If you’re serious about growing organic traffic and improving conversion rate, internal site search data is one of the most underused sources of insight you’ve already paid for. This is the kind of practical, data-led work we obsess over at Totally Digital—because it turns “content” into an asset that drives leads, revenue, and a better user experience.
Below is a step-by-step way to turn your on-site searches into a content roadmap that converts, while also fixing the UX friction that’s quietly costing you money.
Why internal site search data is different (and more honest) than other data
Most marketing data is indirect.
- SEO keyword tools tell you what people search for on the web.
- Analytics tells you what people clicked.
- Heatmaps tell you where people hovered and scrolled.
Internal site search tells you what people couldn’t immediately find.
That’s gold.
It captures:
- The language your customers actually use (not the words you wish they used)
- The gaps in your navigation, IA, and content
- High-intent actions (pricing, logins, demos, returns, delivery, specs)
- Friction points that drive abandonment (“support number”, “refund”, “cancel”)
And it’s happening in a context where trust is already being formed. You’ve got the visit. Now your job is to reduce effort.
It’s also worth remembering: the broader UK search landscape is expensive. Competition and Markets Authority notes that Google handles more than 90% of general search queries in the UK, and UK firms spent more than £10 billion on Google search advertising “last year”. If you can improve journeys on your own site—especially for high-intent visitors—you squeeze more value from every channel, paid or organic.
First: make sure you’re actually tracking internal search properly
Before you build anything, you need clean tracking. The goal is to reliably answer:
- What did they search for?
- Did they get results?
- Did they click the result?
- Did they refine the search?
- Did they convert (or bounce)?
Where internal search data usually lives
You’ll normally find internal search terms in one (or more) of these places:
- GA4 (recommended starting point)
If your site generates a view_search_results event with a search term parameter, you can analyse it right away. - Your CMS or search platform logs
e.g., Elasticsearch, Algolia, Coveo, Shopify search, WordPress search plugins. - Server logs
Useful if search terms are passed in URLs and you’re capturing them reliably. - Customer support tooling
Sometimes the same “can’t find X” demand shows up in tickets and live chat. That’s a bonus layer.
If you’re already investing in measurement and performance, this sits naturally alongside Data & Analytics and Insight & Strategy.
Quick tracking checklist (the boring stuff that makes the insight usable)
- Search term parameter is captured (and not redacted)
- Zero-results searches are trackable
- Search refinements are trackable (search term change within the same session)
- Search result clicks are trackable
- Conversions are defined properly (lead, purchase, enquiry, booking, application)
- You can segment by device, channel, landing page, and new vs returning
If this setup is messy, fix that first. It’s the foundation.
Clean the data so it stops lying to you
Internal search data is messy by default. People type:
- Typos (“prcing”)
- Acronyms (“SLA”, “API”, “VAT”)
- Brand names and product codes
- Half-sentences (“can I pay monthly”)
So you need a quick cleaning pass before you draw conclusions.
What to do in your first cleaning sprint
- Normalise obvious duplicates
“price”, “pricing”, “costs” → cluster together (but keep originals for language insights) - Strip noise
Internal staff searches, bot junk, one-off nonsense strings - Tag intent (simple is fine)
- Commercial: pricing, demo, quote, availability
- Support: login, reset password, returns
- Product: specs, sizes, compatibility
- Content/learning: how-to, guide, examples
- Flag “high pain” searches
- Zero results
- Multiple refinements
- High exit rate after search
This is where your SEO audits and Technical SEO mindset pays off: treat search data like a diagnostic tool, not a “nice to have”.
Find the 3 buckets that matter most: money, friction, and missed demand
Once you’ve got the data in decent shape, you’re looking for patterns that fall into 3 buckets:
1) “Money” searches (high commercial intent)
These are searches that show someone is close to action:
- pricing / cost / fees
- quote / estimate
- demo / consultation
- availability
- delivery times
- “near me” (if relevant)
If these searches are common and the path to the answer is unclear, your site is bleeding revenue.
2) “Friction” searches (UX problems)
These are searches that shouldn’t need search at all:
- login
- contact number
- opening hours
- returns policy
- refund
- track order
- cancel subscription
Frequent searches here often mean your navigation and page hierarchy are not doing their job.
3) “Missed demand” searches (content gaps)
These are the best ones for new content creation:
- comparisons (“X vs Y”)
- use cases (“for agencies”, “for charities”, “for SMEs”)
- integrations (“works with HubSpot”, “connect to Xero”)
- constraints (“minimum contract”, “setup time”, “data residency”)
- education (“how does it work”, “best practice”, “examples”)
These are the searches that can become pages that rank externally and convert internally.
Turn internal searches into a content roadmap that actually converts
Here’s the mistake most teams make: they treat internal search terms like “blog topics”.
Sometimes they are. But the highest-converting content is often not a blog post. It’s a purpose-built landing page, a clear FAQ hub, a comparison page, or a pricing explainer.
Map search intent to the right page type
Use a simple rule:
- If the search is about buying → build or improve a commercial page
- If the search is about deciding → build comparison / proof / detail pages
- If the search is about using → build support / how-to content
- If the search is about finding → fix navigation + internal linking + labels
Examples:
Search term: “pricing”
Best output: Pricing page, pricing explainer section, or clearer pricing blocks on service pages.
Search term: “b2b seo”
Best output: A dedicated service page + internal links from relevant blogs and case studies (not a generic post). For example, B2B SEO.
Search term: “competitor analysis template”
Best output: Downloadable template page + email capture + supporting guide. Tie it into Competitor analysis.
Your “content that converts” framework (simple, reliable, repeatable)
For each high-value search cluster, build content that includes:
- The direct answer fast (above the fold if possible)
- Decision support (proof, examples, process, outcomes)
- Objection handling (pricing, timing, risk, alternatives)
- A clear next step (CTA matched to intent)
- Internal links that help people continue (not wander)
This is exactly where joined-up organic strategy matters—your organic traffic growth is capped if the page experience and conversion path are weak. Algolia summarises multiple benchmarks showing conversion rates through site search can be materially higher than site average (their Jan 2026 roundup cites examples like 4.63% vs 2.77%). You don’t need to obsess over the exact percentage; the point is consistent: searchers are highly intent, and your job is to meet that intent cleanly.
Use internal search data to improve UX (and make navigation pull its weight)
Content is only half the win. The other half is removing friction so people don’t need to search in the first place—or, when they do, the search experience actually works.
Baymard Institute has benchmarked hundreds of leading e-commerce sites and built extensive guidance around on-site search UX performance. Even if you’re not e-commerce, the core principles translate: relevance, clarity, filtering, and handling “no results” properly.
High-impact UX improvements driven by search data
1) Fix “no results” like it’s a revenue leak (because it is)
If people search “refund policy” and get nothing, you’ve created a dead end.
A strong no-results experience should include:
- Suggested alternative searches (based on your clusters)
- Popular links (pricing, contact, key categories)
- A way to contact support (if the query is support-led)
- Optional: “Didn’t find what you need?” feedback box
2) Add synonyms and language mapping
If users search “cost” but your site only talks about “pricing”, you’ve got a vocabulary mismatch.
Build a synonyms list from your top internal search terms:
- “cost” ↔ “pricing”
- “fees” ↔ “rates”
- “book” ↔ “schedule”
- “refund” ↔ “returns”
3) Improve filters and sorting using what people search for
If searches frequently include modifiers like:
- “under £X”
- “monthly”
- “next day”
- “compatible with…”
Those modifiers should often become filters, facets, or page-level options.
4) Use search terms to rename navigation labels
If your nav says “Solutions” but people keep searching “Services”, that’s a signal. Don’t get precious about internal naming—match user language.
5) Create “search-led” hubs for recurring themes
If internal searches constantly revolve around a topic (e.g., “migration”, “tracking”, “hreflang”, “technical audit”), build a hub page and route people into it from:
- Navigation
- Related services
- Contextual internal links
- On-page modules
This pairs nicely with a well-structured insights section like Insights and service architecture like SEO / Organic Marketing.
Prioritise what to build using an “effort vs £ impact” scoring model
You don’t need a perfect model. You need a usable one.
Create a simple score for each search cluster:
Impact score (0–3)
- 3 = directly tied to revenue (pricing, quote, demo)
- 2 = strongly influences decision (case studies, comparisons)
- 1 = UX/support (login help, refunds)
- 0 = low-value noise
Demand score (0–3)
- Based on search volume frequency (relative within your own site)
Friction score (0–3)
- 3 = high zero-results, high exits, multiple refinements
- 2 = some friction signals
- 1 = minor issues
- 0 = working well
Then pick your first 10 actions by total score.
Translate it into £ (without getting silly)
You can sanity-check ROI with a back-of-the-napkin calculation:
- Monthly internal searches for “pricing”: 600
- Click-through to pricing content after search: 30% → 180 visits
- Lead conversion rate on that page: 2% → 3.6 leads
- Lead value (conservative): £1,500
- Monthly value: ~£5,400
If your pricing journey is messy and you can lift conversion from 2% to 3%, the numbers get meaningful quickly. This is the same logic behind UX ROI discussions more broadly: better journeys compound.
Build the measurement loop so content keeps improving
Internal search-led content is not “publish and pray”. You want a loop.
Track:
- Searches per session
- % of searches that lead to a click
- % zero-results
- Refinement rate
- Exit rate after search
- Conversion rate for searchers vs non-searchers
- Assisted conversions influenced by search-led pages
Then review monthly:
- New rising search terms
- High-volume “friction” searches that should be solved via UX
- New “missed demand” clusters that deserve pages
- Underperforming pages that searchers keep trying to find (content not discoverable)
If you want to go further, pipe GA4 + search events into BigQuery for deeper clustering and segmentation. But don’t let “advanced” become an excuse for not doing the basics.
Common mistakes that waste the opportunity
- Publishing blogs for commercial searches
If someone searches for “pricing”, they don’t want a thought leadership post. - Ignoring zero-results because “the volume is low”
Zero-results often reveal high-intent edge cases and language mismatches. - Not fixing the underlying UX
Content won’t compensate for confusing navigation, weak internal linking, or a broken search experience. - Treating internal search as only a UX problem
It’s also a content strategy engine. Use it to guide what you create next. - No ownership
Someone needs to own the loop: insight → action → measurement → iteration.
A practical 30-day plan you can actually execute
Week 1: Instrumentation + extraction
- Confirm internal search tracking in GA4 / platform logs
- Pull top queries (30–90 days)
- Identify zero-results and high-exit searches
Week 2: Clustering + intent mapping
- Group queries by theme and intent
- Tag as Money / Friction / Missed demand
- Pick top 10 opportunities
Week 3: Build
- Create or upgrade priority pages
- Improve no-results experience and synonyms
- Add internal linking routes from high-traffic pages
If you need development support to implement search improvements cleanly, this is where Website design & development earns its keep.
Week 4: Measure + refine
- Track search-to-click and post-search conversion
- Review refinements and exits
- Iterate based on what changed
The takeaway
Internal site search data is one of the few datasets that shows you, in plain language, what people want from your site right now. If you use it properly, it becomes a dual-purpose engine:
- A content roadmap that aligns with real intent and drives conversions
- A UX improvement programme that removes friction and increases confidence
And it doesn’t require a massive replatform, a 6-month project, or a fancy AI dashboard to start. It requires good tracking, a clear framework, and a bias toward action.
If you want help turning your internal site search data into a conversion-focused content plan (and fixing the UX blockers it exposes), explore our case studies to see what “measurable improvement” looks like—then get in touch and we’ll map out the quickest wins for your site.
If you’re tired of traffic that doesn’t convert, Totally Digital is here to help. Start with technical seo and a detailed seo audit to fix performance issues, indexing problems, and lost visibility. Next, scale sustainably with organic marketing and accelerate results with targeted paid ads. Get in touch today and we’ll show you where the quickest wins are.