Dimension Labs

Industry

Guest experience analytics for hotels

Read every review, survey and stay comment across your portfolio. Find which guest issue actually moves your rating at each property.

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Ask Which guest issue is actually costing us stars?

We work with enterprises such as NBA, Travelers, Mars and Concentrix.

[01] The problem

The most-mentioned issue is rarely the one that costs you stars.

In public reviews of Marriott-family hotels in Orlando, front desk check-in was mentioned almost three times as often as cleanliness. Cleanliness moved one-star risk by 34 points. Front desk moved it by about two.

Right now

01Brand-standard audits miss what guests actually notice
02Every property's survey vendor codes differently
03Fees and staff issues get averaged away
04Owners ask why a property dropped, nobody can prove it
05Fixes are funded by complaint volume

Your guests already wrote the renovation plan. It's ranked by volume instead of by effect.

[02] EnrichOne record, structured

Language in. Structured data out.

Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for hotels and hospitality.

enrichment · one record

Raw text

We booked a suite for our anniversary and arrived to hair in the sink and a stained carpet. When I asked to move, the manager was dismissive and made us feel like the problem. Then a $39 resort fee we were never told about. We will not be back.

Google review · 1 star · property joined

Dimensions written to the record

Facility & Cleanlinessbroadly dirty
Primary Frictioncleanliness
Staff Interaction Qualitydismissive · named role
Service Recoveryrefused
Fee & Premium Changeundisclosed fee · $39
Price–Value Perceptionpoor
Loyalty Signalwill not return

Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side.

[03] The product

3.1 · Read everything

Every record, enriched.

Start with one export of reviews, post-stay surveys and guest messages, or connect every channel. Every record comes back with typed Dimensions like facility and cleanliness, staff interaction quality, fee and premium change, service recovery and loyalty signal, written the same way every run, then joined to property, brand tier, room type and RevPAR.

Read every record, not a sample
Join each record to property, brand tier, room type and RevPAR
Add your own Dimensions in plain English
Backfill years of history in the same run

dimension labs · blend

3.2 · Prove the cause

Ask why. Get proof.

Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating and one-star risk, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which guest issue is actually costing us stars
Compare every property on the same Dimensions
Put a figure on undisclosed fees
See which service recovery actually saves the stay

dimension labs · ask

Example finding

Which guest issue is actually costing us stars?

Cleanliness moved one-star risk by 34 points. Front desk, mentioned almost three times as often, moved it by about two.

8,504 rated public Google reviews · Marriott-family hotels in Orlando

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your owners and asset managers needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor.

Produce the monthly readout without building it
Publish a live monitor leadership can open and question
Track whether the fix you shipped moved the number
Export to PDF, slides or your BI tool

dimension labs · report

The priority list, rearranged by cause

Exhibit from “What your CX tools can’t tell you”. Left: the standard CX-analytics ranking. Right: a DoWhy causal model on 8,504 rated public reviews, holding hotel, tier, language, review length and reviewer fixed. Every effect survives placebo, random-cause and subset tests.

[04] How the platform worksRuns daily

From raw conversations to a proven answer.

Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works.

Language · 37 sources

ZendeskIntercomSalesforceLiveChatDriftSlackQualtricsMedalliaSurveyMonkeyQuestionProGenesysAmazon ConnectAvaya Cloud8x8TwilioSMSAdaKore.aiYellow.aiCognigyRasaMicrosoft Bot FrameworkAmazon LexAmazon AlexaOracle Digital AssistantBotcopyCapacityFacebook MessengerKikX (Twitter)OpenAIEmailCall recordingsApp store reviewsGoogle reviewsOpen-ended surveysCommunity forums

Business + world data

SnowflakeDatabricksBigQueryAmazon S3Salesforce CRMCSV + ExcelREST APIRevenueOrdersRenewalsUsage eventsLoyaltyStore + location dataWeatherSeasonalityMarket events

Dimension Labs pipeline · Runs daily

Ingest + normalizeEvery channel and language into one schema

EnrichDozens of Dimensions written onto every record

Map + governValidated for drift, versioned, the same fields every run

Analyze + proveJoined to outcomes and tested for cause

Where it goes

Dimension Labs agentAsk, report, schedule

Your warehouseSnowflake, Databricks, BigQuery

BI toolsTableau, Power BI, Hex

Claude and ChatGPTvia MCP · coming soon

[05] ArtifactsBuilt by the agent