Beware of Plato’s Data Cave, a Key Takeaway from AI in Commerce Business Breakfast


Global e-commerce is among the fastest growing industries globally, experiencing 18% growth in 2018. Worldwide, consumers purchased $2.86 trillion worth of e-goods in 2018, compared to $2.43 trillion in 2017.

Because digital commerce is data-driven, the industry is ripe territory for AI. However, lack of knowledge and uncertainty remain the most prominent obstacles to this technology gaining a stronger foothold. To address these obstacles, and Google Cloud co-organized a business breakfast to discuss the challenges and opportunities and share their remarks on artificial intelligence (AI) in e-commerce. Joining Google and were experts from, Sotrender, and iProspect, all companies deliver sophisticated tools for digital business.

Stuck in Plato’s Data Cave

“When it comes to building AI applications, it’s all about the data,” said Paweł Osterreicher, Director of Strategy & Business Development at, during his presentation. He pointed out that the simplest analytics in smaller businesses can be done within an Excel spreadsheet or pen and paper. Preparing a simple segmentation within a client group or spotting best-performing products is not a huge challenge. But those are only the tip of the iceberg. “The more sophisticated insights we gain, the more complicated the task becomes. And that’s where specialized software comes in,” he said.

“The greatest challenge is a lack of flexibility. There is no jack-of-all-trades among the popular tools, and each has its limitations. The problem is when a tool doesn’t fit a company’s needs. And, to be honest, that’s a common situation,” Osterreicher continued. Companies thus often need to tweak the tools at their disposal to make them fit or get used to missing insights from their data.

Most companies process only a fraction of their data and operate with only half the picture. They are like the prisoners in ‘Plato’s cave’, watching only the shadows customers cast on the wall, with no access to or true grasp of their real form.” [Ed. Note: See Allegory of the Cave.]

The only way to analyze data in a convenient and cost-effective way is to leverage machine learning models. Machines are able to effectively spot patterns even in seemingly insignificant details.

“Sometimes information about how long customers hover over a button or how they go about filling in an online form is a first step to obtaining meaningful information. The model is only as good as the data it was built on,” concludes Osterreicher.

Image Capture to the Cloud Helping to Reinvent Retail

In another presentation, Jakub Skuratowicz focused on the technical aspects of how companies use AI. There are numerous ways for companies to benefit from AI, be it building engagement, personalizing the user experience or detecting fraud.

Google’s expert showed a new application of image search for omnichannel commerce. First applied by the Nordstrom clothing company, the app-enabled users to take a photograph of an item and then search for it in the shop’s database. Thus, the customer could quickly buy the product online or check its availability.

“By using Google Cloud Platform-delivered machine learning tools, the company reached 95% accuracy in recognizing an item shown in a photograph”

It also thrives in recommendation engines. “It was common to recommend the user another version of the product – a different size of a dress, for example. That’s pointless. Why would one need another of the same dress, only slightly bigger?” Skuratowicz asked. Instead, the AI-powered model recommended products that complemented the one that had been searched for, like sunglasses or a scarf to go with the dress.

Skuratowicz also showed how AI spots fraudulent transactions in international e-commerce. “Manual or semi-automatic checking can be effective, but machine learning makes it more scalable,”  he said. By applying AI-based solutions, the international logistics provider Pitney Bowes boosted the accuracy of its fraudulent transaction detection by 80% while reducing false-positives by 50%.

Read the source post at