The old adage, “Faster, better, cheaper. Pick one,” may be soon outdated thanks to advances in artificial intelligence (AI), especially for e-commerce businesses. Traditionally, these are the three areas where businesses can compete. A business can deliver a product faster, but more expensively and at a lower quality. Or it can deliver a higher-quality product, at a higher cost and slower delivery time, of course. Or a business can aim to deliver the least expensive product in their category, at the expense of quality and, possibly, delivery time.
In the past, these three areas of competition were mutually exclusive, and businesses needed to pick one (or two at the most) to compete on. Chasing all three took enormous resources, and rarely could a firm deliver on all three simultaneously. That is, until the introduction of AI.
Today, AI can be used to analyze the millions of e-commerce transactions, but also data points in both customer habits and in logistics networks. Specifically, predictive analytics provides a window of insight into how a business can deliver on all three. Here’s how.
What is predictive analytics?
First, it’s important to understand what predictive analytics is. Basically, this is a branch of artificial intelligence that analyzes large data sets to predict future events. Predictive analytics derives from a variety of AI practices, including data mining, data modeling, statistic analysis, and machine learning. As is the case with many AI programs, the larger the data sets the more accurate the predictions can be. It can be applied in a variety of fields, including finance, healthcare, fraud detection, and many more. Of course, our focus here is its application in e-commerce.
How to apply predictive analytics to e-commerce
There are a number of areas where predictive analytics can have a major impact on an e-commerce business. Perhaps not surprisingly, Amazon is leading the way with its desire to ship products to customers before they even order them. Leveraging their massive logistic network of warehouses as well as their enormous amount of customer transactions, Amazon is developing a system to store and ship products that customers order regularly, without them having to lift a finger (or, in this case, click a button).
Another area where predictive analytics can grow an e-commerce business is in product recommendations. Businesses can analyze their customer’s shopping preferences, and then predict which products they might buy based on past purchases. What’s more, predictive analytics can be used to predict the types of special offers and promotions a specific customer might respond to. This isn’t just about selling ink to someone who buys a printer, though. This is about using data to deliver personalized recommendations to buyers at scale.
Another key area where predictive analytics can help e-commerce businesses is in their supply chains and inventory management. In this area, predictive analytics can not only predict what customers will buy but also when. Businesses can use these predictions to achieve greater efficiencies by producing and stocking products only when they need them, thus reducing costs associated with stock warehousing. Additionally, predictive analytics can be applied to distribution networks, allowing firms with always find the fastest and least expensive way to deliver products to customers.
Ready to give your e-commerce business a boost?
Businesses that apply predictive analytics to their operations today are likely going to leap-frog competitors who don’t. If you’re ready to achieve faster, better, AND cheaper product delivery, wining more customers and sales than ever before, contact us. All.in Data can help you design and implement a predictive analytics program that enables your business to be more efficient and effective than ever before.