FAQ — Hotel Market Interest Index (Monthly)
What is the Market Interest series?
Monthly actual volumes of branded Google searches for your hotel brand, built from a governed query list (official name, variants, misspellings, local language). Ambiguous terms are excluded.
Do you normalize or transform the data?
No. We use raw monthly counts. No normalization, no log scales, no z-scores.
What do you compare it to?
Standard lodging benchmarks—primarily STR/CoStar RevPAR Index (RGI) vs. your comp set. We also review MPI (Occupancy Index) and ARI (ADR Index). If STR isn’t available, we use monthly revenue share or bookings share from your BI stack.
What did your test show?
In Sweden (2020–2022), branded search volumes and RGI had a strong, statistically significant association, r(14) = 0.78. In practice: when market interest rises, competitive revenue performance tends to rise too.
Does this prove causation?
No. Treat it as a decision signal to guide allocation, timing, and risk monitoring—alongside commercial context.
How far ahead can it lead?
We inspect 0–3 month leads/lags. The most useful planning horizon is often 0–1 month ahead.
How do you handle seasonality or volatility?
By default, we compute correlation on the monthly levels. If you want extra context, we can also include a simple month-on-month change chart. That optional view is up to you.
How much history do we need?
At least 18–24 months of monthly observations for stable estimates of correlation and lag.
Will paid campaigns distort “market interest”?
Branded search reflects overall market attention. We annotate heavy campaign months and can run sensitivity excluding those points. The goal isn’t to “buy” the signal but to observe the market.
Our brand name collides with generic terms—how do you prevent noise?
Query hygiene: add disambiguators, exclude ambiguous strings, include local-language forms, and re-validate regularly. All changes are logged.
What correlation is ‘good enough’ to act on?
As a rule of thumb, |r| ≥ 0.6 and stable across windows/transformations is decision-useful—especially if the link strengthens at a consistent +1-month lead.
What granularity can we get?
Brand, sub-brand, city, and segment (e.g., transient vs. group)—subject to search and outcome coverage.
Can we use this without STR?
Yes. Pair the series with monthly revenue share / bookings share or another dependable commercial outcome.
Where does this live operationally?
We publish the monthly volume series alongside RGI/MPI/ARI, add brief annotations (campaigns, pricing, distribution, competitor/PR), and push to Looker / Power BI / Tableau and monthly exec decks.
What do we receive each month?
Updated market-interest volumes (brand/city/segment)
One-page exec readout (what moved, why, what to do next month)
MoM alerts for spikes/drops and divergence vs. STR
Quarterly method refresh (correlation/lag, query hygiene)
What inputs do you need to start?
Brand list (incl. variants/misspellings, locales)
Monthly outcome series (STR RGI/MPI/ARI or revenue/bookings share)
Geographies & comp sets
Notable events for annotation (campaigns, pricing, PR, distribution)
What’s the first decision this helps me make?
Allocation and timing for next month—shift money and attention toward markets where interest is accelerating; schedule offers into those tailwinds; investigate MoM drops early.