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Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.
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Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.

Why machine learning is good for retail and Ecommerce

What comes to your mind when thinking about Machine Learning? Maybe it’s a Tesla’s car with autopilot or a robot produced by Boston Dynamics? Most of the widely known Machine Learning (ML) solutions are some sort of phenomena that (at least for now) is hard to imagine being used in our ordinary lives every day.

But Machine Learning isn’t only about such exclusive and (yet for now) sometimes even impractical products. Actually, almost every person on Earth touches ML almost every day.

We’re talking about retail and e-Commerce. We buy stuff every day and most of it is sold with the help of different Machine Learning applications.

But is ML really helpful and profitable for such businesses? We’re going to find an answer for this (spoiler: it definitely is).

eCommerce can extract a lot of value from Machine Learning solutions.

Elevate your customer experience and sales with smarter, data-powered software.

Top 10 Applications of ML for business in 2021

Basically, there are two main directions of work for ML in retail and e-Commerce: enhancing inner business processes or customer experience. But if we dig a bit deeper into the topic, we’ll see that the number of possible applications for Machine Learning doesn’t boil down to these two. So, where particularly ML can be and is used?
Efficient stock control and inventory management

Inventory management influences business’s financial flows both directly and indirectly. For example, overstocking items gets inventory piled up without any purpose, which can even lead to a dead stock problem. On the contrary, understocking might result in opportunity costs and disappointed customers who couldn’t find the needed item, which will ruin the seller’s image.

Machine Learning can assist in solving a wide range of inventory-related problems, for example:

Tracking products so that there are no mismatches or mix-ups, which can dramatically affect customer experience;
Implementing ML can also help by optimizing the whole stock management, thus making delivery of goods faster, which will enhance customer experience;

Using Machine Learning in stock predicting helps to avoid over- and understocking, which will enhance business’s financial flaws as well as customer experience

More accurate demand and sales forecasting
This point is much like the previous one. Analyzing historical data like sales during previous 3-4 years considering some side variables (like economical and political situation) with Machine Learning makes it possible to predict how the sales will go in the future allowing to make production, logistics and marketing plans more precise and cost-effective. And what is more, ML allows detecting new market trends before all competitors notice them, so you get the time advantage to implement changes or launch new products and earn higher market share.
Predictive maintenance

Another important point for any business is the condition of equipment. Small flaws occur regularly and that’s okay, nothing is perfect. But critical failures can come at a price too high to cover.

That’s why more and more companies start practicing predictive maintenance. They give Machine Learning a set of data about how the system is working in its norm and after learning the algorithm alerts about failures, letting business fix them before it’s too late.

Search engine result refining and visual search

In this field, ML applications have been widely used for a long time already. Thanks to Machine Learning, a search engine can better understand what particularly a customer is searching for, even when the request is not complete or accurate.

The visual search technology makes it much easier for users to find the desired goods – all they need is to upload an image and choose from similar options by different brands. It can also help to detect piracy and counterfeits to prevent their distribution and loss of profit.

Dynamic pricing

When was the last time you ordered an Uber? Was there a higher price due to high demand?

That’s dynamic pricing. Based on the ratio between available drivers and orders, the app calculates the price. If there are too many orders, Uber will raise the price for a ride in order to bring more taxi drivers to the roads so that the demand would be fulfilled. That’s an economist’s dream come true, isn’t it?

By applying ML to pricing decisions, it’s possible to achieve such an effect, which will have a positive impact on a brand’s financial flow. Basically, after learning on provided data, ML will be able to calculate the perfect price for a particular good at a particular moment, which leads to higher sales and revenue growth.

Up-selling and cross-selling
It’s about personalized recommendations. When a customer visits a web-site and puts an item in the cart (for example, a smartphone), the system will most likely offer something related and possibly needed (like protection case & glass). Or, maybe, the chosen good has some better alternative (there’s another smartphone in stock that has better characteristics). By letting Machine Learning to compose offers for related items or possible upgrades, a business can get much bigger revenue.
Immersive customer experience

Nowadays, doing any business is not just about providing services or selling goods. It’s also about how the brand interacts with customers.

The era of waiting for ages while there will be a free specialist at a call center to solve a customer’s problem is over. Everything must be quick, convenient and look natural.

This can be achieved with Natural Language Processing (NLP) technology. A Machine Learning algorithm can be taught to recognize speech or text and retrieve information about intentions of the customer. After this, it’s possible to transfer the customer to the profile specialist passing the call center, thus saving time for the customer and enhancing one’s experience of interaction with the brand.

This solution can be implemented as a chatbot or a virtual assistant when a customer calls a brand’s hotline number.

Customer segmentation and targeted marketing campaigns

Another field of use for Machine Learning is targeted marketing. ML can analyze information about customers and segment them according to their purchasing behaviour. ML empowers marketers to switch from general campaigns for all customers to more tailored offers at the right time which ideally suit each audience and create incentives for purchase. With the same marketing budget and allocated resources, you reach higher conversion, boost sales and increase brand loyalty.

Churn prediction and prevention

There is always a flow of customers. Some of them come, but some go.

With help of ML algorithms, it’s possible to analyze the churn reasons in a more detailed way, segment them into clusters according to their purchase behaviour and identify those who are likely to churn soon. Moreover, a Machine Learning algorithm can detect barely noticeable (manually) correlations and patterns, thus giving a more accurate picture of churn reasons. So you become able to respond in time and provide customers with more tailor-made offers to minimize this unpleasant phenomenon.

Social media monitoring using NLP

Setting up marketing campaigns is important, but knowing how your brand is perceived is vital. Gathering feedback from customers gives an opportunity to see strong and weak sides of a brand.

This feedback can be gathered directly, but also there’s an option to receive information about brand perception indirectly, via social media.

By assigning a Machine Learning algorithm to analyzing social media posts and comments concerning your brand, you can build a model of how the brand is seen by potential and current customers: what they like about the brand, what they don’t. Maybe they have some idea on how to improve.

All this information will help in understanding whether you’re moving in the right direction.

Instead of conclusion

So, Machine Learning is really helpful. Rising revenue, giving better understanding on how everything’s going, giving opportunities to avoid losses and optimize business processes… and even chatting with customers instead of making them wait in line for the next available specialist.

And even though it seems to be pretty expensive, it will pay off. So why not boost a business with such a universal tool that can do so much help?

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