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crafting-man · 2 years ago

Text

https://www.etsy.com/your/shops/me/stats/listings/1476517881?date_range=this_month&channel=etsy-retail

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themepluginpro · 4 years ago

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Download ZF WordPress Category Search on Codecanyon

Description ZF WordPress Category Search :

Download ZF WordPress Category Search. The theme releases on Tuesday 21st October 2014 By The author zufusion on Codecanyon. It’s uses with author,auto suggest,custom post type,date range,dropdown category,easy digital downloads,multiple taxonomies,price range,radio category,tab category search,tab slider category,woocommerce attributes,woocommerce search,wordpress category search. Item Title: ZF WordPress Category Search Category: wordpress/ecommerce Price: $15 Author: zufusion Published Date: Tuesday 21st October 2014 03:02:13 AM More Info / DownloadDemo

ZF WordPress Category Search is a WordPress advanced search box, the ability display categories as tabs/radio/dropdown with advanced fields, support WooCommerce, Easy Digital Downloads, custom post type, taxonomies.

You can search by search categories, tags, taxonomies, authors, date range, sort, order by …

Otherwise, you can also search multiple taxonomies of custom post type. These easily to really refine your searches.

Key Features:

Tab slider category

Add icon popover for tab icon

Tab alignment

Radio category search

Dropdown category search

Auto suggest like google

Support WooCommerce category search, WordPress category search

Support Easy Digital Downloads, custom post type

Drap vs drop taxonomies to display

Search multiple taxonomies, author, price range, date range, order

Translation ready

Widget vs Shortcode ready

Easy to setup and customize

Support dark vs light skin

Cross-browser support

Change Log:

02.10.2019 - Version 2.6 * Work with WordPress 5.2

25.05.2015 - Version 2.5 * Fixed can't open advanced box when category box is empty * Fixed doesn't respond to the tags, categories of post in advanced box * Change default value of min count selected to 0 instead of 3 (only dropdown mode)

25.03.2015 - Version 2.4 * Added match type taxonomy values (categories) on setting page * Allows show open advanced button inside or outside input query * Show advanced button with icon or text or both * Fixed dropdown mode doesn't show categories * Fixed conflict with user pro plugin * Fixed long category name for dropdown mode

23.03.2015 - Version 2.3 . this version you need to save setting page again * Support search form template * Added taxonomy checkbox field * Put Advanced box inside the popup * Unlimited Level Sub Categories

02.02.2015 - Version 2.2 * Fixed menu disappear after using search * Fixed insert icon for Easy Digital Downloads

28.01.2015 - Version 2.1 * Support Easy Digital Downloads * Fixed radio css

04.11.2014 - Version 2.0 * Added auto suggest like google. * Support custom post type, multiple taxonomies * Drap vs drop taxonomies * Moved all options to setting page. * Added some hooks function for the developer.

More Info / DownloadDemo #WordPress #Category #Search

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uphamprojects · 2 years ago

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Kwargs for insurance claims reports

More trees for my forest.Property Insurance/Claims Company│├── Policyholder Summary Report│ ├── kwargs: policyholder_id, date_range│ ├── formulas: COUNT(policy_id), SUM(premium_amount)│ └── returns: total_policies, total_premiums_paid│├── New Policies Issued Report│ ├── kwargs: date_range, policy_type│ ├── formulas: COUNT(policy_id)│ └── returns: new_policies_issued│├── Policy Lapse and…

View On WordPress

#bi #business #businesstheory #career #cram notes #formulas

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cyber-security-news · 4 years ago

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How to Read DMARC Reports?

DMARC in Brief

DMARC (Domain-based Message Authentication, Reporting, and Conformance) is an email authentication standard or protocol that determines whether an email is authentic or not. Its process involves combining SPF and DKIM records to decide the authentication status of an email. It provides transparency of the sending sources of all emails sent from your domain and also ensures better email deliverability. Most importantly, however, it safeguards your domain against malicious cyberattacks like spoofing, phishing, and impersonation.

For detailed information on Domain-based Message Authentication, Reporting and Conformance, read more on What is DMARC?

If you’re wondering what SPF and DKIM protocols are, head to What is SPF? and What is DKIM? to read more.

What Is a DMARC Report?

While DMARC safeguards against several email-based cyberattacks, it also acts as a feedback mechanism that helps the domain owner track security and deliverability issues by generating regular reports. DMARC reports are authentication results containing data on a domain’s usage. They intimate the domain owner of malicious sources and protocol errors. They are a goldmine of data that can be used to strengthen a domain’s security and take action against malicious sources while minimizing errors and deliverability issues.

DMARC reports are periodically sent in XML format to the domain owner’s email address. However, they are highly technical and can be confusing for the average user to interpret. Essentially, DMARC reports are of two types:

DMARC Aggregate Reports

DMARC Forensic Reports

To get a more detailed idea about each of these reports, head to RUA and DMARC Aggregate Reports and RUF and DMARC Forensic Reports.

This is what a raw DMARC report looks like:

<?xml version=”1.0″ encoding=”UTF-8″ ?>

<report_metadata>

<org_name>google.com</org_name>

<email>[email protected]</email>

<extra_contact_info>http://google.com/dmarc/support</extra_contact_info>

<report_id>8293631894893125362</report_id>

<date_range>

</date_range>

</report_metadata>

<policy_published>

<domain>yourdomain.com</domain>

none

</policy_published>

<row>

<source_ip>302.0.214.308</source_ip>

<policy_evaluated>

</policy_evaluated>

</row>

<header_from>yourdomain.com</header_from>

</identifiers>

<auth_results>

<dkim>

<domain>yourdomain.com</domain>

<human_result></human_result>

</dkim>

<spf>

<domain>yourdomain.com</domain>

</spf>

</auth_results>

</record>

</feedback>

Did that make any sense? No, right?

Allow us to break it down for you a little!

ISP/Email Service Provider

<?xml version=”1.0″ encoding=”UTF-8″ ?>

<report_metadata>

<org_name>google.com</org_name>

<email>[email protected]</email>

<extra_contact_info>http://google.com/dmarc/supp

Report ID

<report_id>8293631894893125362</report_id>

Date range

<date_range>

</date_range>

DMARC record

<policy_published>

<domain>yourdomain.com</domain>

none

</policy_published>

IP address

<source_ip>302.0.214.308</source_ip>

Authentication overview

<policy_evaluated>

</policy_evaluated>

From:Domain

<header_from>yourdomain.com</header_from>

DKIM authentication report

<dkim>

<domain>yourdomain.com</domain>

<human_result></human_result>

</dkim>

SPF authentication report

<spf>

<domain>yourdomain.com</domain>

</spf>

The DMARC report can now be easily interpreted even by the average user!

Original source: https://www.evernote.com/shard/s373/sh/585004c2-e125-e6a7-118a-44062e3d669a/01aa3b2af08a207f77c40314612b5cb9

#dmarc #dmarc record #spf #dkim

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wonbindatascience · 5 years ago

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Pandas Tutorial

10 minutes to pandas

https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html

import

import numpy as np

import pandas as pd

Object creation

s = pd.Series([1, 3, 5, np.nan, 6, 8])

dates = pd.date_range('20130101', periods=6) df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))

df2 = pd.DataFrame({'A': 1., 'B': pd.Timestamp('20130102'), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(["test", "train", "test", "train"]), 'F': 'foo'})

Viewing data

df.head()

df.tail(3)

df.index

df.columns

df.to_numpy()

df.describe()

df.T

Transposing your data

df.sort_index(axis=1, ascending=False)

Sorting by an axis

df.sort_values(by='B')

Selection

https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html

We recommend the optimized pandas data access methods, .at, .iat, .loc and .iloc.

Not recommended

Selecting columns

df['A']

df[['A', ��B’]]

Selecting rows

df[:3]

df['20130102':'20130104']

Recommended

Quick intro

df.loc[(for row)]

df.loc[(for row), (for column)]

df.iloc[(for row)]

df.iloc[(for row), (for column)]

Selection by label

pandas.DataFrame.loc

Access a group of rows and columns by label(s) or a boolean array.

e.g.

df.loc[dates[0]]

df.loc[:, ['A', 'B']]

df3.loc['20200606':'20200608', 'B':'C']

pandas.DataFrame.at

Access a single value for a row/column label pair.

Similar to loc, but faster

e.g.

df.at[’20200606’, ‘A’]

Selection by position

pandas.DataFrame.iloc

Purely integer-location based indexing for selection by position.

e.g.

df.iloc[3]

df.iloc[3:5, 0:2]

pandas.DataFrame.iat

Access a single value for a row/column pair by integer position.

Similar to iloc, but faster

e.g.

df.iat[3,2]

Boolean indexing

What does indexing means?

https://www.geeksforgeeks.org/indexing-and-selecting-data-with-pandas/

Indexing in pandas means simply selecting particular rows and columns of data from a DataFrame.

df[df > 0]

df[df['A'] > 0]

df2[df2['E'].isin(['two', 'four'])]

The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses

Setting

Setting a new column

It automatically aligns the data by the indexes.

df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) s1 = pd.Series([1, 2, 3, 4, 5, 6], index= dates) df['F'] = s1

Setting values by label

df3.at['20200605','A'] = 0

df.loc[:, 'D'] = np.array([5] * len(df))

Setting values by position

df.iat[0, 1] = 0

Setting values with where operation

df2[df2 > 0] = -df2

Missing data

pandas primarily uses the value np.nan to represent missing data.

To drop any rows that have missing data

df1.dropna(how='any')

Filling missing data

df1.fillna(value=5)

values = {'A': 0, 'B': 1, 'C': 2, 'D': 3} df1.fillna(value=values)

To get the boolean mask where values are nan.

pd.isna(df1)

Operations

Stats

df.mean()

df.mean(1)

Same operation on the other axis

Apply

df.apply(np.cumsum)

df.apply(lambda x: x.max() - x.min())

Histogramming

s = pd.Series(np.random.randint(0, 7, size=10)) s.value_counts()

String Methods

s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) s.str.lower()

Merge

Concat

https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging

Concatenating pandas objects together with concat()

pd.concat(df1, df2, df3)

<=> df1.append([df2 , df3])

A useful shortcut to concat() are the append() instance methods on Series and DataFrame.

Join

https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging-join

SQL style merges

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=True, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

pd.merge(left=df1, right=df2, how=‘left’, on='key')

DataFrame.merge(self, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes='_x', '_y', copy=True, indicator=False, validate=None)

df1(right=df2, how=‘inner’, on='key')

Grouping

https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#groupby

“group by” involving one of the following steps

Splitting the data into groups based on some criteria

Applying a function to each group independently

Combining the results into a data structure

df.groupby('A').sum()

df.groupby(['A', 'B']).sum()

Reshaping

Stack

Pivot tables

Time series

https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries

pandas.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs)

years = pd.period_range('2010-01-01', '2015-01-01', freq='A') years.asfreq('M', how='S')

Categoricals

https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#categorical

df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']}) df["grade"] = df["raw_grade"].astype("category") df["grade"].cat.categories = ["very good", "good", "very bad"]

Series.cat()

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.cat.html

Accessor object for categorical properties of the Series values.

s.cat.categories

s.cat.categories = list('abc')

s.cat.rename_categories( 'cba’) s.cat.rename_categories({'a': 'A', ’b’:’B’, 'c': 'C'}) s.cat.rename_categories(lambda x: x.upper())

and so on

Plotting

https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization

import matplotlib.pyplot as plt

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) ts.cumsum().plot()

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D']) plt.figure() df.cumsum().plot() plt.legend(loc='best')

Getting data in/out

df.to_csv('foo.csv')

pd.read_csv('foo.csv')

Gotchas

Intro to data structures

https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dsintro

Series

pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

Series is a one-dimensional labeled array(ndarray) capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.)

numpy.ndarray

An array object represents a multidimensional, homogeneous array of fixed-size items.

DataFrame

pandas.DataFrame(data=None, index: Optional[Collection] = None, columns: Optional[Collection] = None, dtype: Union[str, numpy.dtype, ExtensionDtype, None] = None, copy: bool = False)

Parameters

data

ndarray (structured or homogeneous), Iterable, dict, or DataFrame

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.

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t-baba · 8 years ago

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Pandas: The Swiss Army Knife for Your Data, Part 2

This is part two of a two-part tutorial about Pandas, the amazing Python data analytics toolkit.

In part one, we covered the basic data types of Pandas: the series and the data frame. We imported and exported data, selected subsets of data, worked with metadata, and sorted the data.

In this part, we'll continue our journey and deal with missing data, data manipulation, data merging, data grouping, time series, and plotting.

Dealing With Missing Values

One of the strongest points of pandas is its handling of missing values. It will not just crash and burn in the presence of missing data. When data is missing, pandas replaces it with numpy's np.nan (not a number), and it doesn't participate in any computation.

Let's reindex our data frame, adding more rows and columns, but without any new data. To make it interesting, we'll populate some values.

>>> df = pd.DataFrame(np.random.randn(5,2), index=index, columns=['a','b']) >>> new_index = df.index.append(pd.Index(['six'])) >>> new_columns = list(df.columns) + ['c'] >>> df = df.reindex(index=new_index, columns=new_columns) >>> df.loc['three'].c = 3 >>> df.loc['four'].c = 4 >>> df a b c one -0.042172 0.374922 NaN two -0.689523 1.411403 NaN three 0.332707 0.307561 3.0 four 0.426519 -0.425181 4.0 five -0.161095 -0.849932 NaN six NaN NaN NaN

Note that df.index.append() returns a new index and doesn't modify the existing index. Also, df.reindex() returns a new data frame that I assign back to the df variable.

At this point, our data frame has six rows. The last row is all NaNs, and all other rows except the third and the fourth have NaN in the "c" column. What can you do with missing data? Here are options:

Keep it (but it will not participate in computations).

Drop it (the result of the computation will not contain the missing data).

Replace it with a default value.

Keep the missing data --------------------- >>> df *= 2 >>> df a b c one -0.084345 0.749845 NaN two -1.379046 2.822806 NaN three 0.665414 0.615123 6.0 four 0.853037 -0.850362 8.0 five -0.322190 -1.699864 NaN six NaN NaN NaN Drop rows with missing data --------------------------- >>> df.dropna() a b c three 0.665414 0.615123 6.0 four 0.853037 -0.850362 8.0 Replace with default value -------------------------- >>> df.fillna(5) a b c one -0.084345 0.749845 5.0 two -1.379046 2.822806 5.0 three 0.665414 0.615123 6.0 four 0.853037 -0.850362 8.0 five -0.322190 -1.699864 5.0 six 5.000000 5.000000 5.0

If you just want to check if you have missing data in your data frame, use the isnull() method. This returns a boolean mask of your dataframe, which is True for missing values and False elsewhere.

>>> df.isnull() a b c one False False True two False False True three False False False four False False False five False False True six True True True

Manipulating Your Data

When you have a data frame, you often need to perform operations on the data. Let's start with a new data frame that has four rows and three columns of random integers between 1 and 9 (inclusive).

>>> df = pd.DataFrame(np.random.randint(1, 10, size=(4, 3)), columns=['a','b', 'c']) >>> df a b c 0 1 3 3 1 8 9 2 2 8 1 5 3 4 6 1

Now, you can start working on the data. Let's sum up all the columns and assign the result to the last row, and then sum all the rows (dimension 1) and assign to the last column:

>>> df.loc[3] = df.sum() >>> df a b c 0 1 3 3 1 8 9 2 2 8 1 5 3 21 19 11 >>> df.c = df.sum(1) >>> df a b c 0 1 3 7 1 8 9 19 2 8 1 14 3 21 19 51

You can also perform operations on the entire data frame. Here is an example of subtracting 3 from each and every cell:

>>> df -= 3 >>> df a b c 0 -2 0 4 1 5 6 16 2 5 -2 11 3 18 16 48

For total control, you can apply arbitrary functions:

>>> df.apply(lambda x: x ** 2 + 5 * x - 4) a b c 0 -10 -4 32 1 46 62 332 2 46 -10 172 3 410 332 2540

Merging Data

Another common scenario when working with data frames is combining and merging data frames (and series) together. Pandas, as usual, gives you different options. Let's create another data frame and explore the various options.

>>> df2 = df // 3 >>> df2 a b c 0 -1 0 1 1 1 2 5 2 1 -1 3 3 6 5 16

Concat

When using pd.concat, pandas simply concatenates all the rows of the provided parts in order. There is no alignment of indexes. See in the following example how duplicate index values are created:

>>> pd.concat([df, df2]) a b c 0 -2 0 4 1 5 6 16 2 5 -2 11 3 18 16 48 0 -1 0 1 1 1 2 5 2 1 -1 3 3 6 5 16

You can also concatenate columns by using the axis=1 argument:

>>> pd.concat([df[:2], df2], axis=1) a b c a b c 0 -2.0 0.0 4.0 -1 0 1 1 5.0 6.0 16.0 1 2 5 2 NaN NaN NaN 1 -1 3 3 NaN NaN NaN 6 5 16

Note that because the first data frame (I used only two rows) didn't have as many rows, the missing values were automatically populated with NaNs, which changed those column types from int to float.

It's possible to concatenate any number of data frames in one call.

Merge

The merge function behaves in a similar way to SQL join. It merges all the columns from rows that have similar keys. Note that it operates on two data frames only:

>>> df = pd.DataFrame(dict(key=['start', 'finish'],x=[4, 8])) >>> df key x 0 start 4 1 finish 8 >>> df2 = pd.DataFrame(dict(key=['start', 'finish'],y=[2, 18])) >>> df2 key y 0 start 2 1 finish 18 >>> pd.merge(df, df2, on='key') key x y 0 start 4 2 1 finish 8 18

Append

The data frame's append() method is a little shortcut. It functionally behaves like concat(), but saves some key strokes.

>>> df key x 0 start 4 1 finish 8 Appending one row using the append method() ------------------------------------------- >>> df.append(dict(key='middle', x=9), ignore_index=True) key x 0 start 4 1 finish 8 2 middle 9 Appending one row using the concat() ------------------------------------------- >>> pd.concat([df, pd.DataFrame(dict(key='middle', x=[9]))], ignore_index=True) key x 0 start 4 1 finish 8 2 middle 9

Grouping Your Data

Here is a data frame that contains the members and ages of two families: the Smiths and the Joneses. You can use the groupby() method to group data by last name and find information at the family level like the sum of ages and the mean age:

df = pd.DataFrame( dict(first='John Jim Jenny Jill Jack'.split(), last='Smith Jones Jones Smith Smith'.split(), age=[11, 13, 22, 44, 65])) >>> df.groupby('last').sum() age last Jones 35 Smith 120 >>> df.groupby('last').mean() age last Jones 17.5 Smith 40.0

Time Series

A lot of important data is time series data. Pandas has strong support for time series data starting with data ranges, going through localization and time conversion, and all the way to sophisticated frequency-based resampling.

The date_range() function can generate sequences of datetimes. Here is an example of generating a six-week period starting on 1 January 2017 using the UTC time zone.

>>> weeks = pd.date_range(start='1/1/2017', periods=6, freq='W', tz='UTC') >>> weeks DatetimeIndex(['2017-01-01', '2017-01-08', '2017-01-15', '2017-01-22', '2017-01-29', '2017-02-05'], dtype='datetime64[ns, UTC]', freq='W-SUN')

Adding a timestamp to your data frames, either as data column or as the index, is great for organizing and grouping your data by time. It also allows resampling. Here is an example of resampling every minute data as five-minute aggregations.

>>> minutes = pd.date_range(start='1/1/2017', periods=10, freq='1Min', tz='UTC') >>> ts = pd.Series(np.random.randn(len(minutes)), minutes) >>> ts 2017-01-01 00:00:00+00:00 1.866913 2017-01-01 00:01:00+00:00 2.157201 2017-01-01 00:02:00+00:00 -0.439932 2017-01-01 00:03:00+00:00 0.777944 2017-01-01 00:04:00+00:00 0.755624 2017-01-01 00:05:00+00:00 -2.150276 2017-01-01 00:06:00+00:00 3.352880 2017-01-01 00:07:00+00:00 -1.657432 2017-01-01 00:08:00+00:00 -0.144666 2017-01-01 00:09:00+00:00 -0.667059 Freq: T, dtype: float64 >>> ts.resample('5Min').mean() 2017-01-01 00:00:00+00:00 1.023550 2017-01-01 00:05:00+00:00 -0.253311

Plotting

Pandas supports plotting with matplotlib. Make sure it's installed: pip install matplotlib. To generate a plot, you can call the plot() of a series or a data frame. There are many options to control the plot, but the defaults work for simple visualization purposes. Here is how to generate a line graph and save it to a PDF file.

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2017', periods=1000)) ts = ts.cumsum() ax = ts.plot() fig = ax.get_figure() fig.savefig('plot.pdf')

Note that on macOS, Python must be installed as a framework for plotting with Pandas.

Conclusion

Pandas is a very broad data analytics framework. It has a simple object model with the concepts of series and data frame and a wealth of built-in functionality. You can compose and mix pandas functions and your own algorithms.

Additionally, don’t hesitate to see what we have available for sale and for study in the marketplace, and don't hesitate to ask any questions and provide your valuable feedback using the feed below.

Data importing and exporting in pandas are very extensive too and ensure that you can integrate it easily into existing systems. If you're doing any data processing in Python, pandas belongs in your toolbox.

by Gigi Sayfan via Envato Tuts+ Code http://ift.tt/2gaPZ24

#CSS3 #HTML5 Branding #HTML5 Weekly #HTMLl5 demos #Javascript #Javascript Branding #Javascript Weekly

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andreacaskey · 6 years ago

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Google Ads script: How to automatically apply bid modifiers

This script which will save HOURS of your time. That’s not hyperbole, I promise.

Remember the in-market audiences bid modifier script I released last year?

This expanded version of the script automatically applies modifiers for device, location, in-market and remarketing audiences based on performance.

You can set campaign filters, decide which types of modifiers you want to adjust, set minimum impressions, conversions and cost filters, and weight the modifiers it applies according to adjustable volume thresholds.

Honestly, what’s not to love?

What’s new?

To recap, the old script looks at campaigns’ CPC over a given time range and sets bid modifiers to each of the campaign-level in-market audiences based on performance. If there are no campaign-level audiences, the tool will apply bid modifiers to all in-market audiences at ad group-level.

This one does the same, but includes device, location and remarketing audiences!

And where the old script only looked at minimum impressions as a threshold, this one has extra filters to choose from: minimum cost and minimum number of conversions. You can also weight the modifiers if the volume is low.

Just like the old script, it does calculated modifiers based on the following formula: Modifier = Entity CPA / Audience CPA, where ‘Entity’ is the campaign or ad group.

How to use it

As always, copy the script below and paste it in the scripts section of Google Ads.

You’ll need to set all the following variables correctly to make sure the script does exactly what you want it to do.

To start, use CAMPAIGN_NAME_DOES_NOT_CONTAIN and CAMPAIGN_NAME_CONTAINS to exclude or include specific campaigns.

Next up, your targeting options! These are pretty self-explanatory, set them to true to enable them: DO_DEVICES, DO_LOCATIONS, DO_IN_MARKET_AUDIENCES, DO_OTHER_AUDIENCES.

Use DATE_RANGE to determine the time frame for the script to look at, using one of these options.

Set MINIMUM_IMPRESSIONS, MINIMUM_CONVERSIONS, and MINIMUM_COST to the minimum number of each you want a campaign of ad group to have to be considered.

To be on the safe side, use MIN_BID_MODIFIER and MAX_BID_MODIFIER to define the upper and lower bounds for the bid modifiers to fall into.

If you would like to weight the modifiers based on the number of conversions, use CAMPAIGN_BID_MODIFIER_WEIGHTS or ADGROUP_BID_MODIFIER_WEIGHTS

The post Google Ads script: How to automatically apply bid modifiers appeared first on Search Engine Land.

Google Ads script: How to automatically apply bid modifiers published first on https://likesandfollowersclub.weebly.com/

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lindarifenews · 6 years ago