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6 Significant Machine Learning Strategies for eCommerce Businesses

Ecommerce is a necessity of today’s era. It’s true that we all like going to shopping malls or stores and investing some time into ‘quality shopping’. But COVID kind of changed the rules and turned eCommerce into a priority from an option.

Starting from the simplest household items to large electrical appliances, eCommerce stores came to everyone’s rescue. But the evolution of eCommerce also triggered the growth and amalgamation of other technologies, especially Machine Learning (ML).

ML was already being put into use by search engines and networking sites to extrapolate user data. But the growing competition among niche marketing sectors and increased eCommerce traffic have made it mandatory for businesses to incorporate Machine Learning strategies.

Continue reading this article to learn about the six most useful ML strategies that can alter the fate of your eCommerce businesses. But first, let’s look at some numbers that forecast the future of online shopping.

- Statista reported that in 2022, eCommerce sales showed the highest growth in Asia, the Americas, and Australia with Singapore and Indonesia leading the way.

- In 2022, Alibaba Group emerged as the largest eCommerce retailer worldwide and generated approximately 780 billion USD in annual online sales, with Amazon being the second with around 690 billion USD in online sales.

- Statista reports also forecast that Amazon will surpass Alibaba by 2027 and will generate almost 1.2 trillion USD in online sales.

- India and the Philippines are the two Asian countries with the fastest-growing eCommerce sales.

It’s obvious that eCommerce businesses are booming. So it’s important that you prepare your online store with the best ML strategies and keep them market ready. So let’s get to the main discussion.

ML Strategies that Make Your eCommerce Business Better

One of the most basic functions of any online store is its search feature. It holds utmost importance for online stores as it is the key to displaying users their required items. Good optimization means that users get the option of refined keywords.

So if someone searches for mixer and grinder, then they simultaneously get the options of multiple keywords starting with their keywords, for example, mixer and grinder followed by name of the most preferred or searched companies or mixer and grinder followed by colors.

Using ML algorithms can optimize these search results by analyzing the purchasing data of the customers. ML can display related keywords for the searched products and can also display items that are frequently brought together. This increases the chances of improving the click rates and leads to conversion.

When we talk about the effectiveness of recommendation engines, Netflix definitely needs a mention. According to a McKinsey post, 75% of the programs watched by the viewers are suggested via ML algorithms. However, the functioning of a recommendation engine has more to it than data analysis.

It isn’t just the type of shows watched by the viewers; the engine also needs to take into account the location, demographics, top-ranking shows, trending shows, and of course, the shows already watched by the viewers.

The same goes for eCommerce businesses. With AI and ML algorithms, your site can offer customers feasible product recommendations based on their preferences and taste, which in turn increases the chances of sales.

A chatbot is the first thing that comes to mind when we talk about automated customer support. But automated customer support is more than a chatbot. When you place an order, the site sends a confirmation mail and an SMS to you. Also, it regularly updates you on the status of your purchased products.

But chatbots are in the limelight because they serve as the evolved version of automated customer support. The trained algorithms can help customers through trivial matters like tracking food orders, helping with exchange and money returns, etc.

However, critical problems will always need the assistance of real humans. But the chatbots have not only increased the efficacy of customer support but have also helped companies save costs and resources.

Not the least bit surprised, are we? Pricing is one of the decisive factors that hold the key to the success of an eCommerce business. Now there are a lot of aspects that affect the pricing. First of all, you have competitors.

If you keep the margin too high (with shipping charges, taxes, and all), there’s a fair chance that your customers leave your app. At the same time, you have to keep the margin good enough to get your own profits.

Pricing is dynamic too which means that it changes according to occasions, sale seasons, shipping locations, the supply and demand chain, etc. Machine Learning services can help you analyze and assess all these aspects, and show the updated price to your customers.

When going for traditional shopping, you might often have experienced salespeople approaching you to know your specific requirements and offering you items that meet the same.

When it comes to online stores, the same thing is done by ML algorithms. Once again, the filters and predictive analysis are put into action. While purchasing apparel, you might have come across filters like colors, sleeve size, fabric, etc.

These preferences are tracked and extrapolated by ML algorithms. So the next time you search for similar apparel, the store displays products based on your past filters. Such segmentation and personalization have the power to influence the purchasing decisions of the users.

When you open your favorite online magazine, it often displays articles and posts that might intensify your interest. Based on the type of content you read, the stories you save, or the files you download, your magazine presents you with marketing campaigns that have increased chances of getting your attention.

Ecommerce businesses make use of Machine Learning services to design precise marketing campaigns for both their existing and potential customers. The algorithms also keep track of visiting time, the type of marketing emails opened and responded and the sale season in which customers have made their purchasing.

The point is that every day, an online store generates a huge amount of data and proper assessment of these data sets can lead to sensible marketing campaigns created with relevant products and messages.

Final Thoughts

The dynamics of the retail industry are rapidly changing and to keep up with the market, retailers need to include ML mechanisms that enable them to make the most out of the everyday data. With the help of a reliable development partner, eCommerce businesses can make the most out of the data and can make rich profits.



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A Smith

Albert Smith is a Digital Marketing Manager with Hidden Brains, a leading enterprise web & mobile app development company specializing in IoT, Cloud & Big Data