Loyalty Lab implements machine learning without hiring a single AI data scientist

In an article this summer, Harvard Business Review made a strong case for the importance of artificial intelligence (AI) in today’s business world. According to HBR, AI technologies are “poised to have a transformational impact, on the scale of earlier general-purpose technologies” (i.e., databases or the internet).

But the fact that these technologies will soon be ubiquitous doesn’t necessarily make it any easier for businesses without AI expertise to see a path to incorporating and monetizing them—not without huge costs, massive disruptions to how you do business, and many other challenges and complexities.

The good news is, incorporating AI capabilities like machine learning into your operational workflows can be much simpler than you might think.

As one example, we’re excited to share the story of Loyalty Lab, a company with no AI specialists that is now regularly using machine learning to improve the customer experience—at a fraction of the cost of staffing a new AI data science department.

Loyalty Lab is a data-based marketing company based in the Netherlands that works for large enterprise customers. And here are the ingredients in this company’s simple recipe for success: data stored in Amazon Redshift and Amazon Simple Storage Service (Amazon S3) and analyzed by PredicSis.ai, an automated machine-learning solution now available in AWS Marketplace.

Before deploying PredicSis.ai, Loyalty Lab data analysts had a problem many of us are trying to wrap our heads around: the massive expansion in data volume in recent years. “The amount of data we had to work with grew day by day—not the number of records, necessarily, but the number of data categories available for each record,” says Dick Koers, a solution architect for Loyalty Lab. “The more data categories there are, the harder it is for people to find all the correlations.”

Loyalty Lab knew it didn’t want to fire half of its data science team and replace them with AI specialists. It also didn’t want to license an on-premises deployment of a managed solution for machine learning. “Staffing a new data science department would have… increased our personnel costs and been very disruptive,” says Koers. “And as a project-based company, we try hard to minimize costs that won’t always be billable to a specific campaign or initiative.”

The answer? Because Loyalty Lab was already using Amazon Redshift and Amazon S3 to store the data it needed to analyze, it was able to launch PredicSis.ai through AWS Marketplace in just a few clicks and run a powerful analytics initiative on lead qualification for a major European automaker. That initiative required only about 40 minutes of manual work and a few seconds of application runtime. When that was done, the company was able to switch off PredicSis.ai until the next time it was needed to impress a customer and earn more revenue for Loyalty Lab.

“Frankly, when we tell our customers what PredicSis.ai on AWS Marketplace can do for them, they can be skeptical,” says Koers. “Then, in just a few minutes, we can show them a beautiful PredicSis.ai output with results they never expected, and that skepticism turns to amazement.”

Read the full case study here. Check out PredicSis.ai in AWS Marketplace.