“I’ve learned that I still have a lot to learn.” - Maya Angelou
If you stop learning, you grow stale, you grow old and you start to think you know everything.
Not that long ago, we discovered a group who realizes the most value from great data flow––a group who doesn’t want to do the technical work to connect the data but also cannot afford to wait on the technical team to do this for them. Data scientists. The buck, however, does not stop there. Yes, data scientists find our product tremendously valuable, but what else did we need to learn?
Now that we understand data scientists…
We can identify problems and facts about them, their jobs and their thoughts. Things like:
- Data scientists want more data. Always.
- They often have to rely on others (engineers/devs) to perform technical tasks.
- The majority of their job is data cleaning, a menial task that may be automatable.
- Because most data science tools are built by data scientists, they are all useful. This poses a real paradox of choice.
This clarity and understanding helped us talk to data scientists in a way that resonated well with them. We focused our messaging and discussions around how Astronomer can solve these pains. But we needed more.
It seems like, in startups, you run with confidence in one direction, learn something new, stop, and with confidence, run the other direction. This happens again and again until you are worn down. It’s at that point when you find the resilience inside you to keep pushing.
My point here is that every time I feel like I “know” something, I find myself challenging that conviction and ultimately changing it or deepening my understanding. But what does that have to do with our newfound target? A lot.
It turns out that knowing your target and their problems isn’t enough, for a few reasons:
- Your target is deeper than one position or title.
- Your target may not be the economic buyer for your product/service.
- You may need to find new ways to connect with your target.
Let’s dive into these to see what else we can learn.
Your Target Is Deeper Than One Position or Title
Understanding this was a breakthrough for us, but to get there took a bit of light analysis. We identified the pain data scientists feel by talking to over 30 of them. And we figured this pain must be felt by others, right?…but, who? Through a series of positioning exercises obtained from a great book, we laid out who in an organization may feel the same pain. We analyzed our current customer base, asking questions like:
- Who is the buyer?
- Who uses the product?
- Who uses the data once it’s moved?
Soon, we started to realize that not only is our target the data scientist, but “those doing data science-type work,” which really opened my eyes to how much I still didn’t know. This group includes marketers, product managers, founders, and more. So marketing towards data scientists is A tactic… but is it THE tactic? And, if not, how would this change the market positioning that drives our web copy?
These questions kept us up at night… well, not really. They kept us in team meetings for weeks on end––discussing them over and over… and over… again. While it “distracted” us from our normal day-to-day, it helped us understand which conversations we should be having and with whom.
For example, though they are involved in the conversation, folks in the IT department (those most obviously doing technical work) are not our initial targets. While we thought that might be true, we needed to confirm it through analysis. It took the entire team, bringing in a disparate viewpoints (from the product, creative, marketing, etc.), to challenge everything each person thought was true, but had not yet confirmed.
Think of the first blog post as digging the hole. We did the dirty work to really understand what kind of soil we’re dealing with (ie. who our target is). Then it was time to start figuring out how to lay the foundation (ie. positioning).
To do that, we started to have effective conversations with the right people. But we realized that we needed to know: who is involved in approving the spend? Is it the contact we’ve been talking with? That’s where the economic buyer, the person responsible for budget, comes into play.
Are You the Economic Buyer?
When trying to grow an early stage startup, this an important question to ask of your target. Asking this question not only gives your target the comfort that you know what you’re doing and truly want to solve their problems but it also gives you clarity to navigate the buying process, which can save deals and save time.
So we asked ourselves––for each customer and prospective customer––who would approve the spend, and we started to identify what we call “champions” and “buyers.” These are both very important, and generally require very different conversations.
Champions are the targets feeling the pain. They need your solution because the status quo is not moving them closer to their goals. If your product can come in and solve their problems, they are the heroes. If you are genuinely out to solve their problems, they’ll bring you up the ladder with them and do some internal selling to get this solution approved. Hence, champion.
Talking to champions is easy because it’s just like you’d talk to yourself. When you have problems that hinder you from your job, you want…you long…for a solution. One that’s fun to use and worth the cost. Start asking pointed questions related to the problems you solve and continue the conversation if, and only if, they are feeling those pains enough to pay to solve them. It’s at that point that, in the best interest for both parties, you ask to understand the buying process and who the economic buyer happens to be.
This person (or people) is responsible for using budget to purchase products and services that contribute to corporate initiatives and/or short term goals. If the buyer does not firmly believe that you will contribute to their goals, you may as well throw out the white flag and hope someone comes to scoop you up off of the turf.
At this stage, it’s your job to help them understand that the status quo is costing money, wasting time and not contributing to goals. The spend on your product will change this, over time. “Over time” is key here, as it’s important to paint the picture that it may take some time for results to start showing. You can’t do 100 sit-ups a day for a week and expect to be shredded. If you promise immediate results, their too-good-to-be-true radar will beep and delay or kill the decision.
Getting the economic buyer’s attention is as simple as getting an intro from your champion. From there, you can work together to show how the solution affects budget, time savings, and increase in x, y, or z (generally revenue or productivity). The economic buyer can then use these facts to visualize ROI to whomever has final approval, likely someone in leadership or on the board. If your solution can really solve the problems you say it will, your champion will be the hero, the economic buyer will reap benefits and you will become a trusted resource.
But of course, even if you talk to the right people in the right way, you can run into dead deals. Again…a chance for resilience! You might understand WHO we are selling to, WHO the stakeholders in the decision are and WHAT the process is, but it’s important to keep exploring HOW to best connect with them.
Tweak the Process Until You Find Something That Works
Our first tactic was reaching out cold to individual data scientists or those doing data science in their job. But using (quite a few) prospecting tools to get email addresses for marketers, data scientists and product managers was a tedious task. You feel like you’re getting sh!t done because there is a lot of activity, but when you stop and look, the results don’t justify the effort. During Q1 2016, our SDR and I sent over 2,000 cold emails to our targets. We did A/B testing on roles/titles, messaging and subject lines. Still, our response rate was unimpressive… about 1 out of 100 unimpressive.
So we went back to our roots. How did we acquire our current customers? What instigated the best conversations? The worst?
Pondering this a bit, we realized that the way we’ve grown our business is by using our vast network and getting and giving introductions, which meant we needed to completely drop our cold-outreach strategy. After all, building a relationship is more than just saying “hi” every now and again. It’s more than asking, “Hey, do you know anyone doing X or having Y problem?” Building a relationship takes time and, usually, a #GiveFirst mentality. It might even mean connecting them with one of the 500+ connections on LinkedIn and thousands of contacts in your phone. That’s why you have them, after all!
One way this worked was when I reconnected with a now-customer I met through a Meteor.js forum from my pre-Astronomer days. At first, he wasn’t quite ready to engage our platform or services. So I introduced him to a few of his targets (in the higher education space) and talked with him about once a month to help him think through features and how the Lean Startup applies to his business. Eventually, he started using Astronomer to move his customers’ data from system to system.
As exemplified above, this strategy is starting to show results, although “results” may not mean new customers each time––or right away. At Astronomer, we believe all companies want to be data-driven. It’s really just a matter of time, strategy shifts and internal maturity until most companies will have to become data-driven to stay competitive. So we’ll stay focused on reaching our target. And to do that well, we plan to keep on learning.
It definitely takes some grit and resilience to get to the deeper understanding needed to grow the business. It starts by knowing what problem you are solving and for whom you are solving it. And then asking more questions. Never be satisfied with the knowledge you acquire along the way.
I will continue to chronicle our journey as we build our business and understand our customers, so stay tuned. If you’d like to contact me to talk about my journey, Astronomer’s journey or help with data transportation, accessing hard-to-reach data sources and/or automating the analysis of data, reach out to email@example.com. I’d love to chat!