A clearer path from a property question to a qualified inquiry.
A worked 90-day example connecting buyer research, project content, AI visibility, and inquiry quality for a fictional property group.
This is a fictional worked example. Any figures are synthetic demonstration data, not achieved client results. The scope, timeline, and interpretation explain how a real engagement could be documented.
- company
- Example property group
- market
- Mediterranean real estate
- scope
- Buyer research, project pages, technical SEO, and reporting
- duration
- 90-day example program
The report, with the context attached.
Invented baseline → follow-up figures. These are not a forecast or client result.
36 → 63 of 180 sampled answers per period
18 → 36 of 180 sampled answers per period
Synthetic 30-day baseline and follow-up periods
From 8 baseline and 12 follow-up inquiries
“How should we compare property developers before arranging a viewing?”
The challenge
The fictional group has a substantial project portfolio, but its pages mix company claims, location information, and individual developments. A relocating buyer struggles to establish which project fits their needs, what is current, and who can answer a specific question. In our constructed baseline, the brand appears in 36 of 180 sampled answers, while only 18 answers cite an owned page.
A proposed approach
Map the questions behind a viewing
Group 60 example questions around relocation needs, developer comparisons, project verification, and service scope. Keep branded questions separate from discovery and comparison questions. Sample each question once across three platforms to create 180 observations per period.
Give each project a reliable source page
Separate the company profile from individual projects and location guides. Define consistent fields for project status, location, available services, relevant documents, and a named review owner. Avoid financial return claims and direct regulated questions to qualified advisers.
Publish useful decision content
Create a developer evaluation guide, three project-page briefs, two location guides, and a viewing-preparation checklist. Link each material claim to an approved source and connect the guides to an inquiry route with clear next steps.
Connect the observation to the conversation
Repeat the core question sample under documented conditions. Keep answer mentions, owned citations, referral visits, and inquiry qualification in separate reporting sections. Record content release dates and other campaigns alongside the comparison.
A 90-day delivery plan
Establish the baseline
Review the site and project facts, agree buyer cohorts, and record the initial answer and referral sample.
Build and release
Publish the approved guides and project improvements, connect internal links, and clarify the viewing inquiry.
Review and decide
Repeat the answer sample, compare equal 30-day referral windows, and review the quality of each inquiry.
How we would evaluate the work
- Mention rate is answers naming the brand divided by 180 sampled answers in each period.
- Owned citation rate is answers linking to an owned page divided by the same 180 answers.
- Referral visits use equal 30-day periods; privacy and missing referrer data can leave journeys unobserved.
- A qualified inquiry matches the agreed location, project scope, and genuine buying timeline; the sales team makes that determination.
Reading the example results
In this invented dataset, mention rate rises by 15 percentage points and owned citation rate by 10 percentage points. Visits rise from 150 to 240, while qualified inquiries rise from 4 to 7. These figures illustrate how a report could connect observations without treating a mention as a lead. No actual experiment, client engagement, or causal result is represented.
If visibility improves but inquiries remain poorly matched, review project clarity and the inquiry journey before expanding the question set. Preserve the original cohort so a wider program does not erase its baseline.
This educational example was created by Citevia. For our measurement principles, read how to measure AI search visibility.
