(Python) Project: Generating Keywords for Search Campaigns

Generating Keywords for Search Campaigns

Automatically generate keywords for a search engine marketing campaign using Python to send website visitors to the right landing page.

When structuring a search engine marketing account, a key task is mapping the right keywords to the right ads and making sure they send users to the right landing pages.

In this project, you'll practice how to automatically generate keywords for a search engine marketing campaign using Python to send website visitors to the right landing page.

Imagine working for a digital marketing agency, and the agency is approached by a massive online furniture retailer. They want to test your skills at creating large campaigns for all of their website. You are tasked with creating a prototype set of keywords for search campaigns for their sofas section. The client says that they want you to generate keywords for the following products:

sofas
convertible sofas
love seats
recliners
sofa beds

The client is a low-cost retailer, offering many promotions and discounts. You will need to focus on such keywords. You will also need to move away from luxury keywords and topics, as you are targeting price-sensitive customers. Because they are going to be tight on budget, it would be good to focus on a tightly targeted set of keywords and make sure they are all set to exact and phrase match.

Based on the brief above you will first need to generate a list of words, that together with the products given above would make for good keywords. Here are some examples:

Products: sofas, recliners
Words: buy, prices

The resulting keywords: 'buy sofas', 'sofas buy', 'buy recliners', 'recliners buy', 'prices sofas', 'sofas prices', 'prices recliners', 'recliners prices'.

As a final result, you want to have a DataFrame that looks like this:

Campaign	Ad Group	Keyword	Criterion Type
Campaign1	AdGroup_1	keyword 1a	Exact
Campaign1	AdGroup_1	keyword 1b	Exact
Campaign1	AdGroup_2	keyword 2a	Exact

What keywords would help the client sell the listed products through search campaigns?

Save at least 60 unique keywords as a DataFrame called keywords_df and a CSV file called keywords.csv.
Both of these should contain four columns:
- Ad Group: containing the product names
- Keyword: containing the product and keyword combinations, combined in both directions (word + product, and product + word)
- Campaign: with the value SEM_Sofas in every row
- Criterion Type: with the value Exact in every row.

import pandas as pd

1. Pair words with products

Create a list of six to ten strings that contain words you think would work well with the given five furniture products: sofas, convertible sofas, love seats, recliners, and sofa beds. Loop through these words in the list and the list containing the furniture products and append each product and keyword combination (word + product, and product + word) into a new keywords list.

# List of words to pair with products
words = ['buy', 'price', 'discount', 'promotion', 'promo', 'shop']

# Print list of words
print(words)

products = ['sofas', 'convertible sofas', 'love seats', 'recliners', 'sofa beds']

keywords_list = []

for product in products:
    # Loop through words
    for word in words:
        # Append combinations
        keywords_list.append([product, product + ' ' + word])
        keywords_list.append([product, word + ' ' + product])
        
# Inspect keyword list
print(keywords_list)

2. Convert the list into a DataFrame with descriptive column names

Use your newly created keywords list and convert this into a DataFrame called keywords_df and the columns "Ad Group" and "Keyword".

# Create a DataFrame from list
keywords_df = pd.DataFrame(keywords_list, columns=['Ad Group', 'Keyword'])

print(keywords_df, '\n\n', keywords_df.dtypes)

3. Add a campaign and match type column

Add the Campaign and Criterion Type columns with the values "SEM_Sofas" and "Exact" in every row respectively, to your newly created keywords_df DataFrame.

# Add a campaign column
keywords_df['Campaign'] = 'SEM_Sofas'

# Add a criterion type column
keywords_df['Criterion Type'] = 'Exact'

print(keywords_df)

4. Save a DataFrame as a CSV file

Save keywords_df to a CSV file named 'keywords.csv' using the pandas to_csv() method. Exclude the DataFrame index in the saved file by specifying index=False.

The index parameter here refers to the column name or columnar position of an index in your dataset to be included as an index in the saved data. However, if you do not wish to save an explicit index to your dataset, you can set the index parameter to False