Dynamic pricing, price optimization, profit-maximizing software; we find many names for those we love. I will try to answer: what type of software support e-commerce companies use for full automation of changing prices to maximize profit and/or market share.
To keep this text on a high level I will elaborate on the pricing of two important categories of products: key products and tail products.
Like traditional retailers, e-commerce companies are keen on being seen as the most price competitive on the key products. Wheater you are a consumer electronics company offering iPhones and Samsung TVs, a street clothing retailer selling Adidas or Nike sneakers, or you are shipping newly released books to your customers: you always want to have the lowest price on the products where the consumers have the highest price perception. These key products are eye catchers and will drive traffic. It’s common that these key products represent up to 80 percent of an average e-commerce site’s revenue but only half of its profit. Identifying these items is not an easy task but it can be done with a combination of machine learning algorithms, taking into account site visits; transactional sales data; and what products competitors feature. This approach together with a good understanding of the marketplace will give the online store a good starting point in the pursuit of its key products. During my years, most e-commerce sites I’ve had the pleasure to encounter, price these key items manually and in some rare cases even several times per day, though then often notification by competitor and own site data. So to do the job here, to price the key items the software support is:
- 1. Automated web crawling of prices from competitors
- 2. Machine learning to identify key products
These are the products that individually have low sales though they can be great in variety. For the consumer electronics provider, it could be the HDMI cable or a TV offered in an unusual size or color; the socks or the regular t-shirt for the street clothing retailer, or some older books that are still in print for the bookstore. These items are in general less price sensitive and there is room for automation. Here the most sophisticated e-commerce players utilize tools with artificial intelligence(AI) to analyze, suggest and change prices automatically. By introducing automated AI-guided A/B price testing (slowly in steps, either lowering or raising the price and testing the outcome on sales and profit), comparing site customer behavior and automatically identify product groups that both within and between group behave the in the same way in relation to consumer buying behaviors. Of course at the same time considering price, delivery terms and availability of same or similar products at competitors. This is combining the power of price elasticity, product cannibalism, and up-sales. Here automation and dynamic pricing shine. In the case where there is not sufficient data for AI-powered software support, a rule-based pricing framework should be in place. So to do the job here, to price the tail the software support is:
- 1. AI-driven price elasticity software that suggests optimal prices
- 2. Sophisticated web crawlers that can both identify and collect price and availability of similar or same products
- 3. A software that supports sophisticated rule-based pricing. Example of sophistication should be at least, but not limited to: own and competitor stock levels and availability, sales trends, minimum accepted margins and of course competitors prices
To conclude, I would like to make the last observation. My experience is that traditional retailers gone e-commerce (omnichannel) are the biggest buyers of external price optimization software. Traditional e-commerce, in most cases, has developed some internal tool for pricing usually combined with bought or in-house scraping capabilities. In general, I think you would be amazed by the relatively low adoption of dynamic tools, but the times are a changing.
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