My friend has four different magnets for plumbers on his fridge. Three of them are generic rectangular magnets that have generic information and contact details. One of them was in the shape of a dripping tap, mentioning they were experts in leaks and drips. If they had a leaking faucet it's pretty easy to guess which plumber they would call; the specialists in dripping taps. On the other hand if they had a clogged toilet it's down to chance which of the plumbers they would call, although they're less likely to call the dripping tap specialist they're also more likely to forget to look at the fridge and just search for a plumber online.
People will generally assign a higher value to a specialist than a generalist. Would you rather have heart surgery performed by an experienced cardiac surgeon who has done that specific operation hundreds of times, or by a generalist who has performed hundreds of surgeries, but only a couple of heart surgeries? If you were moving a valuable antique piano, would you hire a general furniture removalist who says they will move pianos, or pay a premium for an antique piano removalist? If you found a rat infestation in your house would you be more likely to start searching for "pest control" or "rat catcher"? When you have a specific problem you first look to people who are experts in solving that exact problem; which is why it can be effective for a service provider to have a landing page for different usecases.
This can be counterintuitive from the perspective of the service provider. A trained plumber can fix a leaking tap, a blocked toilet, a burst pipe and many other issues. Why limit their potential market by narrowing thier focus to a specific type of problem? But by advertising themselves to fix a particular problem they stand out for people with that problem, and may actually get more leads.
It's easy to apply these in the abstract, but it's much harder to do in practice. I'll explore some different ways I could position myself.
I currently refer to myself as a "data analyst", which is a very generic term. And I can do all the standard tasks of an analyst:
- Work with a decision maker to understand their objective and the levers they can pull
- Work with business stakeholders, database administrators and developers to identify the relevant data to capture
- Wrangle, extract and clean data from databases and third party sources
- Apply appropriate analytical, statistical and machine learning techniques to solve the problem
- Communicate insights and recommendations to stakeholders to help them make better decisions
- Effectively break down and coordinate the problem across a team, managing stakeholder expectations
- Build and serve production data pipelines, dashboards, and machine learning products
It's useful to use the term data analyst (or data scientist), because it's a commonly known position that people advertise for. However I don't stand out from the crowd; why would a hirer employ me over any other data analyst?
I could perhaps refine this by focusing on the areas I'm better at and want to be known for. I genuinely enjoy working with a decision maker to understand their objective, current perspective and framing the problem. Doing this right is immensely important to make sure we're working on the right problem. My real passion is diving deep into the problem domain and data to understand the problem and finding the insights that will improve outcomes. I'm always happy using whatever techniques will help shed light on the problem, and I really relish new challenged. Finally I appreciate the importance of communication in enacting change, and will work hard on the communication piece to make sure the insights are understood by the stakeholders.
I could position myself as working in Advanced Data Analytics, or Applied Data Science. I've also heard specific alternatives to Data Science such as Product Science and Decision Science. I know I'm not a reporting analyst or dashboard designer because I really enjoy solving new problems more than building consistent reports and dashboards.
It would be very unwise for me to position myself as a differential geometer, because no one is looking for them. However it's quite relevant to a lot of my work. My thesis was on the differential geometry of 4-manifolds and I have a very solid grounding in multivariable calculus. And it turns out that most machine learning algorithms are a combination of linear algebra and differential calculus. Machine learning provides a toolkit for building models for predicting how changes to inputs will yield changes to outcomes under uncertainty, and so is incredibly useful in optimal decision making.
Likewise I have an academic grounding in measure theory and experimental physics which puts me in a good place for understanding statistics. Statistics is incredibly useful in extracting the signal from the noise, measuring which changes actually lead to improvement and for building the models mentioned above. Even better statistician is a recognised occupation in industry, unlike differential geometer.
These are both poor positioning because they talk about the tools to solve the problem rather than the problem to be solved. Very few people would connect trying to work out how to increase revenue with differential geometry, calculus or linear algebra. Similarly if I had a plumbing problem I wouldn't search for a "plunger operator" or "drain snaker", let alone the more specific versions of these.
Python Data Analyst
Focussing on a solution can work when there's a known need. This is why tool-focused jobs like Tableau Analyst, React Developer and AWS Architect work. A large company has already decided to invest heavily in some tool will look to hire people who specifically have experience with that tool (sometimes overlooking the other non-technology requirements of the role).
Positioning myself as a Python Data Analyst communicates my technical skills, taking me away from pure reporting roles, and is associated with machine learning and advanced analytics. However being more technical it ignores the valuable skills of being able to frame a problem, understand the context, and communicate the outcomes. Again it's not close enough to an actual problem domain.
Digital Behaviour Analyst
Most of my professional career has centred around analysing user behaviour. Concepts like sessions, conversion funnels, overlaps (how many people view both of these pages), and behavioural segments are very familiar to me. While I can use typical digital analytics tools like Google Analytics and Adobe Analytics, I'm much more comfortable using databases, even if they're an extract from these products. I like connecting my understanding of flows through a website with what I see in the data, and connecting it to the products. An extreme example of this is when I reverse engineered a database of a customised Yellowfin instance to understand how customers were using the site.
This is better than any of the previous definitions because it selects a type of customer. I can help companies that have a digital product such as a website or an app, and store or export the behavioural data to a database for analysis. This also sets a minimum scale; I probably can't offer much to companies with under 100 customers.
Moving towards a problem
None of my iterations are hitting the critical point of a problem someone is looking to solve. I know that I enjoy framing problems, using analytical techniques to solve them, and communicating and implementing solutions that leads to better outcomes. I prefer using statistical techniques and technical tools to enable new perspectives for hard problems. I have experience understanding user behaviour on digital products to help optimise the experience, influence strategic decisions, and monetise.
The next steps towards my branding are understanding potential customers more, and the specific problems I can help them solve in their own language.