1. The brief
Imagine working for a digital marketing agency, and the agency is approached by a massive online retailer of furniture. They want to test our skills at creating large campaigns for all of their website. We are tasked with creating a prototype set of keywords for search campaigns for their sofas section. The client says that they want us to generate keywords for the following products:
- sofas
- convertible sofas
- love seats
- recliners
- sofa beds
The brief: The client is generally a low-cost retailer, offering many promotions and discounts. We will need to focus on such keywords. We will also need to move away from luxury keywords and topics, as we are targeting price-sensitive customers. Because we 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 we 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, we 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 1a | Phrase |
Campaign1 | AdGroup_1 | keyword 1b | Exact |
Campaign1 | AdGroup_1 | keyword 1b | Phrase |
Campaign1 | AdGroup_2 | keyword 2a | Exact |
Campaign1 | AdGroup_2 | keyword 2a | Phrase |
The first step is to come up with a list of words that users might use to express their desire in buying low-cost sofas.
# List of words to pair with products
words = ['buy','prices','discount','promo','shop','promotion']
print(words)
# Print list of words
# ... YOUR CODE FOR TASK 1 ...
2. Combine the words with the product names
Imagining all the possible combinations of keywords can be stressful! But not for us, because we are keyword ninjas! We know how to translate campaign briefs into Python data structures and can imagine the resulting DataFrames that we need to create.
Now that we have brainstormed the words that work well with the brief that we received, it is now time to combine them with the product names to generate meaningful search keywords. We want to combine every word with every product once before, and once after, as seen in the example above.
As a quick reminder, for the product 'recliners' and the words 'buy' and 'price' for example, we would want to generate the following combinations:
buy recliners
recliners buy
price recliners
recliners price
…
and so on for all the words and products that we have.
products = ['sofas', 'convertible sofas', 'love seats', 'recliners', 'sofa beds']
# Create an empty list
keywords_list = []
# Loop through products
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
from pprint import pprint
pprint(keywords_list)
3. Convert the list of lists into a DataFrame
Now we want to convert this list of lists into a DataFrame so we can easily manipulate it and manage the final output.
# Load library
import pandas as pd
# Create a DataFrame from list
keywords_df = pd.DataFrame.from_records(keywords_list)
# Print the keywords DataFrame to explore it
print(keywords_df)
keywords_df.head()
4. Rename the columns of the DataFrame
Before we can upload this table of keywords, we will need to give the columns meaningful names. If we inspect the DataFrame we just created above, we can see that the columns are currently named 0
and 1
. Ad Group
(example: "sofas") and Keyword
(example: "sofas buy") are much more appropriate names.