Estimating the revenue of an ecommerce company isn’t really difficult. Estimating the eCommerce revenue with a high degree of accuracy is difficult to an extent. But, estimating the revenue with a high degree of accuracy when you have zilch internal data about the ecommerce company available is a Herculean task. At PipeCandy, that’s what we’re doing apart from a hundred other similar tasks. We’ve already talked about the basics of guessing an e-commerce company’s revenue. The formula is simple: Sales revenue = Number of transactions x Average transaction value Average transaction value Average transaction value, a.k.a., Average Order Value (AOV) = Average product price x Average basket size. Let’s say you have 5 pieces of clothing in your shopping cart (Basket size), and the average product price is $20, then, the transaction value is 5x20 = $100.Average product price and Average basket size differ from one category to the other. For example, from a fast fashion brand, an average consumer buys around 3-4 items in one transaction. But for a high-end luxury brand, the average basket size is usually 1 item. At PipeCandy, we crawled a few thousand websites to develop the eCommerce revenue model. So, we have a look-up sheet for the average basket size, average product price and conversion rate for each sub-category. Number of transactions =Conversion rate x Traffic (No. of unique visitors to the website)Conversion rates are easy to come across, thanks to benchmark reports released by companies like Monetate. At PipeCandy, we took average conversion rates for each industry and used machine learning to make it more accurate for each company. Let’s say the average conversion rate for apparel industry is 1.6%. It’s not going to be the same for apparel companies in all revenue ranges. Given the average conversion rate ‘x’, Conversion rates for top 10% (90-99th percentile) and top 25% (75th to 100th percentile) of the traffic is assumed to be 5x and 2x respectively.
Apart from this, we also took multiple other attributes into account which improves the accuracy of our conversion rates, average basket size, and average product price. Some of these include:
Our eCommerce revenue estimates have a 75-80% accuracy level. Our revenue ranges are 99% accurate. How do we know that? We tested. We checked our revenue models with publicly available data and customers’ data. Here’s a quick glossary of all the terms:
Want to know more about this and other kickass things we do? Talk to us.
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