eCommerce machine learning applications: the future of eCommerce marketing
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These days the competition among big online retailers drives each single aspect of their business to be constantly under scrutiny in search of a potential advantage that may result in stealing even the tiniest piece of the marketplace. On the other hand, small eCommerce owners battle it out in either very specific niches of the market or by personalizing their approach to customers as much as possible to retain those who like alternative ways of shopping rather than going mainstream with big brands. Regardless of the eCommerce player in question, striving to get the best technology out there and implement it in one’s eCommerce platform is key to keeping up with the industry’s trends and conform to the latest customers’ expectations in terms of quality of service. Among the best technology that has been making a huge impact in the industry are eCommerce machine learning applications.

eCommerce machine learning applications are not unheard of; however, these applications are still unclear to many, especially when it comes to their functionality and competitive advantage (both from a technical and user experience point of view).

This is why rounded up the key applications in machine learning that are about to make a whole lot of difference when it comes to eCommerce and and user experience modules.

Customer search analysis

customer data analysis

Thanks to machine learning, eCommerce owners have been enabled to analyse customer queries on their websites. Implementing analysis modules of past click troughs, purchase history and preferences, and results based on predictive analytics can easily increase and most of all improve sales.

Product recommendations and promotions

Thanks to predictive analytics, it is possible to determine key recommendations for the customer. Basically by analysing the consumer's purchasing history and browsing behavior as well as the popularity of products on the website in general, the goal is to increase cross selling as well as the conversion of sales by matching the consumer’s demand more efficiently. These system methods include attribute-based, item-to-item or user-to-user correlation.

Sentiment analysis

Thanks to natural language processing, eCommerce shop owners can analyse consumer sentiments in their product reviews (service, price and product quality). This analysis comes in handy when forecasting future product sales.

Image recognition

There are now machine learning tools that allow to identify similar products based on the product image clicked by a customer. This is a great way to display to customers products they are more likely to buy.

Prevention of fraud

To predict frauds attempts such as account takeovers, astroturfing, transaction draw backs,f ake accounts and so on, machine learning analytics is very useful. In particular, there are algorithms now that can detect fraudulent activities and prevent actual frauds way in advance.

Entity resolution frameworks

To resolve problems in non-standardisation, duplication or incorrect values in entity descriptions, entity resolution frameworks are commonly usef. Removing these issues have proven to increase online shopping sales.

Pricing trends

pricing algorithms

In order to maximize revenue, predictive analytics is used to determine data for product sales, customer data, product competition, minimum allocate prices and inventory. By correlating pricing trends with sales trends by using pricing algorithms, pricing becomes much more efficient in terms of maximizing sales.

Improving classification

Today classification is among the biggest challenges in eCommerce due to the increasing size of data used to classify and the response-time which is expected to be real-time. Classification algorithms improve processes of classification in order to maximize the efficiency of classifiers.

Propensity score matching

Sending out too many emails or notifications to customers can do a lot of damage in terms of customer loyalty and satisfaction. Yes, you do want to maintain an ongoing relationship with your customer but you do not want to spam him or her with messages that are not personalized to their shopping history and habits. Propensity score matching is employed to match products which your customer might be interested in purchasing (based on the analysis of historical data). This means that you are able to let the customer know about products that will more likely keep him or her interested in your brand and your product catalog.

Search Engine analysis

The fact that Google updates its search algorithms all the time is no news to anyone. What is vital to everyone in eCommerce is to understand the way Google’s algorithms based on machine learning work in order to adjust search engine marketing strategies. To do that, predictive modelling is employed to analyse Google’s search engine results.

anticipating shopping

Anticipating your shopping

Finally, the latest news to hit the industry is the amazing application of predictive shopping algorithms. Basically, in line with the data of such algorithms, a product is being packaged and ready for shipping even before it's actually purchased - in anticipation of you buying it. These algorithms are called anticipatory machine learning algorithms and if you ask us, it's an application that will revolutionize time constraints of shipping and delivery.

Today, machine learning applications are mainly employed by large companies with the resources to invest in such technologies. However, it is only a matter of time until machine learning becomes more widely employed in the eCommerce industry and exploited for commercial softwares affordable by a larger number of online retailers.

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