Harnessing the potential of data-driven strategies, a global quick-service restaurant chain is undergoing a remarkable transformation, elevating customer satisfaction and fostering revenue growth.
People analytics, once the territory of a select few like Google, has become a focal point across industries. It’s no longer confined to specialized sectors, with businesses increasingly embracing analytics for recruitment, retention, and uncovering unprecedented talent sources, thus redefining employee performance parameters.
In particular, the experience of this restaurant chain offers a significant revelation. By channeling the strength of people analytics towards its frontline staff, the company witnessed substantial enhancements in customer satisfaction, service quality, and overall business performance, culminating in a significant 5 percent surge in group sales within its pilot market.
Unraveling the Challenge: Mapping the Talent Value Chain
Struggling with excessive annual employee turnover across its franchised and corporate-owned outlets, the restaurant chain sought innovative strategies to enhance the customer experience. Recognizing that bridging this turnover gap was pivotal to increasing revenue, the company embarked on a journey to delve deeper into understanding its workforce. This initiative started by setting specific goals and translating frontline employee behavior and experiences into measurable data against real outcomes.
Defining Goals and Gathering Data
Agreeing on critical outcomes upfront was a foundational step for this people-analytics initiative. Rigorous discussions among senior leaders established three pivotal metrics: revenue growth per store, average customer satisfaction, and average speed of service, tracked by shift to ascertain the influential personnel in driving these outcomes. Identifying performance metrics that complemented each other and those that conflicted in specific contexts emerged as a critical insight.
Bridging Information Gaps
Although internal sources provided some relevant data, augmenting it was imperative. Three key areas necessitated substantial data augmentation:
1. Selection and Onboarding: With little information on personality traits impacting performance across various outlets and shifts, the company collaborated with psychometric assessment specialists to profile individual employees through a series of online games, building a comprehensive understanding of their personalities and cognitive abilities.
2. Day-to-Day Management: The absence of a culture or engagement survey prompted the deployment of McKinsey’s Organizational Health Index (OHI). This instrument pinpointed 37 management practices critical to organizational health and long-term performance, offering insights into effective management practices and their influence on frontline operations.
3. Behavior and Interactions: To comprehend employee behavior and collaboration, the company employed sensors to monitor physical interactions among colleagues over time. These sensors captured movement intensity, conversation tones, and time spent conversing versus listening to colleagues and customers.
Challenging Conventional Wisdom: Key Insights
Leveraging data from over 10,000 data points across individuals, shifts, and restaurants in four US markets, the company utilized logistic-regression and unsupervised-learning models to establish correlations between drivers and desired outcomes.
Here are four insights that redefined how the company manages its workforce:
1. Personality Matters: Categorizing frontline employees into four distinct archetypes unveiled surprising correlations. Contrary to popular belief, hiring based on friendliness didn’t maximize performance. Instead, employees focused on their work and minimized distractions, significantly impacting performance.
2. Career Development Triumphs: Monetary incentives showed limited correlation with stronger store or individual performance. On the contrary, career development and cultural norms emerged as more impactful on outcomes.
3. Management Dynamics: Contrary to belief, managerial tenure held no correlation with performance. Identifying effective store manager traits allowed the company to train local leaders effectively and create a stronger team environment.
4. Shift Optimization: Performance dipped notably during extended shifts of eight to ten hours, affecting productivity. Analyses highlighted the inefficiencies of this policy despite easing managerial responsibilities.
The Results So Far
In just four months of piloting these insights in the initial market, the outcomes have been promising. Customer satisfaction soared by over 100 percent, speed of service improved by 30 seconds, new joiner attrition decreased significantly, and sales witnessed a commendable 5 percent increase.
Concluding, while data-driven insights are valuable, they should be complemented by experience-based wisdom in employee management. Nonetheless, these results underscore the immense potential for people analytics to link talent strategies to tangible business value in vast industries such as retail, employing millions across the United States.
Source: Carla Arellano, Alexander DiLeonardo, and Ignacio Felix