BASIC EXAMPLE OF THE USE OF STATISTICAL STUDIES

For our example, let's use a fictional firm that purchases a mailing list of prospective
leads from a third party. They then have their own in-house phone room of telephone 
operators call these leads to setup office appointments with their sales staff.

This list costs them $5,000 for 20,000 leads.
They have five operators which call those 20,000 leads and typically setup 500 appointments. 
Of all the appointments scheduled, 350 of the 500 appointments actually are kept by the 
leads.  Of the 350 appointments that show up, the sales staff turn 200 of them into customers. 
Customer revenue in this example is $110 per sale.

Let's boil these numbers down.
Cost per Lead		$ 0.25
Cost per Appointment	$14.29
Cost per Sale		$25.00
Gross Income		$22,000

Just with these numbers, we can calculate dozens of usable statistics but for this
example, we will focus on the Operators.

The telephone operators have no control over the cost of the names, how many names are 
purchased or how many the sales team actually sells.  They do have some effect however
with the number of appointments they make and how many of those appointments show up. So
we take these statistics:

Percentage of leads called (20,000) in respect to appointments scheduled (500): 0.025%
Percentage of appointments scheduled (500) in respect to appointments showed (350): 70%

In examining how the operators attempt to schedule appointments, we find that they are using
a phone script. This phone script greets the client, qualifies the client, offers a benefit
to the client and then attempts to a close on making an appointment. If an appointment is
made, they give standard directions to the office.

OK, let's play.  We might change something simple in the script like the greeting, maybe
alter the qualification method or add some benefits for them to mention.  Either way, we
would only change ONE thing about how they handle each lead. Over the next few thousand 
calls, we would examine the statistics again to see if that 0.025% has gone up or down 
or remained the same. 

At the same time, we might change the manner in which the directions are given to the 
customer or we might call to confirm the appointment the day before. We might even call those
no-shows back and ask to see if they got lost and would like to reschedule another 
appointment. In this manner, we attempt to raise the 70% show rate. Again, we only change 
ONE controllable function and see how it effects the statistic.

How might we play with sales? We might ask the sales staff what kind of objections they 
generally face and consolidate a list of ways to overcome those objections. The sales staff
might be given a new brochure or handout to give the client. But no matter what, we use the 
prior statistics to determine how any changes effect our statistics.

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