When the job of a young east coast-based analyst – we'll call him James – went remote with the pandemic, he didn't envisage any problems. The company, a large US retailer for which he has been a salaried employee for more than half a decade, provided him with a laptop, and his home became his new office. Part of a team dealing with supply chain issues, the job was a busy one, but never had he been reprimanded for not working hard enough. So it was a shock when his team was hauled in one day late last year to an online meeting to be told there was gaps in its work: specifically periods when people – including James himself, he was later informed – weren't inputting information into the company's database. As far as team members knew, no one had been watching them on the job.

Vonage says it's boosted productivity for its salespeople by 20% using artificial intelligence, blending its own Vonage Business Cloud with Google G Suite, Salesforce and employee productivity measurement tool Prodoscore. Vonage is now offering the technology it uses in-house to its business customers. On the surface, sales productivity is easy to measure -- revenue. Whichever salesperson sells the most is most productive and coffee is for closers. But how do you increase sales productivity, encouraging activities that lead to revenue?

The real-world application of this algorithm used a series of iterative calculations to determine the best routes for traveling salespeople to use to fully and most efficiently cover their territory. Through the iterative process, this appeared to learn routes and improve efficiency over time. Many scholars considered this a little bit of a stretch on machine learning and artificial intelligence. To quote the introduction in the book, Artificial Intelligence: A Modern Approach (2016 edition): "We call ourselves Homo sapiens – man the wise – because our intelligence is so important to us." And while we consider ourselves intelligent, many people think only living, breathing beings can be intelligent and learn. I would have to disagree with that – thanks to the introduction of neural networks, natural language processing and all the other disciplines that touch artificial intelligence, machine learning is a real thing. Machines learn from the data that passes through them and are capable of processing vast amounts of data faster than us humans can. And those machines have the power to remember all that data, find the hidden patterns, the missing links, etc. – better than people can, and even faster than groups of people working together in enterprise businesses. Think about that device in your back pocket for a moment.

If you use G-Suite, this webinar was designed to show you how to unlock the valuable sets of data locked away in your phone calls using Google Machine Learning. Organizations keep hearing the buzz word Machine Learning, but many companies don't even know how to apply Machine Learning to their business with the appropriate use cases. In our 1 hour webinar, we discuss one specific use case and how Machine learning can be applied to hosted VOIP platforms like Vonage, which has API access for business users to consume. Below you can see a recording of how we apply our Machine Learning service to our VOICE enabled API platform from Vonage and leverage all the hidden data of our company's voice activity within one single dashboard. You can also find a copy of the slide deck we used for the webinar.

Investment and interest in Artificial Intelligence (AI) is now white hot as we enter the second decade of the 21st century. Interestingly, AI is now almost a passé term. Machine Learning and advanced pattern recognition is now gaining notoriety as the "new" approach to apply the concept of AI, having been successfully applied to many problems in academia and industry. Several use cases have been identified delivering legitimate Return on Investment. These recent advances can be attributed to the presence of greater access to more data, coupled with new, more powerful computer hardware and processing power.

As new technologies are discovered and developed, widespread adoption doesn't occur until viable business benefits can be identified and validated. Machine Learning, Cognitive Computing or Artificial Intelligence (depending on what you call it) is a "hot," interesting new technology development, and one that is quickly proceeding through the hype cycle to widespread adoption. As a practical use case, Machine Learning can now be used to gain new, perhaps even unexpected insights into sales team engagement to predict sales turnover. Based on the Harvard Business Review, the rate of annual sales turnover among U.S. salespeople is as high as 27%--a rate that twice the average of the overall labor force. As employees pursue new employment, their replacements must be hired and trained, costing time, effort and resources.

You can't improve what you don't measure. In this webinar we discuss how you can leverage machine learning to boost your sales team's productivity and increase sales. Register with your email below to watch instantly!

Machine learning has been one of the biggest buzz words of late. However, in spite of the hype surrounding machine learning, if you pause you realize no one has figured out exactly how we're actually going to use and benefit from this revolution. Join Prodoscore as we demonstrate the first real use case for machine learning in business.