Sales and marketing report different figures because they measure the same reality from a different perspective, with different definitions and different systems. Marketing counts based on behaviour and intent, sales based on qualification and opportunity. As long as those two worlds are not aligned, the figures will continue to diverge structurally.
This is not a sign of poor collaboration or errors in the data. It is a logical consequence of how both teams are set up and what they want to measure. The good news: it is fully solvable. In this article, we answer the most frequently asked questions about this topic, from the causes to the solutions.
How two different versions of the same data arise
Two versions of the same data arise because sales and marketing work with different systems, different measurement moments and different definitions of success. Marketing records behaviour in a marketing platform such as HubSpot, while sales works from a CRM such as Salesforce. Those systems do not always talk to each other, so each department builds its own truth.
Suppose someone fills in a form on the website. Marketing sees this as a conversion and includes it in the report. Sales opens that same lead in the CRM, qualifies it as not interesting enough and skips it. Result: marketing reports ten new leads, sales reports three usable contacts. Both figures are correct, but they measure something different.
Timing also plays a role. Marketing often measures in real time, while sales reports based on deals that have actually been picked up. A lead that comes in on 31 December counts for marketing in the old year, but for sales only in the new year once action is taken. Small timing differences can therefore create large figure differences in monthly reports.
The difference between a lead, MQL and SQL
A lead is any contact that has shown interest, for example by filling in a form or downloading a whitepaper. An MQL (Marketing Qualified Lead) is a lead that marketing has assessed as promising enough to pass on to sales. An SQL (Sales Qualified Lead) is an MQL that sales has confirmed as a real sales opportunity.
The issue lies in the transition from MQL to SQL. Marketing determines when a lead is ready for sales based on behavioural signals, such as the number of pages visited or emails opened. Sales assesses that same lead based on budget, decision-making authority and concrete buying intent. Those criteria do not always overlap.
If the definitions of MQL and SQL have not been jointly agreed and documented, each department assesses leads using its own criteria. Marketing passes on leads that sales does not accept. Sales complains about poor quality. Marketing complains that sales does not follow up enough. The core of the conflict is almost always a lack of shared definition.
Marketing counts more conversions than sales
Marketing counts more conversions because it sees every interaction with the website, a form or a campaign as a potential result. Sales only counts the interactions that lead to a real sales opportunity. Marketing measures broadly and inclusively, sales measures narrowly and selectively. That difference in scope almost always explains the gap between the two figures.
In practice: a visitor who views three pages and leaves their email address is a conversion for marketing. For sales, that same person only becomes interesting if there is a budget, a timeline and a concrete need behind it. Most leads supplied by marketing simply do not meet that threshold.
That is not a problem in itself. Marketing is meant to fill a broad funnel, and sales is meant to filter out the best contacts from it. It only becomes a problem when both teams use their own figures to claim success or blame the other, without a shared view of what the figures actually mean.
The role of attribution in figure differences
Attribution determines which channel or interaction gets the credit for a conversion or sale. Marketing often uses an attribution model that counts multiple touchpoints, while sales attributes the deal to the last personal interaction. That structurally leads to different figures, because both teams attribute the same result to a different cause.
Several attribution models are used:
- First-touch attribution: the first interaction gets all the credit, often used by marketing to show the value of awareness
- Last-touch attribution: the last interaction gets all the credit, often used by sales to show the value of the sales conversation
- Multi-touch attribution: credit is divided across multiple contact moments during the customer journey
If marketing works with multi-touch attribution and sales with last-touch, they report completely different causes of success for the same deal. Neither model is wrong, but if they are used side by side without explanation, confusion arises about who contributed what. A shared attribution model is therefore just as important as shared definitions.
Resolving the discrepancy between sales and marketing data
You resolve the discrepancy between sales and marketing data by creating one central data source that both teams feed and read. As long as sales and marketing work in separate systems that do not communicate with each other, the figures will continue to diverge. The technical and organisational solution go hand in hand.
In practice, this means the following:
- Set shared definitions for leads, MQLs and SQLs. Record them in writing and make sure both teams endorse them.
- Connect your systems so data is synchronised automatically and in real time between your CRM and marketing platform. An API integration between tools such as HubSpot and Salesforce eliminates manual transfer and ensures consistent data on both sides.
- Use one shared dashboard for reporting, so both teams see the same figures and no longer reason from their own systems.
- Agree on a shared attribution model and communicate it internally so everyone understands how results are attributed.
- Plan regular alignment between sales and marketing to identify deviations in time and adjust definitions when practice changes.
The technical integration is often the quickest to realise. The organisational alignment, agreeing on definitions and responsibilities, takes more time but is at least as important.
Small figure differences can be acceptable
Small figure differences between sales and marketing are acceptable when they can be explained and do not lead to wrong decisions. A difference of a few per cent due to timing, rounding or different measurement moments is normal. A structural difference of twenty per cent or more without a clear cause is a signal that something is fundamentally wrong in the alignment.
The question is not whether the figures are exactly the same, but whether they point in the same direction. If marketing says it was a good quarter and sales says it was a bad quarter, there is a problem. If both teams see a similar trend but report slightly different absolute figures, that is usually no cause for alarm.
Accept small differences when they can be attributed to known causes, such as a timing difference in registration or a deliberately chosen difference in scope. Do not accept them when nobody can explain where they come from, because then the data is unreliable and decisions are based on a shaky foundation.
Eenvoud helps with consistent sales and marketing data
The problem of diverging figures can be solved technically. We make sure your CRM, marketing platform and reporting environment talk to each other through smart API integrations, so data is synchronised automatically and you always work from one central source of truth. No manual exports, no versions that do not match, no discussions about which figure is correct.
What we do for you:
- We connect your existing systems, such as HubSpot, Salesforce or other CRM and marketing tools, through custom API integrations and microservices
- We build a central middleware layer that manages the data flow and keeps it consistent
- We develop a shared BI dashboard where sales and marketing see the same figures in real time
- We automate the transfer of leads between systems, so no data is lost in the transition from MQL to SQL
The result: less discussion, more trust in the data and better decisions for both teams. Want to know what this looks like for your situation? Contact us and we will look at the possibilities together.