Interviews, insight & analysis on Ecommerce

Powering the future of ecommerce with machine learning 

By Helena Schwenk VP, Chief Data and Analytics Office at Exasol

Rapid advancements in artificial intelligence (AI) have exploded over the last few years, becoming intertwined into every industry. Countless businesses – especially ecommerce businesses – use it to automate mundane tasks and improve customer experiences. 

A popular branch of AI is machine learning (ML) which can help online businesses to build their predictive analytics capabilities. Using statistics and algorithms to determine patterns in data, it allows the world’s leading companies to move on from limited traditional reports and dashboards that document ‘what is happening’ and ‘why it is happening’, to foresee ‘what will happen’. This enables organisations to better contextualise crucial decisions. 

Given the continued growth opportunities in the current ecommerce industry, it is no surprise that e-merchants are taking a growing interest in ML techniques. The rapid rate of development being undertaken could well be the key to unlocking a new era of efficiency and competitive advantage for many online businesses. 

The benefits for the ecommerce sector 

Digital customer experience lies at the heart of the ecommerce industry and will hugely benefit from machine learning. The technology offers several advantages, including: 

  • Improved customer experience: thanks to personalisation on both a brand’s website and mobile phone applications, machine learning can enable personalised recommendations, helping consumers to feel better recognised as individuals. This can have a positive effect on consumer purchasing behaviour and often lead to higher average sales. 
  • Increased customer loyalty: by detecting the causes of churn, ecommerce businesses can anticipate potential customer abandonment and act proactively. 
  • Risk reduction: by allowing more precise analysis and detection of fraud of the increasingly numerous and sophisticated criminal techniques, online companies can avoid possible streams of lost income. 
  • Just-in-time inventory management: with better website traffic management and identifying and reacting to hot or cold pages companies can, for example, anticipate potential stock shortages or products/services they may need to discount. 
  • Better social media management: through text and sentiment analysis, ecommerce businesses can provide a quick and appropriate response to hot topics, or positive or negative customer feedback. 

What’s needed for an effective strategy 

Machine learning can only trigger these changes if a robust data infrastructure and culture exists to support it. While many are interested in using the technology, not all ecommerce businesses are equal in their data management and activation capabilities. 

The companies that have introduced ML techniques successfully tend to all share a relatively advanced level of analytics maturity – with their data strategies and architecture choices closely linked to their business strategies and goals. Without this alignment, progress will be haphazard, uncoordinated and have low visibility and buy-in within the business. 

If a data culture is ingrained in an organisation from the get go, employees will be better placed to use AI to its maximum potential. Having this data culture in place will help empower every employee — not just data scientists and analysts — to make decisions informed by data and quickly automate time consuming and mundane tasks. 
 
After a data strategy is in place e-merchants can also better evaluate whether on-premises, cloud, or a hybrid approach to their infrastructure is the right option for what they want to achieve. A hybrid cloud approach can often be the most efficient. It allows organisations to manage sensitive workloads on-premises, but also utilises the cloud – which is powerful when it comes to delivering large volumes of data to lots of people in real-time. 

The next step for ecommerce 

The future of the ecommerce industry is bright. With consumers spending more time online than ever before, and with expectations at an all-time high, it is vital that business leaders use every available tool to ensure they are living up to their customers’ demands.    

As it can often make it easier to bring ML algorithms directly to where the data is being kept, the many businesses already choosing to adopt an in-memory data analytics database will soon see huge benefits as they will be better placed to process and handle large volumes of data at speed. But it doesn’t stop there.  

In the next few years, no code and low code machine learning tools will continue to improve, covering more approaches and making the technology more accessible – allowing a greater number of employees to use and benefit from machine learning.  

Thanks to the democratisation of data analytics, more industry and use-case specific solutions will also develop. IT and data science teams will be freed up to focus on more complex use cases that will lead to the creation of new data pipelines and likely further personalisation of the customer experience for e-merchants. 

Across the ecommerce industry we are already seeing the use of AI and ML exploding, but this is only the beginning. Real-time customisation, fraud detection and traffic management will soon become the norm as the technology continues to develop.  

To take the next step forward, online businesses need to establish a robust data strategy that’s aligned to the overall business objectives and build the right infrastructure and culture to support it. If used optimally, ML will revolutionise the ecommerce sector and pave the way for a new era of automation and efficiency. 

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