What is covered?

This 3 days hands-on workshop offers theories and practical skills on creating value with business analytics while developing analytical and critical thinking of analytics-business alignment, security, ethics, and privacy issues (no prior knowledge of analytics is required). The workshop also introduces tools, techniques, and strategies to increase brand loyalty, generate leads, drive traffic, and ultimately make good business decisions. The workshop covers the following themes and topics (please bring your own laptops for the hands-on sessions):

topic 1: Creating Value with Social Media Analytics

Social media data is considered the ‘new gold’ and can be employed to identify which customer behavior and actions create more value. Still, many firms find it extremely hard to define what the value of social media is and how to capture and create value with social media data. In this session, we will cover the following topics:

Have in-depth understanding of social media value creation theories and concepts.
Understand a generic social media value creation model.
Understand different types of social media values to customers and firms.
Have in-depth understanding of social media return on investment (ROI).
Formulate social media metrics for measuring ROI.
Understand social media analytics theories, concepts, tools, history, and industry.
Familiarize with the eight layers of social media analytics framework.
Understand uses of social media analytics by business.
Contrast social media analytics vs., business analytics.
Comprehend four types of social media analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Understand common social media analytics limitations and issues.
Carry out social media analytics vendor assessment (practical)

topic 2: Creating Value With marketing Analytics

This session will provide a theoretical and practical introduction to the most prominent topics in contemporary marketing analytics, which is often called Customer Insights analytics in commercial businesses. The first part of the lecture will provide the theoretical background to the main Marketing Analytics topics, while the second part consists of hands-on exercises with SPSS (no prior knowledge of this statistical package is required).The lecture will cover the theory and practice of the following topics:

Segmentation: RFM-based and cluster analysis based
Market basket analysis
Prospect selection
Assessing long-term effects of marketing strategies
Churn modelling
Customer Lifetime Value calculation
Hands-on exercises with SPSS

topic 3: Creating Value with Text Analytics

Text analytics deals with the extraction and analysis of business insights from textual elements, such as comments, tweets, blog posts, and Facebook status updates. Text analytics is mostly used to understand social media users’ sentiments or identify emerging themes and topics. The following topics are covered:

Understand basic text analytics concepts and tools.
List uses of text analytics by business, government, academia, and financial institutes.
Understand objectives of text analytics for business intelligence purposes including sentiment analysis, concept minding, trends mining, and topic mining.
Understand text analytics cycle and the steps required to mine business insights from text.
Different types text analytics terms, methods, and algorithms.
List common text analytics limitations and issues.
Extract and analyze social media text (with DiscoverText).
Construct precise social data fetch queries.
Use Boolean search on resulting archives.
Filter on metadata or other project attributes.
Tabulate, explore, and set aside duplicates, cluster near-duplicates.
Crowd source human coding.
Measure inter-rater reliability.
Adjudicate coder disagreements, and
Build high quality word sense and topic disambiguation engines.

topic 4: Creating Value with business Analytics

Business Analytics is the practice of improving business performance and making business decisions, combining statistical analysis and software. This session provides an overview of business analytics in the era of big data with R. This session also discusses descriptive analytics, predictive analytics and prescriptive analytics with structured data in areas such as retail, tourism and healthcare.

During this session the following topics are discussed:

Understand terminology associated with data including analytics processes and frameworks in R
Understand descriptive analytics, predictive analytics and prescriptive analytics
Discuss challenges faced with data
Understand data sources
Real examples of analytics using R
Descriptive analytics with descriptive statistics and visualisations using the tidyverse and predictive modeling using R. (Tutorial)

The materials and data are available at this link

https://github.com/kimnewzealand/R-tutorials/tree/master/Analytics

topic 5: Creating Value With Location Analytics

Location analytics, also known as spatial analysis or geospatial analytics is concerned with mining and mapping the locations of social media users, contents, and data. During this session the following topics are discussed:

Comprehend location analytics concepts and tools.
List uses of location analytics by business for business intelligence purposes.
Understand sources of location data.
Grasp challenges associated with location analytics.
List location analytics and privacy concerns.
Understand application and tools of location analytics currently available in the market.
Map location data (tutorials)

topic 6: Creating Value with Networks Analytics

Network analytics extract, analyze, and interpret personal and professional social networks, for example, Facebook, Friendship Network, and Twitter. Network analytics seeks to identify influential nodes (e.g., people and organizations) and their position in the network. During this session the following topics are covered:

Understand network analytics concept and tools.
Understand network and node level metrics (degree centralities, density, clustering coefficient, structural holes, etc.)
Understand different types of networks and its terminologies.
Understand uses of network analytics for business intelligence purposes.
Formulate network strategies
Extract, construct, and analyze common social media networks (NodeXL Tutorial).

topic 7: Creating Value With Search Engine Analytics

Search engines analytics focuses on analyzing historical search data for gaining a valuable insight into a range of areas, including trends analysis, keyword monitoring, search result and advertisement history, and advertisement spending statistics. This session is dedicated to search engines analytics.

Understand search engines types and working mechanism.
Develop comprehensive understanding of off-site and on-site SEO search engine optimization (SEO) techniques (e.g., link-building, social sharing, Alt Text, keywords. Anchor Text, etc).
Differentiate between paid search and organic SEO.
Understand search engine data analytics and its types.
Explain the two main categories of search engine analytics.
Extract and analyze search engine data through Google trends and Google correlate (Tutorial)

topic 8: Financial Analytics

Developing forecasts and determining how global economic and political events affect local markets and attaining a data-driven understanding of how different sectors in the economy integrate to achieve sustainable societal and macroeconomic goals, are the main ingredients of my research activities. In this tutorial I will cover both the theory and application of forecasting house prices. First I will show how to use AR, ARIMA, or Box-Jenkins univariate time series approach to forecast house price and then I will show how to use multiple related variable VAR model to forecast house price using a cointegration. To do sophisticated analysis I will use EVIEWS-10 Software, a student version of which can be bought a relatively cheap price. As a an alternative, I will also show how to use moving average and exponential smoothing models to forecast house price using Excel.

Topic 9: Analytics-Business Alignment

Configuring and understanding analytics tools alone are not enough; to get the most out of it, analytics should be strategically aligned with business strategy.This session discusses strategies and techniques to align analytics with business goals. We also provide a detailed discussion on social media strategy formulation and its components, such as ownership plan, content strategy, account strategy, platform strategy, and implementation plan. The lectures also provides an analytics maturity models that organization use to assess their current state of analytics maturity and provide a structured path towards improving data analytics competence for an enterprise-wide business decision-making. Here are the objective of the session.

Understand the concept of aligning analytics with business goals
Understand social media alignment matrix.
Understand the role of CIO in aligning analytics with business objectives.
Understand social media strategy.
Understand the steps needed to formulate a social media strategy.
analytics-business alignment assessment (Tutorial)

topic 10: Analytics Legal, Privacy, And Security Issue

Internet use and harnessing big data introduces new challenges related to privacy, security, data management, accessibility, governance, and other legal and information security issues such as hacking and cyber-warfare.This session discuss these issues in detail alongside a discussion and framework on social media risk management.

Understand common social media risks and privacy issues.
Understand social media risks management framework.
Understand social media risks mitigation strategies.
Understand the different type of social media data and the privacy issues surrounding it.
Familiarize with techniques and strategies to secure social media accounts.

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