Planning Sale Events for Online Shops: Discount Depth, Break-Even Uplift, Stock Build-Up, Pull-Forward Effects, Shipping Peaks & Post-Event Review

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Tag four normal weeks of orders with their weekday, average the orders per weekday with AVERAGEIFS, turn them into a weekday index and estimate how many orders the sale day would bring without any sale.
• Calculate each product's gross margin, the volume factor and the extra sales a discount needs just to earn the same gross profit as without the sale, and you spot the product where no volume can pay for the discount.
• Price one average order at four discount levels, apply the free-shipping threshold and the payment fee, compare the margin per order and the total contribution of each scenario and pick the discount that earns the most.
• Look up a fictional uplift factor for each product's category, forecast the units and revenue of the main sale day and let the lift fade into a second day.
• Turn the sale forecast into units to order after stock on hand and a safety buffer, round up to full case packs, count back the supplier lead time in working days with WORKDAY and flag every product whose order is already late.
• Compare actual daily units with the baseline in the week before, during and after the sale, measure the lift on the sale days and the dips around them and work out how much of the lift was only shifted demand.
• Spread the main-day order forecast over 2-hour slots, work out how many packers each slot needs, find the peak staffing and check whether the booked carrier pickups can take all parcels.
• Review the sale by category: gross profit after a returns allowance, the incremental profit against the baseline and after the extra costs, the share of orders from new customers and a chart of sale vs baseline gross profit that shows which categories really paid off.