‘The art of
the possible’
It’s a familiar scenario. You’re about to switch the telly off and
turn your attention to something more constructive or edifying
(reading a book, doing the dishes, conversing with your child…).
But what’s this? A ‘for you’ recommendation on Netflix. It’s not
your usual thing, but it does have a strong female lead and
brooding Scandi setting. Before you know it, you’ve settled back
in on the sofa.
Such is the lure of streaming platforms’ fiendishly clever
algorithms, which now learn your viewing preferences from highly
nuanced factors, such as where and when you watch, what you
watched last year compared to this year, and whether you enjoy
‘Visually striking nostalgic dramas’ or ‘Understated
romantic road trip movies’ (genuine Netflix categories).
It comes as little surprise to hear, then, that we now
spend twice as much time binge-watching Netflix as
we do bonding with our families (71 versus 34-37
minutes, according to Streaming Observer analysis).
But most will be unaware of the technical term at the
heart of these highly addictive recommendations:
‘prescriptive analytics’.
And yet it’s a concept that powers a surprising
amount of modern life. Shopping on Amazon, for
example. Or navigating traffic, where your phone can
combine datasets including roadworks and other travellers’
whereabouts to recommend the best route.
It’s something also already used extensively in some areas of
business. And it is now making its way slowly but surely into HR.
The holy grail
But what exactly does prescriptive analytics mean? The received
wisdom goes that prescriptive analytics is the next stage on the
journey after descriptive and predictive.
“In the chain of value, descriptive is just getting access to the data
in a manner you can deal with,” explains Ankur Modi, CEO of
StatusToday. “The next layer is around diagnostics, which is
understanding key trends and why something might be happening.
The third is predictive analytics, which looks at ‘now that I know that
trend, can I predict what’s going to happen?’
“The final stage, and arguably the holy grail, is prescriptive
analytics. In simple terms it’s the action plan based on the data.”
When it comes to HR, Modi concedes that while many in the
profession will have heard the term more of late, few have gotten to
grips with it. “In general, HR analytics awareness is low. And
prescriptive analytics is hard for technical people, let alone for HR
people,” he says.
“This is in its infancy in terms of the ways it’s being used by HR
at the moment,” agrees Olly Britnell, global head of workforce
analytics and HR strategy at Experian. “But it could get very
sophisticated very quickly.”
Is prescriptive analytics the holy grail for
HR data, or is it simply something the
function has always done?
JENNY ROPER explores
In terms of potential applications, both Britnell and Modi cite the
same key area optimised by its predecessor, predictive analytics:
retention. Extending analytics capability from predictive to
prescriptive here means HR professionals can draw significant
competitive advantage not just from foretelling which employees
may soon resign and when, but also how to prevent this.
“One of the predictive models we’ve developed in HR at Experian
which will lend itself well to the prescriptive end of things is around
attrition,” says Britnell. “We have a predictive model that flags who’s
high risk and the factors driving this – such as that they’ve had two
supervisor changes in the last month.
“We’re using machine learning to track interventions
such as changing the team structure or offering more
training, and then tracking which ones are having an
impact. The idea is that in a year’s time we’ll be able
to give our HR business partners (HRBPs) insight into
who’s high risk but also, based on what we’ve seen in
the business, what to do about it.”
Another area Experian is applying this to is D&I.
“Like most organisations, a challenge we face is around
gender diversity, and we’ve done lots of analysis around
‘where do we have the challenge?’, then modelling
around the levers at our disposal,” says Britnell.
Prescriptive
analytics is
hard for
technical
people, let
alone HR
“To me, that is a prescriptive piece of work because effectively
you’re outlining what the future entails, providing the data around
what’s needed to achieve a target, and giving the business the tools to
execute around the right strategy.”
Paul Cutler, group HR director at Travelex, adds workforce
planning to the mix. For a highly seasonal business like his, it’s
critical that HR can prescribe the volume of staff needed in stores
and airport stands according not just to historic customer demand
and passenger flow data, but by also predicting the impact of new,
emerging events.
“So it’s what impact does Brexit have on summer travel numbers
in the UK and how does that lead to staffing recommendations?” he
says. “Because if you were operating in a purely descriptive analytics
world, you would be basing that on last year.”
Something and nothing
The objection forming for some, however, might be that this sounds
very much like what HR has always done – or, perhaps, should
always have been doing. After all, as an HR professional, if you’re not
handling and analysing data in a way that leads to action, what – to
put it bluntly – are you doing?
Britnell confirms this is a fair challenge. The danger is making
prescriptive analytics sound overly new-fangled and complicated, he
says. “I’m not a massive fan of the term because it is just what we
should be doing as a function,” he says, adding that, as such, while it
might be the holy grail of analytics, this doesn’t mean it should be
HR Technology Supplement Prescriptive analytics
8 HR October 2019 hrmagazine.co.uk
/hrmagazine.co.uk