Markup Pricing in Ecommerce With Data Science

Now, let’s look at the reasons behind price markup. Consider an e-commerce company that delivers products to your doorstep. Such companies often charge a marked-up price to make up for various supply chain costs:

• Delivering to remote locations or to certain pin codes

• Delivering the next day or within two days

• Handling cash on delivery

• Handling special instructions like gift wrapping

Similarly, fleet services or food delivery companies charge surge price during heavy demand hours while their supply remains the same or goes down.

You saw how surge pricing can be decided using experiments. In this case, the surge was decided based on the revenue generated by the company. This is fine as long as the cost remains constant and maximising revenue is equivalent to maximising the profit. However, there might be other cost components that may affect the overall profits, as discussed in the example below.  

Let’s see a combination of markup and markdown to optimise the utilisation of fleets.

Often, companies offer 'fixed payments' to incentivise drivers to complete more rides. For example, some companies offer a fixed payment per day even if the driver completes just one ride that day. They may also offer additional fixed payment depending on the number of rides completed.

In scenario 4, if we consider the fixed payment to be made to each registered driver, you will find that the revenue comes down to ₹2,560. The two images below represent two scenarios when fixed payments were offered to the drivers. In the first case, only a markup was done, whereas in the second case, markdown was also done.

The revenue is ₹4,120 and ₹4,240, whereas the profit is ₹2,560 and ₹2,680 in the first and the second cases, respectively. Thus, the profits were higher in the case of both markup and markdown.

This is because even if eight fleets remain unutilised, the company is required to pay the drivers as per their contracts. This usually leads to loss. To overcome this loss, it declared a 5% discount on all fleets at 7 a.m. To avail this discount, four people from the 8 a.m. schedule and four people from the 9 a.m. schedule shifted to the 7 a.m. schedule. In this way, the fleet aggregator achieved 100% utilization of all cabs. This change also offered them an additional ₹120 profit. Thus, an optimal mix of markdown and markup pricing algorithm can optimise the revenue and profit margin.