How To Scale Your Growth Process in 8 Weeks
I had an epiphany during my morning commute last week (don’t you love it when that happens?): Our growth team should be a beacon, an intentionally inconspicuous device designed to attract attention to a specific location.
Aspirational, right? As usual, the hard part is setting out to actually do it. Only two short weeks ago, I joined Astronomer to scale our growth team. For newcomers to Astronomer, like me, it’s important to understand why what we’re tackling is so important, and therefore, why my mission feels personal.
We’ve heard about big data ad nauseum—and we get it, data is everywhere and we need it to make better-directed decisions for our business (and life for that matter!). At the same time, there’s a ton of people like me: non-technical quant analysis folks who need a data infrastructure that’s scalable yet flexible and, most importantly, accessible. I am the future of your workforce. I—and so many like me—need Astronomer to grow.
Here's why: Astronomer simplifies the “plumbing” between data pipes. We integrate disparate data sources into a unified structure. From there, algorithms can be written on top of a consistent dataset, and analytics and reports can extract a full picture of a particular area of focus, even your entire business.
Too tech? Think about it this way: Like the construction of a road, you can’t actually get anywhere until the cement has been laid—a long and expensive process requiring the employment of an entire construction crew to make the infrastructure work. With Astronomer, you’re buying the cement pre-made and commissioning our team to put on the finishing touches.
I want everybody to know they can get the data infrastructure they need this easily.
Introducing "The Beacon Project"
So how will I make growth happen? I have a vision and some thoughts on how to do it, but the honest truth is, I’m not sure what it will look like in 3–6 months. Classic startup story.
The Starting Point
My main objective is to monitor our tactics to ensure they align with our strategic direction, making sure all teams are operating cohesively, intelligently and with data-supported documentation.
To sum up my starting point for this project, we’re in need of a new engine for growth. A repeatable process. Evidence our customers give a shit and we can sell them a valued solution. Everyone on the team feels we have it; now it’s my job to take the amazing work of the team and quantify it.
And that’s the point of The Beacon Project. I want to document my initial hypotheses, tests, iterations and, ultimately, final use cases that have materially improved the way Astronomer acquires and services our customers. Let’s get started!
To build toward my vision of building a growth engine for the entire business, I have to start by understanding unit economics in three key areas of our operations: personnel capacity (per role, not per human), sales process timeline/requirements and implementation capacity per product type.
These capacity stats will be one of five “batched” datasets that will be the raw materials for the Astronomer growth engine (The Beacon).
The five datasets that will be The Beacon’s gasoline are:
- Lead Scoring System - determination of potential customer fit based on stated needs, budget, authority and timeline
- Salesforce Business Data - industry, revenue range, # of employees, personas (titles)
- Salesforce Sales Process Data - MQL, lead to opportunity conversions, opportunity to close conversions, average sales cycle (aggregate and per stage), annual contract value (ACV) and customer lifetime value (LTV)
- Marketing Cohort Analyses - weekly tracking of individual campaigns that produce MQLs; monthly tracking of aggregate activity from MQL to opportunity and beyond
- Astronomer Capacity - # of hours it takes a role to perform a function: marketing (content, messaging and inbound strategy), sales (which opportunities need an account executive and related customer success activities vs. those that can be directed to our self-service SaaS platform), implementation (of specific product types; DPL vs. ETL) and customer success (which ideally routes back to sales)
The nerd in me wants to get scientific about this, so here are the hypotheses I’m setting out to prove (or disprove).
Hypothesis #1: It is possible for Astronomer to rank potential leads based on historical analysis of the characteristics of metadata in our customer profiles and sales processes.
Hypothesis #2: A dynamic growth scoring algorithm needs to consider current team capacity and future potential sales scenarios to back into a sales “hot list” (a target number of accounts with the highest likelihood of quick close and long-term customer value).
Hypothesis #3: Once the scheme has been determined it can be systematized to scale dynamically as more information gets entered into the system. The algorithm gets smarter (initially with manual calculations, eventually through machine learning).
So what’s my plan of attack to make this happen? I’ve got five key steps that I will lay out in future posts:
- Step 1: Inventory all data needs for the organization
- Step 2: Build framework to capture and analyze incoming data
- Step 3: Start running campaigns to populate the system with data, add infrastructure to systems as needed, test framework for automation
- Step 4: Cross-reference the datasets like crazy; focus marketing and sales according to what’s working (manual exercises at first).
- Step 5: Iterate the model accordingly and build proven models into automated systems
So how will it work? We’ll see! I’ll check back in in two weeks with our progress. Until then, is there anything you think I should know as I build The Beacon?
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