Direct Marketing Applications

Direct Marketing Applications

Post by Shaun William » Mon, 21 Jul 2003 19:56:00



Hi everyone,

I am very interested to use SPSS for a number of direct marketing type
analysis.  I have brought a couple of textbooks that talk through stats
and/or spss but none appear to use examples drawn from direct marketing.

Does anyone know of any books/articales which specifically describe direct
marketing applications of spss?

One of the things i would like to do is calculate the sample size for a
number of mailing cells, the data would look like this;

Marketing code (say 5123)
Expected response rate (say 5.5%)
Allowable margin of error (say 0.5%)

These are column headinsg (i.e. variables) and I would have dozons rows, one
for each marketing code.  I know there a way of calculating the required
sample size using these two varaibles, but I am not sure how to do it in
spss.

Thanks
Shaun

 
 
 

Direct Marketing Applications

Post by Rich Ulric » Sat, 26 Jul 2003 05:20:41


On Sun, 20 Jul 2003 11:56:00 +0100, "Shaun Williams"


> Hi everyone,

> I am very interested to use SPSS for a number of direct marketing type
> analysis.  I have brought a couple of textbooks that talk through stats
> and/or spss but none appear to use examples drawn from direct marketing.

> Does anyone know of any books/articales which specifically describe direct
> marketing applications of spss?

> One of the things i would like to do is calculate the sample size for a
> number of mailing cells, the data would look like this;

> Marketing code (say 5123)
> Expected response rate (say 5.5%)
> Allowable margin of error (say 0.5%)

> These are column headinsg (i.e. variables) and I would have dozons rows, one
> for each marketing code.  I know there a way of calculating the required
> sample size using these two varaibles, but I am not sure how to do it in
> spss.

I think the SPSS readers know SPSS and stats, but very little
marketing  -- so none of us recognize your question.
Even if SPSS has something built in for that, we won't know it.
Is your allowable "margin of error"  also something that
could be describe as 0.5/5.5  or 9%, as the "relative"
margin  of error?

Here's a stab at delineating the answer without being
sure of the question.

The distribution of responses, when it is a small fraction, can
be treated as a Poisson count.  

For Poisson,  the square-root  is 'normalizing'  such that
the result has a SD of 0.5.    
 - Or,  SQRT(X) +/-  1.0   is the 95% CI .
 - Or, you can name the sequential square-numbers to get
the 95% CI  in the original metric.

Thus:
If you expect  100  actual responses, as a small fraction, the
95%  CI  is 81  to 121.     (That is a relative margin of error
of +/-     20%.)

If you expect 400  responses,  the CI  is  (381,421).

 - If those definitions in the above  match what you want,
it is simple algebra to extend work out the formulas for the
total Ns.

I imagine that you if you derive a formula  from the above,
you should regard it  as a crude estimate, until you see how
it behaves in practice.
The prediction makes it easier to spot divergences, but don't
be surprised if it performs badly, until you have monitored a few.

 - How many changes are there in the mail-content
and in the audience   between one mailing and the next?

--

http://www.pitt.edu/~wpilib/index.html
"Taxes are the price we pay for civilization."  Justice Holmes.

 
 
 

Direct Marketing Applications

Post by Matt » Wed, 30 Jul 2003 06:34:15


Hi Rich,

I will try and find the formula for you (it's more of a 'rule of thumb'
that we have tucked away in a spreadsheet somewhere...).

Could you send some more details on what you want to measure? i.e. what
is the universe size/definition(e.g. sector/s of population, age groups,
existing customers, etc.??? It helps to know in order to work out the
sample.

If, for instance, you only wanted the opinions of males vs. females, you
would require a small sample. If you wanted the opinions of
males/females across various age categories (e.g. 18-24, 25-34, etc.),
the sample size would grow.

I hope I'm on the right track...

Matthew.


> On Sun, 20 Jul 2003 11:56:00 +0100, "Shaun Williams"

>>Hi everyone,

>>I am very interested to use SPSS for a number of direct marketing type
>>analysis.  I have brought a couple of textbooks that talk through stats
>>and/or spss but none appear to use examples drawn from direct marketing.

>>Does anyone know of any books/articales which specifically describe direct
>>marketing applications of spss?

>>One of the things i would like to do is calculate the sample size for a
>>number of mailing cells, the data would look like this;

>>Marketing code (say 5123)
>>Expected response rate (say 5.5%)
>>Allowable margin of error (say 0.5%)

>>These are column headinsg (i.e. variables) and I would have dozons rows, one
>>for each marketing code.  I know there a way of calculating the required
>>sample size using these two varaibles, but I am not sure how to do it in
>>spss.

> I think the SPSS readers know SPSS and stats, but very little
> marketing  -- so none of us recognize your question.
> Even if SPSS has something built in for that, we won't know it.
> Is your allowable "margin of error"  also something that
> could be describe as 0.5/5.5  or 9%, as the "relative"
> margin  of error?

> Here's a stab at delineating the answer without being
> sure of the question.

> The distribution of responses, when it is a small fraction, can
> be treated as a Poisson count.  

> For Poisson,  the square-root  is 'normalizing'  such that
> the result has a SD of 0.5.    
>  - Or,  SQRT(X) +/-  1.0   is the 95% CI .
>  - Or, you can name the sequential square-numbers to get
> the 95% CI  in the original metric.

> Thus:
> If you expect  100  actual responses, as a small fraction, the
> 95%  CI  is 81  to 121.     (That is a relative margin of error
> of +/-     20%.)

> If you expect 400  responses,  the CI  is  (381,421).

>  - If those definitions in the above  match what you want,
> it is simple algebra to extend work out the formulas for the
> total Ns.

> I imagine that you if you derive a formula  from the above,
> you should regard it  as a crude estimate, until you see how
> it behaves in practice.
> The prediction makes it easier to spot divergences, but don't
> be surprised if it performs badly, until you have monitored a few.

>  - How many changes are there in the mail-content
> and in the audience   between one mailing and the next?

 
 
 

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