Ryan Hopkins, former Future of Wellbeing Leader at Deloitte and author, ’52 Weeks of Wellbeing’, didn’t mince his words this at MAD World this year when he urged professionals to “level up” when it comes to data and measurement:
“Surveys are good for data capture but we have to level up and treat Wellbeing like a discipline, like any other function in the business. You wouldn’t go to the sales department and say ‘Lee, how are you getting on in sales?’ and for him to say ‘Ah, I’m not really sure, we’re not measuring’. That would just not fly.”
More sophisticated with data
In an ideal world, he said, the Wellbeing function would clearly understand data points like attrition rate, long term absenteeism and the rate of engagement, and be able to link these to the survey data, anecdotal data and supplementary data like annual leave taken, to create hypotheses.
“We have to treat measurement and data seriously. You could then work out how much this is costing and it would put it on the C-suite’s agenda,” he said.
More than surveys
His argument that the industry needs to be doing much more than simply using surveys as the main data point was echoed throughout the conference. However, as this is the primary way many in the industry currently measure wellbeing he added:
“It will mean that a lot of us doing this work will come up short, so it’s going to be a difficult moment but we need to make that shift to create work that is better for all of us.”
His words are backed up by Aon’s 11th Benefits & Trends Survey which shows that only 9% of employers are actively measuring the return on investment from their wellbeing programmes. And, of those which are measuring, most of them (55%) are basing their calculations on employee engagement surveys.
Those employers not “falling short” making the biggest shifts with regard to data, and taking data points way past staff surveys, are those working with data experts, either as consultants or those with data analytics departments in house.
Working with data experts
The challenge of working with data specialists was covered in one of the most popular sessions at MAD World on the measurement of workplace wellbeing. This was led by Jordan Pettman, Former Head of Organisation Analytics and Insights at the London Stock Exchange Group. He introduced himself as the “data analytics guy” who sees his role as to create relatable “stories” from the data.
When at the London Stock Exchange his Wellbeing brief was to create business cases that “get funding to do wellbeing initiatives or report back to the executive committee on whether that wellbeing initiative paid dividends to employees, but also back to the business”.
Proving the efficacy of interventions
He gave several examples of how data experts working with wellbeing professionals can lead to that Holy Grail of being able to prove the efficacy of interventions taken. For example, when working at Nestle, he was able to measure the ROI of the wellness interventions taken in some of its factories and able to prove that there was a tenfold financial return to the business in terms of cost avoidance, through employees accessing the EAP.
“We demonstrated the rate that employees took stress leave had decreased by 300% compared to the year prior because of accessing the EAP for stress. This led to reduced absence and we were able to show that the cost of the EAP was ten times less than the cost of the previous absence rate,” he said. “So, not only did this show doing great things for the employees, but it was actually really smart for the business too.”
Building a partnership with data experts
Novartis also has an inhouse people analytics team and panellist Sharon O’Connor, Global Lead Employee Wellbeing at the pharmaceutical firm, shared that it has taken time for the wellbeing team and the data team to find the best way to work together. This is because, typically, departments tend to have quite a “transactional” relationship with the data team. But O’Connor has gone out of her way to create a more collaborative approach.
“My preference when working with them is that we make it more about hypotheses building together, rather than transactional,” she said. “My mantra is partnership, partnership, partnership!”
This is very different to how the data analytics team generally works with most departments, which is more the case of they’re given a question, which they then go away and answer and come back with a report.
Creating hypotheses together
The way she’s nurtured this partnership is through trying to make them feel part of the wellbeing team and be invested in it because, after all, they are employees too that benefit from any initiatives. She’s made it clear that she is really interested in their experiences as employees and to bring those to the table.
“This empowers them more with the data and they have started bringing ideas to the fore, and forming hypotheses themselves,” she said. “This means they have more of an investment in what we’re doing as well.”
The biggest surprise, and lesson she’s learnt, is that it takes quite a bit of time to evolve into this kind of partnership relationship. “It takes patience to nurture that strong business collaboration, which we’ve found so beneficial. A lot of time, and a lot of querying,” she said.
Tips on working with data experts
But, now, the two teams have the kind of relationship where they form hypotheses together and they even do “deeper dive sessions” together, really drilling into the data. For example, they’ve worked on topics including purpose, the engagement survey and work-life balance rates. In these more focused sessions, the teams together look at other options to cut data and brainstorm around potential “stretch objectives”.
Another “lesson” O’Connor said she’d learnt is that collaborating continually in this way – rather than giving them a brief to go away and work on on their own – means “you don’t have a surprise expectation gap a few quarters into execution” of an intervention.
Instead, she’s fostered a culture of continuous reporting where “we don’t wait to be asked for a report and we don’t only report on impact when we’ve had a new campaign; we’ve established a cadence of reporting which includes a year end report, but not just that report.”
Get data expertise early
This is all music to Pettman’s ears.
“Getting the analytics team involved early means you don’t get to the point of needing to tell people about your intervention and then saying there is no data to analyse,” he said. “If you get us [data experts] involved early, we can suggest which questions to add, to create the right data.”
As well as creating a more sophisticated working relationship between data and wellbeing, the other development that Pettman identifies as significant, and a sign of the future, is the fact that employees are also increasingly realising the benefits of giving their employers data. As he said:
The next evolution
“It’s an interesting evolution that employees understand a bit more that ‘if I give my company some information about me, they can use that to adjust their strategies and they’ll tell me about what they’ve done with my data’.”
This cultural change will lead to that “levelled up” situation, which Hopkins spoke of at the beginning of this feature, where wellbeing moves away from basing its results purely on survey data and has a much deeper understanding of employee behaviour, as well as how effective its interventions truly are.
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