Big Data Analytics is an all new application trend in the global retail process. Big Data Analysis in the retail world is quite challenging which has underpinned the emergence of Retail Analytics. Working out on demand of the popular products, predicting the trends, forecasting the demand, price optimization, identification of customer's behavior, changing the traditional way of approach, and planning for what to sell next comes under the umbrella of Retail Analytics.
- Prediction of Trends:
Today, retailers have a wide variety of tools to make out the season's best-selling product – it can be anything starting from infant food to electronic gadgets.
Predictive Analytics works on the social media post to look for the cause of the buzz and analyse the as-buying data to know what products the sales department are going to push. Popular brands are engaging Sentiment Analysis program using sophisticated algorithms to determine the most discussed product along with the product category and thus estimating the future sales for the same and to launch similar products (for competitive companies)
- Forecast of Demand:
After the product trend is analysed and the category is sorted out, the next step is to understand the future demand and work out on the possibility of the sales for the launch of any new product. This includes a study of thorough customer demographics and identification of the key indicator that is needed to build the real-time picture of the spending habits of the customer in a definite zone.
Folgers, for example, is on the top in the USA, while Nescafe is leading the UK market. Hence, the future prospect for coffee can be predicted by the consumer's choice for quality and the class of coffee in each zone.
- Optimization of Price:
Walmart and similar other giant retailers are spending hugely on their real-time merchandising process. Walmart, for example, is to create "world's largest private cloud" to analyse the customer behavior in real-time and to track millions of everyday transaction.
The algorithm for predictive analytics is designed to track demand, product inventory and competitor activity to know the real business insight. And hence, any decision can easily be taken in a few minutes.
Big Data is also used to determine "mark-down optimization" – the time when there should be a price drop. The price drop is not same for all product category, and hence, a general "End-Of-Season" sale will not work. With Retail Analytics playing an active role, shows there must be a gradual decline in price from the exact moment when the demand starts sagging. It, however, increase the revenue even when the demand is falling. US retailer Stores shows Predictive Analytics to beat up to a 90% over traditional "End of Season" sale.
- Identification of Customer:
More than 87% of the customers rely upon Online Recommendation Technology and data collection through both online and offline transaction record and loyalty program.
Though Amazon is not going to deliver a product before you order them, but there is a push in that direction. Demand can be forecasted based on the location and customer demographics. This, in turn, will help in easy, fast and efficient product delivery. Also, Big Data Computation helps in knowing the retailer-customer contact method with the Predictive Analytics Tools. Accordingly, a customer will be informed of any product launch, promotion or customized offers via Email or SMS or Mobile Alert or even Call!
Attracting the right customer in your brick-and-mortar is a key feature to boost sales and build customer loyalty. US department Store – Macy used Proximity Sensors to determine the dearth of vital "millennials" demographic group and hence opened its One Below basement at its flagship New York store, to offer “selfie walls” where you can wait to get personalized 3D-printed smartphone cases. The service strategy is to attract young shoppers to the store who will probably go on to have a lasting value to the Macy's business.