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: 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 (tutorial)

topic 2: 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 (tutorial)

topic 3: 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:

  • Machine learning and big data related aspects
  • Text mining as a learning problem
  • Review of text mining algorithms and text mining applications
  • Basics of Matlab (programming, data visualization)
  • Matlab implementation of a spam classifier using Naïve Bayes Classifier
  • Sentiment analysis of Twitter tweets with Matlab (tutorial)

topic 4: 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 and predictive modelling using R. (Tutorial)

topic 5: Location Analytics

Location analytics involves extracting knowledge or intelligence through the analysis of location (often termed spatial or geospatial) datasets. This session provides an overview of location analytics in the context of big data, and examines the resulting implications. During this session the following topics are discussed:

  • Introduction to location analytics
  • Understand terminology associated with location analytics
  • Identify sources of location data
  • Understand the relevance of geospatial big data and location intelligence
  • Become familiar with a range of geospatial scenarios
  • Identify the implications of location analytics
  • Understand the future of big data analytics
  • Map location data using QGIS (tutorial)

topic 6: 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 (tutorial)

topic 7: 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 (tutorial)

topic 8: Analytics Legal, Privacy, And ethical 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.