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.

The retail industry is among the early adopters and innovative users of big data. But they have the challenge of tackling the huge data since the 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 says 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 & merchandising?
·  How to avoid lost revenues due to stock-outs, lower online sales per visit, a 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

Merchandising:

·        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 an 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 smartphones, 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.

Enhancing Customer experience with Big Data Analytics

The era of Big Data is upon us.

Today, most organizations are still struggling to unlock the full value of this Big data that is available to them. From internet to mobile and social, the amount of customer data is continuously growing.

Company’s ability to extract value from big data through smart analytics will be the key to their business success.

Big data by definition is:

• Volume - Unmanageable volumes by traditional databases
• Variety - Combination of all the internal structured business data (CRM, ERP, POS and all the internal system data) and external unstructured data ( Social media data, feedback surveys, Audios, Videos, streaming data, Call center data, images)
• Velocity - enormous speed at which it comes into the organization.

Channel based marketing is the least priority now. The increased amount of data available at individual customer level has allowed companies to do a personal marketing.  But all this customer data out there is worthless if you can’t process it & turn it into actionable intelligence.

With Big data platforms helping in collection, integration, and transformation of large volumes of data, companies can conduct complex and varied analysis on much larger data sets and reduce the time to action and reaction to customer needs.

Organizations can now impact the entire customer lifecycle and every interaction by being well prepared for each interaction, shaping the interaction in real time as it happens and driving the huge improvements across all the channels for next interaction.

By listening to the data as a signal from customers and working to personalize the experience for the customer, creates the value for the customer as well as business.

Some examples of enhancing the customer experience using Big data Analytics:

• Retail giants are using Big data to personalize the offers to enhance the customer experience
• Healthcare companies are using it for improving the patient care in hospitals
• Banks are using it for cross-up selling and bringing the new products to market
• Insurance companies are making tailor-made policies for their customers in real time
• Manufacturing companies are using Big data to improve their products, predict the failures in their product lines ahead of time to make every customer interaction very smooth

Using Big data to address customer inputs before they become problems is extremely important to ensure they stay loyal and more profitable to the company.

Customers expect to have the best possible experience from their vendors/service providers. They want to be recognized as individuals and not as a part of a segment.

Big data Analytics – A disruptive technology !!

Big data is the most talked term these days in the analytics world. It will have a big transformative impact on all the aspects of the business.

Most of the companies now have realized that there is a huge competitive advantage in analyzing the humongous data quickly & effectively for future insights.

Big data analytics is the disruptive technology bringing the 4^th aspect of Value to the already published TDWI’s 3Vs – Volume, Velocity & Variety.

• It enables business users to process every granular bit of data in a quicker way removing the traditional need for sampling & then applying the models
• It encourages an investigative approach in users for data analysis since they get access to whole data
• It can reveal insights hidden in the data, which were previously too costly due to large data movements
• As per Gartner report, Big data is a priority of SMB & it will drive $232 billion in spending through 2016.

Some of the technology platforms which are used for big data:

• Distributed file based: Hadoop-MapReduce (Cloudera, Hortonworks, MapR)
• Appliance-based: Greenplum, IBM Puredata(Netezza), Oracle, Teradata
• Columnar databases: HP Vertica,  ParAccel, 1010data
• In-memory databases/tools: SAP Hana, Qlikview, Tableau
• Nonrelational/NoSQL: Cassandra, MongoDB, Splunk, Hbase

Hadoop is at the top of the list of technologies used for dealing with Big data due to its ultra-high scalability & low cost compared to other platforms.  It is a suite of products linked together, which breaks up the large datasets into smaller chunks on commodity servers, and data processing is done in a distributed cluster environment to quickly return the results.

Some of the probable Big data use case in various industries:

Insurance: Collecting data from monitoring devices fixed in cars & providing the personalized insurance policies based on driving habits, Underwriting price optimization for insurance products, Claims fraud with social network link analysis.

Retail: Market basket analysis for entire merchandising instead of sample data, Sentiment analysis based on social media for improving brand perception, customer service, competition analysis, Customer & market segmentation, Weblog analysis for customer behavior.

Banking & Finance: Fraud detection with entire history data for better detection, Trade surveillance in capital markets, the More accurate risk score to customers, Text mining on call center data.

Healthcare: Improve patient care by analyzing electronic Health Records (EHR) & reduce insurance payer costs, Reduce hospital readmission rates by analyzing information from discharged cards.

Manufacturing: Forecasting warranty costs & detecting issues in spare parts of finished goods, text mining to understand the complaints from customers for product improvements.

Because of the nascent stage immaturity of Big data initiatives, there are many views of what is it & how it can be applied.  Organizations need to focus on Big data processing while avoiding the movement of large volumes of data which is very costly.

Big data help make better decisions – faster, more efficiently with higher quality.