How do I create an SLA when I don’t know conversion rates?

[JonPayne]

Not all of us have access to a complete data set to be able to calculate our conversion rates to set an SLA between marketing and sales. Join the discussion to get advice on how to create your SLA based on industry estimates.

*To learn more about this, check out the Holding Your Teams Accountable With an SLA lesson via HubSpot Academy. 

[KNair4]

When you don’t know the numbers, build your SLA around process, effort, and responsiveness — then update it later with actual performance data.

[dyg]

Currently working on trying to get internal data to give us internal performance data. Mostly now, I am creating the infrastructure so that we can collect good data and begin looking at insights. For now, using 3rd party sources to create benchmarks. What I like to do in my analysis is look at 3 things:

1.  Once our internal data is cleaned and synthesized, I'll be able to look at how our current companies compare to our companies all time data
2. Then I will compare on a shorter scale, which can be month over month, quarter by quarter, year over year.
3. Then I compare that data to industry standards.

[Allieatom]

Ask questions and do research. What is the company goal and overall vision? Once you have this you can come up with actionable SLA for marketing and sales teams. 

[aguerrau90]

I do not know my conversion rates.

[Jaycee_Lewis]

Hey, @aguerrau90 👋 This thread has a lot of great suggestions, but one easy one to get started with is looking up the benchmarks for your industry or vertical. — Jaycee

[HLee8]

On the Marketing end, the benefit you have with digital Marketing platforms is the ability to speak to the reps and get an idea of average click-through rates. Conversion rates can vary with the business (B2C will vary by product and will again be different from B2B) but you can get good ballpark figures by asking reps for high-level benchmarks, consulting Gen AI tools and perhaps talking to one or two people who have hands-on experience with the channel you're working with.

Then it comes to determining your internal funnel which is going to be more complicated. Hopefully by the time you're scaling, you have some data on your first batch of leads. Remember, your first leads are always going to be the most qualified because you got them through high-touch methods and they're probably your most bought-in set of customers. People who come in through paid digital channels or syndicated content marketing are likely to be less qualified, but you can take a percentage haircut from your historical rates to get at the right ballpark to benchmark these. They may also need a different nurturing approach, so consider building out your CRM (customer relationship management) channel to ensure that leads who come from sources that aren't necessarily bought into your brand get nurtured in a way that's scalable for you.

[Abdulk]

Assuming it's a new sales and marketing steup, and we do not have historic data to support or decisions, then I would look at the industry. What are their rates? I might even bring in an external consultant within our industry so we can do solid projections. 

[OLiu]

That’s such a relatable challenge, many teams don’t have perfect conversion data when they first try to create an SLA. What I’ve found helpful is:

1. Start with industry benchmarks 

2. Use internal historical trends (even partial) 

3. Set directional targets, not fixed numbers 

4. Focus on accountability metrics you can control 

This way, you’re still able to build an SLA that aligns teams and reduces friction, while leaving room to refine it once your reporting improves.

[Atush_Sharma]

That’s such a relatable challenge, many teams don’t have perfect conversion data when they first try to create an SLA. What I’ve found helpful is:

1. Start with industry benchmarks – Even if you don’t have your own numbers yet, you can look at average conversion rates by industry as a starting point. For example, typical lead-to-MQL or MQL-to-SQL benchmarks can guide expectations.

2. Use internal historical trends (even partial) – If you don’t have full data, use whatever you do have—maybe from a single campaign, quarter, or region—as a baseline.

3. Set directional targets, not fixed numbers – Frame your SLA as “initial assumptions” rather than absolutes. This way, both marketing and sales agree to revisit it every quarter as more data becomes available.

4. Focus on accountability metrics you can control – Instead of just conversion rates, you can align on things like response time to leads, number of qualified leads handed over, or follow-up consistency. These create accountability even without full conversion visibility.

This way, you’re still able to build an SLA that aligns teams and reduces friction, while leaving room to refine it once your reporting improves.

[KAgarwal06]

With limitation in our raw data, we can assume the industry standards, for example, a B2B SAAS company has a conversion rate of 10% for lead-to-opportunity and 35% for opportunity-to-deal. Basis these assumptions, we can plan our quarterly targets for the respective teams and review them on monthly basis identifying our delta and planning the next quarter on the basis of the actual data. However, this exercise also helps in identifying the gaps if any and adopting the GTM frameworks which enables us to improve the negative delta. 

[MP49]

You don’t need perfect data to start building an SLA. If your conversion rates aren’t available, you can base your first SLA on industry benchmarks or even conservative estimates, then refine it as you collect your own numbers. For example, assume a 20% lead-to-opportunity rate and a 25% opportunity-to-close rate, then back into how many leads you’d need to hit your revenue goal. Over time, compare estimates against your actuals and reset the SLA each quarter. The key is to start with a baseline rather than waiting for “perfect” data—SLA success comes from iteration, not a one-time calculation.

[PG0701]

When you don’t have full visibility into your conversion rates, an SLA doesn’t have to stall—it just has to evolve. Think of it as a hypothesis that gets tested and refined over time.

One approach is to start with industry benchmarks. For example, if B2B SaaS companies in your sector average a 5–7% lead-to-opportunity conversion rate, use that as a temporary anchor. Then layer in your own qualitative context: the quality of your ICP (ideal customer profile), the maturity of your sales cycle, and the tools you’re using to track engagement.

From there, frame the SLA less as a rigid contract and more as a living agreement. You might commit that marketing will deliver X number of MQLs (marketing-qualified leads) per month, while sales agrees to follow up on 90% of them within a set time window. Over the first 60–90 days, track what actually happens. Even if your numbers aren’t perfect, you’ll quickly see patterns emerge that allow you to adjust your SLA with real conversion data.

In other words: don’t wait for perfect data. Use estimates to set an initial framework, build accountability around activity, and then let the feedback loop between marketing and sales refine your SLA into something data-driven and tailored to your business.

[SBooker4]

When faced with having to create an SLA without knowing the converion rate you have to first start with finding out if there is enough data available to calculate the conversion rate. However, if you are faced with not having enough available data you need to start with a basic SLA that you can adjust and build upon as you scale. This basic SLA will allow you to collect the needed data while you focus on what you have in the moment response times, follow-up cadences, and ownership clarity. 

Response times, follow-up cadences and ownership clarity will produce it's own data and give you a starting point to create bench marks. You will analyze:

• The handoff time between teams
• Lead response times, "How quickly is sales able to contact leads once received from marketing." 
• Follow-Up Attempts: "How many touchpoints are there over a set amount of time?"
  • Example: "3-5 touchpoints over 2-3 weeks"
• First response time. How quickly the customer is acknowledged after a chat, call, message or request.

Basic SLA example: "All MLQs will be followed up by Sales w/in 24 hours."

This should provide you with enough data to eventually evolve your SLA around milestones such as, Lead speed, contact rate, follow-up compliance, and engagement response time. This data will now be enough to calculate your conversion rate to determine your sales velocity. 

[NAlphonsus]

I would apply the following steps in creating an SLA Without Existing Conversion Data

- Define What Matters Most in the Service: i would start by outlining what aspects of the service that I can measure — conversion rate is just one metric

- Use Industry Benchmarks or Proxy Data

• Look at competitors or similar departments (ask peers or check industry reports).
• Use customer expectations: For instance, 80% of customers may expect a response within 24 hours.- - Make My SLA Time-Based, Not Outcome-Based (for now): If I don’t know conversion rates, then i would focus my SLA on inputs and effort, not results.

1. Mark it as a “Baseline SLA” (Temporary, Measurable): I would label my SLA clearly as a starting point to be revised once data comes in.

1. Track & Adjust Over Time

[vijayKumar114]

If I’m asked to create an SLA but don’t have conversion rates, I don’t let that stop me. I start by focusing on things we do know, like how quickly we respond, when we pass tasks to the next team, or how long it usually takes to finish something. I talk with the teams to figure out what’s realistic for everyone involved—both for customers and for our own operations. Instead of waiting until we have all the perfect numbers, I treat the first version of the SLA as a starting point. I’m clear about what’s based on facts and what’s just our best guesses, so everyone knows where the numbers come from. As we get more real data, we can update and improve the SLA. I think it’s better to get something useful in place now and adjust it later, rather than wait until everything is exact.

[Georrick]

"When you don’t have reliable internal data to build your SLA, you reverse-engineer from the industry benchmarks and build assumptions you can test.

Start by identifying a baseline metric—like MQL-to-SQL or lead-to-opportunity conversion rates—from trusted sources in your industry (think: Salesforce, HubSpot, Gartner). Then frame your SLA as a provisional agreement based on those benchmarks.

Example: 'Marketing will deliver 100 MQLs per month with an expected 15–20% SQL conversion rate, subject to 90-day evaluation and adjustment based on pipeline quality.'

This keeps both sides accountable without pretending the data is perfect. You’re not just setting targets—you’re building an iterative system where expectations, handoff timing, and qualification standards evolve with better insight.

SLA ≠ final word. It’s a framework for alignment."

[jessieong]

In such cases, I rely on a combination of approaches to establish baselines and set realistic Sales SLAs:

• Leading indicators and pipeline metrics: Analyzing earlier funnel stages (e.g., qualification and proposal acceptance rates) to approximate conversion trends and identify bottlenecks.
• Sales team insights: Gathering qualitative input from reps and managers to validate estimates, refine forecasts, and capture best practices.
• Pilot programs or controlled tests: Partnering with sales teams to trial new qualification criteria (drawn from best practices) or process changes in smaller segments to gather directional data before scaling.

Once initial estimates are set, I continuously track and adjust them as more data becomes available, improving accuracy over time. These methods help set SLAs that balance ambition with realism and support healthy, predictable growth.

After Sales SLAs are clearly defined and aligned, the next step is to establish Marketing SLAs - for example, around lead quality, volume, and speed to follow-up.

Prioritizing Sales SLAs first ensures that Marketing efforts are designed to support and complement Sales capacity and goals, creating a cohesive handoff, strengthening accountability across the funnel, and driving overall revenue growth.

[rgill4]

If I do not have conversion data, I use industry averages for my business type and work backward from revenue goals. I estimate how many leads are needed and then create a basic SLA. For example, how many qualified leads marketing should send to sales, and how quickly sales should follow up. It is not perfect, but it helps both teams align. We can always adjust the SLA once real data becomes available.

[AlePerotti]

Research market data and create some educated guesses on the initial data points to get things started, then evolve in each iteration. You also have to begin measuring all data points to create your own set of historical information.

[hgonzalezc]

It's necessary to creare a data culture in every organization. You can start from zero to stablish tour data system and structure and grow.  The most important thing is to stablish the system.