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Many marketers are dipping their toes into machine learning and exploring the possibilities of what artificial intelligence can do for them. This curiosity, vital to their growth, belies an important fact: most marketers aren’t ready to embrace machine learning and AI yet. A critical piece of the puzzle is missing: mastery of algorithms.

What is an Algorithm?

An algorithm is a defined, repeatable process and outcome based on data, processes, and assumptions. We use algorithms all the time in our daily lives. We have a defined process with data, processes, and assumptions for everyday tasks.

For example, if we make coffee in the mornings, we have data. We have information in our heads about making coffee, and the desired outcome. We have a process for making coffee, a defined order to do things. We have assumptions, such as that we have enough coffee in the house to make coffee (an assumption which we occasionally prove false).

We’re Bad at Algorithm Documentation

What we don’t often do in our mundane daily algorithms is carefully document those algorithms. Only when something significant changes do we take the time and energy to document them. We leave instructions for the house sitter or pet sitter about our daily tasks. We leave out of office memos for our teams and colleagues. Any time we are transferring information about our processes and outcomes to someone unfamiliar with them, we are sharing our algorithms.

If we only share algorithms when we are working with someone unfamiliar with them, we run the risk of omitting steps that might be critical to the desired outcome. Someone who doesn’t know how our espresso machine works might not know to put water in it first, or set the grinder to a specific grind number – and if we omit those instructions, bad things might result.

Why Algorithms Matter

Machine learning is entirely about algorithms, about teaching machines how to perform our processes. Robust process documentation and encoding of every step for the machines is essential to achieving the desired outcome. If we omit a step, unlike a human which could infer the missing pieces, a machine currently cannot. The machine will simply fail at the task, and our investment of time, energy, and resources is for naught until we fix our omissions.

Consider this snippet of R code from a visualization algorithm for making charts about how topics are related:

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It’s easy to see how a misplaced comma could throw the entire thing off. What’s less easy to see is that if our processes are out of order, or our data is bad, or our assumptions are faulty, we may generate an incorrect outcome. If we don’t have processes with robust documentation and explanations, we could potentially create machine learning that generates faulty outcomes we don’t understand are faulty.

Why Many Marketers Aren’t Ready for AI Yet

Until marketers become expert at process documentation, at building and mastering algorithms, at defining assumptions cleanly and clearly, we are not ready for AI. We risk doing more harm than good to our organizations.

AI and machine learning are very much like the genies of Arabic and Islamic culture, most familiar to Western audiences in stories like Aladdin and the magic lamp.

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Genies granted wishes in fiction with significant, unintended consequences because the asker was insufficiently specific about their wish, often causing the opposite of what they wanted.

Machine learning and AI are similar; if we are not perfectly clear in our instructions, in how we build our algorithms, we will create the opposite of the clarity and insight we seek.

Here’s a simple test to determine if you are ready to embrace machine learning: do you have a fully documented playbook in your organization of your marketing processes and algorithms, including the math behind how you calculated goals and goal values, inferred and explicit ROI, and other key metrics or processes?

If you do, you are ready to dip your toes into the machine learning waters.

If you don’t, fix up your documentation first. Master the habits of building and encoding great algorithms first.


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