Approaching Go-to-Market as a Data Engineer

An Introduction to GTM Engineering

I appreciate everyone’s patience while I’ve been MIA from the newsletter for the past few months. In short, this newsletter (specifically my interview with Chad Sanderson) led to a dream career move that combined my love for data, startups, and go-to-market (GTM) as I joined his venture-backed startup as their first employee. Unfortunately, I highly underestimated the amount of effort required to join a startup this early and leading go-to-market (GTM). The past seven months have been wild, but I couldn’t share details until we moved out of stealth! Well, today, we officially launched Gable.ai, and I can share with you all what I’ve been working on– GTM Engineering.

What is GTM Engineering

Hubspot defines a GTM strategy as “a step-by-step plan designed to bring a new product to market and drive demand. It helps identify a target audience, outline marketing and sales strategies, and align key stakeholders. While each product and market will be different, a well-crafted GTM strategy should identify a market problem and position the product as a solution.” While most startups start from scratch, Chad and I have a unique set of advantages:

1. We collectively have an audience of over 100K followers.

2. We have years of data tracking our content engagement and conversions.

3. Chad manually kept a spreadsheet of every meeting he had and who converted to design partners for the product.

4. We have the technical skills to analyze this data and build data products with it.

In other words, we don’t have an awareness problem. We have an optimization problem. How do we filter down and engage with the right potential customers when we expect to get millions of views of our messaging in a year? Even if a large portion of this audience are qualified leads, there is no way we can individually meet with all of them. We need to identify 1) who is the most engaged, 2) who is in our initial customer profile, and 3) whether it’s the right timing to engage with this lead. It became clear to us that traditional marketing methods would not work for us, and thus, we decided to pull from our engineering skills to solve this problem.

Thus, I define GTM Engineering as the process of using engineering problem-solving, data, and automation to bring a company and or product to market.

Problem Scoping

Even with the above advantages, it’s still challenging to determine what exactly a startup should focus on in its GTM efforts. A common mistake people make is jumping straight into tactics such as webinars, email campaigns, events, etc.. While this is great for quickly getting some traction for a startup, it doesn’t move the needle forward for market adoption. Thus, all of our GTM efforts at Gable start with the following five questions:

1. What does extraordinary success look like?

2. Who are similar people doing it, and how are they doing it?

3. How do we make this end goal possible?

4. What needs to exist for question “3” to be true?

5. Repeat questions “3” and “4” until you have a starting point.

What does extraordinary success look like?

For us, we are basing GTM success on the proxy of our ability to meet the milestones to raise a Series A funding round. For that to happen, we need to have X number of paying customers by the end of Y time. With your north star metric in place, you can begin to work backward.

Who are similar people doing it, and how are they doing it?

You don’t need to start from scratch, as you can leverage the power of hindsight to your advantage in your GTM strategy. I made a shortlist of founders who were in the data space and successfully launched their products, and I studied their online presence leading up to their company announcement. I’m talking going through every post, blog article, webinar, etc., to see how they built up excitement for their proposed solutions and got their first customers. Patterns start to emerge as to what’s effective or should be passed on for our unique business use case at Gable.

How do we make this end goal possible?

As stated earlier, Chad and I had a lot of data going into this GTM strategy development. For example, here is some of my personal LinkedIn data captured from Shield Analytics.

In addition, we have data on Substack engagement, Slack community engagement, Calendly meetings, and a list of design partners. With this data, we determine the conversion rates going from impressions on social media to securing design partners. With these conversions, we can work backward from our target customer count to each stage of the funnel to the number of impressions we need to reach.

Keep in mind that at this early stage, these numbers need to only be directional to help us establish a GTM strategy rooted in our unique business use case.

What needs to exist for question “3” to be true? Repeat until you have a starting point.

Now that we have our data, use cases, and targets, we can begin thinking through which tactics must exist to reach our end goal. The way Chad and I approached this was to:

1. Create categories for each major stage in our marketing funnel.

2. List out as many tactics as possible for each category.

3. Rank each tactic on a 1-10 scale for various attributes such as the reach of the tactic, how the tactic positions us in the market, or time commitment.

Based on these attributes, we can create a heuristic with a total score for each tactic. From there, we rank the best scores for each category and choose the top three tactics for each category. Finally, we ensure that these tactics will help us reach our proposed targets and iterate on the plan until we are confident that we can reach our targets.

GTM Architecture Design

If you thought building a data stack was difficult, try building a GTM stack, and you will quickly learn how disjointed this space is. Everything is held together by duct tape and Zapier integrations, and you need five demos with a sales executive before you even understand if the product is right for you. Even among seasoned sales and marketing professionals I interviewed for help, it was difficult to determine a stack for an early-stage startup that wasn’t cost-prohibitive. More importantly, most sales and marketing platforms are antiquated in that they rely heavily on email marketing. Thus, we had three primary criteria for determining a tool:

1. Ability to natively integrate with other GTM vendors without Zapier.

2. Access to an API for us to build our own integrations and automations.

3. Emphasis on social selling via LinkedIn.

Surprisingly, the resulting GTM tech stack closely aligns with a data stack, and we took full advantage of this. The below image is a drastically watered-down version of our GTM stack architecture, but you can see that similarly to any data workflow, our GTM captures marketing events, we log it into data storage, and then we leverage this data for insights and automation to drive revenue.

What makes viewing GTM stacks similar to a data stack powerful is that it becomes less about GTM tooling and, instead, about what architecture design aligns with with your business model.

GTM Optimization via Data Products

This is the fun part where I get to leverage my technical skills to drive our GTM strategy. Everything above was months of work to establish a strategy that would lead us to revenue and establish the foundational GTM architecture to achieve this. This is precisely what excites me the most about GTM engineering, as it ensures my technical work is directly tied to the business's revenue.

With the above architecture diagram of the flow of marketing data, actions, and expected conversions, we then identify where are the bottlenecks that we can automate. Thus, we create a product requirements document (PRD) to list every action in this value stream for GTM and then map those to technical requirements. Finally, we convert this PRD into Jira tickets and two-week sprints that align with the Engineering team at Gable (including doing sprint retrospectives).

While I can’t go into detail about what exactly I’m building for this data product, I can share that it’s a platform for:

1. Connect as many data points as possible in our marketing and sales efforts to our CRM HubSpot,

2. Identify new actions and or users and determine if they are in our initial customer profile (ICP).

3. If they are in our ICP, take specific actions such as a daily enriched leads list for the CEO or automated engagements.

The goal is to create the infrastructure to enable this automation to be easy to implement for semi-technical sales and marketing professionals.

My Vision for GTM Engineering

Through my data science roles, I quickly realized how little technical skills alone drove impact within a company– it was the combination of domain knowledge and the scalability of tech that was valuable. Thus, the primary purpose of GTM engineering is not to make more technical people involved with marketing and sales. Instead, the focus of GTM engineering is to make data and software engineering best practices as accessible as possible to slightly technical marketing and sales professionals.

Specifically, this looks like technical individuals building internal data products that 1) empower marketing and sales professionals to be data-driven, and 2) optimize their workflows to focus on the most important leads. More importantly, this requires making the technology as accessible as possible so that they can help develop these data products with their domain knowledge.

I’m currently building the V1 of our GTM Engine with Chad as my primary user to see if we can optimize his ability to drive people through a sales funnel and generate revenue. We are both confident in the potential of this platform, but things get interesting when we reach our series A funding round and start to build out the GTM and sales teams at Gable. If this resonates with you, then please reach out to me on LinkedIn as I am looking to build out a team to realize this GTM engineering vision when we begin to scale.