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What Does This Metric Mean To Me?

Partners in Excellence

We have all sorts of pipeline metrics, activity metrics, prospecting metrics, account, territory, retention, renewal, mix, margin, and endless other metrics. We have an entire alphabet soup of metrics, including MQL, SQL, ARR, ACV, TCV, NPS, MRR, LTV, CAC, Churn, and XYZ (OK, I made that up—I think). Both have $10M pipelines.

SQL 111
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A Step-by-Step Guide to Forming a Marketing and Sales SLA That Works

Sales Hacker

At Packhelp , we created an SLA that helped us scale over 9000% over five years and take out Deloitte’s Fast50 of the CEE region. These are usually: Conversion rates between different funnel stages (MQL to SQL, SQL to Opportunity, Opportunity to won deal). MQL to SQL Conversion Rate (CR): 34%. SQL to Opportunity CR: 82%.

SQL 109
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How To Get Started With Email Segmentation

Salesforce

If it’s wintertime, a clothing retailer might segment their audience by sending promotions to customers in extra cold regions. What was once a complex process involving the manual conversion of campaign objectives into SQL code has transformed into user-friendly interfaces where segmentation is as simple as drag-and-drop.

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Is a Sales Operations Career Right for You?

Sales Hacker

The focus is on things like reporting, territory management and later stage tasks such as contract negotiations and finance approvals. Knowledge of SQL is a big plus. A director job posting usually includes these skills: Oversee the GTM strategy from infrastructure and automation to sales processes and regional performance.

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When Things Dry Up!

Partners in Excellence

Sales people wait for the coveted SQL–the Sales Qualified Lead. Regardless of what’s been agreed between marketing and sales, to a sales person the SQL is a buying ready (hopefully PO ready) lead. Regardless of market, solution, region, sales/buying cycle there is one universal truth.

SQL 102
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Mailchimp’s ex-Head of Data Platform: “Data Doesn’t Have to be Hard — Three Data Myths and How to Bust Them”

SaaStr

The speaker also addresses the myth of scarcity, emphasizing that as founders of startups, dealing with limited resources is familiar territory. They initially planned to use a data warehouse for analysis but quickly realized they didn’t have one, so they improvised by using existing tools like SQL and Python.

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Like Leaving Flour Out of a Cake

Pointclear

This led to informal agreements with sales reps to accept almost all leads with little inspection in order to keep the business development rep focused on spending more time on the sales rep’s territory. How can you fix this? With what I call the judicial branch.

SQL 69