Script 899: CC SBA Budget Pacing
Purpose
The Python script optimizes campaign budget allocation to minimize lost impression share due to budget constraints by considering historical spend, remaining budget, and other factors.
To Elaborate
The script addresses the challenge of efficiently allocating campaign budgets to minimize lost impression share due to budget constraints. It considers various factors such as the remaining budget for each strategy group, the number of weekdays left in the month, historical spending patterns, and the potential for future spending. The script also ensures that only active campaigns with recent spending and valid program dates are considered for budget allocation. By calculating a budget allocation ratio and capping potential spending, the script distributes the remaining budget across campaigns. It also calculates the recommended daily budget for each campaign, ensuring it meets a minimum threshold. The script ultimately aims to optimize budget pacing and ensure compliance with pacing targets.
Walking Through the Code
- Configuration and Setup
- The script begins by defining a configurable parameter,
MINIMUM_DAILY_BUDGET, which sets the minimum daily budget for campaigns. - It sets up various column names used throughout the script for data manipulation and analysis.
- The script begins by defining a configurable parameter,
- Data Preparation
- The script coerces the ‘Program End Date’ column into a date type and sets the index of the input DataFrame to the ‘CC SBA Strategy’ column.
- It groups the data by strategy to facilitate further calculations.
- Full-Potential Spend Calculation
- The script calculates the full potential spend by adjusting historical spend based on the lost impression share due to budget constraints.
- Data Aggregation
- It aggregates data by campaign and strategy, calculating metrics such as clicks, conversions, and month-to-date (MTD) spend.
- Campaign Filtering
- The script filters out inactive campaigns, those without recent spending, and those with expired program dates.
- Budget Allocation Ratio Calculation
- It calculates a budget allocation ratio by capping full potential spend and comparing it to total spend within the same strategy group.
- Remaining Budget Calculation
- The script calculates the remaining budget for each strategy group by subtracting MTD strategy spend from the SBA monthly budget.
- Budget Allocation
- It allocates the remaining budget to each campaign based on the calculated ratio and determines the recommended daily budget by dividing the allocated budget by the remaining business days in the month.
- Minimum Budget Enforcement
- The script ensures that the allocated budget meets the minimum daily budget requirement.
- Traffic Budget Calculation
- It identifies campaigns to traffic and adjusts their daily budgets accordingly.
- Pacing Compliance Calculation
- The script calculates pacing compliance percentages to ensure campaigns are on track to meet budget targets.
- Output Generation
- Finally, the script generates an output DataFrame containing updated budget information for campaigns with changes detected.
Vitals
- Script ID : 899
- Client ID / Customer ID: 1306923845 / 60269271
- Action Type: Bulk Upload
- Item Changed: Campaign
- Output Columns: Account, Campaign, Daily Budget, CC SBA Allocation, CC SBA Budget Pacing, CC SBA Rec. Daily Budget
- Linked Datasource: M1 Report
- Reference Datasource: None
- Owner: emerryfield@marinsoftware.com (emerryfield@marinsoftware.com)
- Created by emerryfield@marinsoftware.com on 2024-04-02 23:28
- Last Updated by ascott@marinsoftware.com on 2024-04-03 18:04
> 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
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
#
# SBA Campaign Budget Pacing - Minimize Lost IS (Budget)
#
# Allocates according to:
# * Remaining budget for each Strategy Group
# * Remaining weekdays in month
# * Historical spend and spend potential
# * Campaigns with spend in lookback period
# * Minimum daily budget
#
# Author: Michael S. Huang, Adam Scott
#
# Created: 2023-09-30
# Updated: 2024-02-10
#
##### Configurable Param #####
MINIMUM_DAILY_BUDGET = 10
##############################
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_CONV = 'Conv.'
RPT_COL_IMPR_SHARE = 'Impr. share %'
RPT_COL_LOST_IMPR_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_LOST_IMPR_SHARE_RANK = 'Lost Impr. Share (Rank) %'
RPT_COL_CC_SBA_STRATEGY = 'CC SBA Strategy'
RPT_COL_CC_SBA_CAMPAIGN_BUDGET = 'CC SBA Campaign Budget'
RPT_COL_CC_SBA_ALLOCATION = 'CC SBA Allocation'
RPT_COL_CC_SBA_REC_DAILY_BUDGET = 'CC SBA Rec. Daily Budget'
RPT_COL_CC_SBA_BUDGET_PACING = 'CC SBA Budget Pacing'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_PROGRAM_END_Date = 'Program End Date'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_CC_SBA_ALLOCATION = 'CC SBA Allocation'
BULK_COL_CC_SBA_BUDGET_PACING = 'CC SBA Budget Pacing'
BULK_COL_REC_DAILY_BUDGET_CC_SBA = 'CC SBA Rec. Daily Budget'
COL_SPEND_FULL_POTENTIAL = 'spend_lookback_full_potential'
COL_SPEND_FULL_POTENTIAL_CAPPED = 'spend_lookback_full_potential_capped'
COL_SPEND_MTD = 'spend_mtd'
COL_SBA_ALLOCATION_NEW_FLOAT = RPT_COL_CC_SBA_ALLOCATION + '_new_float'
COL_SBA_ALLOCATION_NEW = RPT_COL_CC_SBA_ALLOCATION + '_new'
COL_SBA_STRATEGY_BUDGET_REMAINING = 'SBA_Campaign_budget_remaining'
COL_SBA_BUDGET_PACING_NEW = RPT_COL_CC_SBA_BUDGET_PACING + '_new'
COL_BUDGET_REMAINING = 'budget_remaining'
COL_DAILY_BUDGET_NEW = RPT_COL_DAILY_BUDGET + '_new'
COL_REC_DAILY_BUDGET_NEW = RPT_COL_CC_SBA_REC_DAILY_BUDGET + '_new'
COL_DAYS_REMAINING= 'weekdays_remaining'
COL_DAYS_TOTAL= 'weekdays_total'
COL_PACING_CALC = 'pacing_calc'
outputDf[BULK_COL_DAILY_BUDGET] = "<<YOUR VALUE>>"
today = datetime.datetime.now(CLIENT_TIMEZONE).date()
print("inputDf shape", inputDf.shape)
print("inputDf dtypes", inputDf.dtypes)
# change back to percent string
if inputDf [RPT_COL_CC_SBA_ALLOCATION ].dtype == "float":
inputDf[RPT_COL_CC_SBA_ALLOCATION ] = round(inputDf[RPT_COL_CC_SBA_ALLOCATION ] * 100.0, 0).astype(str) + '%'
if inputDf[BULK_COL_CC_SBA_BUDGET_PACING].dtype == "float":
inputDf[BULK_COL_CC_SBA_BUDGET_PACING] = round(inputDf[BULK_COL_CC_SBA_BUDGET_PACING] * 100.0, 0).astype(str) + '%'
# coerce Program End Date into Date type
inputDf[RPT_COL_PROGRAM_END_Date] = pd.to_datetime(inputDf[RPT_COL_PROGRAM_END_Date], errors='coerce')
inputDf = inputDf.set_index([RPT_COL_CC_SBA_STRATEGY])
group_by_strategy = inputDf.groupby(RPT_COL_CC_SBA_STRATEGY)
# ## Calculate Full-Potential Spend
# * Adjust Historical Spend by _Lost Impression Share due to Budget
#
adj_ratio = 1 + (inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET] / (1 - inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET]))
inputDf[COL_SPEND_FULL_POTENTIAL] = round(inputDf[RPT_COL_PUB_COST] * adj_ratio, 2)
# ## Remove Date Segmentation
# * Calculate MTD Spend
# SUM Series with Date index and only includes current month
def current_month_sum(x):
x = x.sort_index()
mtd = x[ (x.index.month == today.month) & (x.values > 0)]
return mtd.sum()
groupby_cols = [ \
RPT_COL_CC_SBA_STRATEGY, \
RPT_COL_STRATEGY, \
RPT_COL_PUBLISHER_NAME, \
RPT_COL_ACCOUNT, \
RPT_COL_CAMPAIGN, \
]
agg_spec = {
RPT_COL_CAMPAIGN_STATUS: 'last', \
RPT_COL_DAILY_BUDGET: 'last', \
RPT_COL_CC_SBA_CAMPAIGN_BUDGET: 'last', \
RPT_COL_CC_SBA_ALLOCATION : 'last', \
RPT_COL_CC_SBA_REC_DAILY_BUDGET: 'last', \
RPT_COL_CC_SBA_BUDGET_PACING: 'last', \
RPT_COL_SBA_TRAFFIC: 'last', \
RPT_COL_CLICKS: 'sum', \
RPT_COL_CONV: 'sum', \
RPT_COL_PUB_COST: 'sum', \
COL_SPEND_MTD: current_month_sum, \
COL_SPEND_FULL_POTENTIAL: 'sum', \
RPT_COL_PROGRAM_END_Date: 'last', \
}
inputDf[COL_SPEND_MTD] = inputDf[RPT_COL_PUB_COST]
df_campaign_agg = inputDf.reset_index() \
.set_index(RPT_COL_DATE) \
.groupby(groupby_cols) \
.agg(agg_spec) \
.reset_index() \
.set_index(RPT_COL_CC_SBA_STRATEGY)
# ## Only allocate budget for recently trafficking campaigns
# * Exclude Campaigns that are:
# ** not ACTIVE
# ** without spend in lookback period
# ** Program Date is in the past
inactive_campaigns = (df_campaign_agg[RPT_COL_CAMPAIGN_STATUS] != 'Active') & (df_campaign_agg[RPT_COL_PUB_COST] == 0)
expired_campaigns = df_campaign_agg[RPT_COL_PROGRAM_END_Date].notnull() & (df_campaign_agg[RPT_COL_PROGRAM_END_Date] < pd.to_datetime(today))
df_campaign_agg = df_campaign_agg.loc[ ~(inactive_campaigns | expired_campaigns) ]
# ## Calculate Budget Allocation Ratio
# * Cap full potential spend at 2X (don't spend twice as much as before)
# * Compare full potential spend for each campaign to total spend within same Strategy budget group
df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] = df_campaign_agg \
.apply(lambda row: min(row[COL_SPEND_FULL_POTENTIAL], 2 * row[RPT_COL_PUB_COST]), axis=1)
# use transform to calculate sum for each Stratgey and make it available on every row
# note: no need to build aggregate DataFrame and JOIN back to original
df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] = 100.0 * \
df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] / \
df_campaign_agg.groupby(RPT_COL_CC_SBA_STRATEGY)[COL_SPEND_FULL_POTENTIAL_CAPPED].transform('sum')
df_campaign_agg[COL_SBA_ALLOCATION_NEW] = round(df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT],0).astype(str) + '%'
#
# ## Calculate Remaining Budget
# * For each Straetgy budget group, calculate how much Budget is left by substracting SBA Monthly budget from MTD Strategy spend
df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] = \
df_campaign_agg[RPT_COL_CC_SBA_CAMPAIGN_BUDGET] - \
df_campaign_agg.groupby(by=[RPT_COL_CC_SBA_STRATEGY])[COL_SPEND_MTD].sum()
# ## Allocate Budget
# * Allocate remaining budget to each campaign according to ratio calculated above
df_campaign_agg[COL_BUDGET_REMAINING] = round(df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] * df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] / 100.0, 1)
# ## Calculate SBA Daily Budget
#
# * Calcualte next day Daily Budget by dividing allocated budget by number of Business Days left in the current month
today_numpy = pd.to_datetime(today).to_numpy().astype('datetime64[D]')
next_month_start = (today_numpy + pd.offsets.BMonthBegin()).to_numpy().astype('datetime64[D]')
# for months ending on weekends, use max(1,x) to avoid dividing by zero
days_left = max(1, (next_month_start - today_numpy).astype('timedelta64[D]').astype(int))
df_campaign_agg[COL_DAYS_REMAINING] = days_left
df_campaign_agg[COL_REC_DAILY_BUDGET_NEW] = round(df_campaign_agg[COL_BUDGET_REMAINING] / days_left, 0)
# ### Apply Minimum Rule
# * Bump allocated budget above minimum
allocated_below_min = (df_campaign_agg[COL_REC_DAILY_BUDGET_NEW] < MINIMUM_DAILY_BUDGET)
df_campaign_agg.loc[allocated_below_min, COL_REC_DAILY_BUDGET_NEW] = MINIMUM_DAILY_BUDGET
# ### Traffic Budget
df_campaign_agg[COL_DAILY_BUDGET_NEW] = np.nan
# campaigns to traffic
to_traffic = df_campaign_agg[RPT_COL_SBA_TRAFFIC].notnull() & \
(df_campaign_agg[RPT_COL_SBA_TRAFFIC].astype(str).str.lower() == 'traffic')
print("Not weekend. Traffic count", to_traffic.sum())
# copy budgets over
df_campaign_agg[COL_DAILY_BUDGET_NEW] = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]
# then blank out budget for non-traffic campaigns
df_campaign_agg.loc[~to_traffic, COL_DAILY_BUDGET_NEW] = np.nan
# ## Calculate Salesforece Item ID -level Pacing compliance percentage. Ideally should be 100% each day.
# number of elapsed workdays
current_month_start = pd.to_datetime(today.replace(day=1)).to_numpy().astype('datetime64[D]')
total_days_in_month = (next_month_start - current_month_start).astype('timedelta64[D]').astype(int)
df_campaign_agg[COL_DAYS_TOTAL] = total_days_in_month
prorated_ratio = (total_days_in_month - days_left) / total_days_in_month
print("today", today)
print("current_month_start", current_month_start)
print("next_month_start", next_month_start)
print("weekdays_in_month", total_days_in_month)
print("weekdays_left", days_left)
print("prorated_ratio", prorated_ratio)
# divide MTD spend by prorated total budget
mask = df_campaign_agg[RPT_COL_CC_SBA_CAMPAIGN_BUDGET] > 0
df_campaign_agg[COL_PACING_CALC] = round(100.0 * \
df_campaign_agg.groupby(by=[RPT_COL_CC_SBA_STRATEGY])[COL_SPEND_MTD].sum() / \
(prorated_ratio * df_campaign_agg[RPT_COL_CC_SBA_CAMPAIGN_BUDGET]), \
0).astype(str) + '%'
df_campaign_agg.loc[mask, COL_SBA_BUDGET_PACING_NEW] = df_campaign_agg.loc[mask, COL_PACING_CALC]
# Debug DF with full details
df_strategy_budget = group_by_strategy[[RPT_COL_CC_SBA_CAMPAIGN_BUDGET]].transform('max').dropna().drop_duplicates()
print("Strategy budgets", df_strategy_budget.to_string())
# ## Generate outputDf
# Check for changes
changed = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW].notnull() & \
( \
(df_campaign_agg[RPT_COL_CC_SBA_REC_DAILY_BUDGET] != df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]) | \
(df_campaign_agg[RPT_COL_DAILY_BUDGET] != df_campaign_agg[COL_DAILY_BUDGET_NEW]) | \
(df_campaign_agg[RPT_COL_CC_SBA_ALLOCATION ] != df_campaign_agg[COL_SBA_ALLOCATION_NEW]) | \
(df_campaign_agg[RPT_COL_CC_SBA_BUDGET_PACING] != df_campaign_agg[COL_SBA_BUDGET_PACING_NEW]) \
)
print("Changed rows:", changed.sum())
# Debug
debugDf = df_campaign_agg.loc[changed] \
.reset_index() \
.sort_values(by=[RPT_COL_CC_SBA_STRATEGY, COL_DAILY_BUDGET_NEW, COL_REC_DAILY_BUDGET_NEW], ascending=False)
# print("debugDf", tableize(debugDf))
# Only emit output for changed campaigns
if changed.sum() > 0:
# construct outputDf
outputDf = df_campaign_agg.loc[changed, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, COL_DAILY_BUDGET_NEW, COL_REC_DAILY_BUDGET_NEW, COL_SBA_BUDGET_PACING_NEW, COL_SBA_ALLOCATION_NEW]] \
.copy() \
.rename(columns={ \
COL_DAILY_BUDGET_NEW: BULK_COL_DAILY_BUDGET, \
COL_REC_DAILY_BUDGET_NEW: BULK_COL_REC_DAILY_BUDGET_CC_SBA, \
COL_SBA_BUDGET_PACING_NEW: BULK_COL_CC_SBA_BUDGET_PACING, \
COL_SBA_ALLOCATION_NEW: BULK_COL_CC_SBA_ALLOCATION, \
}) \
.reset_index() \
.sort_values(by=[RPT_COL_CC_SBA_STRATEGY, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET_CC_SBA], ascending=False) \
.drop(RPT_COL_CC_SBA_STRATEGY, axis=1)
print("outputDf shape", outputDf.shape)
print("outputDf", tableize(outputDf.head()))
else:
print("No changes detected, returning an empty dataframe")
outputDf = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET_CC_SBA, BULK_COL_CC_SBA_BUDGET_PACING, BULK_COL_CC_SBA_ALLOCATION])##
Post generated on 2024-11-27 06:58:46 GMT