Redwood City, Calif-based Glint launched last year with $15.5 million in venture capital funding and an idea that employee engagement needed better software support than the market had developed for that space to date.
Its answer was a platform that would send brief surveys to employees asking them about, well whatever the company had identified as important to employee engagement and retention.
Perhaps it was about the onboarding process or the company’s current policy on flextime. Glint called the surveys pulses and designed the system so that the surveys could be easily sent and then, when they were completed, taken apart and analyzed. The system also featured, and still does, "smart alerts," which alert human resources and/or executives that a key employee engagement metrics has declined.
A Crowded Space
Glint, as it entered this space, had some powerful backing in its corner, including Norwest Venture Partners and Shasta Ventures, which led the investment funding along with angel investor Ev Williams, the co-founder and CEO of Medium and former chairman and CEO of Twitter.
It also had some marque launch customers including FICO and Marketo.
Unfortunately it also had plenty of competition. Employee engagement, a long-standing approach to talent management, tends to become more popular at certain parts in the labor market cycle — namely when the labor market shows signs of tilting towards the employees.
Right about now you might be wondering why Glint even bothered and certainly at this point the folks at Glint are thinking the same thing — but about the time they spent briefing me on their update.
The 'Cost' of Disengagement
Glint today rolled out an update to its smart alerts that connect the dots for the company about what that decline in employee satisfaction or engagement could mean to its bottom line. They are called Smart Alerts with Predictive Risk Models and because there is a machine-learning element to the update, these predictions grow more accurate as more data is added.
Now, bear with me because here too, predictive analytics and, increasingly, machine learning, is nothing new.
What is new, however, is Glint’s approach of addressing a frustrating gap in the knowledge base around employee engagement. It seeks to answer what would be the total cost to the company of not addressing a particular employee engagement failing. I can hear you saying 'duh' all the way over here – the cost is the investment the company made in their hiring, training and benefits packages etc.
But the cost of any input is more than just the direct expenses, as anyone who has done a total cost of ownership analysis, and its sudden loss can have a wide-ranging affect.
Predicting the Big Picture Outcome
Glint’s approach is to take an outcome predicted after an analysis of a pulse and spell out what impact it could have on the company’s bottom line. In other words, it predicts the big picture outcome.
For example, with the right data a retailer could predict sales in a particular store based on the employee engagement of the sales clerks in that store, Glint CEO Jim Barnett told CMSWire.
Or a hospital could be able to predict an increase in re-admitted patients, based on the engagement level of the nurses, he said.
A user can begin making these connections from day one based on the historical data and historical engagement scores around certain environments and industry sectors that the system already has, Barnett said.
But the machine learning element that has been added to the update means that, given enough time and new inputs, the system will get smarter and more precise about these outcomes.
This is an area that Glint intends to explore further.
"We believe that machine learning and artificial intelligence are revolutionizing the kinds of insights and predictions that can be automatically extracted from people data," said Goutham Kurra, co-founder and Head of Product at Glint.
"Smart Alerts with Predictive Risk Modeling is just the first of many capabilities that will allow our platform to adapt to each client’s unique culture by learning from its people data."