Perform Content Gap Analysis and Topic Cluster Optimization

Importance of Content GAP Analysis:

This analysis involves identifying keywords that are relevant to the website’s niche and comparing the website’s existing content with that of its competitors. The goal is to identify keywords and topics that the website’s competitors are ranking for, but the website is not. This analysis helps to identify content gaps, which can be filled with new content that targets those keywords and topics.

Step 1: Find you focus keywords and competitive related page

Focus Keyword: child custody lawyer colorado springs

Your domain: https://boallaw.com/family-law/child-custody-support-modifications/

Competitor 1: https://www.chamberlandlaw.com/child-custody-and-visitation.html

Competitor 2: https://www.harrisfamilylaw.com/protect-the-children/child-custody/

Competitor 3: https://coslawyer.com/low-cost-custody-kit/

Step2: Put all the Step1 detail in the below website and then click to compare

Here is the below list for related keywords

https://docs.google.com/spreadsheets/d/1LkWiSZ6ewp2i57e1bXYOo2pyjpHPKFqupnRvX7_LdFA/edit?usp=sharing

Note: Now we need to select those key which are related to our website niche.

Here are some related keywords
child support attorney
child custody attorney
child custody lawyers
colorado child support
child support lawyers
child support colorado
child support attorney near me
affordable child custody lawyers
colorado child custody laws

Recommendation: So, we need to write a fresh SEO friendly content for this page. This content help to improve their website’s search engine rankings, increase traffic, and ultimately drive conversions.

Topic Clusters Optimization Using Python

Using this Python tool we can create a topic cluster from a large number of random keyword list, using those topic cluster we can write blogs.

Step 1:

Create a folder on desktop –

Create a TXT file on that folder and rename it to “keywords”

Now go to any keyword research tool and extract large number of keywords from a broad topic.

For example –

We have searched a broad topic on Seranking keyword research tool –

And the tool provided a large number of random keywords, now we have group them to create topic cluster –

Got 1149 keywords –

Now copy those keywords and paste on that previously created TXT file. And save it.

Step 2:

import csv

import numpy as np

from sklearn.cluster import AffinityPropagation

from sklearn.feature_extraction.text import TfidfVectorizer

# Read keywords from text file

with open(“keywords.txt”, “r”) as f:

keywords = f.read().splitlines()

# Create a Tf-idf representation of the keywords

vectorizer = TfidfVectorizer()

X = vectorizer.fit_transform(keywords)

# Perform Affinity Propagation clustering

af = AffinityPropagation().fit(X)

cluster_centers_indices = af.cluster_centers_indices_

labels = af.labels_

# Get the number of clusters found

n_clusters = len(cluster_centers_indices)

# Write the clusters to a csv file

with open(“clusters.csv”, “w”, newline=””) as f:

writer = csv.writer(f)

writer.writerow([“Cluster”, “Keyword”])

for i in range(n_clusters):

cluster_keywords = [keywords[j] for j in range(len(labels)) if labels[j] == i]

if cluster_keywords:

for keyword in cluster_keywords:

writer.writerow([i, keyword])

else:

writer.writerow([i, “”])

Save the code as python on that folder –

Now open anaconda prompt –

And go to that folder using cd command –

Now install those PIPs –

pip install scikit-learn

pip install numpy

Now run the python code –

python topic.py

After running this code, an excel file will be exported to your folder –

Result:

Open the clusters file –

As we can see the tool has analysed the keyword list and created a group for topic cluster.

Recommendation:

We will create blogs using those keyword group/cluster.

And interlink them to optimize topic cluster.

How To Perform Content Gap Analysis and Topic Cluster Optimization

Importance of Content GAP Analysis:

Topic Clusters Optimization Using Python

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