Flash Sales Troubles AI Can Fix
Apparel online retailers stage flash sales to lure customers into shopping impulsively, boost customer loyalty by ensuring the right price perception, and spur buyers to check out the rest of the offered assortment. Ultimately, flash sales bring a 35 percent lift in transaction rates and help retailers liquidate excess stock.
However, running flash sales is a tricky task. Retailers need to make sure their discount stands out and at the same time does not kill their profit margin. Very often retailers boost their rate of sales by offering too deep discounts and lose gross profit. In other cases, businesses set higher prices and fail to liquidate old and surplus inventory.
Advanced companies use sophisticated pricing software for markdown optimization to ensure timely stock clearance and stop losing money.
Hidden challenges of launching flash sales
There are two major issues retailers need to deal with when it comes to flash sales pricing:
- To forecast the sales of the discounted stock. Traditionally, pricing analysts analyze the first hours of sales and sales dynamics of the marked down items. Then they make an assumption that this particular dress or a pair of shoes behave the same way as, let’s say, a certain item which was sold in four days with the maximum discount 70 percent. Finally, they apply the same rules for the discounted product. In such a scenario, pricing analysts need to have solid expertise to make successful pricing decisions.
- To find a balance between the markdown depth, sales velocity, and profit margin. Retailers need to select the right assortment, time-frame the sales correctly, set the optimal first and last discounts, as well as define every repricing step between them. The challenge here is to mark down so that the stock is cleared off in time, while maintaining gross profit and profit margin.
In the end, it all comes down to the expertise of a particular pricing analyst. Even if their pricing decisions are successful, it is impossible to scale them and repeat their success in the future. Pricing managers need to reinvent the wheel every time. That’s where AI takes the floor in the operations of advanced retailers.
Using AI to stop losing revenue
AI-based retail pricing software has proven to be effective when it comes to letting companies earn more and speeding up repricing. Ilona Baskova, Brand Manager at Eastern European apparel retailer Intertop, comments on the effectiveness of such solutions in terms of financial performance and the way they change the process of pricing.
She says: “Technology does boost the financial performance of your company. When using machine learning algorithms in repricing, we do not do repricing per se, but we set the rules of the game and control the results. Machines do the rest of the job.” With a pricing platform to optimize markdown pricing, Intertop saw its profit margin boost by 200 basis points and gross profit grow by 10.3 percent.
Here is how algorithm-based solutions help retailers optimize markdown management:
- Algorithms can classify any number of items and predict their sales dynamics by using a detailed description of such products. The description has to comprise all there is to know about the product. For example, its color, fabric, and lengths, along with its brand and other parameters. AI can extract this data and define its importance automatically. Then it uses the data to forecast the sales of such an item.
- Based on the elasticity of price, the algorithms can calculate the optimal markdown prices to maximize the gross profit and profit margin during the whole flash sales period. At the same time, they ensure that the stock is cleared off in a timely manner.
To recap, flash sales are a very handy tool to boost sales and entice customers. However, retailers need to learn to run them effectively. That’s where artificial intelligence jumps in by predicting sales dynamics and crafting optimal prices for every repricing cycle. Ultimately, it helps to hit two goals:
- maintain the gross profit and gross profit margin;
- ensure that the stock is sold in time.
Alexandr Galkin is CEO & Co-founder of Competera, a pricing platform for enterprise retailers looking to increase revenue and stay competitive. He is also a Forbes contributor, speaker at IRX, eCommerce, and RBTE conferences.