Cater to the “market of the one” — this has always been the holy grail of marketing.
Brands and marketers have always strived to understand individual consumer necessities and tried to cater to them directly through an open dialog, at scale.
While this was long a pipe-dream, with the advent of deep neural networks, the current crop of machine learning algorithms, and advancements in artificial intelligence (AI) research, the age-old spray and pray marketing is coming to an end.
Now, with machine learning, brands have a good shot of being truly coherent in their narrative and engaging consumers with a consistent voice, tailored to individuals across omnichannel end-points.
To break it down, let’s take a concrete example of advertising a kid’s video game, such as “Plants vs. Zombies — Garden Warfare 2” and compare the two marketing options.
Traditional Marketing vs. Machine Learning in Marketing
In the marketing world, the best course of action for such a game would involve defining the genre of the game, the intended audience behavior and the market segment to advertise.
It’s also worth deciding on a channel strategy along with efficient channel assets, defining the expected performance per channel based on some pre-determined assumptions, and eventually allocating the budget per channel.
The 3-Step Challenge
Step 1: Genre
What is the genre of the game? Is it the mid-core game? A hard-core game? Is it a broad category like a third-person shooter?
The gameplay has characteristics of tower defense as part of it. It also has customizations, multi-player portals, missions and quests.
So how should one template this out?
Traditional Marketing: Marketers love templates. When defining the genre of a game, they will often settle for a broad category such as third-person-shooter.
The reason for this is to avoid reporting and downstream channel complexity. In best cases, additional tags can get added to the template which also must come from a predefined taxonomy — the fewer details the better.
Machine Learning in Marketing: In the world of machine learning, the game does not necessarily need to have a genre. A detailed, written description of the game and gameplay is all that is needed to feed into the machines. In contrast with traditional marketing, the more details, the better.
Better yet, machine learning algorithms thrive on additional text from comments, articles and user descriptions of the game, and can make up its own dynamic internal knowledge representation of the game to truly capture the ‘spirit’ of the game as against a genre.
Here, machine learning techniques such as entity extraction, relationship extraction, sentiment analysis and other retrieval techniques are used to encapsulate the spirit of the game.
Step 2: Audience
Who is the intended audience for the game? What is the behavior of the audience?
Since this is on all game consoles (Xbox One, Xbox 360, PS4, etc.) should we market to all console manufacturers? Or is it more prudent to allocate budgets only for third-person shooter genres…
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