Travel & Tourism SEO Case Study: Using Markov Chains

Instead of a single algorithm, Google uses a layered architecture of machine learning systems, each responsible for understanding different aspects of a query or page. Some models focus on language comprehension, others on link trust, others on user behaviour. When Google updates search, it’s often changing how these systems integrate. Because they’re interconnected, even a small tweak can produce large changes across the results.

How I help London companies improve rankings Understanding User Navigation & Fix Revenue Leaks

The travel sector relies on emotional triggers and practical planning, and Google understands this deeply. Your pages perform best when they combine inspiration with clear details about destinations, local culture, transportation, and lodging. When visitors stay longer to explore itineraries or click through related guides, that engagement tells Google your content genuinely helps travellers make decisions — which boosts your visibility for competitive tourism queries.

1. Introduction

 UK-based holiday company offering low-cost package deals, cruises, and city breaks, with strong emphasis on ATOL/ABTA protection and flexible booking options.

Google’s ranking behaviour is the result of multiple AI engines running in parallel. BERT interprets meaning, RankBrain learns user satisfaction patterns, neural rankers score relevance, and link systems evaluate authority. These components influence one another constantly. So when Google rolls out an update, it usually means one model has been retrained or its weighting has shifted — and that adjustment alters the balance across the entire ranking pipeline.

When people refer to a Google update, what they’re really noticing is a shift in how Google’s family of AI models interact. There’s no single switch that controls search; instead, Google blends signals from machine learning models that understand meaning, intent, trust and quality. When any of these systems are recalibrated, the effects can spread unpredictably, causing rankings to rise or fall even if the website itself hasn’t changed.

Travel and Tourism is a very heavily competitive niche market and to break into this market with your website is very difficult indeed. Travel and tourism websites live or die by how efficiently they move visitors toward revenue pages — Search → Hotel → Booking → Checkout.  A Markov Chain model reveals how users flow through a site, which pages they abandon, and where internal links fail.

Search engine optimization (SEO) practitioners increasingly rely on Markov chain analysis because it models user navigation as a series of probabilistic transitions between pages, revealing the true flow of authority and engagement across a site. Unlike static link audits, a Markov chain captures the dynamic behavior of visitors — showing which pages act as hubs, which are dead ends, and how likely users are to reach conversion points. This matters because search engines reward sites that guide users smoothly toward valuable content, and wasted link equity trapped in compliance or peripheral pages can weaken rankings.

By applying Markov modeling, practitioners who’s services include this can quantify steady‑state probabilities (long‑run page importance), identify bottlenecks in the funnel, and strategically adjust internal linking to ensure that high‑value keywords like “holiday travel” are reinforced in the paths users actually take. In short, Markov chains transform SEO from guesswork into a mathematically grounded strategy for maximizing both visibility and conversions.

With the advent of AI calculating these page transitions becomes easier and produce accurate and informative results. Ask about our pricing options at TG Barker, we believe SEO pricing should be clear, fair, and tailored to your business goals. Contact Gordon who will give you a fair idea of what work may be required to beat your competition and win on the SERPs.

2. Building the Markov Model

2.1. Step 1 — Build States

Each unique “From → To” URL pair represents a transition.
For a travel website, states usually include:

• Homepage

• Category pages (Holidays, Flights, Destinations, Deals)

• Legal pages (Terms, Privacy)

• Product pages (Hotel or package detail pages)

• Basket / Checkout

The example website, in this case study contains transitions like:

• Homepage → Privacy Policy

• Privacy Policy → Homepage

• Terms → Privacy Policy

• And various footer-driven circular transitions

Already we can see a major issue:
Users are being pushed into non-commercial pages due to heavy footer link prominence.

2.2. Step 2 — Create the Transition Matrix

A Markov model works by calculating:

Pij=Number of transitions from page i to page jTotal transitions from page iP_{ij} = \frac{\text{Number of transitions from page i to page j}}{\text{Total transitions from page i}}Pij​=Total transitions from page iNumber of transitions from page i to page j​

Using our inlinks dataset, we would aggregate all links coming from a page and determine the proportion landing on each destination URL.

Example (simplified):

This becomes the transition matrix used for Markov modelling.

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2.3. Step 3 — Add Absorbing States

In travel websites, absorbing states include:

• Successful booking

• Exit from site

• Error pages

Any user reaching one of these states stops moving, which reflects real user behaviour.

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3. Markov Analysis Findings (Based on Our Actual Data)

Finding 1 — Excessive Authority Lost to Legal Pages

Our dataset for this example travel website shows:

• Many links from the homepage → Privacy Policy

• Many links from all pages → Terms & Privacy

• These links appear in footer and header, meaning they “steal” PageRank

Impact:
Legal pages become artificial “hubs” even though they produce £0 revenue.

This is common in travel websites but particularly damaging in competitive tourism SEO.

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Finding 2 — Missing Internal Links to Money Pages

From the sample:

• There are no direct internal links in this data leading toward:

  • Booking pages

  • Category / holiday landing pages

  • Destination hubs

If this reflects the larger website structure, then:

Commercial pages are starved of internal link equity.

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Finding 3 — High Fragmentation of User Flow

If most clicks are forced through:

• Sticky headers

• Heavy footer navigation

• Legal pages

• Redundant links

then users rarely follow the ideal purchase pathway:

$$\text{Homepage → Destination Page → Package Details → Checkout}$$

Instead, they bounce around in:

$$\text{Homepage → Footer → Legal → Back → Footer → … → Exit}$$

This pattern is common with travel sites using overloaded templates.

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Finding 4 — Low Probability of Reaching High-Value Pages

A Markov simulation using the example website link structure would likely show that:

• Only 10–20% of internal flow reaches product/hotel/package pages

• The majority gets trapped in template regions

This means the site structurally discourages bookings.

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4. The Markov Simulation Output (Interpretation)

When we build the steady-state distribution — essentially, “Where does a user end up after many clicks?” — we expect:

Top-ranked pages in the stationary distribution:

1. Privacy Policy

2. Terms

3. Homepage

4. Footer-originating pages

5. Possibly Contact page

Bottom-ranked pages (the ones that SHOULD earn authority):

• Holiday destinations

• Category pages

• Hotel/package pages

• Booking funnel pages

This is the opposite of what a travel business needs.

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5. Recommendations (Based on the Markov Findings)

1. Reduce Footer Legal Link Weight

• Move Privacy/Terms into a flyout menu or collapse them

• Add rel="nofollow" (Google tolerates this on legal pages)

2. Add High-Value Internal Links from Homepage

Link prominently to:

• Top 5 destinations

• Core holiday categories

• “Deals of the week”

• “Late availability” pages

These will dramatically improve authority flow.

3. Build a Hub-and-Spoke Model for Destinations

Example:

This improves both SEO and conversion.

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4. Remove Circular Transitions

Many links loop back:

privacy → homepage → privacy → homepage

These loops create self-reinforcing noise.
Replace with:

privacy → (no internal outgoing links)

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5. Improve Booking Funnel Connectivity

All destination and hotel pages should link directly to:

• View full package

• Price calendar

• Checkout start page

Markov flow improves dramatically when the funnel is direct.

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6. Common structural problems

This Markov Chain analysis highlights a common structural problem in travel websites:

• Too many transitions point to low-value legal pages.

• Too few transitions funnel authority and users toward revenue pages.

Using the Markov insights above, BroadwayTravel — or any travel site — can restructure internal links to:

• Increase visits to package/booking pages

• Improve conversion rates

• Strengthen SEO for key commercial searches

• Reduce wasted PageRank

The model turns chaotic internal linking into a clear, data-driven roadmap for optimisation.