In our modern world, big data permeates the fabric of our society. Google Maps can predict where we are going on any given day, with astonishing accuracy. Facebook has gone far enough to not only know your moods, but manipulate them as well, and Amazon continues to gather massive buying histories on all of its customers - a virtual treasure trove of data to be tapped for purchase recommendations.
Artificial intelligence isn’t just limited only to huge, consumer-based platforms though. Smaller, niche-based businesses are using it to create new opportunities that previously didn’t exist. Here in Austin, Banyan Water provides corporate clients with a platform to monitor and reduce water usage, and Authors.me mines data on thousands of books and movie scripts to recommend future top-performers. You get the idea.
Now let's take a scenario that's near and dear to my heart and relate it to all the data collected by SaaS platforms. Let’s say you have 30-50 SaaS platforms used across your business, including email and calendaring, a CRM, an ERP, a marketing automation tool, a phone system, accounting software, and much more. Separately, the data for any given platform isn’t terribly informative. 6 months worth of call logs for an employee will just tell you when that employee used the phone. However, taking that data over time, in aggregate, and comparing it to other tools may provide invaluable insight - especially when you marry the data with outside events. Consider what you could learn by loading a year’s worth of SaaS platform data into one cross-platform comparison tool, and overlaying it with employee data like performance reviews, hirings, and firings. Just think about the questions you could answer, like:
What exactly are my top performers doing?
You’re probably very familiar with who your top performers are. Do you have any idea what they are doing to get there? Maybe not. And sometimes, it’s difficult for them to articulate it. Instead of asking top performers to donate extra time to share their best practices, you could use this cross-platform learning to see what they are actually doing. Extra credit for breaking it down to a departmental level as the data patterns and behaviors will likely be different for sales, customer service, marketing, and engineering functions.
What type of employee behavior occurs leading up to a performance plan and/or termination?
Managers absolutely dread putting employees on “a plan” - and with good reason. Everything seems to go downhill from there. Cross-platform SaaS data, overlaid with historical termination records, may shed some light on what poor performing employees are doing (or not doing, in many cases). Getting a jump on the situation may help managers avoid an eventual termination, or give a longer runway to proactively support the employee. And who knows - the data may show that the employee’s patterns would perform better in another department or role.
These questions are really just the tip of the iceberg. Think about all the other predictions to be made, armed with the right data - vacations, raise requests, even referrals for new employees. The possibilities are endless.
As we continue to build out our SaaS management platform at Meta SaaS, we plan to help our clients understand the key questions for their specific business. Leveraging cross-platform SaaS data to answer those questions will save precious time on management functions, freeing up managers to support their employees on mission-critical tasks.