Harnessing the power of analytics

Harnessing the Power of Analytics

Ingest Infer Interpret

YOGESH DANDAWATE

Big Data AnalyticsBig data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Such Information can provide advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue.

Descriptive Analytics

• Answers the question, "What happened in the business?

• It looks at data and information to describe the current business situation in a way that trends, patterns and exceptions become apparent.

Diagnostic Or Inquisitive Analytics

• Answers the question, "Why is something happening in the business?"

• It is study of data to validate / reject business hypotheses. This includes analytical drill downs into data, statistical analysis, factor analysis, etc.

Predictive Analytics

• Answers the question, "What is likely to happen in the future?"

• Here data modeling and forecasting are used to determine future possibilities

• Predictive analytics uses data to determine the probable future outcome of an event or a likelihood of a situation occurring.

Prescriptive Analytics

• Answers what, when, why questions

• For example, what should a business do to retain key customers? How can businesses improve their supply chain to enhance service levels while reducing costs?

Created using http://www.mu-sigma.com/analytics/ecosystem/dipp.html

What are Big Data Dimensions?

Volume• Data at Rest• Terrabytes to

Exabytes of existing data to process

Velocity• Data in Motion• Streaming data,

near real time response needs

Variety• Data in multiple

forms• Structured,

unstructured, text, multimedia data

Veracity• Data in doubt• Uncertain

information (incomplete, inconsistent, ambiguous, approximate data)

Analytics in Retail and FMCGApplication Areas

• Identifying of Potential customers• Understanding customer churn• Understanding customer sentiment• Improving/Personalizing Customer

Experience• Recommending products to

Customers• Understand customer behavior• In-store shopper movement

analysis• Loyalty Program Management

• Content Analytics• Demand Forecasting• Operations Analytics• Enterprise Information Management• Supply Chain Optimization• Scheduling Preventive Maintenance

and Repairs

Technology Elements• Content Analytics

• Speech/Audio Analytics• Video Analytics• Text Analytics• Web Analytics

• Sentiment Analysis• Recommendation Engines• Gesture Analytics

• Eyeball Tracking• Expression Analysis

• Social Media Analytics• Activity Stream/Click Analytics• Storage and Search Technologies• Active News and Event Analytics

• Stock & Re-Arrange Essentials on shelves

• Reroute Shipments

• Notify/Update/Recommend Customers

• Enhance store security

Weather Forecast Real Time Decision MakingDisaster management

Call Center Management• What are common customer

complaints?

• Locations from where maximum complaints are getting registered?

• Are issues attributed to Operational Efficiency, Equipment efficiency, People Efficiency ?

• What is the general sentiment of the product/s ?

• Which is the most popular product ?

• Customer feature recommendations?

• Average time for query resolution?

• Query/ Feedback /Complaints Categorization

• Similar queries and their resolutionsStatistics

Geo tagging

Feedback

Complaints Speech Analytics

Text Analytics

Kinect

Expression Analyzer

Confused

Needs Assistance

Enhancing In Store Experience