This post is for sales and rev ops leaders who are being asked to hit a crazy revenue target (shoutout to all the VCs out there) but do so with fewer resources during a recession. I can’t imagine my job without the tools we’ve developed over the last few months so I wanted to share in the hope of adding value (and perhaps even building some pipeline).
Earlier this year, I joined Parabola - a fast-growing startup that helps companies with complex operations, that are spread thin and don't always have the headcount, software or eng resources needed to scale.
And while we have the same (cliche) artisanal cold brew and ping pong set up that we had during my days at Salesforce and Segment, that’s probably where the similarities end.
Nowhere were these differences more apparent than when it came to our infrastructure and resourcing (or lack thereof) in the realm of sales ops.
If you ask anyone in sales ops, their MO has always been “doing more with less” so perhaps more than any other function, they are built to last in today’s climate.
Fortunately, “more with less” is really core to what we do - so it should come as no surprise that with without any sales ops experience, we’ve been able to develop something that isn’t that far off from what’s considered best of breed. What’s more impressive is that it was all done without writing a single line of code and was built by one of our AEs (shoutout to Adam).
Any sales org needs a pulse on the following:
- Leading indicators of potential upsell/churn risk
- Customers who approached key milestones only to drop off
- Integrations and features leveraged across segments
Accessing this information sounds simple but I dare any non-engineer to try to harmonize stripe, CRM, marketing, support & product data AND keep it going in real time.
We need answers quickly – and we don’t have time to wait on data and eng to build or buy long-term solutions. And they’d certainly prefer to not drop what they are doing each day, week or month whenever I ask for data.
The burning question for rev ops, sales, and marketing leaders then becomes: How can we put tools in place that help the team be successful without burdening them with repetitive work or relying on technical teams?
Rev Ops at Parabola
To answer that question, we can draw on our own PLG experience. It’s first important to understand what PLG looks like at Parabola:
- User discovers Parabola and creates an account
- While creating their first flow, they pull data from a core system (like Looker, Salesforce, Google Sheets, or Snowflake) to build an automated workflow
- This flow is published and triggered to run automatically in the future
- Based the value of this automation (which didn’t require engineering support), this user invites a colleague to Parabola and the cycle (hopefully) repeats
Now that we understand the high-level motion, let’s now look at some of the data-points contributing to this growth:
- Number of users on a team, and team growth over time
- Number of triggered automations running
- New integrations configured
- New reports being created and shared
Knowing that, you can imagine how excited our team gets when a team invites more users to Parabola, or connects to their database for the first time.
These moments represent prime opportunities for our Team to ensure customers find maximum value by taking advantage of all relevant features in an effort to deliver real value (and also power the growth flywheel).
Example Use Cases
We have no less than 20 workflows at Parabola powering rev ops. None of these automations required any engineering resources, and have helped us leverage our CRM as a real source of truth, proactively touch base with key customers, and act on opportunities quickly.
Some of those automations include Flows that…
Capitalize on product activity
- Flag user activity in Slack or email, triggered by in-product activity such as adding new users to a team, deploying new automated workflows, and connecting to a new data source for the first time
- Enroll a customer in an email sequence based on specific functionality leveraged (or not leveraged) in the platform
Handle inbound leads
- As new leads are created in Salesforce throughout the day, a flow assigns those leads to the “next up” account executive and surfaces leads with a score above a certain threshold in Slack
Enrich CRM records
- A ‘reverse ETL’ workflow pulls product data from our Redshift database, matches those metrics with Salesforce records, and enriches our CRM with product analytics to paint a more complete picture of leads, contacts, and accounts
- Based on the source of a given lead, their record may not be enriched as it’s created in Salesforce; another workflow finds these un-enriched records, pulls data from various tools to enrich that record, and pushes the complete record back to Salesforce
Calculate sales commission
- Pull data on closed deals to give reps real-time visibility into variable commission, accounting for complexities such as clawback and accelerators
Report on customer health
- Use product and CRM data to assign health scores to key customers, enabling our CX team to proactively reach out to customers at risk of churning
Bundled together, these automations help us drive revenue and positive customer experiences by taking action at the right time. It also cuts out the need for regular Salesforce/ database CSV exports, where we formerly needed to manipulate datasets in a spreadsheet to answer questions through data.
By having our Sales and CX teams build these automations together, we’ve also identified repeatable best practices for calculating user metrics that contribute to health scores and reporting. When we look at reports, there are never any questions around data integrity because we know we’re all calculating results the exact same way, every time.
Takeaways
No two rev ops teams are the same. We evaluated third-party solutions for a handful of processes before embracing the fact that we needed a tool that was as custom and flexible as our processes themselves.
For the rev ops customers we work with, commission structures differ, data stacks vary, intent indicators are custom to specific products, and CRMs require different types of enrichment. This limits the effectiveness of traditional enablement tools with specific point-to-point integrations and pre-built workflows.
Parabola was built to enable operators to seamlessly aggregate data across sources and surface actionable insights to the right people at the right time. Being able to execute on these ideas in hours or days as opposed to weeks or month (and keeping our engineers focused on creating an amazing product) is part of what makes me so excited about bringing Parabola to the world.
