SV Matrix: strategic competitive analysis tool based on the dominance level

Abstract

This article addresses current tools for assessing the level of dominance in industries (Concentration Index (CR), Lind Index (L), Hall-Tideman Index (HT), Herfindahl–Hirschman Index (HHI)), identifies the limitations of the existing toolkit, and proposes its modification based on the developed SV Matrix, which allows analyzing the competitive situation on different markets. Drawing on the RAEX ranking of 2020 for four industries (consulting, audit, outsourcing, information technology), the authors propose classical indices modification algorithms to conduct a comparative analysis of the dominant level on different markets. To conduct a strategic competitive analysis based on modified indices, the article proposes to use the SV Matrix, allowing to reflect the comparative dominant level for different markets, as well as market shares controlled by leading companies. The technique is tested on the sample of 90 Russian industries (according to 2020 official reports) and allows to identify the presence of dominant groups in 31 sectors, assess their size and inner differentiation that resulted in comparative analysis of industries. The SV Matrix expands the toolkit for strategic analysis, allowing to assess not only the competition level in the market and the presence of dominant players in the industry, but also the
differentiation level for companies within the dominant group. Applying this matrix help analyze the characteristics of the market (both for the companies already in the market and those just planning to enter it) and draw conclusions regarding the strategic behavior of companies.

Full text

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To cite this document: Shchelokova, S. V., & Vertogradov, V. A. (2021). SV matrix:
strategic competitive analysis tool based on the dominance level. Moscow University
Economic Bulletin, (6), 137–162. https://doi.org/10.38050/0130010520216.7.
Svetlana Shchelokova
Lomonosov Moscow State University (Moscow, Russia)
Vladimir Vertogradov
Lomonosov Moscow State University (Moscow, Russia)
JEL: L13, L20
SV MATRIX: STRATEGIC COMPETITIVE ANALYSIS TOOL
BASED ON DOMINANCE LEVEL
ABSTRACT
This article addresses current tools for assessing the level of dominance in industries
(Concentration Index (CR), Lind Index (L), Hall-Tideman Index (HT), Herfindahl– Hirschman
Index (HHI)), identifies the limitations of the existing toolkit, and proposes its modification
based on the developed SV Matrix
1
, which allows analyzing the competitive situation on
different markets. Drawing on the RAEX ranking of 2020 for four industries (consulting, audit,
outsourcing, information technology), the authors propose classical indices modification
algorithms to conduct a comparative analysis of the dominant level on different markets. To
conduct a strategic competitive analysis based on modified indices, the article proposes to use
the SV Matrix, allowing to reflect the comparative dominant level for different markets, as well
as market shares controlled by leading companies.
The technique is tested on the sample of 90 Russian industries (according to 2020
official reports) and allows to identify the presence of dominant groups in 31 sectors, assess
their size and inner differentiation that resulted in comparative analysis of industries. The SV
Matrix expands the toolkit for strategic analysis, allowing to assess not only the competition
level in the market and the presence of dominant players in the industry, but also the
differentiation level for companies within the dominant group. Applying this matrix help
analyze the characteristics of the market (both for the companies already in the market and
those just planning to enter it) and draw conclusions regarding the strategic behavior of
companies.
Keywords: competitive analysis, strategic analysis, industry dominance, competition
intensity, economic dominance, SV Matrix.
1
More information you can find on official website http://svmatrix.online

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INTRODUCTION
Traditionally, when analyzing the situation on the market, monopoly, monopolistic
competition, oligopoly, and free competition are distinguished. This approach can be used for
educational purposes and building economic models, but it does not always reflect the existing
institutional market conditions: some companies might have additional advantages compared
to all other companies and can influence market standards and the rules of the game accepted
in the market. A well-known example of this situation is the audit market, where the so-called
Big Four (BIG4) major international audit companies dominate. Despite the absence of
mandatory requirements for the selection of these companies for auditing, there are informally
accepted principles in the market that large companies (especially those operating not only in
the local market) choose BIG4 since their quality standard is internationally recognized by
banks, regulators, investors, and other stakeholders. We can say that all other audit companies
are considered second-tier companies since they account for a relatively small market share.
For example, in Russia, at the end of 2020, BIG4 companies occupy 73.45% of the audit market,
which is generally estimated at 41.351 billion rubles, while the fifth audit company in terms of
revenue is more than three times the smallest of the big four
2
.
According to the theory of economic dominance (Blokhin et al., 2019), market-
leading companies are called alpha-companies, since they use not only classical market
competition instruments (price, quality, supply, etc.) for their leadership, but also institutional
factors (such as predominantly access to the state and less expensive funding sources). And if
these companies, due to natural or artificial reasons, are recognized as market leaders, operate
according to uniform standards and in a similar price segment, then from the point of view of
classical economics, the audit market can be called either an oligopoly (if we separately
consider the audit market represented by BIG4 companies), or, paradoxically, it is a market of
almost free competition, since the same Russian audit market is represented by 117 large and
medium-sized companies, several thousand small ones, and formally it's possible to order an
audit service from any of them.
But from the point of view of the customer, who stands between the choice of audit
services of recognized companies at high prices and relatively cheap services, but provided by
second-tier companies, the situation is not significantly different from the informal oligopoly
2
Official website of «Expert RA» https://raex-a.ru

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of the BIG4 group of companies. If the group of dominant alpha companies is highly
differentiated in terms of revenue (for example, the leader controls 50%), then the situation
requires additional study: there may be a dominant player among several alpha companies, or
these are the features of statistical calculation, when, for example, leaders of different markets
are combined into a single industry, and it makes no sense to talk about dominance in the
industry, since companies are not competitors for buyers.
As a rule, it is possible to single out a group of alpha companies based on subjective
assessments of experts in a particular market (Manchenko, 2020; Studnikov, 2021; Chasovikov,
2021), rank-size analysis methods (Blokhin, Likhachev, 2021), comparison in terms of revenue
and its rate growth (Susslova et al., 2021), but there are no official ratings or criteria for
selecting alphas yet, since each market may have historically different types of institutional
barriers that are known to everyone inside, but are not obvious to external players. The purpose
of this article is to propose an algorithm for determining alpha companies using statistical
indices that allow us to assess the level of market differentiation and the structure of the
dominant group to determine objectively the presence of dominant alpha players in the industry
and competition within the alpha group.
ANALYSIS OF RELEVANT TOOLS FOR ASSESSMENT OF THE
DOMINANCE LEVEL
Currently, there is a large number of tools for the assessment of the competition and
dominance level of companies in a particular market. Among such indicators, the
Concentration Index (CR), the Herfindahl-Hirschman Index (HHI), the relative concentration
coefficient, the market share dispersion, the Gini Index, the Hall-Tideman index (HT), the Lind
Index (L), the entropy coefficient, and others are often mentioned. These indices are well
studied and have several limitations that must be considered when using them.
One of the simplest indices used to assess concentration is the CR index. It shows the
total share of the largest companies, but it does not take into account important factors such as:
1) how uneven the shares of these companies are,
2) how many companies should be considered, so this number is always determined
by experts (Kotsofana and Stazhkova, 2011).
The Lind Index (Linda, 1976) is a generally accepted index for determining the
number of dominant firms in a market and is used to determine the presence of an oligopoly.

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The possibility of using the Lind index, which is calculated based on market shares, to identify
a group of alpha companies will be studied in this article.
It is much more difficult to choose indices for determining differentiation in the
dominant group. The Herfindahl-Hirschman (HHI) and Hall-Tideman (HT) indices are widely
known, but have several well-known drawbacks for small samples, since their minimum value
is inversely proportional to the number of companies in the sample and require accurate
information about all market players so that their sum share was equal to one. Also, the value
of the HHI index depends not only on the completeness of information about the market but
also on the number of players considered (Hirschman, 1964), in connection with which it is
possible to obtain the same values of the index for completely different market situations,
which does not allow comparing markets with each other (Svetunkov, 2016). Indices that do
not consider the company's rank in the market (coefficient of variation, the Gini index, market
share dispersion, the entropy coefficient) are less suitable for assessing the level of dominance,
as they show deviations from the average to one degree or another and are not sensitive to large
gaps in market shares.
METHODOLOGY
As shown above, all existing tools help to analyze only one parameter: the level of
industry concentration or the degree of differentiation of its players. But to make strategic
decisions, a company needs a tool for a comprehensive assessment of both parameters. CR and
HT complement each other best for this purpose, but they do not allow comparison of industries
with each other, and therefore require modification. As part of our study of each industry, to
further compare levels of dominance, we will analyze using the following tools:
1. Indices of concentration (CR - Concentration Ratio) to assess the competition level
of the industry.
2. The Lind Index (L) - to determine the dominant groups in the market and calculate
the corresponding CR index.
3. The Hall-Tideman Index for the group of leaders - to determine the differentiation
level within the group of dominant alpha-companies, determined by the Lind coefficient.
4. Construction of the SV Matrix. Alpha Market Share (the Strength of all alphas on
the market vs Alpha Differentiation (the Variety).

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Concentration indices (CR) - competition level in the market
Concentration ratio (CR) is an index of concentration, calculated as the sum of the
shares of the first N companies in the market and characterizes the competition level. In the
case of a monopoly, the index is equal to 100%; in the case of free competition, with an infinite
(in theory) number of firms, it is equal to zero. As an example, consider the concentration index
of companies in the Russian consulting market in 2020, which was estimated at 104.7 billion
rubles and consisted of 196 companies, according to rating agency RAEX.
Features of this data source are the following:
• it is compiled based on data provided by the companies themselves (and not based on
data, for example, public accounting), which makes it possible to isolate revenue of a particular
business from total revenue;
• provides consolidated data on groups of dependent companies, which, in the
framework of the theory of economic dominance, are called alpha empires (Vertogradov, 2020),
since they operate in the market in coordination with the parent alpha company and have access
to its resources and institutional advantages. In the future, for comparability of information, the
data of the RAEX rankings for the sectors of consulting (1), audit (2), outsourcing, and (3)
information technology (4), as well as some sub-sectors, will be used.
Table 1
CR1-CR15 indices for the consulting services market in Russia in 2020
Place
Group of companies/
company
Total revenue from
consulting services in 2020
(thousand rubles)
CR (accumulated
market share, %)
1
LANIT
18 074 485
17,26
2
EY
11 768 397
28,50
3
PwC
10 046 009
38,10
4
KPMG
9 526 849
47,20
5
CROC
8 540 157
55,35
6
BORLAS
4 627 650
59,77
7
BDO Unicon
4 089 518
63,68
8
SberSolutions
2 157 529
65,74
9
SPECTRUM
1 884 689
67,54
10
ITPS
1 704 862
69,17

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11
Consyst Business Group
1 437 949
70,54
12
SRG
1 223 681
71,71
13
MOLGA Consulting
942 177
72,61
14
EK/Digital Solutions, GK
COMITA
918 755
73,49
15
FBK Grant Thornton
917 546
74,36
Source: Compiled by the authors based on the RAEX 2020 ranking.
The concentration index CR is indicated in the right column of table 1 and is equal to
the accumulated market share of the largest players. Accordingly, CR-5, for example, will be
equal to 55.35%, and CR-10 - 69.17%. Economists do not have a well-established
interpretation of what value of N the index gives the most useful results for applied conclusions
(Knyazeva, 2007; Tropynina, 2020).
Lind index (L) - highlighting a group of dominant companies
To identify a group of market-dominating companies, consider the Lind index (L),
which is used to determine the degree of inequality between market-leading sellers of goods
and is traditionally used to determine the boundaries of an oligopoly. It is calculated as follows:

  

 



 


  


Where
• K is the number of the large sellers;
• i is the number of leading sellers among K large sellers;
•  – market share of the n-th seller;
•  - the ratio between the average market share of I sellers and the
share of (K - i) sellers;
• 

 - market share of i sellers (CRi);
• 

 - market share of K large sellers (CRk).
Accordingly, L can be expressed in terms of CR:

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
   


  

 




  


To identify the group of dominant companies, the Lind Index is calculated for the 2
largest companies, then 3, then 4, and so on until the first discontinuity (Ln is < Ln+1) is found,
where n companies are considered to form a dominant group.
Table 2 shows the calculations of the Lind Index according to the data of the Russian
audit and consulting markets in 2020, where the rows are highlighted when the Lind Index
stops decreasing.
Table 2
Lind indices (L) for consulting and audit markets in Russia in 2020
#
Consulting
Share, %
L
Audit
Share, %
L
1
LANIT
17,26
N/a
KPMG
27,87
N/a
2
EY
11,24
0,768
EY
17,12
0,814
3
PwC
9,59
0,524
Deloitte & Touche
CIS
14,96
0,540
4
KPMG
9,10
0,388
PwC Audit
13,49
0,408
5
CROC
8,16
0,316
BDO Unicon
4,20
0,547
6
BARLAS
4,42
0,333
FBK Grant
Thornton
3,41
0,578
7
BDO Unicon
3,91
0,324
FinExpertiza
1,13
0,827
8
SberSolutions
2,06
0,368
Baker Tilly RUS
0,94
0,948
9
SPECTRUM
1,80
0,383
Crowe Expertiza
0,92
0,956
10
ITPS
1,63
0,383
Mazars Audit
0,92
0,919
Source: Compiled by the authors based on the RAEX 2020 ranking.
The Lind Index confidently singles out BIG4 in the market of audit companies with a
combined share of 73%, confirming the prevailing opinion in the market, as well as the
dominant five consulting companies with a total share of 55%, which includes two largest
Russian IT consulting companies - LANIT and CROC - and three local subdivisions of
international audit and consulting alpha empires.
Similar calculations of the Lind index for the Russian outsourcing and information
technology markets give no less interesting results. In the information technology market, the

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index confidently identifies two alpha players that occupy more than half of the market with a
wide gap from the third company. But in the outsourcing market, despite the presence of a clear
leader (SberResolutions) with a share of 15.42%, almost twice the size of the following
company, the Lind index does not notice the presence of dominant companies in the market.
Due to the peculiarities of the construction of the Lind index, it cannot single out the
only dominant company in the market - the value L for K = 1 does not exist. But if the share
of the second company in the ranking (BDO Unicon Outsourcing) in the outsourcing market
was more than 10.8%, with the unchanged shares of other leading companies, then the Lind
index would show the presence of two dominant companies, but a share of 8.8% is not enough
for this.
Table 3
Lind indices (L) for the markets of outsourcing of accounting functions and
information technologies in Russia in 2020
#
Outsourcing Of
Accounting Functions
share, %
L
IT
share, %
L
1
SberSolutions
15,42
N/a
LANIT
31,99
N/a
2
BDO Unicon Outsourcing
8,80
0,876
Softline
21,37
0,748
3
"1C-WiseAdvice"
5,43
0,733
CROC
5,73
1,170
4
SCHNEIDER GROUP
5,41
0,535
I-Teco
5,05
0,986
5
IBS
5,16
0,416
Jet
Infosystems
4,54
0,818
6
UCMS Group
4,35
0,357
CFT
4,22
0,686
7
TMF Group
4,24
0,305
FORS
3,10
0,635
8
IAS
3,92
0,270
SKB Kontur
3,04
0,567
9
Bellerage Alinga
3,88
0,238
ICL
2,34
0,537
10
Unistaff Payroll Company
3,66
0,215
Sberbank-
Service
2,26
0,498
Source: Compiled by the authors based on the RAEX 2020 ranking.
Similarly, if we calculate the Lind Index for sub-sectors of the audit market (Table 4),
then for financial and legal consulting, the dominant groups will be singled out, and for tax
consulting, the index “sees nothing”, although the first four participants occupy almost 51% of
the market. It is interesting that (all other things being equal) when the share of the second
company PKF MEF changes from 15.28% to 16.7%, the Lind index will single out two
dominant companies.

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Table 4
Lind indices (L) for audit companies in the areas of financial, tax and legal consulting in Russia in 2020
#
Financial Consulting
%
L
Tax Consulting
%
L
Legal Consulting
%
L
1
BDO Unicon
31,94
N/a
UralSoyuz
21,07
N/a
Pravovest Audit
21,16
N/a
2
FinExpertiza
12,49
1,28
PKF MEF
15,28
0,690
HLB Russian Group
17,87
0,592
3
FBK Grant Thornton
10,08
0,84
FBK Grant Thornton
8,79
0,637
FBK Grant Thornton
16,29
0,406
4
HLB Russian Group
5,57
0,79
FinExpertiza
5,58
0,613
KSK Group
14,64
0,319
5
Mazars Audit
5,39
0,65
Crowe CIS
4,72
0,546
Crowe CIS
7,06
0,362
6
Baker Tilly
5,30
0,54
AuditGr
4,58
0,465
Nexia CIS
5,60
0,369
7
Avuar
2,83
0,55
AKG Betroen
3,82
0,420
Mazars Audit
4,35
0,372
8
ADE Professional Solutions
1,83
0,58
Nexia CIS
3,68
0,374
Baker Tilly
2,12
0,444
9
Intercom-Audit
1,80
0,56
AP Nika
2,85
0,355
Korsakov and
Partners
1,74
0,482
10
UralSoyuz
1,54
0,55
Mazars Audit
2,64
0,334
AP Nika
1,68
0,484
Source: Compiled by the authors based on the RAEX 2020 ranking.

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Table 5
Lind indices (L) for consulting companies in the areas of (1) IT consulting and system integration, (2) strategic consulting, and (3)
marketing and PR consulting in Russia in 2020
#
IT consulting
%
L
Strategic Consulting
%
L
Marketing and PR
consulting
%
L
1
LANIT
33,15
N/a
Strategy Partners
35,3
N/a
GMK
23,50
N/a
2
CROC
17,79
0,93
SAGIAR
11,3
1,562
Techart
21,20
0,554
3
BDO Unicon
5,82
1,19
HLB Russian Group
10,1
0,935
Delovoy Profil
13,83
0,493
4
ITPS
4,20
1,098
Financial and
Organizational
Consulting
6,64
0,784
DDVB
11,96
0,405
5
BARLAS
3,46
0,963
EK/Digital Solutions,
GK COMITA
5,17
0,685
Paper Planes
8,55
0,380
6
MSG PLAUT
1,64
1,058
Agency for Direct
Investments
4,54
0,599
HLB Russian Group
7,47
0,347
7
EK/Digital Solutions, GK
COMITA
1,49
1,024
NEXIA CIS
4,47
0,513
ABN Consult
5,04
0,350
8
Consyst Business Group
1,15
1,013
Swiss Consulting
Partners
3,93
0,457
Agency of Industrial
Information
4,01
0,352
9
MOLGA Consulting
1,06
0,965
Mazars
3,20
0,425
Swiss Consulting Partners
2,28
0,393
10
Razdolie Consult
0,68
0,999
ICS Consulting
2,67
0,405
PrCS
2,15
0,400
Source: Compiled by the authors based on the RAEX 2020 ranking.

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When analyzing the consulting sub-sectors (see Table 5) using the Lind index in the
Strategic Consulting segment, where three companies control 55% of the market, this index
does not show the presence of dominant companies. It is because one of the limitations of this
index is the inability to single out the only dominant player (in our case, even with 35% of the
market), the market share decreases quite smoothly starting from the second company, so in
this situation, the Lind index cannot single out leaders. As a result of the calculations carried
out by the authors of this study, the Lind index would show the dominance of three companies
in strategic consulting if the market share of the fourth company "Financial and Organizational
Consulting" would not exceed 4.7%.
The Lind Index confidently shows the dominance of two alpha companies in the IT
consulting sub-sector and 6 companies in the Marketing and PR consulting sub-sector (with
market shares from 23.5% to 7.47%). At the same time, if the share of the sixth company wasn't
7.47%, but exactly 7%, then the Lind index would show that not the six, but the first eight
companies are dominant.
Calculations for the three sub-sectors of the outsourcing industry reveal groups of
dominant companies, while the index does not show clear leaders in "HR accounting " with a
share of 31.49% and in "IFRS reporting" with a share of 41.75%.
Based on the analysis of the application of the Lind Index, we can draw the following
conclusions:
1. This index does not allow identifying dominant companies in the market if there is a
market leader with a large margin of market share from other companies (for example, Table
5, "Strategic Consulting")
2. This index makes it possible to formulate a hypothesis about the presence of
dominant groups of two or more companies (in fact, highlighting an oligopoly in the market),
but this hypothesis requires additional testing to see if this group of players has institutional
advantages characteristic of alpha companies.
3. The Lind index may not show the presence of a group of dominant players if the
decrease in the shares of players occurs smoothly and there is no sharp gap between the group
of leaders and the next largest player in terms of market share (for example, see Table 4 "Tax
consulting").

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Table 6
Lind indices (L) for outsourcing companies in the areas of (1) HR accounting and HR administration, (2) RAS reporting, and (3)
IFRS reporting in Russia in 2020
#
HR accounting and HR
administration
Share(%)
L
RAS Reporting (Russian
Accounting Standards)
Share
(%)
L
IFRS Reporting
(International
Financial Reporting
Standard)
Share
(%)
L
1
IBS
31,49
N/a
SberSolutions
22,32
N/a
FBK Grant Thornton
41,75
N/a
2
BDO Unicon Outsourcing
15,16
1,038
Express Accounting
16,35
0,683
ADE Professional
Solutions
19,64
1,063
3
Unistaff Payroll Company
12,21
0,702
Baker Tilly RUS
13,94
0,477
Mazars
13,07
0,817
4
SberSolutions
11,95
0,498
SCHNEIDER GROUP
13,56
0,352
SberSolutions
7,47
0,786
5
UCMS Group
5,86
0,517
Althaus
8,79
0,337
Crowe CIS
3,94
0,855
6
TMF Group
5,12
0,486
Mazars
7,24
0,319
Bin Audit
3,44
0,812
7
Acsour
4,91
0,436
ADE Professional Solutions
6,33
0,298
Delovoy Profil
2,08
0,856
8
ABU Accounting Services
4,38
0,399
NORD Outsourcing
1,71
0,417
CBS group
1,71
0,859
9
Baker Tilly RUS
2,13
0,436
Delovoy Profil
1,42
0,486
INTEREXPERTIZA
(AGN International)
1,64
0,819
10
1C-WiseAdvice
1,79
0,455
VALEN Group
1,36
0,507
EFIX Group
1,21
0,820
Source: Compiled by the authors based on the RAEX 2020 ranking.

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In general, the Lind Index can be used as one of the tools for identifying alpha
companies in a particular industry, since in most cases it allows you to identify the presence of
dominant groups, but the results of the analysis require expert evaluation, taking into account
the limitations outlined above.
Applying the Lind index together with the concentration index allows us to compare
different industries in terms of the market share held by a group of dominant alpha companies.
Let's call the CRSV index CRn, where n is the number of dominant companies in the industry,
as determined by the Lind Index.
Hall-Tideman Index (HT) for determining the differentiation within a group of
alpha companies
An important characteristic of any market is not only the share occupied by a group of
leaders (CRSV) but also the difference in shares of these leaders. If all N alpha companies have
an equal market share, then we can assume that they have relatively equal market opportunities,
and their consumers have a choice. If one of the companies occupies, for example, 80% of the
total market of N-companies, and the rest of the companies account for only 20%, then the
leading company in terms of share can have a much stronger influence on the market than other
alphas.
To calculate the level of differentiation within the group of leaders, we used the Hall-
Tideman index (HT), which is traditionally calculated for all companies present on the market
and characterizes the degree of market monopolization. But since it is the differentiation of
companies within the dominant group that is important to us, HT will be calculated only for
the group of alpha companies (to do it correctly, we will proportionally normalize their shares
so that they total 100%). The size of the dominant group of alpha companies is obtained from
the calculation of the Lind Index.
The classic Hall-Tideman Index is calculated based on the ranks of firms in the market,
where the largest (usually by revenue) firm has rank 1, the next 2, etc., and their market shares:
 


 , where is the rank of a company in the market (1,2,3…)
If there is one monopoly company on the market, then HT will be equal to one. If two
companies with shares of 50% each, then ½, if 3 companies with 33% each - about 1/3. The
minimum value of HT will be 1/N, where N is the number of firms in the market (HTmin).
This is important to keep in mind when comparing HT indices across markets with different

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numbers of companies. For example, an index value of HT=0.2 would make one economic
sense in a market of 10 companies, but in a market of 5 companies it would simply be its
minimum value.
In Table 7 to illustrate this fact, HT and HTmin were calculated for the industries
already mentioned above and the sub-sectors were ranked in descending order of HT/HTmin
values. It is noticeable that although the HT values in the top (HR Accounting and HR) and
bottom (Marketing Consulting) lines are almost equal - 0.15 and 0.17 - but in the first case, HT
is almost five times higher than the minimum value for the industry, and in the latter only 1.65
times.
It is noticeable that the smaller the number of companies in the sample, the higher the
absolute value of HTmin will be, therefore, to obtain comparable values for groups of different
sizes, HT will be adjusted so that its values are in the interval [0;1] for any n. Let's call the new
index HTSV:
    

 

where n is the number of companies for which the HT index is calculated, and HTn is
the corresponding value for n companies. Accordingly, to determine the level of differentiation
within the dominant groups, the HTSV index was calculated for each sub-sector only for the
dominant group of n companies. So, if the dominant group in "HR accounting and HR
administration" consists of four companies, then when calculating HTSV4, the shares of these
companies were preliminarily normalized to 100% (Table 8).
Table 7
Hall-Tideman (HT) indices for outsourcing, consulting, and audit sub-sectors in
Russia in 2020
Sub-sectors
Number of
companies
HT
HTm
in
HT/HT
min
HR accounting and HR administration (3.3)
34
0,15
0,03
4,94
IFRS reporting (3.5)
21
0,20
0,05
4,17
Payroll (3.2)
38
0,11
0,03
4,16
Legal Сonsulting (2.3)
29
0,14
0,03
3,93
Financial Consulting (2.1)
30
0,11
0,03
3,41
Accounting and tax accounting (3.1)
78
0,04
0,01
3,18
RAS Reporting (Russian Accounting
Standards) (3.4)
21
0,13
0,05
2,79

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Strategic planning (1.1)
20
0,12
0,05
2,40
Tax consulting (2.2)
30
0,07
0,03
2,14
Marketing and PR consulting (1.2)
10
0,17
0,10
1,65
Source: Compiled by the authors based on the RAEX 2020 ranking.
Table 8
The Lind Index, CR, HT indices for the dominant group of companies in
industries and sub-sectors of outsourcing, consulting, and audit in Russia in 2020
Sub-sectors
Lind=
n
n
СRSV
(%)
HTS
V
Payroll (3.2)
10
CR10
=
88,81
0,06
RAS Reporting (Russian Accounting Standards)
(3.4)
7
CR7=
88,52
0,05
Marketing and PR consulting (1.2)
6
CR6=
86,52
0,06
IFRS reporting (3.5)
4
CR4=
81,93
0,17
Audit (2)
4
СR4=
73,45
0,06
HR accounting and HR administration (3.3)
4
CR4=
70,81
0,09
Financial Consulting (2.1)
6
CR6=
70,77
0,12
Legal Сonsulting (2.3)
4
CR4=
69,96
0,03
Accounting and tax accounting (3.1)
13
CR13
=
69,03
0,02
Strategic planning (1.1)
3*
CR3=
56,69
0,21
Consulting (1)
5
СR5=
55,35
0,04
Tax consulting (2.2)
3*
CR3=
45,13
0,11
Outsourcing (3)
2
СR2=
24,22
0,16
Source: Compiled by the authors based on the RAEX 2020 ranking.
*Notes: (1) for the "Strategic planning" and "Tax consulting" sub-sectors, the value of
the size of the dominant group was determined by experts for the reasons indicated above, (2)
the outsourcing industry was excluded from the table (the share of leading companies is 25%,
which does not allow us to talk about the presence of dominance in this market).
In Table 8 the size of the dominant group n was determined by the Lind index, and
CRSV and HTSV were calculated for this group. The larger the market share is occupied by
the group of dominant companies, the larger the CRSV index will be, and the closer the alpha
companies are in market share, the lower the HTSV index will be, which indicates the relative
homogeneity of this dominant group.

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SV matrix as a tool for assessing the dominance level
Let's plot the CRSV and HTSV indices calculated by sectors and sub-sectors on the
graph (Fig. 1). Since there are no industries with a large HTSV in the sample, we added data
on the gas production industry. There are 33 companies gas-producing companies in the
Russian market, while the two largest companies Gazprom and Novatek control 76.5% of
production together, but Gazprom is almost 6 times larger than Novatek in terms of production
volumes
3
.
Figure 1. SV matrix - assessment of the dominance level in different markets
Source: Compiled by the authors based on Table 8
The horizontal axis represents the values of the CRSV index, which shows the total
market share occupied by alpha companies. In the case of a relatively low aggregate market
share (up to 65%), we can say that the position of the group of alpha companies in this market
is relatively weak, these companies experience significant competition in this market. On the
right side of the axis (more than 65%) are highly concentrated markets, controlled by the alpha
companies represented there.
3
Official website of the analytical agency TadAdviser. Accessed 13 September, 2021.
Retrieved from URL: https://www.tadviser.ru/index.php/Статья:Добыча_газа_в_России
HR accounting and HR
administration (3.3)
RAS Reporting (3.4)
IFRS reporting (3.5)
Accounting and tax
accounting (3.1)
Payroll (3.2)
Marketing and PR
consulting (1.2)
Strategic planning (1.1)
Financial Consulting
(2.1)
Tax consulting (2.2) Legal Сonsulting (2.3)
Audit (2)
Consulting (1)
Gas Production
IT (4)
0.00
0.10
0.20
0.30
0.40
0.50
40.00% 45.00% 50.00% 55.00% 60.00% 65.00% 70.00% 75.00% 80.00% 85.00% 90.00%
HTSV (Hall-Tideman Index (HT) for a group of dominant
companies)
CRSV (concentration index CR for a group of dominant companies)

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The vertical axis shows the HTSV index modified by the authors, which shows the
differentiation level of companies within the dominant group. The lower this index for a
particular industry, the more homogeneous companies are (in terms of market share and their
influence in this market). A high HTSV shows that despite the presence of a dominant group,
it is not homogeneous, and more and less large players can be distinguished within it.
We will expand the sample of industries by adding industry data from the information
portal TestFirm.ru (Official website of the TestFirm Project. Accessed 13 September, 2021.
Retrieved from URL: https://www.testfirm.ru/rating), which contains a sample of 2.3 million
legal entities, grouped by 96 industries, which is the undoubted advantage of this source. The
disadvantage of this source is the lack of consolidated information on dependent legal entities
that conduct coordinated market activities. This fact is important to notice when constructing
the SV matrix for applied tasks, but in this case, we are analyzing, in general, the applicability
of the created tool for competitive analysis, so now the lack of this information can be neglected.
The authors carried out calculations according to the TestFirm portal for all industries
for 2020, which made it possible to identify 31 industries with dominant groups according to
the Lind index, and calculated HTSV and CRSV (the results are presented in Table. 9, ranked
by HTSV). The left column of Table 9 shows the TestFirm classification number. Some
industries were subsequently removed from the Table due to the impossibility of economic
interpretation of the data: for example, the leader of one of the industries had a revenue of 366
billion rubles with a staffing of 1 employee, the status of a small business entity, and a profit
of 1 thousand rubles in 2020.
Table 9
Industries with the presence of a dominant group of companies
№
Industry according to TestFirm.Ru classification
Lin
d-n
СRS
V, %
HTS
V
81
Building and territory maintenance activities
2
71,62
0,00
61
Activities in the field of telecommunications
4
61,79
0,02
71
Activities in the field of architecture and engineering design;
technical testing, research, and analysis
4
52,54
0,02
92
Organization and conduct of gambling, betting, and lotteries
4
59,24
0,02
30
Manufacture of vehicles and equipment
9
61,72
0,03
43
Specialized construction works
12
65,15
0,03

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82
Administrative and economic activities, ancillary activities to
ensure the functioning of the organization, activities to
provide other support services for business
13
69,35
0,03
88
Provision of social services without provision of
accommodation
6
38,51
0,03
2
Forestry and logging
7
43,64
0,04
19
Production of coke and oil products
8
63,28
0,04
11
Beverage production
4
60,42
0,05
24
Metallurgical production
5
50,90
0,05
29
Manufacture of motor vehicles, trailers, and semi-trailers
9
68,99
0,05
87
Residential care activities
5
43,36
0,05
79
Activities of travel agencies and other organizations
providing services in the field of tourism
6
64,77
0,06
84
Activities of state administration bodies to ensure military
security, compulsory social security
9
81,78
0,06
17
Manufacture of paper and paper products
7
53,14
0,08
58
Publishing activities
3
38,77
0,09
56
Food and Beverage Activities
4
49,69
0,10
66
Ancillary activities in the field of financial services and
insurance
2
48,69
0,10
8
Extraction of other minerals
2
59,89
0,11
91
Activities of libraries, archives, museums, and other cultural
facilities
5
57,41
0,11
12
Manufacture of tobacco products
4
84,92
0,12
49
Activities of land and pipeline transport
2
49,23
0,21
14
Manufacture of wearing apparel
6
73,88
0,28
37
Wastewater collection and treatment
2
56,49
0,39
75
Veterinary activity
2
73,84
0,56
95
Repair of computers, personal and household items
2
76,62
0,68
Source: Compiled by the authors based on the TestFirm Project data

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Let's put on the graph (Fig. 2) data on industries from Table 8 and Table 9. Since most
companies have HTSV in the range from 0 to 0.1, and the maximum value of HTSV for this
sample is 0.68, we use a vertical logarithmic scale. Datapoint labels correspond to industry
numbers.
Figure 2. SV matrix: a comparative assessment of the dominance level in different
markets
Source: Compiled by the authors
Visually, on the graph, all points fall into 4 quadrants, let's consider each in more detail.
Top right - G quadrant (Gazprom), dominant superalpha
This quadrant includes industries where alpha companies occupy more than 2/3 of the
market, and at the same time, there is a large gap between alphas. We propose to call this square
G - the Gazprom quadrant - as this is a commonly understood example from the Russian market.
In such cases, it is logical to assume that the company that dominates among the alphas - you
can call it super alpha - has some additional capabilities that are not available to the rest of the
industry. In the case of Gazprom, this is a de facto monopoly on the export of produced gas
and other institutional advantages. Also in this quadrant are the veterinary industry (75), where
there are two super-large players, one is almost 6 times larger than the other, and the repair
industry (95), dominated by subsidiaries of Huawei and Sberbank.

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Dominant companies in this market rarely have to face competition from direct
competitors, since the influence of the latter in this market is negligible. The main risk that
these companies can face is the substitutes' emergence, which can lead to a change in the
structure and size of the market (up to its disappearance). For example, in the case of the gas
market, such risks may be a sharp development of markets for alternative energy sources,
which will lead to a decrease in demand for gas and the natural disappearance of this market.
Lower right - B4 quadrant (Big4), natural oligopoly
Industries in the lower right quadrant are characterized by the presence of an established
group of dominant alpha companies that are comparable in terms of market opportunities. The
main concern of the alpha group is the coordinated protection of its position from the entry of
new players, which also requires a stable level of competition within the alpha group of
companies. In a sense, this situation can be called a natural oligopoly (when the efforts of the
dominant group are concentrated not on fighting with each other, but against the rest of the
market companies). For consumers in this market, there is a sufficient choice among the top
companies in terms of quality/ experience/ image.
We suggest calling it B4 in honor of the well-known term Big4 - four audit companies
that occupy 73% of the market in Russia and have a minimum differentiation coefficient
HTSV=0.06.
Alpha companies are interested in maintaining a natural oligopoly and protecting the
market from new entrants. An example of a successful strategy is Tele 2, which entered the
established group of dominant mobile operators in Russia (MTS, Beeline, and Megafon). It
was implemented through price dumping, regional expansion, and partnership with a large state
corporation (Rostelecom). In this case, access to financial and administrative resources played
a huge role, and in case there are no such resources, it's better to focus on a specific niche
market, rather than compete across the entire spectrum of services.
Bottom left - RO quadrant (Red Ocean), fierce rivalry
The lower left quadrant contains markets where alpha companies are relatively
homogeneous and comparable in market share, but still have relatively low aggregate market
share compared to dominant groups in other quadrants. It is the most numerous matrix square.
Considering industries that got here, there is a fairly high level of competition not only with
betas, gammas but between the alphas themselves. So we will call this Red Ocean quadrant,

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after the term introduced by Chang Kim (Kim & Moborn, 2017), characterizing an intense
competition with a low level of differentiation in the market.
High pressure from not only neighboring alpha companies but also from beta players is
forcing alpha companies to consolidate market efforts. It can happen through institutional
associations (industry associations and other forms of cooperation that allow companies to set
additional barriers and restrictions for weaker players) and through mergers or acquisitions
(which often occurs in the automotive industry). In case of successful joint actions, the alpha
companies in this market increase their consolidated market share and move to the B4 segment
(lower right quadrant), and if it is not possible and only one of the companies grows, then the
market transforms into the upper left quadrant.
Upper left - Quadrant I (IKEA) with low or natural barriers.
The upper left segment, the smallest one, is the segment of diverse (from the point of
view of their market advantage) alphas. This quadrant is named I in honor of Ikea, which by a
wide margin leads the dominant group of companies selling furniture and home goods. A group
of heterogeneous alphas can get there, occupying a market share of 30-65% and having a large
number of competitors.
We see two options for industries to fall into this quadrant: (1) the absence of barriers
to entry in the industry, or, almost the opposite, (2) the presence of natural barriers, possibly
characteristic of natural monopolies.
The absence of significant barriers to entry in the industry, as a result of which alphas
can break away from the rest of the market due to their expertise and marketing efforts, but
they cannot further increase their consolidated market share and move into the G quadrant,
since it is impossible to limit entry for other players into the industry. This hypothesis looks
realistic for the consulting industry (1) and its strategic consulting sub-industry (1.1). The lack
of the ability to set strict standards for products or services contributes to the intensive
emergence of numerous new players on the market, specializing in a particular niche with new
products. If alpha companies still manage to establish entry barriers or standardize
requirements, then this market has the opportunity to move into the B4 quadrant. If one of the
alphas manages to significantly increase its market share and significantly break away from the
rest of the alphas, the market can move to the G quadrant. In the case of the consulting market,
it is practically impossible to limit the emergence and entry of new players, since the start of
activity in this market does not require financial or administrative resources.

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The second option for getting into this quadrant is the presence of natural barriers,
which can be overcome only through the acquisition of other players. The industry
“Wastewater collection and treatment” (37) requires the treatment facilities, “Activities of land
and pipeline transport” (49) - the presence of own railways and pipelines. The low, compared
to segment G, consolidated market share of companies of this type is most likely caused either
by the peculiarities of calculating market shares, as in the case of industry 49, where the
dominant two players are Russian Railways and Transneft, or by organizational issues, when
companies are distributed in a certain way between different owners, the change of which will
change the value of HTSV, and the activities of the companies themselves cannot affect the
increase in its market share.
CONCLUSION
In this article, the authors reviewed the existing tools for assessing the dominance level
in industries, identified the limitations of the existing tools, and proposed its modification based
on the developed SV Matrix, which allows assessing the competitive situation in different
markets.
Building an SV matrix for a company in the markets where it operates allows to expand
the tools for strategic analysis and assess not only the level of competition in the market and
the presence of dominant players in industries but also the differentiation level between alpha
companies.
As a result of applying the SV matrix, it is possible to analyze the characteristics of
markets (both for companies that are already on the market and for companies that are just
planning to enter this market), draw conclusions about the successful strategic behavior of
companies in this market. For example, for companies whose market is located in the lower-
left RO quadrant with the strongest competition, it is possible both to combine with other
players to transform the market into the B4 quadrant and to strengthen their positions to move
into the I quadrant.
Understanding the possible trends or direction of development of conditions in the
industry market, as well as the prerequisites for the transformation of the market from one
quadrant to another, can provide additional information, for example, for the regulator, which,
in turn, can make more informed decisions to equalize competitive conditions.
It is important to note that the SV matrix is sensitive to the quality of the source
information about market shares, and in practical application, it is important to ensure that the

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associated companies are consolidated in the source information. According to the authors,
ranking and rating data are more suitable than accounting data to obtain more valid results.
Possible options for the SV matrix development include testing it on new data sets and
analyzing those industries where the Linda index “does not see” dominance, although,
according to industry experts, a group of alpha companies with stronger institutional
capabilities is present.
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