4 Ways Machine Learning Is Impacting The Online Retail Industry

By Published On: July 2nd, 2020Categories: ecommerce, use cases

Guest post by Kayleigh Alexandra. 

Online retail has always been quick to use new technologies, and machine learning is a prime example. Here’s how it’s making a difference.

Talk about AI and most people envision science-fiction scenarios of computers becoming sentient and taking over the world. In reality, what’s meant when we talk about AI is as a practical tool (at this point in time, at least) is machine learning: creating powerful computer systems and deploying them to find patterns in massive sets of data.

Machine learning is artificial intelligence because it takes a skill fundamental to the human experience — pattern recognition — and automates it in a scalable way. Industries of all kinds have found uses for machine learning in recent years, but one forward-thinking industry in particular has used it heavily: ecommerce.

In this post, we’re going to identify four ways in which machine learning is impacting the online retail industry, giving you some ideas for how you might use it in future. Let’s begin:


1. It’s enhancing inventory management

Handling stock has always been one of the trickiest parts of ecommerce. It was already difficult for brick-and-mortar stores to keep enough stock to satisfy demand without running the risk of ordering too much, and that task only got harder with the transition to online retail. Purchases weren’t instantaneous, yes, but the concept of ordering something and waiting a week to collect it lost a lot of its appeal when next-day delivery became standard.

Most ecommerce buyers won’t accept waiting very long, even in tricky circumstances, and that means getting the stock balance exactly right. Having too little leads to delays and lost buys, but having too much means more warehouse space and risking ending up with plenty of products you can no longer sell because they’ve stopped trending.

There are ways around this, such as using dropshipping to avoid having any stock at all, but that’s a niche business model. By deploying machine learning, though, you can ensure that you have the right amount of stock (based on historical data and projections) and place new orders with suppliers at the ideal times.


2. It’s optimizing product pricing

Whatever you’re selling online, you’re going to face a lot of competition. A physical store can do well despite charging high prices because it’s in a great location: this is why cinemas always manage to sell huge amounts of food at exorbitant cost. Online, though, every seller is up against numerous other sellers both nationally and internationally, and being undercut even slightly can lead to a lot of lost sales.

Where it’s essential to be the cheapest (or among the cheapest), pricing optimization software can do the job. It looks at what other sellers currently charge and have charged before at different times throughout the year and in different circumstances, and it adjusts your prices accordingly so you can compete without charging any less than you need to.


3. It’s improving customer support

Handling demand at a high level isn’t just about having enough stock and dealing with the shipping: it’s also about providing strong customer service, and that can be really tricky when you’re fielding queries and complaints from numerous customers. An in-house customer support team can only handle so many questions simultaneously, and having a support ticket system doesn’t impress people much at this point.

That’s where chatbots can really help out. A well-implemented e-commerce chatbot through a service like Onlim can answer basic queries, suggest products based on shopper suggestions, provide order updates, and do various other things — including passing support requests to human assistants if it doesn’t know how to handle them.

This is driven by machine learning. Through looking for patterns in what people ask and how they view the responses provided, you can steadily figure out what support they need and how you can most effectively fulfill that need.

If you’d like to learn more about chatbots, check out our “Ultimate Guide to Chatbots for Businesses”. 


4. It’s refining PPC advertising

Pay-per-click advertising is a key part of the online retail puzzle, consistently driving actionable traffic and making it possible for a new business to generate some interest far before tactics like SEO could become effective. It isn’t always done well, though — and that’s where machine learning steps in to take things to the next level.

Analyzing all the historical data you can provide, machine learning can spool out ads, target audiences and bids that will get results. To a significant extent, this kind of functionality has already been added to PPC platforms: whenever you see an option for automatically adjusting bids, that’s using machine learning.

Doing online retail at a great scale can be a laborious task with a lot of trawling through data and using trial and error to yield improvements. Bring machine learning to bear and you can breeze through much of that work. If you’re planning to get into online retail, or you’re already in the industry, then you must seriously consider implementing it.


Author bio:

Kayleigh Alexandra is a writer for Micro Startups, your online destination for everything startup. She’s passionate about hard-working solopreneurs and SMEs making waves in the business world. Visit the blog for your latest dose of startup and charity insights from top experts around the globe @getmicrostarted.


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