Care
Workshop
[S20]
2015
The Lean Start-up Approach versus Scrum A case study of German startup Student Couch
Gray Literature
[S21]
2015
Accelerating Web-Entrepreneurship in Local Incubation Environments
Journal
[S22]
2015
1) RQ1.1: What is the chronological overview of the
research on the definition of Minimum Viable Product (MVP)?
The 97 papers identified by keywords and selection criteria
were published between 2001 and 2015, as shown in Fig. 3. The
results show an improvement of the research in terms of
quantity from 2012 to 2015. The selected papers were mainly
published between 2012 and 2015.
RQ1.2: How has the definition of Minimum Viable Product
(MVP) evolved compared to the original one?
As reported in Table IV and Fig. 4, the first definition of
MVP was proposed by Frank Robinson [S1] in 2001, with a
strong focus on the economic point of view. Then, in an
interview for Venturehacks in 2009, Eric Ries proposed a new
definition of MVP [S2], then confirmed this in his book in 2011
[6] and adopted as is, or rephrased, in eleven other works. In
ACM digital Library
IEEEXplore Digital Library
Springer Link
Google Scholar
Science Direct
Search Databases Exclusion and
Inclusion (Abstract)
Detailed Reading and
Data Extraction
97 articles 75 articles 22 articles
ti
2010, another new definition was proposed by Steve Blank
[S3], which was then rephrased in 2014 [S18], extended with a
partially new contribution in 2015 [S22] and partially adopted
in five works. In 2011, [S4] reported a definition of MVP
considering the sum of the three previous ones [S1], [S2], and
[S3]. In 2012, two completely new definitions were reported
([S5] and [S7]).
In 2013, another new definition appeared in the literature
[S11], with a small influence, only on the minimum point of
view from Blank [S3]. However, it has never been considered
or extended any further by other works.
In 2014, [S17] proposed a definition of MVP based on the
sum of the definitions by Ries [S2] and Robinson [S1] and
influenced also by Blank [S3].
Table IV lists the different definitions of MVP. When the
definition is based on the sum or on the extension of an existing
one, the original definition is referenced and not copied.
Fig. 3. Distribution of identified and selected
publications per year
TABLE IV. RQ1.2 RESULTS – MVP DEFINITIONS
Year
Study ID
Definition
2001 [S1] MVP is not a minimal product, it is a strategy and process directed toward making and selling a product to customers.
2009 [S2]
MVP is a version of a new product, which allows a team to collect the maximum amount of validated learning about
customers with the least effort. It is an iterative process of idea generation, prototyping, presentation, data collection,
analysis and learning.
2010 [S3] A MVP has just those features (and no more) that allow the product to be deployed.
2011 [S4] [S1] + [S2] + [S3]
2012 [S5]
MVP means releasing early and often, and validated learning means using metrics and A/B testing to find out what really
works and what doesn’t.
2013 [S6] MVP is a product with a minimum feature set targeting market opportunities while profitably solving customer pain points.
2012 [S7]
A MVP is an offer that generates revenue for the company and that motivates customers to provide feedback and
recommend it to other potential customers.
2013 [S8] MVP is the minimum value organization to accelerate sales to first customers.
2013 [S9]
A MVP is typically the first version of a product released to customers, and should contain only the absolute minimum in
terms of features and design for it to become viable to the customer.
2013 [S10]
MVP represents the minimum functionality or set of features within the product, allowing the firm to test the product in
the market and gather customer feedback, consistent with the second principle.
2013 [S11]
MVP is a product with a minimum feature set targeting market opportunities and validate its value and growth hypotheses
as soon as possible.
2014 [S12] MVP is a set of “minimal requirements,” which meet the needs of the core group of early adopters or users.
2014 [S13]
MVP starts the learning and building process quickly. It allows the start-up team to collect as maximum validated learning
about customers with least effort. The goal is to test the fundamental business hypothesis. It is not meant to be perfect-
meant for Early Adopters.
2014 [S14] MVP aims at defining the smallest possible implementation that brings added value to customers.
2014 [S15] MVP is a product capable of being deployed to a subset of customers for evaluation.
2014 [S16] MVP is an experimental object that allows for empirical testing of value hypotheses.
2014 [S17] [S1] + [S2] + MVP is a product that includes just enough features to allow useful feedback from early adopters.
2014 [S18] MVP is a key concept. The goal is to identify the most valuable features by iteratively experimenting the market.
2015 [S19]
MVP is used to test the business model by gathering and measuring customer feedback. Create a viable product with
minimum effort. Requires the generation and testing of numerous ideas
2015 [S20] MVP is a tool to collect customer feedback on the product in order to improve the product.
2015 [S21] MVP is a product with low quality, early prototype.
2015 [S22] [S3] + allow to reason with early adopters; some of whom will pay you money or give you feedback.
Fig. 4. Genealogical Tree of MVP Definitions
TABLE V RQ1.3 RESULTS – MVP MINIMUM DEFINITION AND PURPOSE
Minimum
Main purpose of MVP
Minimum
Functionalities/Feature
To allow the product to be deployed
To target market opportunities
To create a viable product for the customer
To test the fundamental business hypothesis
To allow to test the product in the market
To gather customer feedback
To identify the most viable features by iteratively experimenting the market
[S3] [S4] [S22]
[S6]
[
S9] [S11]
[S11]
[S10] [S18]
[S
10] [S17] [S22]
[S18]
Minimum
Requirements
To meet the needs of early adopters
[S12]
Smallest possible
implementation
To motivate customers to provide feedbacks and recommend it to other customers
To bring added value to customers
[S7]
[S14]
Minimum Effort To collect the maximum amount of validated learning about customers
To test the fundamental business hypothesis
To develop a product that includes just enough features to allow early adopters feedbacks
To gather customer feedback
[S2
] [S4] [S13] [S17]
[S13
]
[S17
]
[S17] [S19]
Minimum Value
Organization
To accelerate sales to first customers [S8]
Fig. 5. Genealogical Tree of Minimum Definitions
RQ1.3: What are the common definitions of “minimum” and
their related main MVP purposes?
Starting from the MVP definitions, reported in Table IV, we
identified the definitions of minimum and their purpose, as
reported in Table V.
Only fifteen works clearly state the meaning of minimum in
the MVP definition, while the remaining seven ones only report
the purpose of the MVP. Among those fifteen works, there are
five different definitions of minimum: “minimum effort”,
“minimum functionalities”, “minimum value organization”,
“minimum requirements” and “the smallest possible
implementation”.
As reported in Figure 5 and Table V, the first definition of
minimum has been formulated by Ries as “minimum effort”
[S2]. The same definition has been reused by two other works
[S4][S17] in combinations with [S3] while two other ones used
the same definition extending the main MVP purpose
[S13][S19].
In 2010 Blank [S3] defined the minimum as “minimum set
of features”. This definition has also been mentioned as is in
other six works [S6][S9][S10][S11][S18][S22] while, as
reported before, it has been combined with [S2] in [S13] and
[S19]. This definition is associated with seven different MVP
purposes. More details can be found in Table V.
In 2013 a new definition of minimum has been proposed by
Bailetti [S8] as the “minimum value for the organization”.
However, this definition has never been extended or reused.
Finally, two new definitions have been proposed in 2014
[S12] and [S14]. Those definitions are not explicitly referred to
any previous one.
RQ1.4: What are the common factors that characterize the
definitions of Minimum Viable Product (MVP)?
Starting from the MVP definitions presented in Table IV we
identified the main key factors that characterize the definitions
of MVP reported in the papers we considered. In Table IV the
key factors are highlighted in bold.
In the Eric Ries definition [S2], two main key factors:
“minimum effort” and “maximum customers validated
learning”. We found these factors in three papers (13.6%),
whereas in the remaining contributions, (86.4%) are
identifiable other key factors.
In terms of technical characteristics, we also found
Minimum in the sense of “minimum set of features”, as
proposed by Blank [S3], in seven papers (31.8%) and
“minimum effort” as proposed by Ries [S2] in four papers
(18.2%).
Taking into account the market and customer aspects, we
identified the key factor “customer feedback / evaluation”
mentioned in seven papers (31.8%). We also found the key
factor “early adopters” in three papers (13.6%).
The remaining factors such as “minimum design” and “low
quality product” are considered only in one paper (4.5%).
B. Discussion of the Results
Several definitions of MVP have been proposed in the last
fifteen years. However, only few have been used or extended.
Most of the new definitions were published from 2012 to 2015.
However, none of them have later been extended or used as a
standard definition. The remaining recently published work
mainly adopts and rephrases the existing definitions[S2] and
[S3].
Among the initial definitions provided by Robinson [S1],
Ries [S2] and Blank [S3], [S2] is the most frequently reused as
is or rephrased (36.3%) while [S3] is reused as is or rephrased
in 18.18%. In 9% of the selected works [S2] is combined with
[S3] while in other 9% of works [S1] is combined with [S2] and
[S3]. Other two new definitions [S5] and [S7] have been
proposed in the grey literature but they have never been reused
or extended.
Considering the definition of Minimum, Blank’s one as
“minimum features” [S3] is the most recurring one (53.3%).
Ries’ minimum definition as “minimum effort” [S2] is reported
in 26.6% of the papers analyzed. Moreover, two other definition
of minimum could be referred to “minimum features” [S3]
(“minimum requirements” [S12] and “smallest possible
implementations” [S14]), increasing the percentage of Blank
definition of minimum to 66.6%.
Taking into account the key factors that characterize the
MVP definition “maximum customer validated learning” is
only reported in 26.6% of the papers analyzed, while it is
extended as “customer feedback/evaluation” in 31.8% of the
contributions. Moreover, “early prototype” is also considered
to a limited extent (9%), whereas other factors cannot be
considered as relevant since they are reported only in one
contribution.
The results also show that the contributions of the gray
literature in recent years can be neglected since we did not find
any relevant contribution to the definition of MVP there.
V. THREATS TO VALIDITY
Next, we will analyze threats to validity, considering
construct validity, reliability, internal and external validity.
Construct validity reflects to what extent the phenomenon
under study really represents what the researchers have in mind
and what is investigated according to the research questions.
The terms Lean Startup, Minimum Viable Product,
Entrepreneur, and Startup are sufficiently stable to be used as
search strings. In order to assure the retrieval of all papers on
the selected topic, we searched broadly in general publication
databases, which index most well-reputed publications.
Moreover, we also included gray literature, so as to consider
possible opinions reported in non-scientific papers.
Reliability focuses on whether the data are collected and the
analysis is conducted in a way that it can be repeated by other
researchers with the same results. We defined search terms and
applied procedures that can be replicated by others. Since this
is a mapping study and no systematic review, the
inclusion/exclusion criteria are only related to whether the topic
of MVP is present in a paper or not, as suggested by [1].
Internal validity is concerned with the analysis of the data.
Since our analysis only uses descriptive statistics, the threats
are minimal. External validity is about generalization from this
study. Since we do not draw any conclusions about mapping
studies in general, external validity threats are not applicable.
VI. CONCLUSION
In this work, we proposed a systematic mapping study on
the definition of Minimum Viable Product (MVP) in order to
obtain an overview of existing definitions and focusing on the
state of the art. We identified 22 main articles to be included in
our study and we found promising results in terms of state-of-
the-art definitions, definition of minimum and and key factors
that identify the MVP.
Starting from the definition of Robinson [S1], Ries [S2] and
Blank [S3], we found that Ries definition is the most
influencing one and its extensions are mainly rephrased without
adding extra details while, in few cases, the three definitions are
combined together.
Unexpectedly the definition of minimum as “minimum
features” [S3] is the most recurring one.
The main key factors identified are related to the technical
characteristics of the product and to market and customer
aspects such as “minimum set of features”, “customer
feedback”, “minimum effort” and “early prototype”.
In conclusion, we suggest to complement Ries MVP
definition [S2] with the “minimum features” proposed by Blank
[S3], as also adopted in several other works.
As regards future work, we want to understand the process
used by entrepreneurs to define MVP in practice.
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APPENDIX: THE SELECTED STUDIES
[S1] F. Robinson, “A Proven Methodology to Maximize Return on
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[S2] E. Ries, “What is the Minimum Viable Product” March.
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