Store teams make many of their most important decisions in the middle of the day. If traffic comes in stronger than expected, they may need more coverage. If a slow period drags, they may need to change the floor focus, push a promotion, or coach the team differently.
But the retailer's sales targets were not moving at the speed of store operations. Leaders wanted a practical hourly view that showed whether stores were pacing ahead or falling behind. Getting that view used to mean waiting on a custom analytics request, with several rounds of explanation and revision before the report was ready.
By then, the useful moment had often passed.
“By the time we used to get the report, the moment to fix the week had already passed.”
The challenge: sales pacing was too slow to guide store action
Hourly targets are only useful if teams can act on them while the day is still unfolding.
The retailer needed a faster way to turn historical sales patterns into clear hourly goals. Store leaders did not just want to know whether sales were up or down at the end of the week. They wanted to see which parts of the day were missing target, where execution needed attention, and whether the team was still on pace.
Before 100xTeam, this required support from analysts. The business team submitted a request, explained the target logic, waited for the report to be built, reviewed the output, asked for revisions, and waited again. A cycle that should have supported same-week execution often took 2–3 months.
The urgency was practical: store teams were making daily staffing, promotion, and sales decisions without a timely view of hourly performance.
The solution: 100xTeam AI made hourly sales pacing self-serve
100xTeam connected AI to the retailer's internal sales data and gave store leadership a simpler way to build the view they needed.
Instead of translating the request through a long reporting queue, the stakeholder could describe the goal in plain business language: use last year's performance as the baseline, set a higher target, break it down by hour, and show where stores were ahead or behind.
100xTeam AI handled the hard part behind the scenes. It interpreted the request, pulled the right data, applied the target logic, and produced an hourly pacing view that the business user could refine quickly.
AI made the workflow faster by removing the technical steps that usually sat between a store leader's question and the answer they needed. Leaders did not have to write queries or wait for a custom build. They could ask the question, refine the pacing view, and move to action much faster.
The results: faster action and stronger hourly performance
The biggest change was speed. What once took months could now happen in minutes or hours.
That gave store leaders a much tighter operating rhythm. They could spot weak hours earlier, adjust coverage or promotions, and keep the team focused on the parts of the day that needed attention.
In the first test month, the team did more than hit the 30% growth target. They exceeded it.
“Now we can ask the question, check the gap, and change the day while the day is still happening.”
For the retailer, 100xTeam AI turned sales targets from a delayed reporting exercise into a live management tool. Teams could stop waiting for the numbers and start using them.
* All names have been modified to preserve privacy.
