I’ve never done Sales before. Most of my professional history was spent working  for a Customer Success company, Gainsight, where I provided the data, insights, and tools for our customer-facing teams. 

At Gainsight, my bonus was tied to 1) verified customer outcomes, 2) renewal rate, and 3) customer advocacy. I learned to be maniacally customer-focused. If customers weren’t seeing value from the product, then I was focused on the wrong things.

When I joined Monte Carlo last year, the company’s Go-to-Market function was in its early days, and there was a real need for people to lead Sales. I immediately jumped in and engaged with prospects the only way I knew how — by treating them like they were a customer.

We’d collaborate to implement and adopt the product, I ensured they were seeing a lot of value, and then, finally, we’d discuss commercial terms. When doing discovery, I didn’t even care if you had budget (much to the chagrin of our new Head of Revenue). I was just excited if you had a problem that we could solve.

Turns out, this works really well. If I sample the first tranche of companies that joined me in a Monte Carlo trial, ALL of them converted to happy and paying customers. 

A great example is Gopi Krishnamurthy, the Director of Engineering at Blinkist, who co-hosted a webinar with us about how Monte Carlo helped them dramatically improve ROI on their data (stay tuned for more on Blinkist’s Data Observability journey!). 

What about Monte Carlo makes these proofs of concept (PoC) so successful?

  • Fast set up: We plug into where your data lives (warehouse, lake) and where it’s presented to the business (BI). For commonly used data stacks, it’s easy to complete in a single 30-minute meeting, or we have docs if you want to do it on your own time.
  • Fast time-to-value: Many customers get incredible value without ever instrumenting anything. In some cases, I’ve onboarded customers by alerting them to a major incident in their data that they weren’t even aware of yet. How? Our machine learning automatically defines rules for the behavior of each table (e.g. data freshness, growth rate, and size) and alerts you when your data looks wrong, stale, or otherwise erroneous. In addition to ML-powered anomaly detection, you can also set thresholds and write custom rules if you have specific business context that you want to set monitors for. Within 24 hours of set up and without any manual input, you get a complete view of your data’s lineage all the way down to your BI and analytics.
  • Customer obsession: Our CEO, Barr Moses, and I come from Gainsight, the Customer Success company. It has deeply influenced Monte Carlo’s approach to working with customers and the people within them. Among other differentiators, we offer: same-day bug fixes, customer-requested features shipped within weeks, daily/weekly customer success interactions, and value criteria defined upfront and carefully tracked. On top of that, we track feature usage extensively (and automatically) to ensure product development is aligned with what’s top of mind for our customers.