800K revenue opportunities unlocked in a month
100+ hours of manual analytic work eliminated every month
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25% annual spend saved on GTM stack
Moogsoft is an AI Ops incident management platform for engineering teams responsible for maintaining high availability. In the face of increasing telemetry data, systems and tools, Moogsoft's machine learning algorithms help automate noise reduction, correlation and data classification across incident workflows – so IT teams can get to root cause and remediation faster. Founded in 2012, Moogsoft has 100s of customers worldwide, including American Airlines, Fannie Mae, Fiserv, HCL Technologies, Yahoo and more.
Mike Cabot is Chief Revenue Office at Moogsoft and runs their go-to-market functions, including Marketing, Sales, Customer Success, Customer Support, and Revenue Operations teams. He has 20+ years of experience in building and growing GTM teams at multiple B2B SaaS companies.
Kevin Clough runs Sales Operations at Moogsoft and is passionate about instituting data-driven GTM tactics as a competitive advantage. Before Moogsoft, Kevin spearheaded data and analytics for GTM teams at Hubspot for eight years.
We have a state-of-the-art data stack, including a cloud data warehouse and ELT tools. However, operationalizing actionable insights and rapid experimentation proved to be a huge challenge.
GTM optimization is an iterative process for most businesses. Depending on the need, the GTM teams want to optimize the top, mid or bottom of the funnel on any given week/month and want a quick solution.
Recently, our GTM team wanted to score prospect interest to help our sales team prioritize their time. This scoring needed to incorporate third-party intent signals, website engagement, product usage insights and more.
This project would have taken weeks to iterate through nuanced data requirements with stakeholders and negotiate priorities with our oversubscribed data team. Once that was done, the data team could start building the solution. Worse, any change to the business logic would mean another round of the same process. Given the process burden, limited bandwidth and the required time & effort, we had to resort to the old-school approach of prioritizing prospects based on limited data, ultimately limiting our topline growth.
Most CRM systems, like Salesforce, have built-in data models and integrations capabilities to pull in external data. For example, adding account and contact data to Salesforce is pretty straightforward. What is hard is then joining data from multiple objects, running time-series analytics and creating business insights. For example, creating alerts based on a change in prospect scores, customer health scores or pipeline to help prioritize GTM activities is incredibly hard in CRM systems.
Initially, we created workarounds like creating object links, but that bloated our CRM and created complex dependencies that were difficult to maintain. Eventually, we had to resort to exporting data from our CRM and running reports manually in Excel, SQL and python – severely limiting our ability to scale a data-driven GTM.
Like many companies, we use Salesforce as our source of truth for customer data. The upside is that the entire revenue team has one place to find information. But the downside is that everyone needs a Salesforce license to access timely customer data. That's expensive!
For RevOps teams, Savant is like playing video games with cheat codes. We use the platform to power any and all GTM analytics. The Savant bots collect, stitch and analyze data from our business apps and data warehouse and activate the insights when and where they're needed. All that without a huge lift.
The first bot we built in Savant activated customer health alerts for sales and customer success teams. With Savant's low-code UI, we could pull data from multiple data sources, build analytics and activate it within a few hours. A months-long process of detailing the data requirements, prioritizing the projects across teams and then having data engineers build a solution was reduced to an analyst spending a few hours in Savant. And for the first time, we had the agility to deliver analytics at business speed.
Within a month, we created several other analytics bots, and the timely insights from those bots have already created $800,000+ of new opportunities. We finally have the analytics development agility that GTM teams want and are not shy about taking on rapid data-driven experiments.
We buy from third-party intent sources and integrate the data into our CRM system. But due to Salesforce cross-object join and time-series analytics limitations, we had to pull the data out and build intent scores every week for our sales team. My team and I have automated building and refreshing the intent scores using Savant.
This is just one example of several reports we have automated with Savant. As a result, we are saving 100s of hours of manual work every month, spending time on strategic initiatives and helping grow our business faster.
Our support teams need a 360-view of our customers to serve them better when they call us. Because Salesforce was our single source of truth, the entire team needed a Salesforce license. That was ok initially, but as our business grew and the teams expanded, our licensing costs skyrocketed.
Savant solved this problem for us. Savant bots help us create a 360-view from data in a data warehouse and deliver it directly to the apps our support teams use. We no longer have to buy Salesforce seats to provide our customer teams 360-view to our support team. It's a win for our budget, our customer teams and, most importantly, for our customers in need of world-class support.
In addtion, we plan on leveraging Savant’s cutting-edge AI/ML capabilities next year. This capabilities obviates the need for us to buy point-solutions for predictive analytics solutions like lead and account prioritization and demand and revenue forecasting.
The CRM licensing costs reduction and tech stack simplification from not having to buy predictive analytics solutions are expected to reduce our total GTM stack costs by at least 25%.
