Using people analytics to drive business performance: A case study
A quick-service restaurant
chain with thousands of outlets around the world is using data to drive a
successful turnaround, increase customer satisfaction, and grow revenues.
People
analytics—the application of
advanced analytics and large data sets to talent management—is going
mainstream. Five years ago, it was the provenance of a few leading companies,
such as Google (whose former senior vice president of people operations wrote a book about it). Now a growing number of businesses are applying
analytics to processes such as recruiting and retention, uncovering surprising
sources of talent and counterintuitive insights about what drives employee
performance.
Much of the work to date has
focused on specialized talent (a natural by-product of the types of companies
that pioneered people analytics) and on individual HR processes. That makes the recent experience of a global
quick-service restaurant chain instructive. The company focused the power of
people analytics on its frontline staff—with an eye toward improving overall
business performance—and achieved dramatic improvements in customer
satisfaction, service performance, and overall business results, including a 5
percent increase in group sales in its pilot market. Here is its story.
The challenge: Collecting data to map the talent value
chain
The company had already exhausted
most traditional strategic options and was looking for new opportunities to
improve the customer experience. Operating a mix of franchised outlets, as well
as corporate-owned restaurants, the company was suffering from annual employee
turnover significantly above that of its peers. Business leaders believed
closing this turnover gap could be a key to improving the customer experience
and increasing revenues, and that their best chance at boosting retention lay
in understanding their people better. The starting point was to define the
goals for the effort and then translate the full range of frontline employee
behavior and experience into data that the company could model against actual
outcomes.
Define what matters.
Agreeing in advance on the outcomes
that matter is a critical step in any people-analytics project—one that’s often
overlooked and can involve a significant investment of time. In this case, it
required rigorous data exploration and discussion among senior leaders to align
on three target metrics: revenue growth per store, average customer
satisfaction, and average speed of service (the last two measured by shift to
ensure that the people driving those results were tracked). This exercise
highlighted a few performance metrics that worked together and others that
“pulled” in opposite directions in certain contexts.
Fill data gaps.
Internal sources provided some
relevant data, and it was possible to derive other variables, such as commute
distance. The company needed to supplement its existing data, however, notably
in three areas:
·
First was selection and
onboarding (“who gets hired and what their traits are”). There was
little data on personality traits, which some leaders thought might be a
significant factor in explaining differences in the performance of the various
outlets and shifts. In association with a specialist in psychometric assessments,
the company ran a series of online games allowing data scientists to build a
picture of individual employees’ personalities and cognitive skills.
·
Second was day-to-day
management (“how we manage our people and their environment”).
Measuring management quality is never easy, and the company did not have a
culture or engagement survey. To provide insight into management practices, the
company deployed McKinsey’s Organizational Health Index (OHI), an instrument through which we’ve
pinpointed 37 management practices that contribute most to organizational
health and long-term performance. With the OHI, the company sought improved
understanding of such practices and the impact that leadership actions were
having on the front line.
·
Third was behavior and
interactions. Employee behavior and collaboration was monitored over time by
sensors that tracked the intensity of physical interactions among colleagues.
The sensors captured the extent to which employees physically moved around the
restaurant, the tone of their conversations, and the amount of time spent
talking versus listening to colleagues and customers.
The insights: Challenging conventional wisdom
Armed with these new and existing
data sources—six in all, beyond the traditional HR profile, and comprising more
than 10,000 data points spanning individuals, shifts, and restaurants across
four US markets, and including the financial and operational performance of
each outlet—the company set out to find which variables corresponded most
closely to store success. It used the data to build a series of
logistic-regression and unsupervised-learning models that could help determine
the relationship between drivers and desired outcomes (customer satisfaction
and speed of service by shift, and revenue growth by store).
Then it began testing more than 100
hypotheses, many of which had been strongly championed by senior managers based
on their observations and instincts from years of experience. This part of the
exercise proved to be especially powerful, confronting senior individuals with
evidence that in some cases contradicted deeply held and often conflicting
instincts about what drives success. Four insights emerged from the analysis
that have begun informing how the company manages its people day to day.
Personality counts. In the retail business at
least, certain personality traits have higher impact on desired outcomes.
Through the analysis, the company identified four clusters or archetypes of
frontline employees who were working each day: one group, “potential leaders,”
exhibited many characteristics similar to store managers; another group,
“socializers,” were friendly and had high emotional intelligence; and there
were two different groups of “taskmasters,” who focused on job execution.
Counterintuitively, though, the hypothesis that socializers—and hiring for
friendliness—would maximize performance was not supported by the data. There
was a closer correlation between performance and the ability of employees to
focus on their work and minimize distractions, in essence getting things done.
Careers are key.
The company found that variable
compensation, a lever the organization used frequently to motivate store
managers and employees, had been largely ineffective: the data suggested that
higher and more frequent variable financial incentives (awards that were material
to the company but not significant at the individual level) were not strongly
correlated with stronger store or individual performance. Conversely, career
development and cultural norms had a stronger impact on outcomes.
Management is a contact sport.
One group of executives had been
convinced that managerial tenure was a key variable, yet the data did not show
that. There was no correlation to length of service or personality type. This
insight encouraged the company to identify more precisely what its “good” store
managers were doing, after which it was able to train their assistants and
other local leaders to act and behave in the same way (through, for example,
empowering and inspiring staff, recognizing achievement, and creating a
stronger team environment).
Shifts differ.
Performance was markedly weaker
during shifts of eight to ten hours. Such shifts were inconsistent both with
demand patterns and with the stamina of employees, whose energy fell
significantly after six hours at work. Longer shifts, it seems, had become the
norm in many restaurants to ease commutes and simplify scheduling (fewer days
of work in the week, with more hours of work each day). Analysis of the data
demonstrated to managers that while this policy simplified managerial
responsibilities, it was actually hurting productivity.
The results (so far)
Four months into a pilot in the
first market in which the findings are being implemented, the results are
encouraging. Customer satisfaction scores have increased by more than 100
percent, speed of service (as measured by the time between order and
transaction completion) has improved by 30 seconds, attrition of new joiners
has decreased substantially, and sales are up by 5 percent.
We’d caution, of course, against
concluding that instinct has no role to play in the recruiting, development,
management, and retention of employees—or in identifying the combination of
people skills that drives great performance. Still, results like these, in an
industry like retail—which in the United States alone employs more than 16
million people and, depending on the year and season, may hire three-quarters
of a million seasonal employees—point to much broader potential for people
analytics. It appears that executives who can complement experience-based
wisdom with analytically driven insight stand a much better chance of linking
their talent efforts to business value.
By Carla Arellano, Alexander DiLeonardo, and Ignacio Felix
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/using-people-analytics-to-drive-business-performance-a-case-study
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