An MQL is a contact your marketing team believes is interested, based on observed behaviour. An SQL is a contact where a conversation has established a named trigger, a rough timeframe and who is involved in the decision. The line sits exactly where observed behaviour ends and a checked statement begins. When you take a definition built on your home market into Germany, that line moves, because the behaviour you were reading is produced differently here.
The definitions themselves travel fine. The thresholds do not. German buyers research quietly, decide in a wider circle and reveal intent later, so a scoring model tuned elsewhere will hand over the wrong contacts and hide the ones worth calling.
What an MQL proves and what an SQL has to prove
Both terms describe one contact in two states. The difference is the burden of proof.
An MQL proves attention. Someone opened a document, joined a webinar, returned to the same service page. That is an observation, not a statement. It shows a topic mattered enough to a person to spend time on it. It does not show that a project exists inside the company, who owns it, or when it will be decided.
An SQL has to carry more: a named trigger, a rough timeframe, and clarity on who takes part in the decision. Remove any one of those and you have an interesting contact, not a qualified one. We set out the criteria that hold up in the piece on when a lead is actually qualified. The step between the two states is not a higher score. It is a conversation, which is why lead qualification sits between the two functions rather than inside either of them.
Why MQL and SQL thresholds misfire in the German market
Three things change at the border, and all three touch the same scoring model.
Research happens before contact, not during it. German buyers tend to read, compare and check well before they identify themselves. By the time a form is filled in, the informal shortlist is often already drawn. A model that treats early engagement as a warming curve therefore sees a flat line followed by a sudden request, and flags the wrong moment.
Engagement in English is a noisier signal. Eurostat data from the Adult Education Survey shows that in Germany 87.7 percent of people aged 25 to 64 know at least one foreign language, but among those, only 28.0 percent describe their strongest foreign language as proficient, against 35.9 percent at basic level. Reading an English page therefore costs a German reader more effort than the same page costs a reader at home, and shallow engagement metrics such as time on page or scroll depth measure that effort as well as interest. The same effect is described in more detail in our piece on why an English pitch does not land in Germany.
The decision circle is wider. One person accumulating points does not represent an account. A German buying process routinely pulls in a technical owner, a commercial owner and, above a certain size, a procurement view. A single high score can sit with the person least able to move the project forward.
Which signals still hold and which ones stop meaning anything
| Signal | What it still proves in Germany | What it no longer proves |
|---|---|---|
| Content download | topic relevance for one person | an active project |
| Newsletter sign-up | willingness to stay in touch | urgency or authority |
| Repeat visits to a service page | sustained attention | that the visitor may decide |
| Time on page and scroll depth | effort, part of it linguistic | strength of interest |
| Inbound question about delivery or scope | a named problem | timing and the people involved |
| Call with a named trigger and timeframe | a real project | the outcome of the decision |
| Second participant joining a meeting | internal support for the topic | the size of the budget |
The top four are MQL material anywhere. The bottom three are what an SQL has to carry, and in Germany they are almost always produced by a call rather than by a form.
How to redefine the handover before your first German campaign
- Write the handover condition as a sentence, not a number. A sentence can be checked by two people from different teams. A score cannot, because it no longer shows what it is made of.
- Set an exclusion rule first. Exclusions decide faster than inclusions. Anything outside the agreed target profile is not handed over, whatever the score says.
- Lower the volume expectation and raise the evidence requirement. Fewer contacts, each carrying a checked statement, beats a longer list in a market where the buyer will verify you before replying.
- Define what travels with the contact. A structured handover means the contact person, the notes from the conversation and the agreed next step. Without those three, your sales team restarts the conversation and the head start is gone.
- Review the rule monthly against returned contacts. Accepted contacts teach you little. The reasons given for rejections show exactly where the definition is too soft.
This is also the real subject of any conversation about outsourcing part of the work. Whether you are looking at B2B lead generation or appointment setting, what you are actually agreeing is that one sentence. Our process page shows how that agreement is put into practice step by step.
What happens when the line is not checkable
The failure pattern is the same everywhere, but it is more expensive in a market you do not know yet, because you cannot tell whether the problem is the definition or the market itself.
- Nobody can state the return rate. That is the clearest sign, because it means the rule has never been tested.
- Contacts are rejected as poor quality without naming which criterion failed.
- MQL volume rises while the number of first conversations stays flat.
- Individual sellers build their own pipeline around the process rather than through it.
When a company entering Germany sees this, the usual conclusion is that the German market does not respond. Often the market responded and the model could not read the answer. A German prospect who goes quiet for three weeks is not saying no, and a scoring system reads that silence as decay. What actually changed is covered in why foreign companies stall in Germany and on our Germany market page.
Does the MQL and SQL split fit German company sizes?
Partly, and it is worth knowing why. The vocabulary comes from organisations with two separate departments and one shared system between them. According to the Federal Statistical Office, 99.3 percent of the 3.2 million enterprises in Germany were small or medium sized in 2024, while those same companies accounted for 27 percent of turnover. So most of the companies you will call do not have a marketing department producing MQLs, and much of the revenue sits with a small number of large organisations where the decision circle is at its widest.
Practically, that means one definition will not cover your whole target group. A mid-sized manufacturer with 80 people and a group with 4,000 do not qualify the same way, and a threshold set for one will quietly break for the other. Splitting the rule by company size is less work than it sounds and removes most of the arguments before they start.
All of this assumes the phone is the right channel for your offer in the first place. The four cases in which it is not, and what changes when the target market is Germany, are set out in when telemarketing is the wrong way into the German market.
MQL, SQL, ICP and buying centre each get a short entry in the B2B sales glossary, which is easier to forward to a colleague than a whole article.
Where the line sits also depends on your business model. SaaS and software companies entering Germany have product usage signals that other sectors simply do not have, which makes a tighter threshold workable. The condition is the same as everywhere else: the signal has to evidence a problem, not curiosity.
For IT services firms, the gap between a marketing qualified and a sales qualified lead is usually a question of project timing, not interest. We describe how this plays out on our page for B2B lead generation for IT services companies in Germany.
Frequently asked questions about MQL and SQL in Germany
Should we translate our existing MQL definition into German?
Translate the wording, but re-test the thresholds. The criteria themselves usually survive. The point values behind them were calibrated on a market where buyers identify themselves earlier, so they will hand over contacts too soon. Run the first eight weeks with the sentence-based rule and set numbers afterwards.
Is a lead score useless in the German market?
No, but treat it as a priority list rather than a handover threshold. A score tells you who to call first. Whether the contact is qualified only becomes visible in that call. Using the score as the threshold replaces a check with an assumption, and you lose the ability to find the mistake later.
How many criteria should the definition contain?
Three or four. A named trigger, a rough timeframe, the responsible role, plus one exclusion rule. Longer lists are not asked in a live conversation, they are filled in afterwards, and at that point they describe expectations rather than what was actually said.
Who should own the definition when the calling is done by a partner?
Both sides, in writing, with a return right for your sales team and an obligation to state which criterion failed. That single mechanism is what turns a disagreement about quality into a measurable rate you can improve, instead of a recurring argument about individual contacts.