Machine Learning and AI: The benefits and common obstacles to avoid
What eCommerce machine learning strategy are you enforcing as a business leader?
What challenges do you face and what solutions do you have while implementing machine learning in eCommerce? The increasing competition amongst eCommerce companies and the growing need to provide a personalised shopping experience to customers are fuelling the market demand. eCommerce companies are utilising the capabilities of AI in retail solutions, to enhance customer service by optimising the delivery and return of products.
AI in retail solutions works on machine learning and deep learning algorithms to understand the customers’ requirements and recommend relevant products.
According to a study on eCommerce machine learning implementation;
- 78% of AI/ML projects stall at some stage before deployment
- 96% of enterprises encounter data quality and labelling challenges,
- 63% have tried to build their technology solutions
- 81% admit that training AI with data is more complex than expected
In-House ML or Vendor’s/Expert Support: What Should Leaders Implement?
Adopting an in-house ML or a vendor solution has different advantages and disadvantages. Understanding each model enables leaders to implement the model fitting their business.
In-House AI/ML Solution
Let’s look at the Pros and cons;
Pros
- Customisation and Flexibility: Enterprises can develop custom and flexible solutions that meet their needs
- Intellectual Property Ownership: eCommerce enterprises have intellectual property ownership of their in-house solutions
- Independence: In-house development enables enterprises to develop internal independence in their solutions
Cons
- Lack of Expertise: In-house ML solution developers may lack the needed experience when developing complex solutions
- Increased Time Commitment: In-house solutions take time to develop
- Lack of Delivery-Oriented Approach: First-time in-house developers may lack a delivery approach when developing solutions
Vendor ML Solutions
Vendor ML solutions are always updated with the latest tech and trends, which benefits eCommerce retailers in the long run.
Here are the pros and cons of a vendor ML solution;
Pros
- Time and Cost Saving: Vendor ML solutions save enterprises the time and money involved in ML solution development
- Create a Single View of Data: A single view of data reduces silos across all departments to optimise operations
- Access to Expertise: Vendor ML solutions are built by experts to meet diverse business needs
Cons
- Integration: Some vendor ML solutions may not integrate with some enterprise systems
- Compliance: Some ML solutions may not meet legal compliance over time with the ever-shifting rules and regulations
What Are the Main Obstacles and Options for eCommerce Leaders?
The eCommerce sector has seen meteoric growth in the last ten years. Any positive growth in an industry comes with many obstacles to success, and the same applies to eCommerce.
Here are the main challenges eCommerce leaders face when implementing machine learning;
- M-Commerce Dominance: According to a study, more consumers purchase items online using smartphones. Many eCommerce retailers face challenges in optimising their eCommerce websites for the M-Commerce market.
Leaders must offer a clear, easy-to-use, frictionless M-commerce shopping experience similar to a desktop website.
- Dependency on Machine Learning Technology: Machine learning is the most sought technology to enhance customer experience, automation and personalisation. Enterprises lack the means to actualise machine learning offerings.
Leaders can opt to work with small and meaningful data to overcome data generated by machine learning.
- Big Data: According to a study, ML has generated more data than ever in recent years. Gaining clear insights into the data insights is never easy.
Business leaders should future-proof their technology and IT, via identification and enhancing important metric visibility. They can achieve this by adopting advanced eCommerce technology,
- Compliance and Customer Data: General Data Protection Regulations (GDPR) hinder how enterprises connect with their customers and data. Enterprises face sanctions and penalties if they do not uphold the regulations.
Business leaders should adopt custom solutions built with expert security and data privacy measures that securely manage customer data while personalising their shopping experience.
What Are the Main Obstacles to Good AI/ML Implementation?
The rise of AI/ML has seen rapid implementation in the eCommerce sector to streamline and optimise their process.
Here are the main challenges that enterprises face when implementing AI/ML:
- Determining the right data set
- Data security and storage
- AI integration into existing systems
- Infrastructure
- Complex algorithms and training of AI models
What are the latest use cases for AI in eCommerce?
Retailers leveraged the power of machine learning and artificial intelligence sooner than any other industry.
Here are the everyday use cases of ML/AI in eCommerce today;
- Voice and Visual Search
- Customer service during the sale and after-sales
- Order update
- Free delivery (78% of customers are selecting brands offering free delivery)
- Loyalty Scheme
- Lead Engagement and Retargeting
- Big Data Analytics
- Conversational AI Chatbots
- Dynamic pricing
- Abandoned cart recovery emails
- Personalised product suggestions
Are you a leader looking to optimise your eCommerce enterprise? Do you want to enhance the customer experience, while lowering the costs of returns? Start a free trial or book a demo today. Our AI platform uses AI/Ml integration to streamline your workflows for better service delivery.
If you;d like to find out how DigitalGEnius can help your retail operation, reach out to them here.
