Ditch the data scientists and weaponize your data with AI tech (VB Live) Posted on : Jun 13 - 2019

A significant investment in AI technology doesn’t guarantee a positive return. Join this VB Live webinar to learn about the five biggest mistakes companies make when they bring cutting-edge customer service technology to their workflows, and how to leap over these pitfalls and into real results.

Most business leads are aware of the importance of AI, says Michael Butler, head of customer success at Ople, but often don’t know how to get started – or if an investment in AI technology is the smartest route to stacking up real ROI.

Previously, as director of global ecommerce at VMWare, he was relying entirely on his data science team, Butler says. The team consisted of about 35 people on staff full time, and the problem was that they were slow to produce models and results. For instance, coming up with a model to score customers most likely to buy a new release would take six weeks; when an anniversary sale came along, it would take another six weeks, starting from scratch each time.

It should take a matter of days, if not hours, Ople thought. When he began to tinker, he realized that if he had clean data sets he could get a model up and running in a matter of hours, whether that was using AWS Sagemaker and their neural network, Google’s TensorFlow, or the plug-and-play Ople platform.

“I don’t want to rely primarily on data scientists,” he says. “What we want to do is elevate their job so it’s not just data wrangling, data prepping, and data cleaning.”

Data scientists, good ones, are hard to find and very expensive and in short supply. Small- and medium-sized companies can’t create a data science team, with the best getting hired away by Google, Facebook, and Apple. But if you do have a data scientist team, it can do more, and more quickly, when you turn over the initial work to an AI-powered software platform.

“Data scientists are all about the quality of the model, tuning it and getting two percent more or 50 basis points — that’s what gets them really excited,” Butler says. “But if you’re a business line owner or you’re running a $100 million renewal business, it doesn’t need to be 99.9998 percent correct. I want to be 90 percent, which is worth $20 million this quarter, so let’s forget the next two percent and put it into production.”

The other piece, he says, is that data scientists consider their models and how they get their results as their secret sauce, which translates as job security for them. But a business owner doesn’t care about the underpinnings of the model, they just care about the model kicking out business results — showing which customers are going to be at risk for not renewing, and then offering insights in how to get them from a yellow status to a green status. View More