eCommerce Machine Learning 2019: What to Expect

According to BI Intelligence, around 72 percent of retailers will invest in machine learning and cognitive computing by 2021. Let’s explore what makes ML and AI in general worthwhile to become an important part of the field development and ergo its future.

5 min read, Jul 12

Providing personalized experience to every customer it’s not just a trend anymore. It’s a must-do for each eCommerce and retail company to survive in the fierce competition in the constantly growing market. Time shows that it can’t be done without the application of machine learning to business processes.

Machine learning allows creating solutions based on statistical algorithms and models that can perform complex data analysis and make predictions and decisions basing on it. The machine is able to execute such tasks without constant instructing, in other words, it learns how to do them using given patterns.

Machine learning trends 2019

All subsets of artificial intelligence are taking over business domains. Implementation for predictive analytics allows overcoming and even in advance avoiding challenges which fields have been struggling with for years due to insufficient level of strategic planning.

AI became the driving force of the digital transformation of the whole supply chain and its links like logistics and retail. As its integral part, machine learning for eCommerce allowed improving product handling as well as shorten its way to a customer by helping to define one’s needs within seconds.

Machine learning use cases in eCommerce and other scopes are focused on establishing stronger relationships with customers from day one. 2019 is promised to be productive in this direction. The availability of more successful results of going AI attract more attention from companies of different level and ergo investments.

ML scope also will continue to be involved in resolving modern burning issues concerning cybersecurity. Avoiding conflicts of interest between steps to keep user privacy and customize their experience through personal data analysis is already a challenge that requires an efficient solution.

Machine learning in eCommerce: what are results?

Advanced analytics is one of the steps towards workflow automation of various aspects of the business. Let’s explore where ML brings and potentially can bring changes to online store operations.

Business running

Machine learning use cases in eCommerce and other fields are mostly focused on workflow optimization through automation and an increase of its efficiency. They can be applicable from the first stages of the supply chain for demand forecasting, shipment planning, inventory management, anti-theft and fraud precautions, etc.

ML models can help setting up and maintaining dynamic pricing appropriately by processing in real-time conditions competitor strategies for the matter, corresponding trends and manufacturing, and logistics expenses.

Digital marketing

Created for analytics of big data eCommerce machine learning solutions are extremely helpful instruments in a toolkit of any marketer to form and if needed adjust a company or brand strategy. This way is easier to set and follow trends and target the right audience with more efficient campaigns.

Image processing also allows not relying just on search query wordings that can’t always clearly describe the required product. Analysis of customer visual content and supplying products with details using AR/VR and 3D technologies are still new approaches that into the future can’t take over traditional techniques of digital marketing.

Machine Learning for eCommerce

Customer support

Chatbots are the most spread solutions across the field. They give the ability to stay in touch with potential and existing clients 24/7 without excessive expenses on multinational customer support teams to operate successfully on the international level.

Modern models can provide extensive details on company services and orders if they connected to CRM or other management software. Machine learning algorithms for eCommerce needs allow constantly improving answers accuracy and relevancy.

Customer experience

It’s easier to build a customer engagement if any new, existing and especially returning visitor of an online store can feel a personalized approach from the first minutes of website exploration. It should cover typing predictions, content suggestions and specifically selected search results.

Processing eCommerce data machine learning models allow transforming a search engine into a provider of custom quest results based on analysis of previous queries and preferences and following forecasting of actual customer needs.

Accurate enough predictions allow leading customers to your online marketplace and guiding them around. By analyzing tremendous amounts of personal data concerning location specifics and social network activity it’s possible to form custom recommendations.

Predictive recommendations in eCommerce

Actually, other eCommerce machine learning examples of applications that are gaining appreciation across the field are recommendation engines or systems that analyze already performed user activity information to predict future customer preference.

The filtering process is usually implemented according to collaborative or content-based approaches. The collaborative one allows building a model basing on analytics of past behavior of a particular user as well as other customers who have made similar purchases.

Content-based filtering is focused on the item itself. Considering its characteristics it’s able to provide alternative suggestions to the one currently explored by a customer with resembling properties. Other approaches take into account user dislikes (risk-aware), device type (mobile), etc.

Recommendations systems are mostly hybrid. They make a fusion of various models for more accurate results. For operating more efficiently such engines should also provide option diversity and user privacy, consider demographics and serendipity as well as show reasonable persistence in content presentation.

Such machine learning eCommerce applications allow raising item discoverability and improving customer engagement by saving time on search efforts and showing a personal approach. They also allow raising the efficiency of automated advertising campaigns.

Predictive models are already extensively used by such conglomerates as Amazon. In fact, they provide approximately 35 percent of sales and significantly raise the number of returning customers by disguising marketing efforts in this direction by mixing technology and human touch.

The collaboration of machine learning and eCommerce is a future for both scopes. For AI it’s a way to progress through solving actual challenges. For online retail, it’s the path to optimization business approaches and processes when the main focus remains on customers since the battle for their attention has more participants than ever including everyday distractions.

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