December 24, 2021.
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The digital revolution has given us a lot of data. This is exciting for a number of reasons, but mostly in terms of how artificial intelligence will be able to further change the enterprise.
In the world of B2B, the number of transactions is vastly lower than in B2C, which is why we are still experiencing a shortage of data. In order to deliver on its promise of revolutionizing the enterprise, it must be able to solve these small data problems as well. Thankfully, it can.
The problem is that many data scientists turn to bad practices, creating self-fulfilling prophecies, which reduces the effectiveness of artificial intelligence in small data scenarios.
Data science practices and avoiding bad ones are the keys to applying artificial intelligence correctly to small data problems.
In the world of data science, self-fulfilling prophecy is simply described as "predicting the obvious." Sometimes companies find a model that predicts what already works for them and apply it to different scenarios.
A retail company can determine that people who fill their cart online are more likely to purchase than people who don't, so they market to that group. They are predicting the obvious.
They should use models that help convert first-time buyers who don't already have items in their cart. This retail company will be much more likely to impact sales and acquire new customers if they solve for the latter.
The process you should follow for applying artificial intelligence to small data problems is below.
When you don't have a lot of data to work with, the first thing you should do is enrich it. External data can be tapped into to apply look-alike modeling. Thanks to the rise of recommendation systems, we see this more than ever. Even if you only have a few purchases on Amazon, they can make predictions on your next purchase based on the information they have about the products and people who buy them. If you are a B2B company that uses a single dimensions to categorize your deals, follow the example of Pandora and divide each customer by the most detailed degrees. The richer your data gets, the more you know about it. Powerful prediction and recommendation models can help you go from low-dimensional data to high-dimensional knowledge.
The process you should follow for applying artificial intelligence to small data problems is below.
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What is the meaning of 2022, for marketers?
Capital is a commodity, the platform is the most important, but working with the right people may have the most profound impact.
