Contact rate for word of mouth
-
- Junior Member
- Posts: 4
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Dear colleagues,
I am currently trying to model positive word of mouth as one driver of
adoption a prescription medication (for a minor chronic ailment) that will
soon be available over the counter (OTC).
The current model structure assumes that adopters will be actively
generating word of a certain period, after which they may continue using the
medication but are less likely to mention it to other sufferers (it is no
more news for them). The adoption fraction is model separately.
The contact rate is for now set at 10 people per month, but I am having
problems calibrating this parameter, and would very much appreciate your
suggestions and learning from similar cases.
I have searched the optimilator, but havent found previous discussions on
the list on this issue.
Thanks very much for your help,
Vittorio Raimondi
From: "Vittorio Raimondi" <vittorio_r@hotmail.com>
Telephone 44 (0) 7739170933
I am currently trying to model positive word of mouth as one driver of
adoption a prescription medication (for a minor chronic ailment) that will
soon be available over the counter (OTC).
The current model structure assumes that adopters will be actively
generating word of a certain period, after which they may continue using the
medication but are less likely to mention it to other sufferers (it is no
more news for them). The adoption fraction is model separately.
The contact rate is for now set at 10 people per month, but I am having
problems calibrating this parameter, and would very much appreciate your
suggestions and learning from similar cases.
I have searched the optimilator, but havent found previous discussions on
the list on this issue.
Thanks very much for your help,
Vittorio Raimondi
From: "Vittorio Raimondi" <vittorio_r@hotmail.com>
Telephone 44 (0) 7739170933
-
- Member
- Posts: 22
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Hello Vittorio,
To calibrate a word-of-mouth model, I typically break the word-of-mouth sales
into real world pieces and then calibrate the pieces.
In word of mouth models Ive built, Ive separated sales into two categories:
word-of-mouth sales, and repurchases (by customers who are in the installed
base). Word-of-mouth sales is an outflow from Potential Customers and an inflow
to the Installed Base.
The formula I typically use for word-of-mouth sales is:
Word of mouth sales = Installed Base Customer x Number of Contacts Per Month x
Fraction of Potential Customers x Probability of Adopting if Contacted
The formula assumes that each Installed Base Customer interacts with a number of
other people each month (number of contacts per month). Some of those people
have already purchased the product and some are encountering it for the first
time so, for word of mouth sales, were only interested in the fraction of the
population that might buy the product for the first time (fraction of potential
customers). These potential customers that encounter the product through
interacting with a customer have a chance of being convinced to buy the product
through each interaction with a customer who is in the installed base
(probability of adopting if contacted).
Probability of Adopting if Contacts should be > 0 and <= 1.
Fraction of Potential Customers = Potential Customers / (Potential Customers +
Installed Base)
Anyone who would like a simple but complete complete copy of a word-of-mouth
model that uses this formula, please send me an email and Ill be happy to send
you a copy.
Best regards, Michael
______________________________________
Forio Business Simulations
Michael Bean
mbean@forio.com
To calibrate a word-of-mouth model, I typically break the word-of-mouth sales
into real world pieces and then calibrate the pieces.
In word of mouth models Ive built, Ive separated sales into two categories:
word-of-mouth sales, and repurchases (by customers who are in the installed
base). Word-of-mouth sales is an outflow from Potential Customers and an inflow
to the Installed Base.
The formula I typically use for word-of-mouth sales is:
Word of mouth sales = Installed Base Customer x Number of Contacts Per Month x
Fraction of Potential Customers x Probability of Adopting if Contacted
The formula assumes that each Installed Base Customer interacts with a number of
other people each month (number of contacts per month). Some of those people
have already purchased the product and some are encountering it for the first
time so, for word of mouth sales, were only interested in the fraction of the
population that might buy the product for the first time (fraction of potential
customers). These potential customers that encounter the product through
interacting with a customer have a chance of being convinced to buy the product
through each interaction with a customer who is in the installed base
(probability of adopting if contacted).
Probability of Adopting if Contacts should be > 0 and <= 1.
Fraction of Potential Customers = Potential Customers / (Potential Customers +
Installed Base)
Anyone who would like a simple but complete complete copy of a word-of-mouth
model that uses this formula, please send me an email and Ill be happy to send
you a copy.
Best regards, Michael
______________________________________
Forio Business Simulations
Michael Bean
mbean@forio.com
-
- Senior Member
- Posts: 117
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Following Mike Beans nice reply on word of mouth models, these
models are treated in depth in Chapter 9 of Business Dynamics. For
an excellent application, see Urban, G., J. Hauser, and J. Roberts
(1990) Prelaunch forecasting of new automobiles, Management Science
36, 401-421. Also chapter 17 in Richardson, G. (ed.) (1996) Modeling
for Management, Vol. 1. Aldershot, UK: Dartmouth Publishing Co.
John Sterman
From: John Sterman <jsterman@MIT.EDU>
models are treated in depth in Chapter 9 of Business Dynamics. For
an excellent application, see Urban, G., J. Hauser, and J. Roberts
(1990) Prelaunch forecasting of new automobiles, Management Science
36, 401-421. Also chapter 17 in Richardson, G. (ed.) (1996) Modeling
for Management, Vol. 1. Aldershot, UK: Dartmouth Publishing Co.
John Sterman
From: John Sterman <jsterman@MIT.EDU>
-
- Junior Member
- Posts: 2
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
For a consumer product, such as mangos, I have just completed a model
that separates out first-time-users, and repeat-users ("loyals").
Similar as in Michaels case, word-of mouth is augmenting the influx of
first-time users, proportional to total sales. The contact rate is then
interpreted as frequency of seeing other customers standing at the mango display
or with a mango at the check-out.
The model assumes promotions and ads etc to stimulate first-time usage, while the
experienced quality determines the transition from first-time user to repeat user.
Many learnings can be derived concerning the co-development of promotions and quality offering
(e.g. ripeness), and co-financing of promotions and product loss cost between importer and retailer.
The model will be presented at the 6th international conference on Chain and Network Management
in Ede, The Netherlands, May 2004.
Hans
Hans Schepers
Wageningen UR
Agrotechnology and Food Innovations B.V.
P.O.Box 17, 6700 AA Wageningen
E-mail: Hans.Schepers@wur.nl
that separates out first-time-users, and repeat-users ("loyals").
Similar as in Michaels case, word-of mouth is augmenting the influx of
first-time users, proportional to total sales. The contact rate is then
interpreted as frequency of seeing other customers standing at the mango display
or with a mango at the check-out.
The model assumes promotions and ads etc to stimulate first-time usage, while the
experienced quality determines the transition from first-time user to repeat user.
Many learnings can be derived concerning the co-development of promotions and quality offering
(e.g. ripeness), and co-financing of promotions and product loss cost between importer and retailer.
The model will be presented at the 6th international conference on Chain and Network Management
in Ede, The Netherlands, May 2004.
Hans
Hans Schepers
Wageningen UR
Agrotechnology and Food Innovations B.V.
P.O.Box 17, 6700 AA Wageningen
E-mail: Hans.Schepers@wur.nl
-
- Junior Member
- Posts: 16
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Hi Vittorio
I feel not being and by far the more qualified to give you an answer, having
never used a model with a word of mouth hypothesis.
I have the same kind of problem then you.
I am working currently on a price setting problem, and one of the hypotheses
included in the layered diagram representing how the hypotheses are working
together is the famous word of mouth.
The word of mouth is important in our service activity (short term rent a
truck, van and car).
In the layered diagram it is driving the behaviour of many other hypotheses.
The problem is that the difficulty to calculate it is as high as its effect.
I have seen many models, mainly in Stermans Business dynamic book and
others and the way the word of mouth is calculated does not satisfy me. In
fact it is in my model the part that is the more ambiguous.
I have started converting the diagram in a model, and worked on the first
hypotheses and left aside the word of mouth that I will integrate later on.
This is the method I will try to follow to resolve the word of mouth
problematic, providing that I may change it depending on my further
experiences.
First let us criticize the usually found in text books formula that Michael
Bean has sent in his mail.
Word of mouth sales = Installed Base Customer x Number of Contacts Per Month
x
Fraction of Potential Customers x Probability of Adopting if Contacted
The number of contacts per month does not specify if the contacts have the
same effect.
For instance you can have revolving contacts, and occasional contacts.
A revolving contact is by example the contact with your wife or children or
colleagues at work etc.
The first contact with a revolving contact will have more effects then the
second or the third.
There are a lot of revolving contacts.
You can always say that it is an average and you have the probability of
adopting if contacted that will make the difference.
Imagine by example that the base of customers is relatively stable and the
only interesting contacts are the non revolving, because the revolving have
already been contacted.
In that case, logically there is very little word of mouth, depending only
on the revolving customers. Then the word of mouth is depending too on the
customer base being instable.
Another problem comes from the needs of the person being contacted. It the
person being contacted has the need only one year afterward, he may have
forgotten the contact.
All this to say that the number of contacts per period has an influence, but
it needs some other information.
I think that it is easier to consider the product number of contacts x
probability of adopting if contacted that makes sense.
The difficulty is to calculate the parameters: number of contacts and
efficiency.
But the worse problem comes from the fact that the probability of adopting
is influenced by the world of mouth of the competitors.
Your word of mouth is influenced by your own word of mouth plus the word of
mouth of competitors.
The result is that the efficiency can vary considerably with the time.
It can be negative too.
One can argue that a model can only consider endogenous factors. But the
objective is generally to find policies and is it worth considering an
endogenous factor that can vary suddenly and change the results of the
policy?
Other problems can be considered: for instance the fact that some people
contacted can be occasional or regular buyers. It will make a difference for
the efficiency. Other factors can be considered depending on the kind of
product.
Whatever the problem, word of mouth is worth considering it.
I will follow several steps.
1. Calculate the impact of word of mouth on the goal of the model.
What is its influence? Is it worth investing money and time and how much, to
add word of mouth to the model?
For instance, in my model, I am interested in price setting. Maybe whatever
the word of mouth effect, it may have no influence on the price setting or
the influence may be small.
If I was interested in a more general model whose goal is to maximize the
result on a long range period, then word of mouth should be considered.
2. Once I have an idea of the influence of the word of mouth, I will have a
close look at reality.
Going down to earth, talking to revolving or not revolving customers or
anybody linked to the subject, is a paramount way to understand how things
are working, get a feel of reality and is often better than tons of data.
3. After this close inspection of live data, I will decide if it is worth
estimating by some ways the effect of word of mouth, independently from any
other factors.
I want simply to know if there is an influence and how much.
To do this, I can make qualitative in depth questioning or quantitative
questioning.
I can for instance, ask every new customer in my agencies, why he decided to
choose our Society or choose some customers and talk with them a longer
time.
4. If the third step is positive, I will have proved that there is word of
mouth influence.
But I still do not know how. I personally think that for my case, it is
depending on the basis of
customers and potential customers multiplied by a factor. I can estimate the
factor if I know the customers the potential customers and the effect. One
of the factors that influence the word of mouth is the quality of the
product, relatively to the competitors.
I will have then to establish a close survey of the quality of our product
compared to the competition.
I have then the formula:
Word of mouth per period =
Installed Base Customer x
Fraction of Potential Customers x Quality of our product compared to the
competition x
a constant factor.
The constant factor can be evaluated by dividing the word of mouth per
period estimated by survey by the other calculated variables.
This is of course a lot of work and it is necessary to prove its usefulness.
After all I may simply leave the word of mouth effect for the next model.
Commercial policy.
Regards.
J.J. Laublé, Allocar, rent a car company.
From: =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <JEAN-JACQUES.LAUBLE@WANADOO.FR>
Strasbourg France
I feel not being and by far the more qualified to give you an answer, having
never used a model with a word of mouth hypothesis.
I have the same kind of problem then you.
I am working currently on a price setting problem, and one of the hypotheses
included in the layered diagram representing how the hypotheses are working
together is the famous word of mouth.
The word of mouth is important in our service activity (short term rent a
truck, van and car).
In the layered diagram it is driving the behaviour of many other hypotheses.
The problem is that the difficulty to calculate it is as high as its effect.
I have seen many models, mainly in Stermans Business dynamic book and
others and the way the word of mouth is calculated does not satisfy me. In
fact it is in my model the part that is the more ambiguous.
I have started converting the diagram in a model, and worked on the first
hypotheses and left aside the word of mouth that I will integrate later on.
This is the method I will try to follow to resolve the word of mouth
problematic, providing that I may change it depending on my further
experiences.
First let us criticize the usually found in text books formula that Michael
Bean has sent in his mail.
Word of mouth sales = Installed Base Customer x Number of Contacts Per Month
x
Fraction of Potential Customers x Probability of Adopting if Contacted
The number of contacts per month does not specify if the contacts have the
same effect.
For instance you can have revolving contacts, and occasional contacts.
A revolving contact is by example the contact with your wife or children or
colleagues at work etc.
The first contact with a revolving contact will have more effects then the
second or the third.
There are a lot of revolving contacts.
You can always say that it is an average and you have the probability of
adopting if contacted that will make the difference.
Imagine by example that the base of customers is relatively stable and the
only interesting contacts are the non revolving, because the revolving have
already been contacted.
In that case, logically there is very little word of mouth, depending only
on the revolving customers. Then the word of mouth is depending too on the
customer base being instable.
Another problem comes from the needs of the person being contacted. It the
person being contacted has the need only one year afterward, he may have
forgotten the contact.
All this to say that the number of contacts per period has an influence, but
it needs some other information.
I think that it is easier to consider the product number of contacts x
probability of adopting if contacted that makes sense.
The difficulty is to calculate the parameters: number of contacts and
efficiency.
But the worse problem comes from the fact that the probability of adopting
is influenced by the world of mouth of the competitors.
Your word of mouth is influenced by your own word of mouth plus the word of
mouth of competitors.
The result is that the efficiency can vary considerably with the time.
It can be negative too.
One can argue that a model can only consider endogenous factors. But the
objective is generally to find policies and is it worth considering an
endogenous factor that can vary suddenly and change the results of the
policy?
Other problems can be considered: for instance the fact that some people
contacted can be occasional or regular buyers. It will make a difference for
the efficiency. Other factors can be considered depending on the kind of
product.
Whatever the problem, word of mouth is worth considering it.
I will follow several steps.
1. Calculate the impact of word of mouth on the goal of the model.
What is its influence? Is it worth investing money and time and how much, to
add word of mouth to the model?
For instance, in my model, I am interested in price setting. Maybe whatever
the word of mouth effect, it may have no influence on the price setting or
the influence may be small.
If I was interested in a more general model whose goal is to maximize the
result on a long range period, then word of mouth should be considered.
2. Once I have an idea of the influence of the word of mouth, I will have a
close look at reality.
Going down to earth, talking to revolving or not revolving customers or
anybody linked to the subject, is a paramount way to understand how things
are working, get a feel of reality and is often better than tons of data.
3. After this close inspection of live data, I will decide if it is worth
estimating by some ways the effect of word of mouth, independently from any
other factors.
I want simply to know if there is an influence and how much.
To do this, I can make qualitative in depth questioning or quantitative
questioning.
I can for instance, ask every new customer in my agencies, why he decided to
choose our Society or choose some customers and talk with them a longer
time.
4. If the third step is positive, I will have proved that there is word of
mouth influence.
But I still do not know how. I personally think that for my case, it is
depending on the basis of
customers and potential customers multiplied by a factor. I can estimate the
factor if I know the customers the potential customers and the effect. One
of the factors that influence the word of mouth is the quality of the
product, relatively to the competitors.
I will have then to establish a close survey of the quality of our product
compared to the competition.
I have then the formula:
Word of mouth per period =
Installed Base Customer x
Fraction of Potential Customers x Quality of our product compared to the
competition x
a constant factor.
The constant factor can be evaluated by dividing the word of mouth per
period estimated by survey by the other calculated variables.
This is of course a lot of work and it is necessary to prove its usefulness.
After all I may simply leave the word of mouth effect for the next model.
Commercial policy.
Regards.
J.J. Laublé, Allocar, rent a car company.
From: =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <JEAN-JACQUES.LAUBLE@WANADOO.FR>
Strasbourg France
-
- Member
- Posts: 22
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Jean-Jacques Laublé makes a good point about the traditional word-of-mouth
product diffusion model not working in quite the same way for services such as
telephone service contracts, internet service subscriptions, or rental cars.
There are (at least) two reasons why the *service* word-of-mouth model is
different than the *product* word-of-mouth model:
1) the relationship with the customer is ongoing, so revenue is calculated from
the installed base stock multiplied by the revenue per customer per year,
instead of the word-of-mouth sales flow and the replacement sales flow.
2) because services are worthless if not used by a specific date, they must be
replenished. There is no replacing an old service for a new service because once
a service expires, it ceases to exist. Therefore there are no replacement sales
for services.
I believe the basic word-of-mouth adoption for service is essentially the same
and is based on subscribers in the installed base interacting with potential
customers. The difference for services is, once these customers adopt, they
continue to pay based on the average life of a subscription or service contract.
And after customers end their subscription, they usually no longer generate new
word-of-mouth sales from interactions with potential customers.
Heres a comparison of the product diffusion revenue and service diffusion
revenue calculations:
The basic revenue equation for *product* diffusion is:
Total Revenue = (Word-Of-Mouth Sales x Price) + (Replacement Sales x Price)
where Replacement Sales = Installed Base / Average Product Replacement Time
The basic revenue equation for *service* diffusion is:
Total Revenue = Subscription Revenue Per Period x Installed Base of Subscribers
For services, Total Revenue goes down when customers exit the installed base by
terminating their long-term contracts or subscriptions. This is somewhat similar
to Replacement Sales declining because customers are no longer using the
product.
Of course, for both services and products, a model based on a real example is
likely to be considerably more sophisticated than these simple, generic
examples.
Best regards, Michael
______________________________________
Forio Business Simulations
Michael Bean
mbean@forio.com
product diffusion model not working in quite the same way for services such as
telephone service contracts, internet service subscriptions, or rental cars.
There are (at least) two reasons why the *service* word-of-mouth model is
different than the *product* word-of-mouth model:
1) the relationship with the customer is ongoing, so revenue is calculated from
the installed base stock multiplied by the revenue per customer per year,
instead of the word-of-mouth sales flow and the replacement sales flow.
2) because services are worthless if not used by a specific date, they must be
replenished. There is no replacing an old service for a new service because once
a service expires, it ceases to exist. Therefore there are no replacement sales
for services.
I believe the basic word-of-mouth adoption for service is essentially the same
and is based on subscribers in the installed base interacting with potential
customers. The difference for services is, once these customers adopt, they
continue to pay based on the average life of a subscription or service contract.
And after customers end their subscription, they usually no longer generate new
word-of-mouth sales from interactions with potential customers.
Heres a comparison of the product diffusion revenue and service diffusion
revenue calculations:
The basic revenue equation for *product* diffusion is:
Total Revenue = (Word-Of-Mouth Sales x Price) + (Replacement Sales x Price)
where Replacement Sales = Installed Base / Average Product Replacement Time
The basic revenue equation for *service* diffusion is:
Total Revenue = Subscription Revenue Per Period x Installed Base of Subscribers
For services, Total Revenue goes down when customers exit the installed base by
terminating their long-term contracts or subscriptions. This is somewhat similar
to Replacement Sales declining because customers are no longer using the
product.
Of course, for both services and products, a model based on a real example is
likely to be considerably more sophisticated than these simple, generic
examples.
Best regards, Michael
______________________________________
Forio Business Simulations
Michael Bean
mbean@forio.com
-
- Junior Member
- Posts: 3
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Hi
I think ³contact rate for word of mouth² suggested by Mike Bean and
described similarly in different books, assumes that capacity or inventory
is always available at the desired level. Capacity constraints or inventory
levels should be modeled to determine the impact of available
capacity/inventory on the flow from potential customers to actual customers
I think ³contact rate for word of mouth² suggested by Mike Bean and
described similarly in different books, assumes that capacity or inventory
is always available at the desired level. Capacity constraints or inventory
levels should be modeled to determine the impact of available
capacity/inventory on the flow from potential customers to actual customers
-
- Junior Member
- Posts: 2
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Just to add to the number of references that have been sent on modeling and
calibrating WOM, a colleague at Yale is doing some interesting work in the
area. The link to her page and her working papers is
http://www.som.yale.edu/faculty/dm324/papers.asp The titles of her working
papers are listed below.
<soc_networks.htm>The Influence of Social Networks on the Effectiveness of
Promotional Strategies" (.pdf) Dina Mayzlin, July 17, 2002
<wom2.htm>Using Online Conversations to Study Word of Mouth Communication
(.pdf), David Godes and Dina Mayzlin, August 2003
<PromoChat2.htm>Promotional Chat on the Internet (.pdf) Dina Mayzlin, June
28, 2003
<draft10_5.htm>The Effect of Word of Mouth Online: Online Book Reviews
(.pdf) Judith Chevalier and Dina Mayzlin, Oct 5, 2003
Paulo Goncalves
paulog@miami.edu
calibrating WOM, a colleague at Yale is doing some interesting work in the
area. The link to her page and her working papers is
http://www.som.yale.edu/faculty/dm324/papers.asp The titles of her working
papers are listed below.
<soc_networks.htm>The Influence of Social Networks on the Effectiveness of
Promotional Strategies" (.pdf) Dina Mayzlin, July 17, 2002
<wom2.htm>Using Online Conversations to Study Word of Mouth Communication
(.pdf), David Godes and Dina Mayzlin, August 2003
<PromoChat2.htm>Promotional Chat on the Internet (.pdf) Dina Mayzlin, June
28, 2003
<draft10_5.htm>The Effect of Word of Mouth Online: Online Book Reviews
(.pdf) Judith Chevalier and Dina Mayzlin, Oct 5, 2003
Paulo Goncalves
paulog@miami.edu
-
- Junior Member
- Posts: 3
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Hi
I think ³contact rate for word of mouth² suggested by Mike Bean and
described similarly in different books, assumes that capacity or inventory
is always available at the desired level. Capacity constraints or inventory
levels should be modeled to determine the impact of available
capacity/inventory on the flow from potential customers to actual customers
I think ³contact rate for word of mouth² suggested by Mike Bean and
described similarly in different books, assumes that capacity or inventory
is always available at the desired level. Capacity constraints or inventory
levels should be modeled to determine the impact of available
capacity/inventory on the flow from potential customers to actual customers
-
- Junior Member
- Posts: 16
- Joined: Fri Mar 29, 2002 3:39 am
Contact rate for word of mouth
Hi everybody.
Mike Bean, is right when he says that a model based on a real example is
much more complicated then those found in text books.
He is for instance considering the telephone service contracts and rent a
car service.
But in the rent a car service, there is great difference in a short term
rental service and
in long term service which is more similar to the telephone service
contracts.
In a short term rental service as in a hotel of a restaurant, the customer
has generally no contract and may change his suppliers very easily, or even
has at the same time different suppliers for the same service, generally for
a reason of disposability.
So the behaviour of customers is very different in these two kinds of
service.
1. The customers can change easily their suppliers.
2. There is often a problem of short term capacity.
3. The impossibility to assume the service for a problem of capacity will
have a very different
cost if it concerns a revolving customer or an occasional one because
the revolving
customer may choose to switch durably to another supplier.
4. This very lack of capacity will not only have an effect on the sales but
on the word of mouth, because the ability to deliver a service is an
important part of the quality of service.
Arif pointed out the capacity problem and its relation with the flow from
potential to customers, but it has to an effect on the word of mouth.
In our business the capacity problem is critical and has always been
considered by customers as the more important attribute of the service
quality.
Regards.
J.J. Laublé, Allocar, rent a car company
Strasbourg France
From: =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <JEAN-JACQUES.LAUBLE@WANADOO.FR>
Mike Bean, is right when he says that a model based on a real example is
much more complicated then those found in text books.
He is for instance considering the telephone service contracts and rent a
car service.
But in the rent a car service, there is great difference in a short term
rental service and
in long term service which is more similar to the telephone service
contracts.
In a short term rental service as in a hotel of a restaurant, the customer
has generally no contract and may change his suppliers very easily, or even
has at the same time different suppliers for the same service, generally for
a reason of disposability.
So the behaviour of customers is very different in these two kinds of
service.
1. The customers can change easily their suppliers.
2. There is often a problem of short term capacity.
3. The impossibility to assume the service for a problem of capacity will
have a very different
cost if it concerns a revolving customer or an occasional one because
the revolving
customer may choose to switch durably to another supplier.
4. This very lack of capacity will not only have an effect on the sales but
on the word of mouth, because the ability to deliver a service is an
important part of the quality of service.
Arif pointed out the capacity problem and its relation with the flow from
potential to customers, but it has to an effect on the word of mouth.
In our business the capacity problem is critical and has always been
considered by customers as the more important attribute of the service
quality.
Regards.
J.J. Laublé, Allocar, rent a car company
Strasbourg France
From: =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <JEAN-JACQUES.LAUBLE@WANADOO.FR>