Big Data Analytic
“Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization” – [Wikipedia]
Big Data has applications in just about every industry retail, telco, healthcare, financial services, manufactures, government.
Any organization that can assimilate data to answer nagging questions about their operations can benefit from big data.
Big Data For Retails
Big Data and Analytic Tools can help collect and analyse customer information from a wider range of sources than before including POS systems, online transactions, social media, loyalty programs, call center records and more. That information deepens their understanding of customer preferences, helps them more accurately identify shopping patterns and enables them to generate more precise customer segmentation.
Effectively analysing the large volume and variety of customer data opens new opportunities to gain a deeper, more complete understanding of each customer and create a smarter shopping experience.
Predict optimal pricing and maintain a price leadership position by analyzing price and demand elasticity.
Select the right merchandise for each channel and fine-tune local assortment planning by drawing on insights from social media, market reports, internal sales data and customer buying patterns.
Optimize inventory across multiple channels by using leading indicators such as customer sentiment and promotional buzz to anticipate future demand.
Reaching your customer big data has brought gains in multi channel sales. data has made multi-channel customer tracking and management more profitable.
Understanding behavior retailers have made financial gains in customer relationship management through big data. increasing the amount of information held about customers last year.
Big Data For Financial Services
Big Data insights can help you uncover what’s hidden and suspicious – and in time to mitigate risks. For example, analyzing data can help you reduce the operational costs of fraud investigation, anticipate and prevent fraud, streamline regulatory reporting and compliance (for instance, for HIPPA), identify and stop rogue traders, and protect your brand. But this requires aggregating and analyzing data from a myriad of sources and types and analyzing it all at once – no small task. Think financial transaction data, geo-location data, merchant data, and authorization and submission data. Throw in data from lots of social media channels and your mainframe data, and you have a significant challenge on your hands.
Truth is an additional dimension of big data to consider: Build and maintain confidence in the data presents a challenge as the sources and varieties grown. Banks can take advantage of big data, in the form of transactions, real-time market feeds, customer service records, correspondence, and social media posts, can gain more insight into their business than ever before and build competitive advantage.
Successfully utilizing big data can help banks achieve three important goals for Big Data in banking can help you achieve your business goals by implementing big data and analytics solutions for major business use cases.
Optimize Offers and Cross Sell
Big data and analytics capabilities provides banks the ability to understand their clients at a more granular level and more quickly deliver targeted personalized offers. This enables higher offer and cross sell acceptance rates that improve customer profitability, satisfaction and retention. Big Data and Analytic Tools provides the platform needed to analyze, predict and deliver more targeted, personalized and effective offers. Benefits include:
- Increased revenue through better offer response rates
- Improved cross-selling and greater product penetration
- Higher asset/balance values and increased customer advocacy
- Lower campaign and infrastructure costs