Predictive Analytics as an Engine Of R&D and New Product Launches
Predictive analytics is not only the way to discover the underlying patterns, but it can also help you with innovation. Here, we discuss the ways to innovate by combining it with business logic, marketing and bridging demand supply factors.
By Lana Klein, (Co-Founder, 4i) http://www.kdnuggets.com/2015/08/predictive-analytics-rnd-product-launches.html
Businesses Will Need One Million Data Scientists by 2018
International Data Corporation (IDC) predicts a need for 181,000 people with deep analytical skills in the US by 2018 and a requirement for five times that number of positions with data management and interpretation capabilities. http://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html
“Andy Frawley pours gasoline on the hot spots for marketers in Igniting Customer Connections. It’s about time we linked marketing success to customer satisfaction.
This smart, practical and approachable book clearly demonstrates why and how businesses can create meaningful connections with consumers.
“In Igniting Customer Connections, Andy Frawley provides an easy-to-follow roadmap for measuring and improving customer experience and engagement by leveraging relevance. Frawley draws upon his three decades of experience to give marketers sound strategies to improve ROE² (Return on Experience x Engagement).”
“Consumer interaction today is changing, and companies of all sizes need to take a new look at how they’re engaging with customers in ways that are truly relevant. Igniting Customer Connections is an important read for every marketer, offering a compelling guide to differentiating brands through a superior customer experience.”
http://www.ignitingcustomerconnections.com/book
Epsilon uses data analytics and marketing analytics to help our clients make better decisions and do smarter marketing in traditional and emerging channels, including social, mobile, web, and targeted display.
With an array of identification, segmentation, predictive modelling and measurement services, we transform massive amounts of data into useful insights. We turn chaos into clarity, transforming “what could be” into “what’s possible”. Because everything we do has measurable outcomes, we can continually monitor, re-evaluate and adjust your marketing mix to get the best results. Epsilon has over 125 statisticians, analysts and consultants who provide a comprehensive range of services to help you identify, reach, engage, convert and retain more customers.
Epsilon was founded in 1969, primarily as a database marketing company, and expanded into customer management and loyalty marketing programs--a rich source of customer data--in the 1970s and '80s. It was acquired by Alliance Data Systems, the Texas-based marketing, loyalty program and credit vendor, in 2004. In 2014, Alliance purchased digital marketing and personalization company Conversant and integrated it with Epsilon, creating fresh strengths in display, video, and especially mobile advertising.
Managing Social Media and Marketing Analytics for Competitive Advantage (Live Session)
Duke University - The Fuqua School of Business
Professor Christine Moorman.
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Duke University - The Fuqua School of Business
Google Analytics for Enhanced Marketing Measurement
Swapnil Sinha – Head of Conversions at Google India
He is a BE in Computer Science and an MBA from University of Utah.
digitalvidya
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Analytics for Marketers with Bitly CEO Mark Josephson
General Assembly upload
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Wearables, smart houses, and smart hotel desks - more and more smart consumer utilities are going to developed. Connected objects are set to become a part of consumers' lives.
The connected objects will do a better job finding you parking places, taking care of your health, and coordinating your deliveries from various vendors It could almost be like having a personal valet.
Some specific IOT consumer applications are given in MIT Sloan Management Review.
1. Famed design firm IDEO is trying to create a headband that lets people measure their brain activity and track their mental focus.
2. An Internet-connected washing machine model is quite smart. It may become the model for what we want in our appliances: connectivity without complexity.
3. The Internet of Things could mean smart light bulbs that wake you up by getting brighter without the sound alarm that disturbs people in other rooms also.
4. There’s already a big market for sensor-driven wearables, like Jawbone and FitBit wristbands. A smash hit will make its appearance shortly.
5. IoT connected smart locks and rooms will be provided by hotels.
6. IoT connected apparel would help sportsmen get better at sport. Also they will indicate injuries and warn players and coaches.
7. Of course, the consumer Internet will be subjected hacking risks and hence needs anti hacking software.
- Sparse Learning
- Why Sparse Learning?
- Sparse Learning: Joint dimension reduction and estimation
- The Lasso: The most popular sparse learning method
- The Group Lasso: Extends the lasso to accommodate grouped selection
- Various types of sparsity on matrices
- Various types of sparsity (matrix factorization)
- Application to GWAS - Genetic Basis of Complex Diseases
- Application to climate change attribution
- The approach: Sparse Learning with spatio-temporal data
- Application: Key influencers in online communities
- Application: IBM Lotus Bloggers
- Sparse Learning on Matrices: Image denoising
- Sparse Learning on matrices: network inference
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AnalyticsZone
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
IBM Business Analytics
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Honglak Lee, Assistant Professor - Computer Science and Engineering, University of Michigan
The 4th University of Michigan Data Mining Workshop
This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
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Michigan Engineering
Mod-04 Lec-28 Feature Selection : Problem statement and Uses
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nptelhrd
Mod-04 Lec-29 Feature Selection : Branch and Bound Algorithm
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Lec-30 Feature Selection : Sequential Forward and Backward Selection
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NPTELHRD
Mod-04 Lec-32 Feature Selection Criteria Function: Probabilistic Separability Based
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Mod-01 Lec-30 Principal Component Analysis (PCA)
Part of Multivariate Statistical Modeling
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Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis
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Clustering analysis is used in applications such as market research, pattern recognition, data analysis, and image processing.
Clustering can also help marketers discover distinct groups (Segmentation) based on the purchasing patterns in their customer base.
In the field of biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functionalities and gain insight into structures inherent to populations.
Clustering also helps in identification of areas of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city according to house type, value, and geographic location.
Clustering also helps in classifying documents on the web for information discovery.
Clustering is also used in outlier detection applications such as detection of credit card fraud.
As a data mining function, cluster analysis serves as a tool to gain insight into the distribution of data to observe characteristics of each cluster.
Mod-03 Lec-25 Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.
