big data & hadoop applications in marketing

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com

Applications of BIG Data in Marketing


Introduction to BIG Data

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

Retail industries generate extremely high volumes of data due to high churn of transactions & inventory.

These companies can utilize BIG Data insights to ensure higher profitability.


Applications of BIG Data in Marketing

• Improved Granular Targeting • Dynamic Forecasts and Planning• Optimizing Campaigns in Real-Time• Measuring channel effectiveness using Multi-touch Attribution Models• Enhancing Customer Loyalty using Sentiment Analysis


Improved Granular Targeting

Big Data Analytics allows marketers to segment their prospects and customers into micro-targeted groups based on behaviour, not just demographics or transaction histories.

For example, sentiment analysis from social media can combine with CRM data to create a more holistic view of customer lifetime value and improve cross-sell and upsell opportunities.

BIG Data Applications in Marketing

Dynamic Forecasts and Planning

Big Data enables marketers to combine the customer information with contextual factors such as competitive behaviour, pricing positions, new trendsA and other external factors to determine the magic formula to scale customer retention & acquisition.

This increase in the volume and range of information sources also enables dynamic real-time forecasts resulting in informed decision-making.

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Optimizing Campaigns in Real-Time

Big Data technology can provide the sheer processing power needed to optimise campaigns in a scalable, real-time manner. For example it can be used to determine the proper bid for each keyword in a portfolio so that they are all working together towards increasing customer acquisition at an optimized cost.

Marketers will be able to gain more closures with the same advertising expenditure.

Measuring channel effectiveness using Multi-touch Attribution Models

Marketers can use multi-touch attribution (MTA) models to get a better insight into campaign performance data and the see what is actually driving consumers to engage. MTA models derived from Big data deliver deeper understanding of the factors impacting conversions; which is better than the traditional way of assigning to the last-touch metrics.

Nearly half of all US marketers still measure their efforts through last-touch metrics, with less than 5% using algorithmic models.





Enhancing Customer Loyalty using Sentiment Analysis

The growing number of channels through which customers communicate has resulted in brands needing to understand what their customers are saying about their products and experiences in order to ensure customer satisfaction.

Not only this, even brand sentiment analysis can be conducted which is an essential componened of online reputation management.

Marketers can use Big data applications to analyse comments on social media or product review sites, enabling them to quickly respond to negative or positive comments.




Examples of BIG Data in Marketing

• GoogleThey’ve got the biggest advertising network in the world today, and this is powered by BIG Data. For example, you can target all individuals interested in travel & leisure, living in New York between the age of 18 – 25. Not only this, you can also ensure your advertisement is only placed on specific chosen websites or topic-themed websites.

• AmazonNot only are they the biggest eCommerce company in the world, they’ve also got one of the largest banks of consumer data. Based on this, they’ve created a recommendation algorithm which suggests relevant products to consumers thus, boosting sales. In addition to this, they’ve also used to generate personalized offers & marketing campaigns. Next time you see the product you want waiting for you on their home page, remember, that’s BIG Data.

© 2015 BlueCamphor Technologies (P) Ltd.

How Are Insights Derived from BIG Data?

Say Hello to Hadoop Development :)




Introduction to Hadoop

Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of commodity computers using a simple programming model.

It is an Open-Source Data Management technology with extensive storage and distributed processing.

Hadoop CharacteristicsFlexible

Reliable

Economical

Scalable Get Started with BIG Data & Hadoop



Why Should You Learn Hadoop?

• Hadoop is a modern BIG Data processing framework which utilizes distributed computing clusters.

• It can process, manage and store unstructured data with absolute ease.

• Due to its scalability & effectiveness, companies are heavily adopting Hadoop.

• It has become the standard for storing, processing and analyzing big data.

• BIG Data & Hadoop professionals are in extremely high demand.

• This is the first step to becoming a data scientist or data architect.




Reports – 2015

Here are some of the other Reports that convey the same message:

• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.” (Markets and Markets Research Report)

• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.” (Technology Research Organization, Wikibon)

• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)

• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge demand.” (According to the Big Data Executive Survey 2013)

• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their salaries.” (According to Analytics Industry Report)

Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data




Predictions for 2015

According to the Forrester Report:

• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014, are bound to adopt it in 2015

• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software engineers who will upgrade their skills

• The creation of SQL-on-Hadoop options will further enhance usability & adoption

• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will transform into an application platform giving birth to new frameworks in the Hadoop ecosystem

• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red Hat, VMware and Microsoft will include Hadoop in their operating Systems




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Course Curriculum

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Course Topics

Module 1

Introduction to Big Data and Hadoop

Module 2

HDFS Internals, Hadoop

Configurations and Data Loading

Module 3

Introduction to Map Reduce

Module 4

Advanced Map Reduce Concepts

Module 5

Introduction to Pig

Module 6

Advanced Pig and Introduction to Hive

Module 7

Advanced Hive Concepts

Module 8

Extending Hive and HBase Introduction

Module 9

Advanced HBase and Oozie Introduction

Module 10

Project Set-up Discussion




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