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Prodoscore Leverages Google Machine Learning To Help Increase Team Productivity

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Prodoscore lets you capture and measure accurate real time data about your sales team’s productivity and engagement. Prodoscore uses Google Machine Learning to analyze rich data about employee behavior, trending topics & time management. Workers learn where they can improve. Managers can discover and improve on team strengths and weaknesses, and HR can receive alerts for at-risk employees. Below is our interview with Harpreet Bhui, Vice President, Product at Prodoscore:


Q: Tell us more about the technology behind Prodoscore?

A: Prodoscore uses our Prediction Algorithm to bring in data from all the places you work: G Suite, CRM, VoIP system, etc. Then we apply Google’s Machine Learning on our data set to understand patterns related to employee productivity.

Traditional scoring and analytics methods depend on a manager to call/meet/manually gather data points in order to review employee performance as well as intangible sources of inputs and the need to manually create data points to understand employee performance. Teams are limited with no systematic way to rank employee performance within organization, roles, or teams.

Prodoscore standardizes the scoring process for your organization and bases it on what is actually getting done by each employee, so you genuinely see how performances compare both across workers and time.

We are then able to find areas where improvements can be made and give recommendations to help your entire team be top performers.

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Q: How do you calculate productivity?

A: Prodoscore takes employee activity spent while working in G Suite business applications (mail, calendar, docs and hangouts), your CRM, and phone system (dialer, conference calls, and text messages) to build a simple visualization of your productivity.

Based on their level of activity, and our secret sauce, each employee is given a unique Prodoscore (productivity score). Quick analysis of this score, and our machine learning, identifies areas for coaching and sales improvement.

Our algorithm calculates metrics across various dimensions: Organization, User Role, Manager, & Time.

Q: What makes you the best choice?

A: Prodoscore is the only solution that distills productivity down to a single, easy to understand score automatically and with no customization or configuration. Other solutions provide a host of complicated graphs and data points that are difficult to properly configure and create the same traditional issues for managers and teams.

We’re also the only solution that can assist with Machine Learning recommendations, employee engagement tracking, employee retention, and integrates with G Suite, CRM, and VoIP. With Prodoscore, you’re getting the complete package and the best Machine Learning on the market for improving employee productivity.

Our customer, GroveGroup, saw a 2X increase in Salesforce usage with Prodoscore.

“Prodoscore was great because it gave a full picture of everyone’s productivity, but provided a single number to keep track of,” said Pip Withering, CEO of Grove Group.

“We were able to go in and see exactly what was happening on low productivity days and see if particular people weren’t using Salesforce as much others, and then deal with those issues directly.”

Another one of our customers, CloudCodes, saw a 20% increase in leads generated. Their CEO, Debasish Pramanik said, “I got to know exactly what they are doing with their work hours and could see who was performing well and who was doing poorly and try and help those who needed it to do more follow-ups. I’ve seen faster responses to customers and more campaigns. Before, the best workers would be doing a lot and others wouldn’t, but now we’re all sharing best work practices.”

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Q: What are your plans for the future?

A: We want to continue to invest in complex levels of Machine Learning that can provide better and easy to understand work recommendations, based on employee behaviors along with models that we train, to display the right type of data for the company. More importantly we want to keep the product simple. We are in the early stages of machine learning and there is still so much more we can do, that has not been done.

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