Marketing analysis of Italian HighSpeed Train sector

ATTRIBUTES BENEFITS FUNCTIONAL VALUES FINAL VALUE



Really fast



Punctuality 

To live life in a



 Saving time

Comfortable 

Less stress comfortable way



Arriving on time

wagons  

More efficiency To be professional:



 Comfortable travel

Working facilities self‐actualization

(e.g. Wi‐Fi, electrial

outlets, etc.) Figure 16 – Means‐end chain

3.3 Market concentration

The purpose of this section is to analyze in deep the market power owned by the two

competitors. In a market analysis, it is necessary to know the distribution of market share

and, in general, the influence that each competitor can have within the competitive arena.

These types of evaluation are necessary to define the competition in the industry, because

they are made with quantitative information. In particular, we will use specific indexes such

as the Herfindahl‐Hirschmann HII index and the Lorenz’s measure R*.

In order to compute those indices, we collected the last available data 2014 about the

number of passengers carried by the HST. Passengers who travelled on Freccia’s train are

29mln, while the ones who chose NTV’s trains are 6.6mln for a total of 35.6 million. To

compute market shares we used the passengers representing the "number of the services

provided" due to lack of data about the sales of Trenitalia’s HS service because Trenitalia

also offer low speed travels . According to these data, the market shares corresponding to

81.46% for Trenitalia and 18.54% for NTV. 29

Chapter 3 – Industry overview and marketing analysis

MARKET SHARES: PASSENGERS

NTV (Italo);

18,2%

Trenitalia

(Frecce);

81,8%

Figure 17 – Market shares

The HII index is equal to 0.6979.

For non‐experts, the HII index is computed as:

where is the market share of the firm and the number of the firm.

For the considered market, a value of 0.5 means a perfect competition; therefore, the found

value suggests we are facing with a medium‐concentrated market.

Using market shares, it is possible to draw the Lorenz curve of the chosen industry.

Lorenz curve

1

0,9

0,8

0,7

0,6

0,5

0,4

0,3

0,2

0,1

0 0 0,5 1

Lorenz curve Maximum concentration bound Monopoly curve

Figure 18 – Lorenz curve 30

Chapter 3 – Industry overview and marketing analysis

The red line represents the line that identifies the maximum geometric detachable area, while

the blue one is the Lorenz curve and the green one represents the same curve in the case in

which the sector would become a monopoly. Through the chart just built, we compute the

quantities necessary for the R index that gives an assessment on the level of market

concentration. Concentration area Max concentration area R*

0,1573 0,25 0,6292

Figure 19 – Values of the indices

concentration area

This value of suggests that the market concentration is medium because,

since a value of 0.25 represents a monopoly the maximum concentration and, in the case of

perfect competition, this value tends to 0, the found value is near to the middle.

Similar considerations could be done about the R index: the higher is the value of R, the lower

is the competition. In particular, when R tends to 1 maximum value we are facing a

monopoly, therefore the found value suggests a medium concentration in the market. This

result was expectable looking at the distribution of market shares: only two firms, one of

which own the 81.46% of the market in terms of passengers .

We can explain these results saying that, although a single firm owns 81.46% of the market

and so you can think that the market is high‐concentrated , the rest of market shares are

owned by only a second firm, making market shares less heterogeneous.

3.4 Market macro-segmentation

Companies have to identify the target market in order to evaluate it and to generate an offer

adequate to the needs of the market. We used the Abell’s model to represent in more details

the boundaries of the business of high‐speed trains and identify strategic business units

SBU .

It makes possible to identify all possible product/market combinations through a three‐

dimensional graph.

Since the high speed is a segment of the railway transport market, we felt that it was

interesting to represent through the Abell’s model the railway transport services in general

and then go into details.

We developed the model by answering three questions:

 WHAT: we wondered what was the aim of the service, so focusing on the needs that the

service satisfies; three main functions of use have been identified:

1. Leisure trip holidays, culture, sports, ..

2. Business trip meetings, exhibitions, training, ..

3. Travel for other reasons health, visiting relatives, study, ..

 WHO: it concerns the groups of customers who use the service, and they may be:

1. Families with medium / high income 31

Chapter 3 – Industry overview and marketing analysis

2. Single

3. Young people under 26

4. Business travelers

5. Families with low income

 HOW: It's about the technology perceived by the customers and for this reason we

made three simple distinctions:

1. HIGH SPEED TRAINS.

2. REGIONAL / REGIONAL FAST: they are trains for local transport dedicated to

travel within a region and between neighboring regions. They do stops in

almost all the stations of the route, connecting the small towns with each other

and with the great metropolitan centers. They are divided into "REGIONAL" and

"REGIONAL FAST". These last, unlike the "REGIONAL", make fewer intermediate

stops in the covered space, obtaining a reduction of travel times.

3. INTERCITY: Intercity trains connect about 200 large and medium cities, from

Rome to Ancona, Reggio Calabria / Sicily, Lecce, Taranto, from Turin to Genoa,

from Milan to Naples, helping to achieve an efficient interchange with trains

local transport and with the High speed.

Figure 20 – Abell matrix 32

Chapter 3 – Industry overview and marketing analysis

For our study, it is necessary to keep fixed the technology on high‐speed trains; in this way,

the three‐dimensional graph is reduced to a plan where it is possible to identify all possible

combinations of functions and customer. Theoretically, each company can identify the SBU to

focus on and define the best strategy to use according to the characteristics of each unit. In

our case, both firms of the sector try to serve all identifiable SBU, providing a service that

could reach all customers groups with any combination of “functions”. 33

Chapter 4 – Consumer analysis

CHAPTER 4 – Consumer analysis

4.1 Customers clusterization

The purpose of this section is to investigate the consumer of the given market, trying to go

even deeper on their needs, preferences and consumption methods, trying to find out if these

can be divided into clusters. To carry out this analysis it is first necessary to obtain data and

information on these issues, since such considerations cannot be made without a quantitative

support. However, we are talking about qualitative and subjective facts: preferences, needs,

and so on are qualitative aspects. Therefore, the team developed a questionnaire that could

make quantitative consumer ratings. The questionnaire is divided into three phases: in the

first section, "About You", you are asked to provide personal information and identification

such as age, occupation, reasons and frequency of travel. In the second section, "Preferenze e

priorità", it asks the traveller to give scores according to a Likert scale to aspects of the

service such as "price", "comfort", "customer services," and many others. The third section

covers only two service providers, asking customers to express a preference‐motivated

judgment on the two firms. Thanks to this survey , which was broadcasted to an audience as

13

diverse as possible, the team was able to obtain exploitable information to carry out the

necessary "Cluster analysis". Below is some information about the 229 respondents, to get an

idea of the profiles of people who use the service under consideration.

13 Questionnaire’s link can be found in sitography. 34

Chapter 4 – Consumer analysis Figure 21 – Survey respondents’ profiles

First, we wondered if the preferences about the variables were somehow related to each

other e.g., a consumer who looks very much the price will average be more interested in the

offers/agreements? . The choice of these variables were carried out trying to build a set of

factors that enclose all the features of the service of interest to the consumer. By placing the

Cluster Analysis on the "STATISTICA7" software based on the variables instead of the cases, it

has obtained the dendrogram in the figure, which shows how some variables are related to

each other various methods of analysis have been used, and the results were the same .

Figure 22 – Variables tree diagram

As shown, consumers give similar importance to quiet and cleanliness, as well as consumers

who express a judgment about the comfort on board, will have similar views as regards the

staff and the facilities on board, and the same goes for the price and special offers for

travel/conventions. The dendrogram shows how this bond is stronger for couples

“Tranquillity‐Cleaning" and "Price‐Offers", rather than for the tern "personnel‐services‐

comfort" to observe the linkage distances . For further confirmations, we see at next page the

matrix of correlations, obtained from the questionnaire data reported on Excell. 35

Chapter 4 – Consumer analysis

Figure 23 – Excell screenshot of correlation matrix

In the matrix, the factors that are related in the dendrogram results were colored with the

same colour. Indeed, we see how the two pairs of the above have a very high linear

correlation. The highest correlation is between the staff and the services on board congruent

results with what is shown in the dendrogram . All non‐coloured cells are characterized by

low values of correlation, allowing us to assert that there are no further links between the

factors in play. In addition, it notes that there are no strong negative correlation, and hence

there are no variables that "run in the opposite direction". You can find the highest negative

value in the correlation between "Price" and "Service on board": as you might guess, very

concerned at the price consumers tend to have low interest about the additional services, and

vice versa. In the figure below, there is the table of means and standard deviations of the

factors from the entire sample of consumers.

Price Comfort Service on Personnel Offers/Conventions Cleaning Tranquillity

Board

MEAN 3,576 3,450 2,742 2,834 3,3