Big data Analytics in Retail

All the industry leaders like Wal-Mart, Axa, Citibank, Humana, GE and several others are exploring how Big Data analytics can be used to better understand customer needs, pinpoint risk, improve marketing, enhance the customer experience, combat fraud, and drive profitability. Companies are seeking ways to rebuild their customer relationships in this time of extremely high customer expectations. Retail industry is among the early adopters and innovative users of big data. But they have the challenge of tackling the huge data since 1970s when barcodes were first introduced to scan the products at POS. All sorts of supply chain data came into effect later in 1980-90s while RFID and other sources such as surveillance video cameras started sending humongous data to data centers recently. These have challenged Retailers to capture, store, cleanse & analyze all the data they collect.

Further to flood the data centers are consumer’s interaction with social media & internet which is generating billions of data points that can be measured via clicks, page views, time spent on per page and path traversed from landing to conversion.
Big data analytics is helping retailers to collect and analyze this fine grained shopper visit data and optimize page designs, placements and tailor promotional messages. McKinsey report say that using big data analytics can raise the operating margins by as much as 60%

Some of the questions Retailers have are:

• How to drive critical decision around market segmentation, personalization & merchandizing?
• How to avoid lost revenues due to stock outs, lower online sales per visit, lower visit to buy ratios?

Here is a glimpse of what retailers can do in big data analytics:

Customer:

• Enhancing customer experience across all the channels such as calls, emails, campaigns, catalogs, mobile offers, brick & mortar stores
• Customer sentiment analysis to know the market pulse and market dynamics
• Call center data analysis for customer feedback
• Build loyalty programs based on purchase data & customer segmentation
• Staffing optimization based on weather forecasts & promotional campaigns for better customer experience

Merchandizing:

• Optimizing the product placements and layouts based on video data
• Price optimization based on competitor pricing
• Market basket analysis for revenue growth
• Optimizing seasonal markdowns
• Store analysis for best location & better effectiveness
• Improve in-store sales by leveraging past data with current economic, weather & season/holiday data

Marketing:

• Consumer segmentation, cross selling
• Campaign analytics to channelize advertising dollars in optimal medium for highest ROI
• Sentiment analysis from social media, call centers, surveys, blogs, product reviews
• Identify new products, service & market opportunities by real time monitoring of these customer sentiments
• Location based personalized offers on smart phones, tablets
• Web log analytics for customer behavior analysis & next best offer

Supply Chain:

• Inventory optimization to avoid stock outs
• Demand driven forecasting fueled by structured and unstructured data
• Route optimization for cost reductions
• Warehouse space optimization
• Vendor performance analysis for better competitive prices

Ultimately, the goal of big data analytics is to develop an effective Omni-channel experience that integrates many different factors of supply chain including supplier effectiveness, warehouse optimization, and inventory / logistics optimization for real-time customer engagement.

Big data analytics provides the required ammunition & tools to accelerate growth, boost profits, control risks and meet regulatory & competitive demands.