Since I started mentoring with Springboard, I’ve been reflecting on how the students might go about finding analytics jobs, and what exactly someone means when they say that they want to find a job with a startup. “Startup” means many things to many people. How big is too big to be considered a startup? There’s no clean cut definition, but I want to point out what different types of analytics jobs tend to be available at companies in different stages of their growth.

You can think about these stages in terms of funding levels, team size, or responsibility sets – they all move roughly in sync with each other. I will try to point out company attributes that I consider important. If you’re looking at starting your career in analytics, or at what your trajectory could look like, I hope the following thoughts are helpful. Make sure to note – I naturally gravitate towards tech because that is what I know and where I have the most passion. Similar thoughts may or may not hold as you move to other industries. Finally, if the company that you are working for has an analytics component as part of the core functionality (think Tableau – an analytics tool, Salesforce – a product where data provides core information to a manager, or Baremetrics – a product where data and visualizations ARE the product) then there could be a role for an analytics person at any stage.

With regard to the examples, be sure to keep in mind the time of publication of this article – I’m hopeful that the information here holds over time, but the specific companies will definitely move through different stages!

Super-Early: 1-5 employees, pre-seed or early seed (0-$750k)

This stage is all about proving traction – to yourself or potential investors. Analytics will help you measure it.

Where to look: angel.co – it’s free for startups and pretty focused on serving them effectively. It’s my go-to for early stages. Hacker News Jobs – YC’s hacker news jobs board is a terrific list, too, but it’s heavily trafficked. Standing out is hard, so be sure you’re a rock star.

Demand Generation/Growth Hacking/Marketing

The most impactful use for analytics at this stage only matters insofar as it generates additional growth. Help the team focus on a particular metric that matters to the business, but be prepared to adjust that metric quickly as you learn what’s important. You should be testing the effectiveness of marketing channels, either in terms of benefit for your time, or benefit for your money. When tracking product analytics, and the behavior of your users in the product, don’t get sucked in to looking at the number exclusively – understanding when you need to use qualitative feedback here is crucial. My advice? If it’s something that you’re interested in, reach out and see if you can do a free or cheap project for them. If you can prove that you can get them 10 (or 100) users, and why your data shows you that those could be loyal users, they’ll find room for you.

This role may or may not even be advertised – you sort of need to look around at local incubators and get involved in your local tech community and startup a conversation with folks working in areas that you are interested in. Be prepared to work on a “prove it” project.

Founder

          

Early Stage: 3-10 employees, small amount of funding ($500k-$2M), some traction

A company at this stage might be hiring for a real analytics person, but it’s more likely that you’re coming on as a marketer. Additionally, you’re likely on your own, to sink, swim, or fly. Try to find similarly placed folks at other companies at a similar stage (or a stage further) to help guide you. The expectations on you are likely to be intense, and you need to be ready to measure your own success along with that of the team. It’s an incredibly exciting time at a company, but is absolutely stressful. As long as you keep up with your growth goals, you can start understanding the product analytics as well, and ensuring a robust tracking implementation so that when you hit the next stage, you’re ready to rock and roll.

Where to look:  Angelist – you can sort by size/stage, (and maybe Linkedin or TheMuse – some companies will be there but sorting through the noise is harder). Same deal with Hacker News Jobs as above.

Marketing Analytics (Growth Marketing / Growth Hacking, Paid Ad Specialist, Content Analytics) –

Early-Mid: 10-30 employees (<$5m funding) – Decent traction

Product Analytics (Data Analyst, Data Analytics)

At this stage you’re at least likely to be focused on product metrics, but still related to growth of the product. Depending on the makeup of the rest of the team, you might get some guidance from your executives on Lean Startup style metrics, but be prepared to learn as you go and be mostly self sufficient. It’s important here to focus on Dave McClure’s Pirate Metrics – What events, marketing sources, or user product choices lead to Acquisition, Activation, Retention, Referrals, and Revenue?

A company at Chariot‘s size is looking for a growth analyst.

Product management

Middle Stage: 30-100 ($5-30m funding) – significant traction and user base

If I was trying to start my career and break into the startup world, I would look hard at the opportunities in this stage. If you find a company that’s growing quickly, your role can grow quickly, too. You’ll have more senior folks there to learn from, and you’ll understand how analytics can really drive a company forward: you’ll have large enough data sets for your analyses to make noticeable differences. Finally, this is the stage where your ability to buy top shelf analytics tools starts to appear. As a startup techie, you probably keep abreast of the latest greatest software, but in order for your organization to afford it, you’ll have to have proved your value as an analyst and the value of that tool. The more data you have, the more value the tools bring, the more likely that you can see positive ROI.

These teams are also where you start needing multiple people in roles, so you don’t need to be the World’s Best Analyst on day 1. You “only” need to be very good, or be able to prove to them why you will be.

Check out my friends at VideoBlocks hiring for an entry or midlevel Analytics Specialist:

Late Stage: 100-300 employees ($30M+ raised)

Late stage companies have enough folks on the team to start to really specialize. The marketing analytics folks may start to branch from the product analytics, and you’ll start to see an analytics group form. Additionally, it’s where you start to drift away from really being a “startup” to me. In the context of companies over all time, sure, these are potentially still young, but the atmosphere really starts to be more like a “real company” and less like the early days of a startup. That has benefits – higher salaries, lower risk of the business folding at any given time, and better resources for doing what you need to do. But it also has drawbacks – this seems to be about the stage where not everyone can have a real relationship with the executive staff, and it’s also typical at this stage to have much lower equity offers due to the market salary and lower risk profile.

If you’re looking for your first job, try checking out Springboard to build your skills before you apply. I’m a mentor for them and think that they give you a good baseline set of skills.

Segment (coincidentally, one of my favorite tools of all!) shows some of the specialization happening:

Not-really-a-startup (but could still be awesome): 500+ employees, Tons of Funding (or Profit)

In a data-driven organization, setting metrics and Key Performance Indicators (KPIs) help drive your business forward. While knowing various metrics about your business can help you in different ways, I’m a big proponent of the Lean Analytics concept of One Metric That Matters (OMTM). A savvy entrepreneur (and hopefully, the entire team) will know at any given moment what the most important metric to them happens to be at that moment. With this type of singular focus, you can drive meaningful change to your business without suffering from data paralysis.

An organization’s OMTM can and should change depending on the stage of the company, the short-mid term goals that you’re trying to achieve, and the type of business that you’re in. During my time at Speek, we changed our core metric a couple of times when our goals or strategy changed, and at Sestra, we know exactly what our focus is for the short term. Today I’m going to walk through how we landed on each of these metrics and how they impacted the business.

Speek’s Early Days: Registered Users

First, let’s talk about the early days. I wasn’t with Speek at the very beginning, but looking back at the data from that time period, and talking with the people who were there, it’s pretty clear that the goal was very much top-of-the-funnel. We were positioned as a B2C product with a friendly brand and we were looking for registered users. The team focused heavily on marketing and optimizing the signup process in order to encourage users to try us out. The biggest giveaway here is that we were measuring campaign effectiveness in terms of cost-per-registration.

Classically, you want to look at some number further along the line, but let’s be realistic here. How are you supposed to test any theory or figure out what’s working and what’s not when you don’t have any users? How can you measure a loyal user acquisition if you’ve only been around 6 months and half of your users are from the last 6 weeks. You have to make SOME assumption about certain types of users being better or worse than others, and basing your earliest days on “all users look alike – so let’s acquire registrations as cheaply as possible” is not the worst way to go. It’s crucial though not to mistake a shortcut in the early days for what you should be doing in an ongoing manner.

Registered Users:
–Easy to Measure
–Cheap to Acquire
–What else are you going to use when you don’t know what’s important
–Not ideal, but not terrible

Phase 2: Active organizers

As the company grew and users churned off or stuck around, we started to have more actionable data around user loyalty and usage. This was around the time that I started, and one of my first projects was a deep dive on our marketing spend from a loyal-user acquisition stand point. The biggest takeaway was that one of the best acquisition sources based on a cost per registered user metric had one of the highest churn rate of any source. This helped us realize that it was time to move to a more robust OMTM, but we certainly wish that we had migrated sooner.

After some more data digging around our best users, we shifted our focus to the concept of an Active Organizer. This type of user is someone who has hosted at least one call with at least one other person. That gives us the opportunity for a viral interaction, and shows someone who is actually using the product. Sure, it still has some drawbacks like not distinguishing tire kickers, but it’s a much better indicator of growth: these people are showing speek to someone else and are many, many times more likely to pay.

Active Organizers / Active Users
–Much more meaningful indicator
–Create Viral Growth
–Still thinking of B2C, but encouraged the transition to B2B

In a textbook case of “you get what you measure for” our new registrations dropped 30-40% mostly losing paid acquisitions who didn’t convert anyway, and bumped our activation rate by almost 100%.

Again, we made product and marketing decisions based on this metric. For example a scheduling widget enabling people to invite another person or spending marketing dollars on another salesperson since the salespeople inherently pull someone onto a call with 2 people.

Best Fit: B2b Sales Demos

This leads me to phase 3, where we really saw success. Our number 1 leading indicator of revenue was number of sales demos set. The product/marketing/word of mouth generate inbound leads, and we made substantial improvements geared towards generating even more inbound leads in our subsequent releases. We even shifted our onboarding from a freemium model to a free trial model, to help turn all of those free registrations that we get into true sales leads.

Additionally, we worked the predictable revenue model of setting outbound sales demos. These inbound and outbound sales demos are just two sides of the same coin working to increase the number of demos that we set. And so when you asked me up through the time of Speek’s Acquisition, “what is your One Metric?”, my answer was sales demos.

Sales Demos
–Best indicator of future revenue
–Drove switch to a free-trial model and away from freemium

Make sure your One Metric Keeps up

Each adjustment to the One Metric was driven by the needs of the company at the time. When you’re deciding on your one metric, be sure to keep in mind your company stage, needs, and business model. Those can all change and your OMTM may change along with it!

What’s the One Metric That Matters for your business?