The data available to retailers comes from web sites, in-store video and analytics systems, customer demographics, mobile location data from tablet computers and smart phones, social media, weather, and embedded sensors.
Data collected from all these sources is being analysed and used to create better customer focused shopping, more effective systems for delivery and numerous efficiencies.
Retailers such as Walmart are using Big Data analytics to optimise its inventory based upon the specific tastes and seasonal preferences of customers in each geographic area. Walmart has been increasing the scale of its Big Data collection for years, in 2012 they moved to a 250-node Hadoop cluster.
Walmart combines public data from the web, social data and proprietary data such as customer purchasing data and contact information. As over 200 million customers visit Walmart stores each week, the data that the company can collect is vast.
Williams Sonoma have been mining Big Data repositories of customer purchase data, clicks, click-throughs, demographics and web browsing history. This leads to predictive models for each customer being created, which then informs targeted emails being sent.
Another area where big data is impacting retail is in predicting customer purchases and customer micro-segmentation.
Target stores in the U.S recently analysed historical purchase data and identified 25 shopping items which could be used to indicate when a shopper is pregnant, leading targeted, pregnancy specific coupons and special offers being sent to those shoppers via email.
Other areas in which Big Data is helping retailers is cross selling and upselling, location based marketing through geographical location data, pricing optimisation through connected smart shelves to big data applications. The shelves will be able to automatically update prices based on dynamic market conditions.
Moving away from the shop floor, retail businesses have been using big data to help with supply chain and logistics optimisation. Sensors in delivery trucks and Radio frequency ID tags on products can help stores know the exact location of products in transit.
Retail fraud can also be spotted through monitoring – by modelling and analysing high volumes of data from transactions and extracting features and patterns, retailers can prevent credit account fraud.
Big Data has been used in the financial markets for a number of years, some would argue they have been pioneers in Big Data use. However, due to strict data protection laws there are limitations on how they can use it.
The challenges facing finance is the consolidation and streamlining of inefficient operations that have spawned from mergers and the upgrading of systems. The creation of new products that replace existing, declining products and adhering to financial regulations.
One way in which the finance market is using Big Data is with precision marketing, where big data alters the information structure, allowing financial institutions to collect and analyse customer data. This, therefore, provides a more individualised and tailored service to the customer.
Danske Bank implemented STATISTICA in 2011, a software solution designed for risk modelling and reporting, providing real time analytical data. The system from StatSoft, was designed to help reduce risk in credit scoring. The concept of applying big data analytics should make the data work easier in areas of predicting risk, detecting fraud, and making decisions regarding credit.
For example, with making decisions regarding credit, the system will be able to forecast the behaviour of a new credit applicant by predicting loan-default choices or poor-repayment behaviours, at the time the credit is granted.
Big Data in risk management plays an extremely important role and the better the data and analytics, the more accurate it can be. In this process, Big Data reworks the traditional risk management model with cloud computing that assists in creating the most accurate risk estimation at the lowest cost.