Marketing & Customer Analytics Training training centre in Bangladesh

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Marketing & Customer Analytics Training

  • Date : 30 March - 19 April 2019
  • Duration : Evening(6:30 PM-9:30 PM)
  • No. of Classes/ Sessions : 6 Sessions
  • Class Schedule : Friday and Saturday
  • Total Hours : 18
  • Last Date of Registration : 29 March, 2019
  • Venue : Bdjobs Training, BDBL Building (Level 19), 12 Kawran Bazar C/A, Dhaka 1215.

Introduction

Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions.

What is this course about?
This course will equip participants with capability to segment, profile and better understand customers. These abilities are crucial in order to offer appropriate product to targeted customer through the most relevant channel.

You will learn how to identify profitable customers so that business can strategically acquire them and offer appropriate services and product. Business can increase the return on investment by applying targeted database marketing to acquire and keep customers instead of mass marketing.

This is a hands-on course that teaches you how to use different analytical techniques to solve different business objectives. Three practical workshops will demonstrate how to segment and profile your customers using R Modeler.

After completion of this training course, you will be able to:
At the end of the course, participants will be able to:
1. Understand customer segmentation so that different marketing tactics can be applied to different customer segments. For example, the most profitable customer segment might receive bigger discounts than other customer segments
2. Understand customer profiling so that customer segments are better understood not only descriptively, but, behaviorally (e.g. how much they spend per month, how long have they been buying from the business)
3. Apply R Modeler analytical techniques, such as customer lifetime value, latency and RFM scoring, to solve business problems
4. Appreciate the Business Customer Game and learn when to market to each customer segment with the right product/service and through the right channel.

What background do I need?
The pre-requisites for learning 'Marketing & Customer Analytics' include basic statistics knowledge and Business Knowledge.

I am from a non-technical background. Will I benefit from this course?
Yes, the course presents both the business and technical benefits of Marketing and Customer Analytics. The data mining and technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.

Methodology

PowerPoint Presentation,Handouts,Hands on Lab Practice, Brainstorming

Contents of Training:

Session 01 :

Introduction
How can Amazon recommend the right product whilst the customers are buying? How can an organization know which customers are interested in leaving their services? Why do we receive fair deals when we are interested in buying a product? The key behind these questions is the effective deployment of Customer Analytics by these organizations, that means the efficient and effective use of customer data.
Methodology
Each chapter is explained from the business problem point of view and it includes the following sections: The problem, Algorithm to solve the business problem, Main concepts (related to the technique), Implementation Process, Benefits, Use cases, How to implement the algorithm using R, References

Session 02 :

Customer Analytics
In this chapter we will introduce what is Customer Analytics ? Origins of Customer Analytics? new consumer ? Characteristics ? Some Business Jargon such as : CLV (Customer Lifetime Value),Customer equity, Value Proposition etc, Types of strategies : Customer Acquisition ,Customer Development ,Customer Retention, Acquisition-Retention Optimization.? How Increasing the analytical maturity of the organization?
Methodologies, Technology and Techniques
In this chapter we will introduce some methodologies, technology and techniques that can be used to analize customer data. Customer Pipeline , Customer Journey , Technologies : Business Intelligence, Data Analytics, Big Data, Data Management, Data Brokerage ??,Types of analysis : Customer understanding, Customer acquisition, Customer Loyalty and profitability, Customer retention, Efficiency and how they related ????,Understanding your customer : What does it mean understanding your customer? There are different perspectives: Behavioral perspective, Profitability perspective, Lifecycle perspective, Loyalty perspective, Interest perspective, Campaign perspective?? , Types of analysis and Techniques Knowledge, Profitability, Life-cycle, Loyalty, Interest, Campaigns

Session 03 :

Customer Lifetime Value
The business problem ? Technique to solve the business problem? Main Concepts ? CLV formula ? A simplification ? Final consideration ? Implementation Process ? Benefits ? Use cases ? How to implement CLV using R ? ARPU/ARPA as CLV aproximation ?
RFM Analysis and Modelization
The business problem ? Technique to solve the business problem ? Main Concepts ? Implementation Process ? Benefits ? Use cases ? How to implement RFM using R ?

Project#1: Customer Lifetime Value
Industry : Retail
Description : The goal of this project is to predict the Arrival Time of a flight given the parameters like:"UniqueCarrier", "DepDelay", "AirTime", "Distance", "ArrDelay", etc. Whether these attributes affect the arrival delay and if yes, to which extent? Construct a model and predict the arrival delay.
Compute the (Source Airport - Destination Airport) mean scheduled time, actual and inflight time with the help of MapReduce in R and visualize the results using R.

Project #2: RFM Analysis and Modelization
Industry : Retail
Description : This problem is about making predictions on the stock market data.The dataset contains the daily quotes of the SP500 stock index from 1970-01-02 to 2009-09-15 (10,000+ daily sessions). For each day information is given on the Open, High, Low and Close prices, and also for the Volume and adjusted close price.

Session 04 :

Customer Satisfaction
The business problem? Technique to solve the business problem ? Main Concepts ? What is Customer Satisfaction ? What is NPS ? Promoters, Passive and Detractors ? Pros & Cons ? Implementation Process ? Benefits ? Use cases ? Alternatives ? How to implement this algorithm using R ?
Association Analysis
The business problem ? Technique to solve the business problem ? Main Concepts ? Implementation Process ? Benefits ? Use cases ? How to implement this algorithm using R ?

Project #3: Customer Satisfaction
Industry : Retail
Description : This problem is about social media analytics. This can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. This problem is mainly about how to use twitter analytics to find meaningful data by performing Sentiment analysis of the tweets obtained and visualizing the conclusions.

Project #4: Association Analysis
Industry : Retail
Description : The problem of creating recommendations given a large data set from directly elicited ratings is a widely potential area which was lately boosted by players like Amazon, Netflix, Google to name a few. In this project, you are given a collection of real world data from the different users involving the products they like, rating assigned to the product, etc. and you have to create and come up with recommendations for the users.

Session 05 :

Customer Segmentation
The business problem ? Technique to solve the business problem ? Main Concepts ? Customer Segmentation Techniques ? Implementation Process ? Benefits ? Use cases ? How to implement this algorithm using R ?
Cohort Analysis
The business problem ? Technique to solve the business problem ? Main Concepts ? Implementation Process ? Benefits ? Use cases ? How to implement this algorithm using R ?

Project #5: Customer Segmentation
Industry : Retail
Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.

Project #6: Cohort Analysis
Industry : Retail
Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.

Session 06 :

Churn Analysis
The business problem ? Technique to solve the business problem ? Main Concepts ? Reasons for churn ? Implementation Process ? Benefits ? Use cases ? How to implement this algorithm using R ?
Overall business health of the company
Discuss how healthy is your business

Project #7: Churn Analysis
Industry : Retail
Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.


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Resource Person

Md. Rabiul Islam

Md. Rabiul Islam has been working with Business Analytics & MIS, Process Reengineering & Automation, Business Research & Development, Project Management, OpenERP customization based on Clients’ Requirement.
He has completed his Graduation on Computer Science and Engineering from the University of Dhaka. He has served Dhaka University Library Management Project, Dhaka University Admission Project. He has been involved with some Research Based work on FMDV Modeling and BT bio-pesticide Toxin and TSS Prediction.
His major accomplishments and Contributions are: Big Data, Data Mining, Semantic Web and Machine Learning.

He has been a reputed public and corporate trainer for several years on subjects like python, Bioinformatics Algorithm using Python, Django frame work, Java, PHP.

Mr. Rabiul is also experienced working with Php, Php framework(php codeigniter ,zend framework), Jquery Mobile, Phone Gap,OpenERP , Python , Java, C / C++, Shell (bash),Python, Python framework(pyramid , Django ) ,Ruby, Ruby on Rails ,Java, java framework (spring and hibernate) ,PHP CMS(Drupal, WordPress), Ajax framework(dojo),CSS3,Ext-js,Sencha touch, Javascript, javascript framework (jquery, angular js, backbone, js),MYSQL, MSSQL, Postgre SQL,NOSQL (HBase) ,Android ,Cross Platform (Phonegap ,Titanium, Jquery Mobile),WSDL/XML/JSON, restful, security.

PowerPoint Presentation,Handouts,Hands on Lab Practice, Brainstorming