Artificial intelligence (AI)—like most disciplines—has made staggering advancements in the 2010s due in large part to powerful leaps forward in cloud computing.
Through AI and machine learning (ML), systems can now perform functions based on what they learned from data rather than what was explicitly programmed by a human.
This allows the software to adapt, become more robust and process information that the software wasn’t specifically coded to handle.
An individual user experience can now be influenced, enriched and personalized using the learnings from millions of similar (or dissimilar) user experiences. And the tools to get started have never been easier to access.
For the first time in recent history, digital brands are empowered to meet or exceed some of the loftier user experience expectations with essentially minimal effort.
Marketers will be able to use AI to better predict customer lifetime value and target customers more accurately.
Using ML, each potential customer’s likelihood to churn can be compared to current high-value customers by comparing profiles. And while this propensity can be guessed, it would be without any strong degree of fidelity.
ML offers objective analysis at scale for your dataset or other datasets you may want to compare against.
Product teams will be able to link data from disparate data sources to craft a richer experience.
When developing digital (or physical) products in which the goal is to inspire utility, it is paramount to continue to gather usage information from users: every screen viewed in an app, every level completed in a game, every cup of coffee digitally initiated, all produce usage logs.
At scale, this could amount to millions of records sent from hundreds of different touchpoints. And for product teams working with designers and engineers, some insights are required to continually improve product offerings.
Crowdflower ran a recent survey with data scientists from a broad range of backgrounds, including those who were new to the field and those who were at a chief data officer level.
The survey revealed 50 percent of respondents noted ML had significant importance for their companies and their departments. The problem is that most companies don’t know where to start or are boiling the ocean by including too many things.
Investment in AI and ML should be focused and tied to a single real-world problem such as reducing customer service costs or improving conversion rates.
The demand for ML keeps growing. As businesses are shifting to stay relevant in the cognitive era, ML will both support and drive today’s data scientists and advanced analytics leaders into the future.
Adolphus Nolan III is a solution architect at digital experience consultancy, Bottle Rocket.
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