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understanding Competitive Market Structure 代寫

       1
“Radio Listening”
An exercise in
Gavin Lees, Victoria University, Melbourne.
With special thanks to: John Dawes, Ehrenberg-Bass Institute for Marketing Science, University of South Australia for the
original ‘Salty Snacks from which this has been copied.
January 2013.  Feedback to: 
Gavin.Lees@vu.edu.au
This is the ‘student’ version.  No answers are provided in this version.  Use of this exercise is absolutely free and without
restriction.  Feel free to contact me to give feedback.  
 
The purpose of this exercise is to:
(a) Provide some skill development in utilising aggregated consumer purchase data using Excel.
(b) Better understand the broader concept of competitive structure, the “Duplication of Purchase
law” and the idea of market partitions – that is, pockets of the market comprising certain brands
that compete more intensely with each other and less intensely with the rest of the market.  
(c) Use this understanding to consider a marketing issue.  The brand manager for The Radio
Network is considering a line extension.  Into which sub-market should it be launched?  
 
This exercise takes you through the process of analysing a purchase duplication table of real consumer  
data.  It uses data on   ‘radio listening’ from New Zealand.  What is special about this data set is that it
comprises particular ‘radio listening’ genres – music and talk.  Therefore the data provides the
opportunity to apply the Duplication of Purchase law to brand-level competition but also to examine
the broader competitive level of product type.  With the different product types it is more likely there
will be some sub-markets or what are called ‘partitions’ that we can identify and confirm using the
data.  
Your task is to work through the exercise and re-create the Excel worksheets shown in the ‘screen
shots’ (you don’t have to re-create the written notes on them).  Answer the numbered questions that
appear through the exercise.  
 
 
 
Special thanks to Massey University for supporting the collection of the data upon which this exercise is based        2
Introduction
We start with a basic table of purchase duplications.  This technical term has a simple meaning – the
proportion of the buyers of one brand who also bought another particular brand in the time period
covered by the data (for example, a twelve month time period).  Such data are generated from
consumer panels in which consumer’s purchases are recorded over time.  This data is widely used in
consumer packaged goods markets.  The Excel file showing this table is called “Radio.xls”.  Open the
Excel file to inspect the data.  The brands shown are a small subset of the total market that comprises
over twenty brands, but these are the main ones.  
The brands are ordered by the name of the brand - in alphabetical order.  The column titled
‘Penetration’ indicates the proportion of the radio listeners that listened to a radio station at least once
in the survey week period.  For example, 26% of the listeners listened to Classic Hits at least once, 17%
listened to ZM, 19% to Solid Gold.  The ‘TRN’ stands for The Radio Network, ‘RW’ for Radioworks
and ‘RNZ’ for Radio New Zealand which are the New Zealand radio networks.  
The cells in the table under the columns “Classic Hits …. to … ZM” represent the proportion of
listeners of the row station who also listened to the column station in the period.  For example look at
the first entry under the column titled “More FM”, which says “48”.  This means of the people who
listened to Classic Hits, 48% also listened to More FM in the period.  If you look under the column
titled “Classic Hits”, go down to the row listing for The Breeze.  It says “37”.  This means of the people
who listened to The Breeze, 37% also listened to Classic Hits - and so on.  
Table 1:  Table of Purchase Duplications, ordered alphabetically by the name of the
Station
    
  % also listening to …….
Genre  Network  Brand  Penetration  
(% buying
at least
once)
Classic
Hits
More
FM
National
Radio
Newstalk
ZB
Other  Radio
Live
Radio
Pacific
Radio
Sport
Solid
Gold
The
Breeze
The
Edge
The
Rock
ZM
Music  TRN  Classic
Hits
26    48  17  18  27  9  13  19  28  22  20  30  18
Music  RW  More
FM
30  40    13  13  27  11  12  11  21  14  29  34  23
Talk  RNZ  National
Radio
28  16  14    23  46  16  9  16  11  8  7  10  7
Talk  TRN  Newstalk
ZB
21  21  19  29    34  13  19  20  17  24  21  9  12
Music/Talk    Other  28  27  27  46  34    19  35  30  26  30  28  34  40
Talk  RW  Radio
Live
12  19  26  36  23  19    20  20  20  12  19  18  19
Talk  RW  Radio
Pacific
11  31  34  24  38  35  24    34  17  20  29  18  15
Talk  TRN  Radio
Sport
14  35  26  32  32  30  18  26    23  18  18  22  18
Music  RW  Solid
Gold
19  39  35  16  19  26  13  9  17    20  24  34  17
Music  RW  The
Breeze
15  37  27  15  33  32  9  14  16  25    19  19  13
Music  RW  The
Edge
19  28  47  11  24  28  12  17  13  24  16    35  33
Music  RW  The
Rock
21  36  49  12  9  34  10  9  14  29  13  31    28
Music  TRN  ZM  17  27  40  11  15  40  13  9  15  18  11  36  35  
        3
In this format it is difficult to discern any general pattern or ‘take-out’ from the data.  All we see is an
arrangement of numbers.  We want to be able to use data like this to give us insights into the market we
operate in.  Therefore we begin to perform some operations on the data to make any patterns easier to
identify, as outlined below in Step 2.  
Step 2
Competition between brands in simple terms means the extent to which they gain and lose sales to each
other.  This occurs at the level of the individual consumer who may buy the same brand over
consecutive purchases, or switch around between brands.  Note that the word ‘switch’ does not
necessarily mean the consumer who bought a last time and buys b next time has permanently changed
allegiance from a to b.  It simply means that consumers may buy the same or different brand on
different purchases.  In this exercise we use the term ‘purchase sharing or share of listening’ to mean
the extent to which consumers/listeners share their purchases or listening between brands or stations
over time; and that term is used interchangeably with another term, namely ‘brand switching’.  
There is considerable evidence that this ‘brand switching’ is proportional to brand size – that is, the
market share of the brand.  In other words, consumers are more likely to switch to a big brand
compared to a small brand.  This switching is also affected by functional differences that exist in the
category such as product formulation.  For example in the instant coffee category, there is a lot of
switching between the decaffeinated brand variants, and a lot of switching between the caffeinated
brand variants; but not as much switching between the caffeinated variants and the de-caffeinated
variants (ie from one to another).  A selected reference list of prior research on this topic is included at
the end of the paper.  
However, in the first instance brand size is a very important factor.  Arranging our data in this way
would therefore seem to be advisable.  So the next task is to sort the data by brand size.  This involves
sorting the rows and the columns in descending order according to their penetration levels.  To do so,
you need both a row and a column that shows penetration data so that any sorting can be based on these
figures.  You already have this data in a column.  For sorting the columns, you will need to have a row
showing the penetrations for each brand along the row.  You can copy the penetrations from the
penetration column and paste special -> transpose them into a row (see screen shot 1).  Insert them
above the brand names.          4
Screen shot 1: paste -> transpose data command
 
 
Now sort the data.  Do the rows first, in descending order of penetration.  Then the columns (data ->
sort -> options -> sort left to right: use the row with the pasted penetrations as your sorting row).  
Check that both rows and columns are in descending size order.  One way to tell is to make sure the
‘diagonals’ are all blank.  The term ‘diagonals’ refer to the cells that signify the same column and row
brand – e.g. where National Radio is the column and the row.     
Next, calculate the average duplications for each brand.  This is shown in Screen Shot 2.          5
Screen shot 2: Average calculation 
 
 
 
Your table should now resemble Table 1.
 
 
 
 
 
 
 
 
 
        6
Table 1:  Purchase Duplications ordered by the size of the brand – rows and columns
in descending brand penetration level. 
    
Penetrations  30  28  28  26  21  21  19  19  17  15  14  12  11
                                
            
% also
listening
to …….    
                
 
 
                        
Genre  Network  Brand
Penetration   (%
buying at least
once)
More
FM
National
Radio
Other  Classic Hits
Newstalk
ZB
The
Rock
Solid
Gold
The
Edge
ZM
The
Breeze
Radio
Sport
Radio
Live
Radio
Pacific
      
                          Music  RW  More FM  30    13  27  40  13  34  21  29  23  14  11  11
12
Talk  RNZ  National
Radio
28  14    46  16  23  10  11  7  7  8  16  16
9
Music/Talk    Other  28  27  46    27  34  34  26  28  40  30  30  19
35
Music  TRN  Classic Hits  26  48  17  27    18  30  28  20  18  22  19  9
13
Talk  TRN  Newstalk
ZB
21  19  29  34  21    9  17  21  12  24  20  13
19
Music  RW  The Rock  21  49  12  34  36  9    29  31  28  13  14  10
9
Music  RW  Solid Gold  19  35  16  26  39  19  34    24  17  20  17  13
9
Music  RW  The Edge  19  47  11  28  28  24  35  24    33  16  13  12
17
Music  TRN  ZM  17  40  11  40  27  15  35  18  36    11  15  13
9
Music  RW  The Breeze  15  27  15  32  37  33  19  25  19  13    16  9
14
Talk  TRN  Radio Sport  14  26  32  30  35  32  22  23  18  18  18    18
26
Talk  RW  Radio Live  12  26  36  19  19  23  18  20  19  19  12  20  
20
Talk  RW
Radio
Pacific  11  34  24  35  31  38  18  17  29  15  20  34  24
 
 
Average
Duplications    20  33  22  32  30  23  25  22  23  23  17  19  14  16
                                
 
Why are we bothering to calculate the average duplication?  The answer is that we are interested in any
overall pattern that exists in this data that is managerially useful.  It is easier to start by looking at
averages rather than the numbers within each particular cell.  This is part of a process called ‘data
reduction’ whereby we simplify complex data in order to then interpret it.  
Question 1:  What appears to happen to the average levels of duplication as you look across the
columns from left to right?  Answer this in words to being with.  
We can also be more precise and express the answer quantitatively - by using numbers in the answer.   
To answer this question quantitatively, we can begin by constructing a graph of penetration versus
average duplications.  Do this now using the following instructions:  
1.  Request a scatterplot of the data in excel.  Ensure that penetration is the x axis
and duplication is the y axis.  (See screen shot 3 over the page). The scatterplot
shows a strong linear relationship between the two variables.  
2.  Insert a ‘trendline’ (chart -> insert trendline) and request a linear trendline.  In
‘options’ ask for the equation and R2
 to appear on the chart and ask for the
trendline to go through the intercept.          7
Screen shot 3:  Scatterplot
 
 
The result is:  duplication = 1.1 x penetration, but for one station it looks as if it does not  
fit the line.  Note that we round the result for clarity.  We talk about the poorer fit of that  
brand later. 
Question 2: What is your interpretation of this result?  Explain what “duplication = 1.1 x
penetration” means.  
 
More analysis
The next step is to more formally construct a model that more fully accounts for the broad pattern
exhibited by these data.  
To do this, we calculate a statistic called the “purchase duplication coefficient”.  The duplication
coefficient is a multiplier.  It expresses the expected relationship between the size of a brand A and the
average proportion of other-brand users that would be expected to also buy brand A in a time period.  
We firstly calculate the duplication coefficient for our entire table of purchase duplications.  In a sense
we have done this already in the preceding step using the graph, which produced a figure of 1.1.  But
we do it again now using the following formula, which becomes the basis for some more detailed
calculations later.  
        8
The formula is:
Purchase duplication coefficient = average duplication / average penetration.  
Steps: Calculate the average duplication for all the brands in the data.  Then calculate the average
penetration of all the brands in the data.  Then calculate the purchase duplication coefficient using the
above formula.  
Screen shot 4: Calculations for duplication coefficient.   
 
 
 
Question 3:  What is the average duplication and average penetration for the brands in this table?  
Question 4:  What is the purchase duplication coefficient for this data table?
 
Why are we doing this?
The reason we are doing this is to create a summary statistic, namely the ‘duplication coefficient’
which is a useful summary of the extent of purchasing sharing, alternatively called switching, in the
market.  Also, we are doing this to create ‘expected’ purchase duplications for each combination of
brands.  These ‘expected’ duplications are useful to identify exceptions – brands that are possibly
‘partitioned’, with higher than expected duplications or lower than expected duplications.          9
We know from our previous graph that the average duplication for a brand is around 1.1 its penetration,
except for one brand - which would be somewhat higher.  
Using the formula on the previous page, we produce a duplication coefficient of 1.1.  
Question 5:  This method produces a figure of 1.1.  When we used the scatterplot method we also got
1.1.  Now, interpet what the figure of 1.1 means.  
We can now use this 1.1 figure to create ‘expected’ duplications for each brand.  What this means is,
we can say ‘given the overall amount of sharing in the market and the size of this brand, we expect X
percent of any other brand’s buyers to also buy brand Z’.  
The way we do this is to multiply the penetration for each brand by the purchase duplication coefficient
which gives us the ‘expected’ duplication for each brand.  Do this now.  Then compute the difference
between the actual average duplication and the expected duplication.  Refer to screen shot 5 if you need
to.  
Screen shot 5: Expected duplications
 
        10
The outcome is shown in Table 2.  
 
Table 2:  Purchase Duplications showing ‘expected’ duplications from the
duplication coefficient
 
    
Penetrations  30  28  28  26  21  21  19  19  17  15  14  12  11
                                
      
% also listening to …….
  
 
 
                        
Genre  Network  Brand
Penetration  
(% buying at
least once)
More
FM
Nation
al
Radio  Other
Classi
c Hits
Newst
alk ZB
The
Rock
Solid
Gold
The
Edge  ZM
The
Breeze
Radio
Sport
Radio
Live
Radio
Pacific
      
                          Music  RW  More FM  30    13  27  40  13  34  21  29  23  14  11  11
12
Talk  RNZ  National
Radio
28  14    46  16  23  10  11  7  7  8  16  16
9
Music/Tal
k
  Other  28  27  46    27  34  34  26  28  40  30  30  19
35
Music  TRN  Classic Hits  26  48  17  27    18  30  28  20  18  22  19  9
13
Talk  TRN  Newstalk
ZB
21  19  29  34  21    9  17  21  12  24  20  13
19
Music  RW  The Rock  21  49  12  34  36  9    29  31  28  13  14  10
9
Music  RW  Solid Gold  19  35  16  26  39  19  34    24  17  20  17  13
9
Music  RW  The Edge  19  47  11  28  28  24  35  24    33  16  13  12
17
Music  TRN  ZM  17  40  11  40  27  15  35  18  36    11  15  13
9
Music  RW  The Breeze  15  27  15  32  37  33  19  25  19  13    16  9
14
Talk  TRN  Radio Sport  14  26  32  30  35  32  22  23  18  18  18    18
26
Talk  RW  Radio Live  12  26  36  19  19  23  18  20  19  19  12  20  
20
Talk  RW
Radio
Pacific  11  34  24  35  31  38  18  17  29  15  20  34  24
 
                                
Average Duplications
20  33  22  32  30  23  25  22  23  20  17  19  14  16
    
                          
Duplication coefficient  1.1
                        
Expected Duplication
 
34
32
32  29  24  24  21  21  19  17  16  14  12
Expected - Average
  
-1  -10  0  0  0  1  0  2  1  0  3  0  4
                                
 
We can now read down each column and compare the actual duplications to the expected figures.  For
example, the expected duplication for More FM is 34.  This means we expect 34% of the listeners of
any other station to also listen to More FM.  Cast your eye down each column now and compare the
actuals to the expected figures.  
But generally the model describes the data well, with a correlation between average duplication and
expected duplications of r = 0.881.  If you want to check the correlation, click on the “=” icon next to
                                                
1   The Mean Absolute Deviation is 12% when we compute the difference between actual duplication and estimated
duplication for every brand pair.             11
your formula bar, select “correll” from the drop-down menu, select your average duplications as array
1 and expected duplications as array 2.  
 
Deviations
We can also see, by looking down each column, that there are considerable differences in the
duplications in many of them.  For example, the duplicated listeners of the music stations with music
stations are around 20 to 50 percent, whereas the proportions of music listeners who also listen to talk
stations are around the 9 to 17 percent level.  These differences in duplications within a column suggest
the market exhibits some ‘partitioning’ or clustering of brands that compete more intensely with others. 
This is because each column represents the proportions of each brand's buyers that also buy brand X. 
Our ‘default’ expectation is that these proportions should be all about the same when we look down
each column, once the data is ordered by the size of the brand.  
Question 6:  Why might we normally expect (in a market with no marked functional differences
between the brands) that the duplications down each column are approximately the same? 
Given that we are analysing a broad ‘market’ with at least two identifiably different product types, we
may well expect some partitioning.  This suggests that our first-stage model that simply orders all
brands by size is inadequate, because it results in a fair amount of error between our estimated
duplications and the actual duplications.  Also, one station is not fitting that well to our overall model. 
Further adjustments need to be made to accommodate these structural features of the market. 
Therefore we re-order the table according to product type – biggest first, and then in order from the
largest to smallest brand within each product type.  In other words, have the music stations in order of
size, then the talk stations in order of size.  
Do this now.  
Then calculate the average of the duplications for each of the four groups of duplications (e.g. the
average of the music -> music duplications, the average of the talk -> talk duplications and so on –
these total to 2 x 2 = four combinations).  Insert these into an appropriate cell under each group of
duplications.          12
Screen shot 6:  Groups of duplications
 
 
The result should look like Table 3.          13
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
        14
Table 3:  Purchase Duplications ordered by size within product groups
                              
      
% also
listening
to …….
                  
 
 
 
Music
            
Talk
            Penetration  
(% buying
at least
once)
More
FM
Classic
Hits
The
Rock
Solid
Gold
The
Edge
ZM
The
Breeze  
National
Radio
Newstalk
ZB
Radio
Sport
Radio
Live
Radio
Pacific
    
                          Music  More FM  30
 
40  34  21  29  23  14
 
13  13  11  11  12
  Classic
Hits
26
48
 
30  28  20  18  22
 
17  18  19  9  13
  The Rock  21
49  36
 
29  31  28  13
 
12  9  14  10  9
  Solid
Gold
19
35  39  34
 
24  17  20
 
16  19  17  13  9
  The Edge  19
47  28  35  24
 
33  16
 
11  24  13  12  17
  ZM  17
40  27  35  18  36
 
11
 
11  15  15  13  9
  The
Breeze
15
27  37  19  25  19  13
  
15  33  16  9  14
                             
             28                14  
                               
                               
  Talk  National
Radio
28
14  16  10  11  7  7  8
  
23  16  16  9
  Newstalk
ZB
21
19  21  9  17  21  12  24
 
29
 
20  13  19
  Radio
Sport
14
26  35  22  23  18  18  18
 
32  32
 
18  26
  Radio
Live
12
26  19  18  20  19  19  12
 
36  23  20
 
20
  Radio
Pacific  11
34  31  18  17  29  15  20
 
24  38  34  24
 
 
                            
        
19
            
24
  
                              
 
Overall
Average*
    
24
            
17
  
  
* So the overall average under ‘Music’ is the average of the purchase duplications for the two product groups: 28 and 19.  In other words,
on average 28% listeners of any type of music station will also listen to other music station; 19% on average will also listen to talk
stations.  
The structure of the market according to product type becomes more apparent from this re-ordering. 
This is most obvious when we look at the duplications for the proportions of music listeners who also
listen to other music stations, compared to the proportions of music listeners who listen to talk stations.
The music -> music proportions are around 11 to 40 percent with an average of 28, whereas the music -
> talk proportions have an average of 14 precent – just on half.  
Question 7:  Interpret the point raised above.  What is the significance of the higher duplication
proportions in the music -> music cells compared to the music -> talk cells and the talk -> music cells?
The next way of summarising the data is to now calculate the ‘duplication coefficient’ for each of the
product types.  We have two product types and we want to know what the relationship is between
penetration and duplication for each of these product types, and with each other product type.  So we
need four duplication coefficients, for example music -> music; talk -> talk; and so on.          15
How to do this:
Construct duplication coefficients for each of the product types in the table.  For example, the
duplication coefficient for music ->music2 is: average duplications for music -> music divided by
average penetration for the music stations.  Where we want to know the duplication coefficient across
product types, for example music -> talk we use the average duplication for music listeners who also
listened to talk stations, divided by the average penetration for the talk stations.  
 
Screen shot 7:  Purchase Duplication Coefficients for each Product type
 
 
                                                
2  The use of this “->” symbol is my shorthand way of saying, the proportion of X buyers who also buy other X, or Y, etc.          16
The results are shown below.  
Table 5 Duplication coefficients for each product type 
    Duplication coefficient a -> b   
 
 
  Average
Penetration 
(% buying at least
once)
Music  Talk     
Music  21  1.3  0.8     
Talk  17  0.9  1.4     
AVERAGE  19        
 
In other words, the proportion of music station listeners who will also listen to talk stations is estimated
to be 0.8 times the average penetration for talk station listeners.  
Question 8:  Interpet the meaning of the figure of 1.4 under the ‘Talk’ column.   
 
Model Fit  
By calculating duplication coefficients for each product-type pair (music -> music, talk -> talk and so
on) we effectively have constructed a ‘model’ of this market and can use it to generate estimated
duplications for each brand-pair.  Comparing these model-estimated figures to the actual duplications
results in a mean absolute deviation of approximately 5 percentage points.  Stated more simply, our
model estimates the expected duplications to within 5 percentage points of the actual figure, on
average.  So our slightly more complicated model has enabled us to create quite accurate ‘predicted’ or
‘estimated’ purchase duplications based on the size of the brand, as well as catering to the heightened
levels of competition between some of the product types.  The original model based simply on ordering
by size had an average error of 12 percentage points.  
 
Question 9:  Imagine you are the marketing manager for The Radio Network.  You are considering
launching a new radio station called Radio Hauraki, and you could readily launch it as either a Music,
or Talk station.  Which options would you choose?  To keep the issue simple, assume the margins and
profitability are the same in each sub-market.  Thanks to. Professor Malcolm Wright (Massey University) for the
original question.          17
Summary
In summary we can say:
1.  Overall the proportion of buyers of one brand (of any product type – music or talk) who
buy another brand (of any type) is mainly a function of the size of that other brand.    
2.  That said there is distinct partitioning between some of the different product types. 
Listeners of one genre are much more likely to listen to other stations of the same genre
compared to other genres given the size of those other genres.  
3.  The structure of the market can be put into somewhat more precise numerical terms.  For
example, the proportion of music listeners who also listen to talk stations is around 0.8
times the penetration levels of those talk stations.  This compares to the figure for the
proportion of music listeners who will also listen to music stations as being around 1.3
times the penetration level of the music stations.  
4.  In terms of understanding the extent to which a brand of music or talk station will gain or
lose listeners to other stations, a music station will share listeners with other music
stations   more than would be expected, given their size.  
        18
 
Overall the managerial implications of this analysis are that:  
1.  This approach helps the manager to understand how a market ‘works’ in terms of which other
brands – and which other product types they share customers (or more correctly, customer
purchases/listen to) with over time.  
 
Methodological note
Note that there is an argument that we cannot always infer the directness of competition from simply
examining brand switching (e.g. Lattin and McAlister; Ratneshwar and Shocker, see list over the page). 
The principal reason for this is that data on switching ignores the impact of occasion – that is, the need
that may be being satisfied by the brand on that occasion.  For example, suppose a listener listens to a
talk station at home and a music station on the way to work.  One could argue these two stations are not
competing as substitutes for each other, because they are listened to for different reasons.  (Note also this
is an argument that is similar to the stated limitations of consumer panel data – namely that because the data represent
households, there is the danger of misinterpreting brand switching as an indicator of competition because the brand switch
may reflect purchases for different members of the household).   
However, in this case I believe that inferring competition purely from switching is reasonably valid. 
Here we are viewing competition as the extent to which buyers of one brand, or product type also buy
other brands/products that (a) share the same broad product characteristics; (b) to which from our own
consumption behaviour we know are consumed for approximately the same sorts of reasons; and (c)
can be access at the push of a button!          19
 
Selected Reference list: Competitive Market Structure.  This list is not meant to be exhaustive.  
“Repeat Buying – Facts Theory and Applications” by Andrew Ehrenberg.  Available as Vol. 5, Journal of Empirical
Generalisations in Marketing Science 2000.  See .  Among many other contributions, this text outlines
the ‘duplication of purchase law’ – that purchase sharing generally falls in-line with brand size unless there are clear
functional differences between the brands.  
“Understanding Brand Performance Measures” by Ehrenberg, Uncles and Goodhardt.  Journal of Business Research Vol.
57, 2004.  This paper provides an overview of the fundamental patterns in buyer behaviour arising from the ‘Dirichlet’
model.  It further re-inforces the ubiquity of Dirichlet-type patterns, one of which is the duplication of purchase law.  
“A Probabilistic Model for Testing Hypothesized Hierarchical Market Structures” by Grover and Dillon, Marketing Science
Vol. 4, 4, 1985.  This paper tests various structures for the ground and instant coffee market (which are analysed separately). 
It shows how switching in this market is structured according to functional differences in the product (caffeine content,
formulation).  
“Diversity in analysing brand-switching tables: The car challenge” by Colombo, Ehrenberg and Sabavala, Canadian Journal
of Marketing Research Vol. 19, 2000.  This paper shows how different forms of analysis may be used for contingency-table
brand switching data.  It finds support for the proposition that switching is proportional to share, moderated by functional
differences between the brands.  
“Using a variety-seeking model to identify substitute and complementary relationships among competing products” by
Lattin and McAlister, Journal of Marketing Research Vol. 27, August 1985.  This paper discusses how inferences about
brand competition derived from brand-switching may be confounded by consumer variety seeking.  It develops a model to
mitigate this problem.  The model is, however, very complex.  
“Competitive Market Structures: A Subset Selection Approach” by Kannan and Sanchez, Management Science Vol 40, 11,
1994.  This paper analyses two consumer good markets, namely coffee and flavoured crackers.  It develops an approach for
analysing competitive structures and testing for variety seeking.  It also features a very clear pictorial approach for
presenting the results for a partitioned market.  
“Substitution in Use and the Role of Usage Context in Product Category Structures” by Ratneshwar and Shocker, Journal of
Marketing Research Vol. 28, August 2001.  The paper examines how the common vs distinctive features of products
interact with usage situations to affect substitutability in use.  Among other findings, the paper concludes that common
attributes have a stronger association with similarity than do common usages (p. 286).  For (my own) example: a person
might use chocolate or popcorn to eat in a movie (similar usage) but there is little commonality in the attributes of the two
products, so they are not seen as similar.  
“Does the Duplication of Viewing Law apply to Radio Listening?” by Gavin Lees and Malcolm Wright European Journal
of Marketing. Vol 47 Iss 3 (Date Online 28/5/2012. This paper examines the long standing interest in the duplication of
audience between media vehicles and finds that the Duplication of Listening does broadly follow the Duplication of
Viewing Law. Contrary to popular belief most of the deviations from a mass market are not due to micro-formats (e.g.
classic rock) but rather are explained by a broad partitioning of the market between ‘talk’ and ‘music’ segments, although
they do identify a unique station that still deviates from its parent partition.
 
 

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