EXAMPLE  PREV Function
This example describes how you can use the PREV function to analyze data that is available in a window in rows before the current one.
Functions:
Item  Description 

PREV Function  Extracts the value from a column that is a specified number of rows before the current value. 
IF Function  The 
The following dataset contains orders for multiple customers over a period of a few days, listed in no particular order. You want to assess how order size has changed for each customer over time and to provide offers to your customers based on changes in order volume.
Source:
Date  CustId  OrderId  OrderValue 

1/4/16  C001  Ord002  500 
1/11/16  C003  Ord005  200 
1/20/16  C002  Ord007  300 
1/21/16  C003  Ord008  400 
1/4/16  C001  Ord001  100 
1/7/16  C002  Ord003  600 
1/8/16  C003  Ord004  700 
1/21/16  C002  Ord009  200 
1/15/16  C001  Ord006  900 
Transformation:
When the data is loaded into the Transformer page, you can use the PREV
function to gather the order values for the previous two orders into a new column. The trick is to order the window
transform by the date and group it by customer:
Transformation Name 


Parameter: Formulas  PREV(OrderValue, 1) 
Parameter: Group by  CustId 
Parameter: Order by  Date 
Transformation Name 


Parameter: Formulas  PREV(OrderValue, 2) 
Parameter: Group by  CustId 
Parameter: Order by  Date 
Transformation Name 


Parameter: Option  Manual rename 
Parameter: Column  window 
Parameter: New column name  'OrderValue_1' 
Transformation Name 


Parameter: Option  Manual rename 
Parameter: Column  window1 
Parameter: New column name  'OrderValue_2' 
You should now have the following columns in your dataset: Date
, CustId
, OrderId
, OrderValue
, OrderValue_1
, OrderValue_2
.
The two new columns represent the previous order and the order before that, respectively. Now, each row contains the current order (OrderValue
) as well as the previous orders. Now, you want to take the following customer actions:
If the current order is more than 20% greater than the sum of the two previous orders, send a rebate.
If the current order is less than 90% of the sum of the two previous orders, send a coupon.
Otherwise, send a holiday card.
To address the first one, you might add the following, which uses the IF
function to test the value of the current order compared to the previous ones:
Transformation Name 


Parameter: Formula type  Single row formula 
Parameter: Formula  IF(OrderValue >= (1.2 * (OrderValue_1 + OrderValue_2)), 'send rebate', 'no action') 
Parameter: New column name  'CustomerAction' 
You can now see which customers are due a rebate. Now, edit the above and replace it with the following, which addresses the second condition. If neither condition is valid, then the result is send holiday card
.
Transformation Name 


Parameter: Formula type  Single row formula 
Parameter: Formula  IF(OrderValue >= (1.2 * (OrderValue_1 + OrderValue_2)), 'send rebate', IF(OrderValue <= (1.2 * (OrderValue_1 + OrderValue_2)), 'send coupon', 'send holiday card')) 
Parameter: New column name  'CustomerAction' 
Results:
After you delete the OrderValue_1
and OrderValue_2
columns, your dataset should look like the following. Since the transformations with PREV
functions grouped by CustId
, the order of records has changed.
Date  CustId  OrderId  OrderValue  CustomerAction 

1/4/16  C001  Ord001  100  send rebate 
1/7/16  C001  Ord002  500  send rebate 
1/15/16  C001  Ord006  900  send rebate 
1/8/16  C003  Ord004  700  send rebate 
1/11/16  C003  Ord005  200  send rebate 
1/21/16  C003  Ord008  400  send coupon 
1/7/16  C002  Ord003  600  send rebate 
1/20/16  C002  Ord007  300  send rebate 
1/21/16  C002  Ord009  200  send coupon 