Certificate Program in
Retail Analytics Course
- Get Trained by Trainers from ISB, IIT & IIM
- 16 Hours of Intensive Classroom & Online Sessions
- 24+ Hours of Practical Assignments
- Receive Certificate from Technology Leader - IBM
- 2 Capstone Live Projects
- 100% Job Placement Assistance
Overview of Retail Analytics Course
The Retail Analytics course is designed for individuals who want to foster in the Retail Industry. This course helps students to analyze the data and draw valuable insights by which companies will gain a competitive edge. The Retail Analytics course covers important topics that include RSCA process, Sentiment Analysis, Google Analytics, NLP, Recommendation systems, Deep learning concepts, Text Analysis and Behavioral Analytics of customers.
Furthermore, this course helps students to make data-driven decisions and build innovative solutions in the Retail Industry.
Learning Outcomes of Retail Analytics Course
360DigiTMG provides the best training in Retail Analytics with a meticulously framed curriculum. The curriculum of the course introduces and explains in detail all the prominent topics and techniques in the Retail Industry. This course will enlighten on topics like the Introduction of Analytics in Retail, Sentiment Analysis, and many more which helps students to derive insights from the data and build new strategies that help companies to escalate the production and generate revenue.
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Who Should Sign Up?
- Retail Analysts
- Business Analysts
- Data Analysts
- Risk Managers
- Certified Financial Analysts
- Credit Analysts
- Math, Science and Commerce Graduates
Modules of Retail Analytics Certification Course
The Retail Analytics course offered by 360DigiTMG helps students to gain valuable insights and make data-driven decisions. The course curriculum is designed by industry professionals focusing on prime concepts. From this course, students will learn the applications and possibilities of the Retail and Supply chain and also learn about the applications of IoT. Learn the applications of Segmentation and will be able to build strategies from the segregated data. Students will learn how to build recommendation engines for any e-commerce portal. Learn the concepts of the usage of NodeXL and also learn to generate word clouds to perform sentiment analysis to draw meaningful insights using an R programming language. Students will be introduced to learn about how to use linear regression in understanding increasing paid ads performance. Also, you will learn about the various keywords used in contemporary customer analytics. At the end of this topic, you will learn how to keep your customers engaged with the utmost satisfaction. Learn how to address frequently asked questions with the latest information pulled out from the database. Understand how the context can be addressed using NLP concepts, using voice to text and text to voice models. Learn how machines (computers) see an Image or a Video input using Computer Vision. Process the images and videos using CNN to develop AI applications in capturing the customers' purchasing behaviour. Face recognition systems to identify a repeat customer from a new customer. The curriculum includes real-time projects and numerous assignments which helps the students to understand concepts thoroughly. It helps students to gain in-depth knowledge and required skill sets to grab lucrative jobs in the Retail Industry.
Get about, CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance. Get introduced to the principles of big data and learn about the opportunities being created. Understand about how Data is generation and explosion of data, Innovations in the space of analytics. Learn how to distinguish between data types, Exploratory data analysis, the Various moments of Business decisions and various Graphical techniques. Learn about probability and probability distribution namely Z distribution and Student's t-distribution.
Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples
Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.
At the end of this topic, you will learn about the applications & possibilities in the space of Retail & Supply Chain. You will also learn the possibilities of IoT applications in general and in particular in Retail & Supply Chain. Also, the analytics-driven landscape of digital marketing analytics in the space of Retail is explained in detail.
- Data Generation Sources
- Typical RSCA Process
- Data in RSCA
- Analytical Techniques in RSCA context
- Data Analytics & Decision-Making Models
- Promise of Advanced Analytics in Retail
- Introduction to IoT (Internet of Things)
- Applications of IoT in Retail
- Introduction to Contemporary Analytics
At the end of this topic, you will learn about the applications of Segmentation. You will learn about the techniques used to measure the similarity between the records. Understand how organizing and managing the entities (Products/Customers) will become so much easier and beneficial. You will learn the strategies that can be applied to segregate the data.
- Introduction to Segmentation/Clustering
- Clustering Techniques
- Distance measures to measure Similarity
- Clustering Approaches
- Hierarchical Clustering
- Non-Hierarchical Clustering
- Linkage Functions
- Within the Sum of Squares
- Elbow Curve
- Choosing the best number of Clusters
At the end of this topic, you will learn how to build a basic recommendation engine for any eCommerce portal. You will be able to appreciate the various strategies involved in building a recommendation engine to make the organization profitable. It will be a blend of theoretical concepts & usage of R programming to accomplish this practically.
- Introduction to Recommender Systems
- Traditional Collaborative Filtering
- Distance measures such as
- Cosine-based similarity
- Correlation-based similarity
- Strategy for Analytics Solution
- Runtime vs. Quality of recommendation
- Reducing Computation Burden
- Search-Based Methods
- Addressing Cold Start
- Usage of Dimension Reduction techniques
At the end of this topic, you will learn how to ensure the rack placement, slotting fee, marketing & distribution fee. Increase the purchases by offering bundling fees & the discount strategies will be discussed. How to analytically determine the products getting sold together will help strategize the increase in sales. It will be a blend of theoretical concepts & usage of R programming to accomplish this practically.
- Introduction to Association Rules
- Emergence of Data – POS
- Strategies & Applications of Association Rules
- Apriori Algorithm
- Performance Measures
- Life Ratio
- Innovative Applications
- Sequential Pattern Mining
At the end of this topic, you will learn how to effectively use the travel routes by the logistics firms to ensure that the goods are transported faster. How many warehouses should be placed & at what distance & at what locations to be most profitable? Also, this can be extended to the use of social media to improve brand awareness. It will be a blend of theoretical concepts & usage of NodeXL to accomplish this practically.
- Graph/Network, Vertices/Nodes & Edges/Links
- Network Characterization
- Node properties (Degree, Closeness, Eigenvector, Betweenness, Page Rank, Egocentric)
- Edge properties (How to weigh)
- Network properties (Path, Shortest path, Diameter, Cluster coefficient)
- Link Prediction
- Entity Resolution
- Fast Greedy
- Leading Eigenvector
At the end of this topic, you will learn how to deal with unstructured textual data. You will be able to generate word clouds & also perform sentiment analysis to draw meaningful insights. You will be able to perform analysis on social media data (Twitter) by extracting the tweets using R. You will be able to extract topics for performing Natural Language Processing. It will be a blend of theoretical concepts & usage of R to accomplish this practically.
- Introduction to Text Analysis
- Tokenization procedures
- Simple Bag-of-words (BOW) representation
- Term-Document Matrix structures
- Basic display aids
- Sentiment Analysis
- What and Why
- Sentiment scoring schemes
- Tidytext introduction
- Clustering BOW structures
- Intro to NLP using OpenNLP
- Annotations, POS tagging, NER, Chunking, etc.
- Customizing Chunking – Homebrewed
- A Quick look at CoreNLP capabilities
- Latent Semantic Analysis
- GloVe and Linguistic Regularities
- Wordnet explorations
At the end of this topic, you will learn how to track the various metrics such as the number of people visiting the website, time spent on each page or product, navigation path followed by user leading to sales. You will learn about how to use linear regression in understanding about increasing paid ads performance. Also, you will learn about the various keywords used in contemporary customer analytics. It will be a blend of theoretical concepts & usage of Google Analytics & R to accomplish this practically.
- Introduction to Google Analytics
- Install Tracking Tag, Create Views & Adding Filters
- Understanding Reports, Metric & Dimensions
- Interact with Graphs
- Segmentation, Basic Filters
- Audience Demographics, Reports & Interests
- Use of Behavior Report & Technology Report
- Acquisition Reports by Channels, Sources & Mediums
- Social Report & Network Referrals
- Measuring Value of Social with Conversions
- Behavior, Site Content Reports
- Track Events & Real-time Data
- Integrate with BigQuery & R
- Cohort Analysis & Filters
- Advanced Google Analytics
- Search Engine Advertising Analytics
- Sponsored Search Advertising
- Linear Regression for Enhancing Advertisement Position & thereby Profits
- Modeling Click-Through Rates
At the end of this topic, you will learn how to keep your customers engaged with the utmost satisfaction. Learn how to address frequently asked questions with the latest information pulled out from the database. You will learn how revenue increases by the application of Chatbots. Understand how the context can be addressed using NLP concepts, using voice to text and text to voice models.
- Chatbots in Retail
- Introduction to Conversational Chatbots
- Building Block of Chatbot: Intents & Entities
- Taking actions based on Context using NLP
- Chatbot Deployment
At the end of this topic, you will learn how to keep the unstructured data using Deep Learning concepts. Learn how machines (computers) see an Image or a Video input using Computer Vision. Process the images and videos using CNN to develop AI applications in capturing the customers' purchasing behavior. Face recognition systems to identify a repeat customer from a new customer.
- Video Analytics for Customer Behavior Analysis in Retail
- Introduction to unstructured data generated in Retail
- Processing Videos using Computer Vision & OpenCV
- Convolution Neural Network & its Architecture
- Image processing using CNN
- Feature Maps
- Pooling Layers
Trends in Retail Analytics
Many retailers started adopting Retail Analytics immensely as they have realized that they bring significant returns. Here is the curated list of the latest Retail Analytics trends that are going to transform industries in near future. These trends help companies to gain in-depth insights and will replace inefficient data management tools and in-house approaches. Further, these trends will help companies to be more responsive, agile, and build new strategies. Omnichannel experience is one of the latest trends in Retail Analytics. The companies in the retail industry should be able to bridge a gap between online and in-store platforms. The company’s objective should be to enhance the customer shopping experience and manage the availability of the required products. Furthermore, customer-centric models will help the companies to imbibe online platforms to offer the omnichannel experience to their customers. The personalization concept is trending now. Companies are coming up with innovative offers and sales to attract new customers and target specific location customers.
This concept helps companies to retain their customers by engaging them with quizzes, interactive sessions, videos, and apps. Predictive Analytics is one of the key trends in Retail Analytics. By analyzing the historical data, companies can predict the future or unknown events. It will enable companies to enhance their supply chain operations. By predictive analytics, companies can know the climatic conditions, local programs, and other conditions that will affect business. So based on the data they can plan accordingly and release the products. This will reduce inventory, increase the lifetime of the customer, and operations costs. Dynamic Pricing is considered to be the most important trend in Retail Analytics. This model helps companies to design their price structure based on the demand for the products, market, and competitors’ strategies. This will also enable companies to improve customer loyalty and satisfaction. So, there is tremendous scope for Retail Analytics in providing a wide range of opportunities in different sectors.
How we prepare you
Additional Assignments of over 60+ hours
Live Free Webinars
Resume and LinkedIn Review Sessions
Lifetime LMS Access
Job Placements in Data Science fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in Live Projects
Offline Hiring Events
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