# 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 |