If the most valued people and top performers in your
company were thinking about leaving, wouldn’t you want to
know? If a growing wave of disengagement was coming,
wouldn’t you want to stop it? If there were early warning
signs, wouldn’t you want to act on them?
Data has long been used to highlight patterns after the fact. Recently, it’s also started to provide insights about what’s happening in the present. But its predictive ability is relatively untapped – and extremely valuable.
Even data that’s relatively crude by today’s standards is useful. Think about spreadsheets populated at the end of every month:
they enable businesses to make comparisons with last month, or with the same month last year. Are our sales up or down?
What are our customers’ buying patterns?
One of the things about platforms like bountiXP, though, is that you don’t need to wait until the end of the month to get this data. It’s available any time, in real time.
Take the example of a spike in engagement. If that anomaly happened in the first week of the month, why wait another three weeks to see it, by which time it might be too late to act on?
Engagement platforms should be able to show such a spike as it happens. It might be something obvious, like winning a new account or big piece of business. But what if it’s not that apparent? That might be a cue to look at things in more depth and detail.
For example, bountiXP encourages instant, regular, cross-company communication. That spike in engagement might be because people in the business, or in a particular department or team, are talking about something specific – maybe a team member had a baby, or reached a milestone. Or, it could be that there was an incident, or someone said something hurtful or political, which might otherwise have gone unnoticed. Or, it might be something operational that’s proving an obstacle to an urgent client request.
Whatever it is, that spike in engagement could be a nudge to simply read the posts on the platform, or to see which part of the business the engagement is coming from. It might be nothing. But it might be something that can be acted on; an issue that can be nipped in the bud.
And it’s thanks to data that’s visible in real time – and sometimes just in time.
But what about data that can help ahead of time?
One of the things that an engagement platform like bountiXP does is to encourage visible recognition, in the moment, at the right moment. A colleague went out of their way for you? You can thank them on the platform. Someone in your team made a client look good, or was an outstanding example of living company values? You can give them a congratulations, thumbs up or reward.
But beneath those small and significant gestures, there’s useful, valuable data.
Let’s say there’s a particular manager. She leads a top-performing team, in part because she’s so good at recognising them. In fact, she posts at least once or twice a day. A simple “well done” here and a “thank you” there. Over the last few months, however, she’s been engaging much less, and last week didn’t post anything at all. Could this be a sign that she’s disengaged? Could it be that she’s burnt out? Maybe.
What about a star employee who regularly earns good rewards as part of the company recognition program? In past years, she’s tended to redeem most of those rewards just before Christmas – but now she’s redeemed them all in July. Sure, it could be nothing. But could it be a sign that she’s planning to resign from the company in August?
Or what about someone who tends to use all their leave at the end of the year, and has recently, unusually, taken a day here and a day there? Again, it could be nothing. But could it be that he’s using his leave to attend job interviews?
All of these are examples that we might not notice or think much of in the normal course of events. But in the form of data, they might be telling us important things. In fact, they often do.
The predictive power of data
How would this actually work? Well, let’s say a company, hypothetically called Acme Inc., has several years’ worth of data around employee behaviours such as those just mentioned – platform engagement, rewards redemptions, leave – and resignations. That data would indicate norms and patterns, as well as exceptions to those norms.
For example, it might show that when employees changed their engagement/rewards redemption/leave patterns in particular ways, they were likely to resign from the company within a certain time period.
Now, all of that data can be mapped so that Acme Inc. has a picture of the average or aggregated behaviours over time. Then, Acme Inc. can overlay the current behaviours to compare them against the past, and it can pick up the kind of “if x then y” behaviours that could, for example, indicate an intention to leave the company.
And it can do this kind of thing not just at an organisational level, but at an individual level.
While it might sound like shades of Big Brother watching, data-driven intelligence doesn’t replace human or emotional intelligence, but rather complements it.
“If your employees were feeling under-appreciated, or disengaged, or burnt out, wouldn’t you want to know? And wouldn’t you want to do something about it? If your top performers were thinking of leaving, wouldn’t you want to know how you could persuade them to stay?” asks Jean-Claude Latter, Workforce Solutions Director at Achievement Awards Group.
Valid question: if you could persuade them to stay, would it be worth it?
Jannie Els, a data science strategic partner of Achievement Awards Group, modelled the cost-saving potential of analysing absenteeism data to predict voluntary resignations. The hypothesis was that staff who were looking for new jobs used leave days differently to those who intended to stay.
Using the absenteeism data, they could predict with 71% accuracy who intended to resign. That is an impressive statistic.
Calculating the average costs associated with employees leaving and hiring new people, and assuming that interventions would be successful 70% of the time, that company (which has around 11,000 employees) could potentially save R28 million.
That’s to say nothing of the benefits to morale and performance.
And that’s just one example.
Seeing the future more clearly
The saying goes that hindsight is 20/20 vision. Foresight, on the other hand, is regarded more as guesswork, even if it’s educated guesswork. Data changes that, making the future more predictable and, in the context of employee engagement, giving organisations the opportunity to positively influence things, even before they happen.
