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. 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 (“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.
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
July 2017
http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/using-people-analytics-to-drive-business-performance-a-case-study?cid=other-eml-alt-mkq-mck-oth-1707ts&hlkid=d6579922cf3a42de9be7b9ec342e16fc&hctky=1627601&hdpid=c9f72ddd-fc7a-4a84-9456-153535153813
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