Wall Street Journal's AI desk explores how the problem-solving platform can address questions such as where to locate a new store or when to change prices.
Software startup SparkBeyond wants artificial intelligence to move beyond parsing data for insights and start coming up with ideas of its own.
The seven-year-old company says it has developed a problem-solving research platform designed to generate concrete solutions to specific business problems such as where to locate a new store, how to make quicker deliveries, or when to cut or raise prices and by how much.
It works, in part, by detecting complex patterns in large pools of data and formulating an array of possible strategies.
SparkBeyond recently helped Swisscard AECS GmbH, a Switzerland-based credit-card issuer, identify the best way to prevent customer churn by detecting hidden patterns in online transactions and other customer data.
The analysis revealed individual customer preferences for being solicited by email, phone or regular mail, and when they were most likely to respond to upgrades and other deals, said Swisscard Chief Executive Florence Schnydrig Moser.
These patterns identified by the platform are “typically things you would not see as a human,” Ms. Moser said.
Or with traditional data analytics.
Where most AI-powered data-analytics programs are limited by the number of hypotheses fed into them manually by data scientists, SparkBeyond’s platform tests millions of algorithmic hypotheses pulled automatically from online libraries of open-source code like GitHub, the company said.
That enables the platform to break through a “cognitive bottleneck” and significantly expand the scope of analytics, said Sagie Davidovich, SparkBeyond’s co-founder and CEO.
While the platform can be applied to a company’s own data—as deployed by Swisscard—it can also draw information from a sprawling network of billions of webpages across the internet, including Wikipedia, news sites, weather maps, scientific papers, economic studies, patents and other sources, Mr. Davidovich said.
By gathering, sorting and cross-referencing all this material in real time, SparkBeyond is able to “connect the dots” in disparate data sets and information spread across the web and generate solutions, Mr. Davidovich said.
These can range from obvious to impractical—such as selling more products or building a thousand more stores—but all are based on concrete data and solid reasoning, Mr. Davidovich said.
To help corporate decision makers assess the solutions, every idea is assigned a credibility score based on the range of data that supports it, he said.
“The entire web is a messy database,” Mr. Davidovich said, “but there are good ideas out there.”
The approach is catching the attention of large customers such as PepsiCo Inc., MetLife Inc. and Anheuser-Busch. It is also attracting private investors.
SparkBeyond currently has about 200 employees and has set up offices in New York, London, Singapore and Melbourne, Australia. To date, it has raised more than $50 million in venture capital, the company said.
Swisscard said it is currently working with SparkBeyond to develop strategies to minimize credit risk and improve fraud detection.
Mr. Davidovich said his goal is to eventually apply the platform to help solve broader social problems, such as climate change, poverty and homelessness.
While many of the solutions it proposes will be a bit crazy, he added, “we need crazy ideas.”
This article is written by Angus Loten and originally appeared in WSJ Pro: https://www.wsj.com/articles/sparkbeyond-says-its-ai-can-autonomously-tackle-business-problems-11579043950
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis