Purpose

Python script to promote campaigns to a new Publisher Bidding Strategy if they meet minimum spend and ROAS requirements.

To Elaborate

The Python script solves the problem of automatically promoting campaigns to a new Publisher Bidding Strategy based on their performance. The key business rules are:

• If a campaign’s ROAS (Return on Ad Spend) is greater than or equal to a minimum threshold, it should be promoted.
• If a campaign’s spend is greater than or equal to a minimum threshold, it should be promoted.
• The new Publisher Bidding Strategy to be assigned is ‘TargetCpa’.
• The new Publisher Target CPA should be set to the campaign’s Cost per Conversion.

Walking Through the Code

1. Set the minimum ROAS and the target Publisher Bidding Strategy as configurable parameters.
2. Define column constants for the input and output dataframes.
3. Create temporary columns in the input dataframe to store the new Publisher Bidding Strategy and Target CPA values.
4. Assign the new Publisher Bidding Strategy as ‘TargetCpa’ to campaigns that meet the minimum ROAS threshold.
5. Set the Target CPA value as the campaign’s Cost per Conversion.
6. Identify the campaigns that have changed their Publisher Bidding Strategy.
7. If there are changed campaigns, print a table of the campaigns with their changed Publisher Bidding Strategy.
8. Create an output dataframe with the columns: Account, Campaign, Publisher Bidding Strategy, and Publisher Target CPA.
9. Rename the temporary columns to match the output dataframe columns.
10. Print the output dataframe.
11. If there are no changed campaigns, print “Empty outputDf” and create an empty output dataframe.

Vitals

• Script ID : 237
• Client ID / Customer ID: 1306922587 / 2
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Publisher Target CPA, Publisher Bid Strategy
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2023-07-13 11:21
• Last Updated by Byron Porter on 2023-12-07 21:03

> See it in Action

Python Code

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
#
# Promote to new Publisher Bidding Strategy if meet minimum:
#  - Spend
#  - ROAS
#
#
# Author: Byron Porter
# Date: 2023-07-13
#

########### Configurable Params - START ##########

MIN_ROAS = 1
PUB_STRATEGY_TARGET = 'TargetCpa'

########### Configurable Params - END ###########


RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER_BIDSTRATEGY = 'Publisher Bid Strategy'
RPT_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
RPT_COL_ROAS = 'ROAS'
RPT_COL_CPC = 'Cost/Conv. $'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_PUBLISHER_BIDSTRATEGY = 'Publisher Bid Strategy'
BULK_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'

# temp columns for new values
TMP_PUB_STRATEGY = RPT_COL_PUBLISHER_BIDSTRATEGY + '_'
inputDf[TMP_PUB_STRATEGY] = np.nan

TMP_TARGETCPA = RPT_COL_PUBLISHER_TARGETCPA + '_'
inputDf[TMP_TARGETCPA] = np.nan

# if both Spend and ROAS meet mininum threshold, switch to new pub strategy

inputDf.loc[(inputDf[RPT_COL_ROAS] >= MIN_ROAS), \
             TMP_PUB_STRATEGY \
           ] = PUB_STRATEGY_TARGET

inputDf[TMP_TARGETCPA] = inputDf[RPT_COL_CPC]

# find changed campaigns
changed = (inputDf[TMP_PUB_STRATEGY].notnull()) & (inputDf[RPT_COL_PUBLISHER_BIDSTRATEGY] != inputDf[TMP_PUB_STRATEGY])

if sum(changed) > 0:

    print("== Campaigns with Changed Pub Strategy ==", tableize(inputDf.loc[changed]))

    # only select changed rows
    cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_PUB_STRATEGY, TMP_TARGETCPA]
    outputDf = inputDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                        TMP_PUB_STRATEGY: BULK_COL_PUBLISHER_BIDSTRATEGY, \
                        TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
                    })

    print("outputDf", tableize(outputDf))
    
else:
    
  print("Empty outputDf")
  outputDf = outputDf.iloc[0:0]

Post generated on 2024-05-15 07:44:05 GMT