In 2016, total retail sales across the globe were predicted to reach $22.049 trillion, up by 6.0% from the previous year. eMarketer has further estimated sales to top $27 trillion in 2020, even as annual growth rates slow over the next few years, owing to global trends.
Big data revolution is playing a major role in driving these massive figures of growth for the online retail industry. The use of advanced analytics and predictive modelling is changing the face of retail, and helping us all get what we want, when we want it, where we want it and more often with an array of pricing on the same product.
Here are 5 ways retailers are using (with varied levels of success) big data technology to create an advantage in the retail sector.
Recommendation engines are arguably one of the trendiest uses of data science in online retail today. By training machine learning models on historical data, the savvy retailer can generate accurate recommendations before the customer leaves the Web page. It solves the problem of connecting your existing users with the right items in your massive inventory.
The key to have a successful recommendation engine is the domain expertise, algorithm development skill, massive inventory, and frictionless user data entry design.
360 Degree Customer View
Customers today share tons of personal data as they engage with an organisation through an increasing number of channels and applications. Big Data is increasingly used to bind this available and meaningful data, to design an enhanced 360-degree customer view, to deliver the information and analytics needed to improve the way organisations deal with customers on a day-to-day basis and leverage data to make better decisions about products, services and promotions. Thereby driving better engagement, more revenue and long term loyalty from the customer.
‘Knowing your customer’, is the mantra to success
For years, the fashion industry and retailers worked on intuition and best guess to predict fashion trends for the upcoming seasons. Today however fashion forecasters have data from a multitude of both expected and unexpected channels including history, current market offerings, fashion shows, social media platforms, customer reviews and feedbacks etc to logically and accurately predict forecasts. Using retail analytics, image and information databases, comparison shopping tools, social monitoring, and internal analytics systems.
Having shelves full of products that don’t sell is simply not an option in today’s business environment. Simply saying, “Having shelves full of products that don’t sell is simply not an option in today’s perilous financial climate”. And accurate trend forecasting is the difference between a ZARA and a Marks and Spencers.
Right price is the difference between making a sale and losing a customer! With The flood of data being captured from multiple digital and physical touchpoints. The retailers today have the power and the opportunity to make significantly better and dynamic pricing decisions. Unlocking substantial benefits If they can overcome the complexity of Big Data.
On average, a 1 percent price increase translates into an 8.7 percent increase in operating profits (assuming no loss of volume, of course). Yet per estimates 30 percent of the thousands of pricing decisions companies make every year fail to deliver the best price. That’s a lot of lost revenue! (Source: McKinsey)
Gone are the days when customers had limited options for shopping. In the current scenario, if a customer does not find the desired merchandise at one retail shop, they would explore an alternate brand or store. Unavailability of merchandise, empty shelves leave a negative impression on the customers and they might be reluctant to visit the store in near future. Inventory management prevents such a situation.
In this age of digital, we are experiencing a major upheaval in the way inventory is managed. This revolution is a result of the availability of the huge amounts of real-time data that are now routinely generated on the internet and through the interconnected world of enterprise software systems and smart products.
Key Challenges include:
Advanced machine learning and optimisation algorithms can look for and exploit observed patterns, correlations, and relationships among data elements and supply chain decisions. Such algorithms can be trained and tested using past data. They then can be implemented and evaluated for performance robustness based on actual realisations of customer demands.
With this article, we’ve just highlighted some of the top use cases for Big Data Analytics in the retail industry. There are indeed more ways that big data analytics can help retailers bring in more operational efficiencies and personalised customer engagement. Please write back if you wish to have a more detailed discussion on any aspect of Big Data in the retail industry.