The Importance of 90: Why peak coverage defines retail success
Executive summary
Retailers have long known that peak hours make or break the week, but few measure just how effectively those hours are being supported. The Peak Coverage Score changes that by quantifying alignment between selling labor and traffic demand.
What emerges is a powerful truth: coverage is not simply an operations exercise—it’s a performance driver. Strong coverage unlocks higher sales, stronger customer experiences, and more engaged teams. Weak coverage, by contrast, means even the best traffic opportunities slip through the cracks.
The pursuit of 90% peak coverage is not about perfection, but about focus. Leaders on the floor, clarity in scheduling, and incremental gains all move the needle. For executives, the challenge is to make peak coverage a board-level conversation, not just a store-level one. The result is a measurable impact on comp growth and a stronger foundation for long-term performance.
Defining peak coverage
Peak segments: Ten 2-hour blocks representing a store’s busiest 20 hours each week.
Peak coverage score: A measure of how effectively selling labor is scheduled to cover peak traffic.
This framework shifts labor planning from hours and budgets to customer alignment, making scheduling a driver of revenue rather than just a constraint.
Why store leadership at peak hours drives performance
The presence of a store manager on the sales floor during peak hours consistently outperforms averages across all metrics:
Higher visit value
Higher average basket
Higher conversion (+88%, +81%, +75% vs. Baseline)
In contrast, when no sales leader is present, performance falls:
Lower visit value
Lower average basket
Lower conversion (-56%, -87%, -87% vs. Baseline)
The lesson is clear: leadership coverage is non-negotiable during peak.
Common pitfalls that undermine peak coverage
Even with peak identified, execution can falter. The most common causes include:
– Gut-feel scheduling instead of data-driven planning
– Incorrect apportionment of labor
– Employee availability and callouts
– Mismanaged non-sell vs. selling labor split
These factors don’t just reduce efficiency—they erode the very hours most critical to sales and customer experience.
The limitations of traditional peak coverage analysis
Traditionally, peak coverage analysis uses a store’s yearly score to divide locations into two groups: above and below 90%. While simple, this method has limitations:
– Weeks below 90% get averaged into the “above 90%” group, watering down accuracy.
– Small sample sizes for lower store-count retailers.
This method is still effective for high-store-count chains looking for quick directional insights, but it doesn’t provide the precision needed for deeper analysis.
How weekly data unlocks smarter peak coverage insights
A better approach is to analyze peak coverage weekly.
100 stores = 5,200 weekly data points.
Across 21 retailers, a full fiscal year yielded 157,820 weekly data points.
The analysis used comp stores only, ensuring consistency across retailers.
This method provides more granular insights and avoids distortion of annual rollups.
The link between peak coverage and comp sales growth
From September 2024 to August 2025, results showed a direct relationship between peak coverage and year-over-year sales performance:
– 60% of weeks achieved coverage above 80%.
– Moving from lower bands to higher ones delivered 2% to 6.5% comp sales growth variance.
– Even if 90% peak coverage is difficult to sustain weekly, incremental gains—from 70% to 80%—produce measurable impact.
For example, stores making these improvements recorded +1.8% comp growth even outside holiday periods. Quarterly analysis also revealed shifts in coverage, underscoring that performance outcomes fluctuate over time and reinforcing the need for continuous monitoring rather than relying on annual averages.
The challenges of reaching 90% peak coverage
Consistently reaching 90% coverage is aspirational, but several realities make it challenging:
Low-volume stores with minimum staffing (baseline: Min Sell = 1, Open Staff = 1, Close Staff = 2)
50% of stores operate with PPOH below 2.2 when not in a holiday period.
Balancing mandatory baseline coverage with peak alignment often requires difficult trade-offs, especially in smaller fleets.
Still, the upside is clear: when stores sustain 90% or higher, they capture an additional +1.5% in comp sales compared to those that remain below.
Key roadblocks preventing strong peak coverage
Executives seeking to close the peak coverage gap should focus on the following:
Leadership at peak | Ensuring managers are present on the floor. |
Data over gut-feeling | Using traffic and coverage metrics to guide scheduling. |
Employee availability | Accounting for callouts and scheduling flexibility. |
Recommended coverage | Adopting system-driven allocation guidance. |
Non-selling vs. selling balance | Preventing task time from eroding selling hours. |
Building toward 90% peak coverage through incremental gains
For many retailers, jumping from 70% to 90% coverage overnight is unrealistic. But incremental gains are both achievable and valuable.
Raise stores from 70% to 80% coverage first.
Reinforce leadership presence at peak.
Audit non-sell allocations to protect selling labor.
Track quarter-to-quarter shifts to catch coverage gaps early and adjust before the impact results.
Over time, these steps move to the fleet toward the aspirational goal of 90% peak coverage while still producing meaningful comp growth along the way.
Executive takeaway: Why Peak Coverage should be a strategic priority
Peak coverage is not just a scheduling metric—it’s a sales strategy. The closer retailers align selling labor to peak traffic, the stronger their comp growth.
While 90% peak coverage remains the benchmark, even modest improvements yield measurable gains. For executives, the call to action is clear: focus on leadership at peak, diagnose roadblocks, and pursue incremental growth.