Eskom has announced enhancements to its digital platforms, including a new chatbot called Alfred to report faults and an upgraded customer portal and app….
Technology has changed the way customers engage with retailers and the days of simply adding an online site to an existing bricks and mortar business are long gone. Today’s consumers expect an instant response and use a multitude of channels to search and interact with retailers, from the internet and mobile applications to digital TV, social media and online gaming. By doing so they are revealing an increasing amount of information about themselves and their shopping habits.
With the shopper’s consent retailers are using cloud and big data technologies to collect, store and analyse some of this information, but the sheer volume and scale of the data means that this analysis is not happening fast enough. By the time a response, recommendation or personalised suggestion is produced the opportunity to help the customer has passed.
Enter machine learning
Machine Learning is a technique that allows computers to look for patterns in data and that powers the online recommendation engines that for example, suggest books, music or films you might enjoy. Amazon has a long legacy in machine learning and has been using it since its early days, when it needed a way to help its editors make recommendations from the millions of books in its library. Today Amazon uses machine learning in almost all areas of the business. It is what makes Amazon Echo able to respond to voice commands instantly, and it is what allows Amazon to unload an entire truck full of products and make them available for purchase in as little as 30 minutes.
Up until now running these types of complex machine learning tasks have been confined to big business because it required expertise in statistics, data analysis, and technology infrastructure. Amazon Web Services (AWS) has automated these steps making machine learning accessible for everyone and today developers can create as many models as they need, experiment and scale as their applications grow all and do all of this with no setup cost.
For traditional retailers machine learning can be used to deliver recommendations to shop workers to action immediately, or it can automatically make a decision based on what it has learnt from previous engagements with a customer. For example, if a shopper is looking for a pair of black boots online and has ‘liked’ or ‘shared’ an image of them, a discount for that pair could be automatically applied in store.
Where machine learning becomes even more interesting to retailers is in the context of IoT. Adding a layer of smart systems to a network of sensors, beacons and automated machinery allows more data to be collected and worked with in ways than can redefine a retailer’s entire business model and streamline its operations.
Using machine learning online, retailers can use it to make useful recommendations in store. For example, data collected from a customer’s smartphone through an iBeacon (a Bluetooth device that broadcasts or receives data within a short distance) in store can be linked to sales and stock data and analysed by machine learning services in the cloud. By rapidly crunching the data the system can, in real-time, push a discount code to the customer.
Retailers can also automate their warehouses using smart machines, enabling the items to be dispatched based on incoming orders, with no human intervention. This frees up people to focus on bringing new services to market for customers and the development of new applications. It could also mean having more people on-hand in store to support customers.
This level of automation could be spread across the entire supply chain from manufacture to delivery providing a 360 degree view of both customers and operations and when combined with feedback from customers and analysis of social media, could dictate future product development.
Machine learning for all
Up until now, machine learning has been available only to those few who could afford the computing power required to crunch the massive amounts of data and had the in-house expertise to be able to interpret the numbers. But with AWS, machine learning is available to all organizations, large and small. For the boutique retailer who wants to understand what colour bag sells best to larger retailers running loyalty programmes, cloud-based services are reducing the time and money needed to learn the skills to get started.
For retailers the days of simply adding an online site to their existing business are long gone. The amount of data-driven and cloud-powered technology available to retailers to take advantage of will create more ways to innovate and delight customers, more ways to deliver operational excellence and with machine learning uncover more ways to do business.