Analytics with Big DataIntroduction: Robust Designs, Jan 2015

Contents

Analytics Market:

Questions our

customers have asked

us

Market Evolution: Business

Intelligence, Analytics, Big

Data

Our Credentials:

Analytics Samples from our

Work

Enterprise Analytics

Applications

About Us: Robust Designs

Analytics Market:Questions From our Current Customers

Analytics Market:

Questions our customers

have asked us

Telecom: Consumer Marketing

• Tell me about my customers’ behaviours• Who is moving out• Who is moving in• Who is spending on what• What can I cross sell

• Tell me what can I offer them• Based on what others who are

similar have bought

• Micro segment and create personalized offer for a marketing campaign• Track the campaign and learn and

refine

• How do I find out who are the distributors who are loyal to me• Design a loyalty management programme which is more likely to be

successful

• Which collection cases should I target today• What is the strategy for each case:• Should I write a letter, call• What figure I should settle for

Financial Services: Sales, Loyalty, Collections

Healthcare

Medical• Show me how dengue fever is

spreading in a city• And predict how it is likely to

spread

• What are the chances of readmission given history of a patient and treatment received

Financial• Predict billing trends by DRG• Predict likely defaulters

Retail / Manufacturing

• What stocks make best basket of things to carry in my showroom

• Which stocks I should carry how much keeping in mind the order trends

Market Evolution

Market Evolution: Business

Intelligence, Analytics, Big

Data

Data Evolution

You asked questions,

IT gave you

answers

You discovered answers

by yourself

Big data gives you answers –

any questions

?

Core system reports

Business

Intelligence

Distributed

Computing +

Statistics

Enablers

Data Evolution

Large scale computing power is available now for big data to have a justifiable ROI

What’s Changing

Business Intelligence• Analysis is top down, done by

humans• Identifying root causes for

behavior and variance to set goals is a main target• Action addresses the root causes

identified

Big Data / Analytics• Analysis is bottom up, done by

machines• Identifying root causes is considered

not necessary• Deriving consumer behavior, patterns

is paramount

• Action is not based on cause – effect• Action is based on patterns and on the

entire population - using Micro-segmentation, Personalization

Descriptive to Prescriptive Analytics

• Track, Segment, Recommend Action

Customer Delight

-----Prescriptive

• Business KPIs, Insights, Analytics

Better Business

----Predictive, Diagnostic

• Dashboards, Reports, Data

Efficient Operations

----Descriptive

• Identify clusters based on some variables

Correlation

• Predict behaviour of this set at a future point in time

Prediction

• Suggest best actions to meet a desirable outcome

Prescription

AnalyticsModel

Big Data

Is Big Data About Largeness of Data?• Not necessarily

• And it is not just about Twitter, Facebook posts• But big data scales linearly, and can work with large data sets

not handled before by conventional technology like databases and data warehouses

• Big data is deployed where there is big money at stake, regardless of the size of the data• E.g., an asset management company managing assets of

thousands of high net worth individuals can take advantage of big data as much as a telecom company with millions of consumers

• Big data applications are relevant for small businesses too, but • the cost of big data has not yet come down to that price

point, and • the expertise to apply the technology and into a domain is

scarce

What is Big Data?• It is about the ability to

• take in all the data in your context, • being able to process it fast enough for your

business, • apply statistics, and • generate so many graphs that no human can read

them all in reasonable time, let alone analyze

• But big data machines analyze them, come up with correlations, predict behaviors

• To the point of knowing if this customer is going to churn, if this product offer is going to be successful with what degree of certainty, and if the lady walking around your store is pregnant

Analytics Services

Our Credentials:

Analytics Samples from

our Work

Experience - Analytics with R

Understand Data Enhanced Data VisualizationsQuantile Based Estimates, Distribution & Probability PlotsDerived Variables Custom Aesthetics

Group ‘things’Clustering

Find SimilaritiesAssociation Analysis

Interactive Association Rule Mining

Mining for RHS & LHS Relationships (After vs Before)

Plotting Associations

Recommend Next Best Recommendation Engine Item Based Collaboration Filtering (Based on Product Similarity) - Generating Top x recommendations based on a Consumption Frequency Count Table

ForecastTime Series Analysis Forecasting via Holt-Winters, adjusting for Seasonal Trends, Arima

Social Data Mining Text Analytics & Connecting to Social DataPlotting Text Data in Wordclouds using various source formats (plain text, pdf, XML)

Getting Associations between Text Data

Connecting & searching Twitter Public Data

Forecasting Seasonal Sales

Visualizations: Boxplots, Binning

Clustering: Comparing Identified Clusters by Mean/Median Time Series Data

Market Basket Analysis:Hospital Bills – What items are billed together?

Recommendations: Item Based Collaborative Filtering: based on Product Similarity [Cosine Rule Method]

Twitter Word Cloud

Enterprise Analytics ApplicationsUse Cases and Solution Methods

Enterprise Analytics

Applications

Analytics Uses Across Functions

Strategy & Planning

Marketing/Sales

Customer Service

Business Question Analytics Suggestions

Are my customers who get in touch with the company happy? What kind of feedback is received, how has this changed over time, and what is likely to be expected in the future?

Sentiment analysis:Text Analytics & Visualization

Logistic regression forecasting, Hypothesis testing, Conditional probability employing methods

What changes to customer service have impacted customer satisfaction the most?

Correlation Analysis: Predictor vs response variable testing

Which customers are most valuable – and are these customers being served appropriately?

Auto clustering Customer lifetime value through weighted scoring

Where do new customers come from? Can we offer better services with less hassle for customers?

Location analysis, clustering

Operations

Business Question Analytics Suggestions

What activities are most profitable? Pattern Identification through Data Mining

Are resources being maximized? How can I get more out of my current resources (e.g. reduce system downtimes, improve service efficiencies)?

Pattern Identification through Data Mining

Can I make an improvement to logistics/ supply chain processes?

Pattern Identification through Data Mining

FinanceBusiness Question Analytics Suggestions

What are the revenue and cost projections for next year?

Forecasting Methods: Seasonal Adjustment Forecasting(Holt-Winters), ARIMA, Linear Regression

Which (groups of) customers are likely to default payment?

Logistic regression & response modelling

What is the revenue breakdown? Quantile based estimates, box & whisker plots

How can I cut finance approval times ? What items (are likely to) take the longest for approval?

Pattern Identification and Data Mining Conditional probabilistic estimates

Requests approvals vs rejected statistics, breakdown

Better Visualizations

Marketing/SalesBusiness Question Analytics Suggestions

What are my sales forecasts for 2015? Forecasting Methods: Seasonal Adjustment Forecasting(Holt-Winters), ARIMA, Linear Regression

What are our customer segments? Auto- Clustering Derived variables/segments through weighted scoring

What is the distribution of my annual sales (or numerical data) by price of item? What prices command the greatest proportion of sales?

Quantile based diagramsBox & Whisker Plots

What are the unusual trends in my data (customers, employees, sales, dates…)? Where are the exceptions and how can we explain these?

Outlier analysis :Quantile based (wrt Interquartile range), Confidence Interval based (beyond 95% CI), Distribution (2 SDs)

What are the influencers for sales? Correlation Analysis: Predictor vs Response Variable Testing

Will this type of product pricing work for this category of brand? Forecasting Methods (see first row), Hypothesis Testing, Logistic Regression

What are customers saying about my company /brands on social media? Social media connection (Twitter, Facebook), Sentiment/Text Analytics, Hashtag/keyword searches, Likes/Comments/Shares statistics

Strategy/PlanningBusiness Question Analytics Suggestions

Where should I invest more capital next year? ROI estimation & complete summary statistics based on departmental statistics Weighted scoring & sales forecast estimates

What kind of people should I be hiring? Scoring of employees, department performance statistics & activity profitability estimates

What can improve the company’s brand? Social media, CRM, Internal e-mail & chat text analytics

Where are the gaps in operational efficiency? Downtime statistics, resource utilization %, time-related patterns

How can I maximize company profitability? Pattern Identification through Data Mining

About Robust Designs

About Us: Robust Designs

Robust Designs and CUBOT

is a software company specializing in BI solutions• Operational since 2004• Privately held• Offices: Singapore, Mumbai,

Bangalore• 15 people

is our BI product with over 40 customers in India, Singapore, Malaysia, Singapore, Vietnam & Netherlands• Developed with the vision: • Faster to Implement, Simpler to

use

Experience with BI over the YearsIndia, Singapore, Malaysia, Vietnam, Netherlands

2005

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Stayed 4-6 years

Stayed 2-4 years

Stayed 1-2 years

PAST CUSTOMERS

APAC

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