Quality of sales
Margin, attachments, and basket mix show whether a sale helped the business, not just whether it happened.
Case study · Part 2 · Sales performance
Staffed right. Now, sell right.
Part 2 of my Lowe's story. Once the right people were on the floor, the question became what they were selling, and how to give every specialist a fair goal and a clear history.
The story · Lowe's
From hours to what sells.
This came after the staffing work. Once stores were staffed right, the next question was what was being sold, and by whom. I developed the Sales Performance tool, a proprietary application that monitored employee performance and set sales goals on its own.
More than the top line. Margin, attachment rate, service attachment, credit attachment, lead generation, and basket mix all mattered, not just total sales.
Sales through the specialists. Our specialists were the most trained people in the building, so we wanted the bulk of sales to route through them.
Goals from the forecast. Every goal came from the store and departmental forecasts, so it reflected real opportunity.
Twelve months of history. Employees and their managers could review performance together and talk about where to improve. Managers always had a list of who had the most room to grow. Not in a bad way, just a clear view of where the opportunity was.
The demo below is not that tool and uses no Lowe's data. It's one example store with fictional specialists, built to show how the pieces fit.
Later, this work expanded into employee bonuses. That's Part 3: Incentives.
Interactive · Example data
One example store, end to end.
Three steps: forecast to goals, a specialist's scorecard, and the manager's opportunity list. Every number is an example you can change in the assumptions panel.
1Example data
Forecast to goal
The store forecast splits by department, then into each specialist's monthly goal by hours worked.
Store forecast this month
2Example data
Specialist scorecard
Twelve months against goal, the view a specialist and their manager review together. I looked at a full year because any one month can swing with the season; the pattern is what mattered, and what the incentives in Part 3 were built to reward.
3
Where the opportunity is
The manager's list: biggest gap first, with a strength to start the conversation.
Example assumptions
How it works: department forecast = store forecast × department mix. Each specialist's goal = department forecast × the share of department sales we want through specialists, split by scheduled hours. The 12-month histories are generated from fictional profiles and a seasonal curve; they are not real people or real results. The opportunity list ranks each specialist's largest shortfall over the last three months.
Beyond the store
Why this matters.
Goals, history, and coaching that all come from the same forecast tell one consistent story to everyone on the floor.
Goals people trust
When a goal traces back to the store and department forecast, it feels earned, not arbitrary.
Coaching, not blame
An opportunity list starts the conversation with a strength and one focus area. It points managers where help pays off most.
History makes it fair
Twelve months of trend separates a slow month from a real pattern, for the employee and the manager.
Right work, right person
Routing big-ticket sales through the most trained people puts expertise where it changes the outcome for the customer.
Beyond retail
Sales teams, branches, contact centers: any team with a forecast can cascade goals, track quality, and coach to the gaps.
Example data only: the store, departments, specialists, histories, and all assumptions are fictional and editable. Built by me with AI. Updated Oct 2026.
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