Why do dealership marketing numbers never agree between systems?
Dealership marketing numbers never agree between systems because each system counts a different thing over a different window with different rules. A platform counts an interaction it influenced, the CRM counts a lead it received, and the DMS counts a delivered unit. These are not the same event, and reconciling them requires agreeing which system is authoritative for which measure before comparing.
Why do dealership marketing numbers never agree between systems?
Dealership marketing numbers never agree between systems because each system counts a different thing over a different window with different rules. A platform counts an interaction it influenced, the CRM counts a lead it received, and the DMS counts a delivered unit.
These are not the same event, and reconciling them requires agreeing which system is authoritative for which measure before comparing. Units and gross come from the DMS.
Opportunities and follow-up come from the CRM. Impressions and clicks come from the platform.
Reporting that mixes sources without saying so produces arguments, not insight. Attribution windows, view-through counting and last-click versus multi-touch models all change the number legitimately.
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Name the authoritative source per measure
Units and gross come from the DMS. Opportunities and follow-up come from the CRM.
Impressions and clicks come from the platform. Reporting that mixes sources without saying so produces arguments, not insight.
Windows and models differ
Attribution windows, view-through counting and last-click versus multi-touch models all change the number legitimately. Ask each provider to state its window and model, then compare like with like.
Deduplicate before you conclude
The same customer arriving through several channels is counted several times. Without deduplication, the sum of channel-reported results exceeds the store's actual sales.
Reconcile a small set monthly
Pick three figures and reconcile them every month. Persistent unexplained variance is a data problem worth fixing; a new dashboard is not the fix.
A reconciliation exercise
Choose a month and list sales delivered from the DMS. For each, look in the CRM for its source, then in advertising and analytics for the touches recorded.
Note where the trail breaks: no CRM record, a wrong source code, a phone call not tracked. The exercise usually reveals two or three process gaps that account for most disagreement and can be fixed at the source.
Agreeing a reporting standard
Publish a one-page reporting standard that lists each measure, its source system, definition, window and owner. Require vendors to report in those terms alongside their own.
When numbers still differ, the standard tells you which is authoritative and why. It also helps new staff and vendors understand how the store measures itself.
Common mistakes in reconciling numbers
Adding platform-reported conversions to get a total, comparing periods with different windows, blaming a vendor before checking the tagging and rebuilding dashboards instead of fixing data are common. Another is expecting exact agreement. Set an acceptable variance, investigate outliers and record the explanation of any known differences.
Questions to ask each vendor
How do you define a conversion? What window and model do you use?
How do you deduplicate across channels? How do you match to sales?
What do you not measure? Ask for the answers in writing and compare them to the store's reporting standard so differences can be explained rather than argued.
What to verify
Name the authoritative source for each measure
Ask each provider for its attribution window and model
Deduplicate customers before summing channel results
Reconcile three figures every month
Continue the research
Analytics & attribution
Cross-channel measurement, lead-to-sale matching and executive reporting.
Compare providers →Dealer intelligence & BI
Operational, financial and group-level reporting across dealership systems.
Compare providers →Customer data platforms (CDP)
Identity resolution, first-party data unification, audiences and activation.
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