How To Build Up Your Business Base Fair And Square
Business and martial arts have a lot in common. In martial arts,what you learn at white belt beginning rank, are the basics plus the problems you’re most likely to run into going forward. In the tradition I practice, that’s one of the unusual distinguishing characteristics - the techniques we learn at white belt aren’t necessarily the easiest or simplest, but they deal with problems we’re most likely to have. Someone grabbing you and punching you in the face? Someone getting in your face and setting you up for a sucker punch? Many of our basics revolve around giving people answers to practice against those problems.
So, here’s an interesting question: how do you apply that to your marketing and overall startup business? What are your marketing white belt techniques? What are the problems your startup company runs into most, and how well do you do at solving them - and critically, at teaching your team how to solve them?
Then take a look at the solutions in the marketing technology marketplace. How well do the various tools out there help you solve your most common problems? I’d hazard a guess that the answer is, “not very well at all”. Why? When I look at the search terms and queries and social media comments I receive most often, they look like one or more of the following: How do I know what’s really working? How do I optimize my website for Google’s algorithm today? How do I show any kind of results for social media marketing? And variations thereof.
These are the problems that millions of marketers face every day. These are the white belt problems, but we’re not delivering white belt solutions to those problems, as an industry. Here’s the funny thing about white belt techniques in the martial arts: you never, ever stop practicing them.
My senior teachers call it polishing the mirror, making your basics better and better over time. Yes, you learn more techniques, and deal with more elaborate problems and their solutions, but if you stop practicing the basics, you lose touch with your most common problems. Consider some of the problems we’re constantly chasing in marketing; let’s use optimizing your website content for search. What’s the fundamental problem? We’ve got more competition than ever (including from Google itself) and we want to be found for search terms relevant to us. What’s the general solution, the white belt basic?
Create content so good that everyone relevant to you wants to share it and link to it. The fundamental, the basic, the white belt problem is that most marketing content really, really sucks. No one cares about it. No one wants it. Now, how many different SEO tools, services, agencies, and team members are focused on solving that basic, that fundamental? The answer is almost none. Tools only help you do more, faster - if you create crap content, tools help you create more crap, faster. If you want your marketing to succeed, if you want your customers to be deliriously happy, if you want to make a great big pile of money, then figure out what the most important, most common problems your marketing is supposed to solve, and go solve them.
The path to black belt begins by being really, really good at the white belt techniques, and the path to becoming a master marketer is solving the biggest problems that plague your customers.
Fairness is a difficult subject to tackle in business , because people have many different ideas of what constitutes fair treatment. In the context of things like bank loans, citizens’ rights, being hired for a job, etc. what is fair? The dictionary definition is both straightforward and unhelpful: “impartial and just treatment or behavior without favoritism or discrimination” What constitutes fairness?
This is where things get really messy. Broadly, there are four different kinds of fairness, and each has its own implementation, advantages, and pitfalls: Blinded: all potential biased information is removed, eliminating the ability to be biased based on provided data Representative parity: samples are built to reflect demographics of the population Equal opportunity: everyone who is eligible gets a shot Equal outcome: everyone who is eligible gets the same outcome For example, let’s say we’re hiring for a data scientist, and we want to hire in a fair way based on gender.
We have a population breakdown where 45% identifies as male, 45% identifies as female, and 10% identifies as something else or chooses not to identify. With each of these types of fairness, how would we make the first step of hiring, interviewing, fair? Blinded: gender and gender-adjacent data (like first names) are removed from applications. Representative parity: our interview pool reflects the population. If we’re in China or India, there are 115 males for every 100 females, so our interview pool should look like that if we’re using representative parity. Equal opportunity: we interview everyone who meets the hiring criteria until we reach 45% male, 45% female, 10% other.
Equal outcome: we interview everyone until we have second-round candidates in the proportions of 45% male, 45% female, 10% other. Each of these scenarios has its drawbacks as well, either on excluding qualified candidates or including unqualified candidates. Blinded fairness doesn’t address underlying structural fairness problems. For example, if women feel excluded from data science jobs, then the pool of applicants would still reflect an overall bias, blinded or not. Representative parity doesn’t address the structural fairness problem as well, though it does do slightly better than purely blinding data. Equal opportunity may exclude qualified candidates in the majority, especially if there’s a substantial imbalance in the population, and potentially could include lower quality candidates in the minority.
Equal outcome may achieve the overall intended quality benchmarks but could take substantially longer to achieve the result - and depending on the imbalance, might not achieve a result in an acceptable timeframe. Why does any of this matter? These decisions already mattered when it was humans like you and me making decisions, but they matter much more when machines are making those decisions based on algorithms in their code, because the type of fairness - and its drawbacks - can have massive, even society-level impacts.
From everything like determining what the minimum wage should be to who gets hired for a job to even how a supply chain should function , fairness algorithms can either reduce biases or magnify them. How should we be thinking about these kinds of algorithms? We have to approach them from a balance of what our ethics and values are, balanced with our business objectives. Our ethics and values will dictate which fairness approach we take.
Many different simulation tools exist that can evaluate a dataset and provide projections about likely outcomes based on a variety of fairness metrics, like IBM’s AI Fairness 360 Toolkit and Google’s What If Toolkit. But the onus to think about and incorporate fairness techniques is on us, the humans, at every stage of decision-making for each business.
Keep these tips in mind as you build up your business base fair and square.
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