Business

3 Tips to Fix Keyword Cannibalization from Real Guest Blogging

Published

5 years ago

July 23, 2020

Have you ever seen two or more of your web pages ranking for the same keyword? At first, this might seem like a good thing. After all, the more pages showing up on the first page, the more traffic you get, right? As any wise webmaster would tell you, however, this problem could be costing you potential customers and sales.

Real Guest Blogging specializes in content marketing, and one of the most common problems they encounter with their clients is keyword cannibalization. This happens when you target the same keyword across two or more pages on your website. Without an efficient content strategy in place, it proves challenging to prevent keyword cannibalization, particularly if you have a huge site with hundreds or even thousands of indexed pages.

It’s worth noting; however, that keyword cannibalization isn’t always a bad thing. But if you know that fixing the problem can improve your conversion rate and profit margins, then you should equip yourself with the right knowledge to stop your posts from cannibalizing each other once and for all.

1) Identify what pages are affected

Your first step is to identify what pages of your website are affected by keyword cannibalization. The easiest way to do this is to head over to Google and use the search query: “domain + keyword.” For instance, typing “bestwidgets.com + red widgets” will return a list of all indexed pages on your blog that contain this particular keyword or similar variations.

You need to sift through this list and identify what keywords these pages are ranking for. From there, it’s all a matter of finding out whether one or more pages rank for the same keyword.

2) De-optimize

When you see a page cannibalizing another, and you’re sure that your site will be better off without its ranking for the same keyword, then it’s time to start de-optimizing. The easiest thing to do is to remove the keyword in question from the page you want to withdraw from the search results pages. But this usually doesn’t cut it. In most cases, you have to look at the internal links pointing to that page and de-optimize the anchor texts as well.

To take things up a notch, you may want to use a backlink checker to see the external inbound links of the page. This entails reaching out to the webmasters of the sites that link to your page, and you can’t expect them to reply all the time. But it’s worth trying if you want the best shot at de-optimizing the page.

3) Merge similar content

Merging is the best route to take when two or more pages have very similar topics. Usually, you can combine these pages into a brand new page, allowing you to create a more comprehensive post that could have a higher ranking potential. Just remember to use a 301 redirect from the old pages to the new URL. Doing so will preserve “link juice,” giving the new page a better chance of ranking for your target keyword quickly.

4) Delete the page

Of course, you also have the option of merely deleting the pages that cannibalize others. Many webmasters might find this a bit extreme, and it can be not easy to delete a page knowing that you put in the time and effort to create it. But if you know that it hurts the chances of ranking another page, then it’s best to delete it. This applies in particular to pages that get little traffic and have no backlinks.

In Summary

Keyword cannibalization can be a severe problem for any website, yet it’s easy to forget about it as you focus on creating content and optimizing other website elements. Real Guest Blogging recommends preventing the issue before it even arises. Determine whether any of your existing pages already rank for the target keyword you have in mind. This helps you figure out whether it’s worth creating content from scratch or you’re better off updating an old post.

Business

MetaWorx: Building Full-Stack AI Teams, Not Just Automation

Published

1 week ago

June 6, 2025

Matthew Wiles

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way.

McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people.

“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”

The pod blueprint

When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:

An infrastructure architect to tame compute costs.
A data engineer to secure and shape pipelines.
An applied scientist to prototype models against live feedback loops.
An MLOps engineer to automate rollback and retraining.
A domain product lead translates forecasts into features users actually notice.
Ethics and compliance analysts to stress test outputs for bias and keep the audit.

The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever.

Recruiting for curiosity, not credentials

With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.

“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.

A culture of rapid experiments

Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.

That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.

How leaders can steal the playbook

Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:

Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.

Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.

Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.

Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.

Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.

Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.

Character still scales faster than code

MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.

“Tools level out,” Kite says. “Culture compounds.”

The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.

And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.