How Machine Learning and Push Computing Can Pull Employees In
You have a problem: Businesses all over the world are facing a serious issue. Employees are increasingly overworked, disengaged, and bogged down by inefficient processes. A Gallup poll of more than 80,000 workers found that just 31.5% described themselves as being engaged at work. The majority, 51%, say they’re disengaged, and the final 17.5% reported actually being “actively disengaged.”
Legacy enterprise software is arguably one of the least engaging aspects of any job. However, many organizations have invested heavily in dated IT systems and will keep investing in their maintenance because those systems keep their business running, and any changes or upgrades would be incredibly costly and time consuming. Unfortunately, a system of record built in the 1980s may harbor valuable insights, but an archaic, unusable interface results in data being ignored because accessing it and finding relevant tidbits is such a hassle.
Scrolling for a Solution
Do you know what is engaging? Social media. So much so that a recent study found the average American checks their mobile devices for updates from social networks 17 times a day. The same study found that adults, employee-aged people from 25 to 54, spent the most time of any age group checking their phones for those updates. These are your employees.
Social media networks are built to keep people hooked with must-have, relevant updates that are unique to them. There’s a scientific blend of psychology and UX design at work to keep people coming back to their Facebook feed for more. Machine learning and push notifications are an integral part of that system.
Last year, Pinterest built machine learning “Pinnability” to surface more relevant content to its users. By balancing historical user behavior, data filtering, and real time requests, Pinterest saw a 20 percent uptick in repinning from the home feed. By surfacing the most relevant data and “pins” for each individual user, its users became much more engaged.
In addition, a little ‘ping’ can drive anyone to pick up their phone and check for the latest update. Without that nudge, they likely wouldn’t check their social networks as often.
Enterprise software should take note of machine learning and social networks’ tactics to increase engagement rates. Imagine if your organization could catch employees’ attention by using a system that extracts data from enterprise systems and surfaces important and relevant updates to them – the same way the consumer apps they love do.
Push Computing and Machine Learning
Sapho CTO Peter Yared says, “While push got its start in the consumer realm, the case for business-based push is in many ways much stronger.” He makes a good point. Consider the push notifications you get from some of your most-used apps:
“It’s Jennifer’s birthday!”
“There’s a 50% chance it’s going to rain in the next hour.”
“Your clan is being attacked!”
In the consumer world, these notifications are pushed to our devices to keep us up-to-date with current happenings. Furthermore, these notifications are personalized and based on information our apps believe we want to see. However, often times they come in too late and there is nothing we can do: it’s too late to send Jennifer a gift, you forgot your umbrella at home, and your fortress has already been pillaged.
But, the enterprise world is different. A push notification delivered from an enterprise system could be notably more important and actionable, especially given the power of machine learning to learn each employee’s behavior. HR programs can alert managers of a new employee that will need onboarding or a CRM can push a request for follow up on a sales lead, without an employee doing so much as checking email. Additionally, a push notification can kick off a workflow that enables multiple people to sign off on a project directly from the notification so that it remains on track – no more waiting for people to read their email! Machine learning is ushering in a new, more sophisticated era.
Push computing enables an entirely new way of working. Now, important system information is pushed to the right person before they request it instead of forcing employees to search for it in various systems. Push computing leverages machine learning to learn the likes and dislikes of its users or to find relevant updates for specific people. It then uses multiple channels, such as device notifications, a Facebook-like work “feed,” or even bots in a messenger client, to ensure people have the information they need before they have to look for it, so that no important updates or changes in your organization’s systems goes ignored or unnoticed.
Making IT Work for You
Two separate interesting data points help make this case.
According to Teradata, 43% of CEOs think relevant data is captured and made available in real time, but less than a third of employees think the same. And another study found apps receive 88 percent higher engagement when push notifications are enabled.
This tells me that 1.) employees don’t feel they have access to relevant data they need to do their jobs and 2.) employees are more engaged when important information is pushed to them.
With the advent of social platforms and other highly engaging mobile apps, people have become accustomed to apps delivering all kinds of information they want or need right to their device’s home screen. It’s becoming counter-intuitive to sit through slow, complicated system logins and painstaking searches to complete simple tasks (pull computing).
Enterprises systems need to operate like consumer services, making it easier for employees to be as productive as possible with minimal effort spent on inefficient workflows. Machine learning and push computing solves this problem. It ensures relevant and actionable information is delivered in real time to employees so they remain engaged and can get their tasks done quickly.
About the author: Natalie Lambert is the vice president of marketing at Sapho. Previously, she was at Citrix where she held multiple product marketing leadership positions and was most recently responsible for the company’s multi-product solutions. Prior to Citrix, Natalie spent seven years at Forrester Research, where she was the leading expert on end user computing.