Understanding Keyword Cannibalization in SEO: Why It’s Harmful and How to Avoid It

Search Engine Optimization (SEO) is the art and science of aligning your website with the algorithms of search engines to gain higher rankings and more visibility. Keywords play a central role in this process, acting as the lighthouses that guide users to your content. However, mismanagement of these keywords can lead to a phenomenon known as ‘keyword cannibalization.’ This term may sound dramatic, but its impact on your SEO efforts can be equally significant.

What is Keyword Cannibalization?

Keyword cannibalization occurs when multiple pages on a website target the same or very similar keywords. Instead of one page consolidating the potential ranking power and authority, several pages compete against each other. This not only confuses search engines but also splits the click-through rate (CTR), conversions, and page authority among the competing pages.

Why is Keyword Cannibalization Bad for SEO?

1. Diluted Page Authority: Instead of one strong page, you have multiple weaker ones. Search engines like Google value authoritative pages, and when the authority is spread thin, none of the pages may rank as highly as a single, more focused page would.
2. User Experience Issues: Visitors may not find the most relevant or comprehensive content if it’s spread across multiple pages. This can lead to higher bounce rates and lower user engagement.
3. Wasted Crawl Budget: Search engine bots have a ‘crawl budget’ for each website, which is the number of pages they’ll crawl at a given time. If multiple pages are very similar, it’s a waste of this budget and can prevent other, more unique content from being indexed.
4. Internal Competition: When multiple pages compete for the same keywords, they can outrank each other in a constant tug-of-war, resulting in unstable SERP positions.
5. Ineffective Content Strategy: If a website has multiple pages covering the same topic, it could indicate a lack of a coherent content strategy, which is essential for long-term SEO success.

How to Prevent Keyword Cannibalization

1. Perform a Keyword Audit: Regularly review your site’s content to ensure that you have a clear keyword strategy that assigns unique primary keywords to each page.
2. Use Canonical Tags: If you have similar content that serves different purposes, use canonical tags to tell search engines which page is the ‘master’ version.
3. Consolidate Content: Combine similar pages to create a single, comprehensive page. This often involves 301 redirects to ensure that all the ‘SEO juice’ from the old pages is transferred to the new, consolidated page.
4. Unique Value Propositions: Make sure each page provides unique value and isn’t just a rehashed version of another page on your site.
5. Consider the User Intent: Understand the different search intents behind keywords and create content that matches each unique intent.
6. Use a Strategic Internal Linking Structure: Guide search engines and users to your most important pages by using a strategic internal linking structure.
7. Monitor Your Analytics: Keep an eye on your page performance. If certain pages aren’t performing well, they might be cannibalizing each other.

Using Code to determine Sentences have the same semantic

Okay, so imagine you have a magic notebook that can read and understand words just like a person, but it’s super quick and smart because it’s actually a robot brain! Now, suppose you have a long list of titles for stories or essays, and you want to make sure they all sound different and special.

What I’ve done is I’ve taught this robot brain, which is really just a very clever program on the computer, to check the list and see if any of the titles are too much alike. It uses something called a “BERT model,” which is a way of helping computers understand language the way we do. Just like you know that “dog” and “puppy” are pretty similar, BERT helps the computer see that too.

The robot reads all the titles, thinks about them really hard, and then uses a kind of “similarity score” to tell us how much they are alike. If the score is really high, like getting an A on two different tests, it means the titles are very similar, and maybe we should change one to make it more unique.

I put all these instructions into a special code that the computer can understand, and whenever we get new titles, I can run this magic notebook to check them again. I promise to keep updating and teaching the robot brain new tricks to be even better at this job!

The way we see how alike the titles are is by using something like a game of “hot or cold” — the closer the titles are in meaning, the “hotter” they are, and the robot tells us that with a high score. I keep all the titles in a list, and the robot goes through them, pairing them up and giving each pair a “hot or cold” score. The “hottest” pairs, which means they’re super alike, are then saved in another list that I can look at later to help me decide if I need to make some titles more unique.

And that’s how I use my magic notebook to check the titles and make sure they’re all special!

import pandas as pd
from transformers import AutoTokenizer, AutoModel
import torch
from scipy.spatial.distance import cosine
import csv

# Load pre-trained BERT model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

# Read the CSV file containing sentences
input_csv = "/content/title.csv"
output_csv = "output.csv"

# Define a function to calculate cosine similarity
def calculate_cosine_similarity(sentence1, sentence2):
    inputs1 = tokenizer(sentence1, return_tensors="pt", padding=True, truncation=True)
    inputs2 = tokenizer(sentence2, return_tensors="pt", padding=True, truncation=True)

    with torch.no_grad():
        outputs1 = model(**inputs1)
        outputs2 = model(**inputs2)

    embedding1 = outputs1.last_hidden_state.mean(dim=1).squeeze().numpy()
    embedding2 = outputs2.last_hidden_state.mean(dim=1).squeeze().numpy()

    return 1 - cosine(embedding1, embedding2)

# Read the CSV file into a pandas DataFrame
df = pd.read_csv(input_csv)

# Create a list to store similar sentence pairs
similar_pairs = []

# Compare each sentence to each other
for i in range(len(df)):
    for j in range(i + 1, len(df)):
        sentence1 = df.iloc[i]["Blog Post Title"]
        sentence2 = df.iloc[j]["Blog Post Title"]
        similarity = calculate_cosine_similarity(sentence1, sentence2)

        if similarity >= 0.75:
            similar_pairs.append((sentence1, sentence2, similarity))

# Write the similar pairs to the output CSV file
with open(output_csv, mode="w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Sentence 1", "Sentence 2", "Similarity Score"])
    for pair in similar_pairs:
        writer.writerow([pair[0], pair[1], pair[2]])

print(f"Similar pairs saved to {output_csv}")

Example output

Conclusion

Keyword cannibalization is a sneaky issue that can undermine your SEO efforts. By being vigilant and proactive in content planning and site structure, you can avoid this pitfall and ensure that your website’s pages are working together harmoniously to boost your overall search presence. Remember, in SEO, it’s not just about getting traffic to your site; it’s about getting the right kind of traffic to the right place on your site. By eliminating keyword cannibalization, you can achieve just that.