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.
by Carla Arellano, Alexander DiLeonardo, and Ignacio Felix
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 it1). 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
1 See Laszlo Bock, Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead, New
York, NY: Hachette Book Group, 2015.
July 2017
2
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
(Exhibit 1):
• 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
3
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 (“what employees do in the
restaurants”). 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.
Exhibit 1
Analysis identified which employee features correlated to the
desired outcomes.
Q3 2017
People Analytics
Exhibit 1 of 2
Who gets
hired
How they are
managed
Personality traits
Cognitive ability
Demographics
Commute distance
Previous retail experience
Time allocation
Physical in-location movement
Frequency/duration of interactions
Quality of interactions
Shift length
Shift size
Level of management on shift
Training/capability building
Management behaviors
Compensation structure
What they do
intrinsic
extrinsic
Global restaurant chain,
example
Affected outcomes1
Did not affect outcomes
Myth busting (thought to affect outcomes but did not)
1 Targeted outcomes were customer-satisfaction scores by shift, revenue growth by store, and speed of service by shift.
4
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 (Exhibit 2).
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.
5
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.
Exhibit 2
Frontline employees fell into four personality archetypes.
Q3 2017
People Analytics
Exhibit 2 of 2
1 Emotional Quotient, a measure of self-awareness and sensitivity to others
Distribution of employees at a global restaurant chain
0 25% 50% 75% 100%
Potential leaders
High EQ
Good at multitasking
Follow up with others
Most like high-
performing general
managers
High EQ,1 more
altruistic and trusting
Risk takers, highly
spontaneous
Socializers Conservative
taskmasters
Low EQ
Good at planning
and execution
Very focused, not
good at multitasking
Conduct work within
boundaries provided
(not risk seeking)
Entrepreneurial
taskmasters
Lower EQ, less
altruistic
Good at planning
and executing
Higher appetite for
risk and innovation
6
Copyright © 2017 McKinsey & Company. All rights reserved.
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.
Carla Arellano is a vice president of, and Alexander DiLeonardo is a senior expert
at, People Analytics, a McKinsey Solution—both are based in McKinsey’s New York office;
Ignacio Felix is a partner in the Miami office.
The authors wish to thank Val Rastorguev, Dan Martin, and Ryan Smith for their contributions
to this article.
