By Oksana Stowe, Principal, True

Ever since Tesco launched the first loyalty card, data has been at the heart of pretty much every customer transaction. And with the surge in social shopping and ever more sophisticated e-commerce offerings, the type of data collected has exploded, resulting in a proliferation of data-led technologies flooding the market offering the very best analytical tools and promising transformation.

In a retail environment experiencing unprecedented change, the prospect of being able to have the competitive edge is tantalising. However, using data to become competitive means more than just collecting vast swathes of it: it means compiling, analysing and manipulating data in the right way – and usually in an automated, in real time fashion.

Indeed, in a fast-paced, competitive sector such as retail, brands must use data ever more strategically and quickly in order to gain the edge. 

Many of the technologies that the data-heavy retail industry is introducing is focused on machine learning systems. These promise tangible improvements in collecting, analysing, and applying data derived from various channels to inform better decision-making.

Indeed, the variety of learning capabilities machine learning systems possess means that they are now able to perform intelligent predictions, clustering of data masses, and training in simulated and real-life scenarios. 

ML is a burgeoning technology providing deeper and more purposeful insights into consumer behaviour, enabling intelligent and flexible inventory planning, and offering complex data analysis in real-time.

ML enriches modern retail business practices with comprehensive prediction, optimal pricing, and multiple decision options based on the interaction of various uncertain factors to make data-based, informed decisions. 

With these facts in mind, businesses that understand the benefits of ML — and those that are able to deploy the right methodology – are set to reap the rewards of big data to improve all aspects of their business processes, boost customer engagement, offer more personalised shopping experiences, and adjust pricing sensitively for greater visitor-to-customer conversions. 

Here we take a look at the most impactful use cases for retailers.

Commerce marketing and customer engagement.

Machine learning can boost customer engagement in two ways – by offering the website’s visitors a personalised buying experience and by adjusting the price appropriately. Personalisation is accomplished via the ML system’s analysis of customer data and customer categorisation into distinct clusters.

Once a new visitor comes to the site, the ML algorithm assigns the visitor to a cluster and adjusts the presentation of merchandise accordingly. Smart price adjustment works the same way, allowing the merchant to offer ideal pricing based on the time and day, alongside a host of other demand drivers. For instance, Amazon has employed a smart real-time price adjustment mechanism allowing it to optimise sales.

Product research and competitor analysis. 

Each business wishing to keep tabs on the competition conducts thorough competitor analysis. ML systems enable in-depth analysis of huge masses of competitor data and provide a transparent overview of data of interest, clustered in an understandable way.

These insights can be used to benchmark prices against competitors and run marketing campaigns at optimal times.

Manufacturing and reinforcement training.

Smart forecasting in inventory planning has for a long time been hindered by numerous sources of uncertainty, such as promotional cadence, market cannibalisation, seasonal changes, and other criteria that a conventional forecasting tool could not embrace.

ML systems can be trained in simulated environments to achieve specific goals, and their analytical potential covers all the data complexities mentioned above to enable smarter real-time inventory planning without trial and error.