I don’t always have complete data, so I estimate conversion rates using industry benchmarks and small sample data, then refine the SLA as more real data becomes available.
I enjoyed learning about SLAs. I was interested in the part that elaborates the connection bewteen sales and marketing. I also liked the part that mentiones when you have to contact a customer, how soon is subjective.
I have a question regarding the worksheet on creating a sales and market SLA. Are we provided sales data to enter into the worksheet, or do we make up our own data?
I believe that a lack of historical data should not paralyze operations. In the absence of internal metrics, the first step is to leverage industry benchmarks to build a Baseline SLA.
I do not have access to such data. We never really put systems in place to measure these. But now I see the relationship, how having an SLA will provide clarity to the sales and marketing teams instead of working blindly or simply pulling figures out of thin air. I would start with a one-month period of tracking these metrices to build an SLA, then continue to iterate the SLA as I get more data over a period. I like the idea of a judicial branch, the oversight it gives which ensures that neither marketing or sales are allowed to abandon a lead. Each lead is important.
If data is not available to define the sla. Then it should be an progressive implementation/collection. Research industry standards to have a starting point, talk to employees and get their insights into written content (processed by ai later). have a person call, converted and lost leads to get feedback, turn insights into written content (processed by ai later). Collect any data/registry that relates to conversion rate/process. Process all this data to acquire insights. This will provide a good starting point for the initial SLA. After, set up correct data colection systems and update SLA iteratively. An ongoing process.
Not everyone has access to full data, especially when you are just starting or working with small teams. In those cases, using industry averages can help as a starting point to create an SLA. The important thing is to see it as something flexible, not perfect. As more data becomes available, the SLA can be adjusted over time. What really matters is that marketing and sales are aligned and have clear expectations from the beginning.
As someone starting at a fairly small team I believe creating a strong SLA would be staying open to creative and flexible ideas and approaches in your applied market.
Agree, data is key and if teams or companies have not been thinking about leads and conversions in this way it takes time to build up the data. I liked the idea of testing against marketing benchmarks of your industry, size, etc. I also like the idea of giving marketing a revenue goal, not just a total leads goal, which helps adjust for the different closing % of lead source and potentially different ASP.
When you don’t have a complete dataset, you can still build a solid Marketing–Sales SLA by working backward from revenue goals using industry benchmark conversion ranges (visitor→lead, lead→MQL, MQL→SQL, SQL→customer) to estimate how many qualified leads marketing must deliver and how fast sales must follow up, then agree on clear definitions (what counts as an MQL/SQL), minimum lead quality fields, response-time commitments, and a simple tracking plan (even manual at first) to validate assumptions and tighten the numbers over time.
If you dont have a complete dataset, I think it would make sense to review the dataset you have and review what metrics you can achieve with current info. depening on missing metrics I think its fair to collaborate with teams or research industry standards to apply to your business if possible.
what if I don’t have any data or any company yet to get data ?
I’d love to ask my Sales colleagues to call a lead within five minutes, as a Marketer. What would you advise regarding leads that “raise their hand” outside office hours? It could be noticed 63 hours later that some lead filled out a form on a Friday afternoon, for example.
That’s where sending a follow-up email, giving an expected timeline for response, is important and filtering new leads as being more pressing for when sales is back in office.
Creating an SLA with limited historical data is definitely a practical challenge.
I believe starting with benchmark assumptions, aligning lead definitions clearly between marketing and sales, and then refining based on actual conversion data is the most effective approach. Looking forward to learning how others structure SLA accountability in growing teams.
This is a really common challenge, especially in earlier-stage or less mature RevOps environments where data isn’t fully reliable yet.
If you don’t have a complete dataset, I think the goal isn’t to find the perfect conversion rates—it’s to establish a reasonable starting point that you can iterate on. A few approaches I’ve found helpful:
- Use directional internal data, even if it’s imperfect. Even partial or messy data can give you a baseline. For example, looking at a subset of recent deals or a shorter time window can still help you estimate lead to MQL to SQL to closed-won conversion rates.
- Leverage industry benchmarks as a starting point. While they won’t perfectly reflect your business, they can help anchor expectations. The key is treating these as placeholders, not targets set in stone.
- Align on definitions before metrics! In many cases, the bigger issue isn’t the numbers- it’s inconsistent definitions. Getting Marketing and Sales aligned on what qualifies as an MQL or SQL is often more impactful than perfect conversion rates early on.
- Build an SLA that’s flexible and evolves. Instead of positioning the SLA as “final,” treat it as a working agreement. Revisit it regularly as your data improves and your funnel becomes clearer.
- Focus on feedback loops, not just thresholds. Creating a mechanism for Sales to provide feedback on lead quality can be just as valuable as setting strict conversion targets upfront.
Ultimately, I think of early SLAs as a way to create alignment and accountability - not precision. As your data matures, the SLA can become more data-driven and refined over time.
Useful
When you lack historical conversion data, shift the focus of your SLA from outcomes to activity and quality. Start by establishing “input” commitments: Marketing agrees to deliver a specific volume of leads that meet a pre-defined Ideal Customer Profile (ICP), while Sales commits to a strict response time (e.g., follow-up within 2 hours). Using industry benchmarks—such as a 10-15% conversion rate from Lead to Opportunity—can serve as a temporary baseline to calculate initial volume goals.
The key is to treat this initial SLA as a dynamic experiment rather than a fixed contract. Schedule bi-weekly “Smarketing” meetings to analyze the first batch of data and adjust your conversion assumptions in real-time. By prioritizing a fast feedback loop and aligning on the definition of a “qualified lead,” you can build a data-driven framework from scratch within 30 to 60 days.
Not everyone has access to perfect data — but waiting for it is the biggest mistake.
The truth is simple: companies that grow don’t wait for full clarity, they build hypotheses and validate fast.
If you don’t have enough data to define an SLA between marketing and sales, start with 3 things:
Industry benchmarks → better than guesswork
Realistic internal estimates → even if imperfect
Short feedback loops → test, learn, adjust
The problem isn’t starting with incomplete data.
The problem is not starting at all.
An SLA isn’t a fixed contract — it’s a living system.
It evolves as your team learns.
Companies that wait for perfect data stay stuck.
Companies that operate on hypotheses build predictability.
Now I’m curious:
Did you start with real data or estimates? What worked best for you?
Siendo totalmente honesta, uno de mis mayores retos actuales es la falta de datos históricos limpios. Como todavía dependemos de algunos procesos manuales y hojas de cálculo externas, nuestras tasas de conversión hoy son más una ‘estimación educada’ que una realidad estadística.
Para mi primer SLA, estoy utilizando promedios de la industria como base, pero mi prioridad estratégica es automatizar la captura de datos desde ahora. Mi objetivo es que, para el próximo trimestre, ya no tengamos que adivinar y podamos basar nuestro acuerdo en nuestra propia fuente única de verdad dentro del CRM. Es la única forma de pasar de ser reactivos a ser verdaderamente escalables!
If I don’t have my own data yet, I’d start with industry benchmarks as a rough baseline and adjust over time as I collected real numbers.