Machine learning in ecommerce involves the adoption of systems powered by self-learning algorithms to forecast sales trends, fine-tune marketing strategies, streamline inventory management and order deliveries, personalize the shopping experience, and mitigate online retail risks.
Explore the key use cases, benefits, and adoption best practices of this technology to find out how ML is reshaping the way we buy or sell on the web and why online businesses should consider involving machine learning consultants in their ecommerce projects.
in revenues by implementing ML-based product recommendation systems
McKinsey
+ $1.7 trillion
the value AI, including ML, is expected to create in the retail industry
McKinsey Insights
+ 25%
in customer satisfaction and revenue achieved by ML in ecommerce
Gartner
+ 94%
in qualified new site visitors due to leveraging social data and machine learning in retail and ecommerce
Deloitte
Top 10 machine learning use cases in ecommerce
From marketing and customer care to logistics and security, ML in ecommerce is paving the way for innovations in various business functions. Let's find out how machine learning algorithms are applied in this field and what advantages ecommerce companies can get from implementing ML in their operations.
Recommendation engines
ML-based recommendation systems are a staple in all major ecommerce platforms and online stores as they drive upselling. These engines can follow two distinct approaches to provide suggestions. Those relying on content-based filtering check a customer's purchase history and recommend other products with characteristics similar to those already bought. Systems using collaborative filtering, on the other hand, will suggest products that have already been ordered and positively rated by other users with similar buying patterns.
Benefits
Increased average order value, greater customer lifetime value
Several organizations operating in the industry have already implemented machine learning solutions. Take a look at case studies from major retailers and ecommerce-oriented tech companies.
AiBUY’s shoppable video platform
AiBUY’s shoppable video platform
AiBUY, one of the US leaders in shoppable media technology, partnered with Itransition to enhance its flagship video ecommerce solution with machine learning-based capabilities. The platform integrates CNN-powered image recognition features to detect items across millions of online images and videos and highlight them with customized image overlays. Then, it scans marketing affiliate networks and identifies the closest matching products in their catalogs, enabling viewers to purchase them directly and increasing conversion rates.
Starting with machine learning in ecommerce
When it comes to implementing machine learning in your ecommerce business, you should take into account some general guidelines:
Decide for or against ML
Concern
For many companies looking to implement machine learning, the first challenge is deciding whether it’s worth opting for an ML solution (usually more complex and financially demanding) over conventional software. This makes sense if ML adoption delivers good ROI and addresses key inefficiencies that could not be solved with “traditional” technologies. After all, the prospect of a potentially rewarding adoption can foster stakeholders’ and executives’ buy-in despite the cost and complex implementation.
Recommendation
McKinsey highlights the potential of ML in ecommerce for marketing and sales and supply chain management. Accenture, on the other hand, recommends chatbots as a good first investment. All in all, these tools offer a massive boost to the user experience and customer care without requiring huge investments, as you can build your bot on already-existing services and solutions (such as Amazon Lex and Microsoft Bot Framework) or develop one from scratch.
Partner with Itransition to craft your machine learning solution
In recent years, artificial intelligence and machine learning in ecommerce has acted as a catalyst for retail digital transformation, enabling businesses to better handle the explosive growth of online shopping in terms of data-driven decision-making, process optimization, and customer experience. However, implementing this technology can take time and effort. To make this journey smoother and avoid missteps, rely on Itransition's experience in ML consulting and development.
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