Retail Analytics Masterclass

Description

This 2-day Retail Analytics Masterclass is designed to equip Retail professionals to make data-driven decisions to successfully transform their organizations into Intelligent Enterprise, bringing efficiency and accuracy with data analytics and new technologies

This course explains how Big data effectively analyzes large volumes of diverse data and helps companies gain a deeper understanding of customer demand. Applying retail data analytics makes shopping more relevant, personalized and convenient, which can help you sell more and boost consumer loyalty, as these examples of big data trends in retail prove. Specifically, this Masterclass will enable you as a Retail profession to;

1. Make Customer centric marketing decisions. It’s one of the most popular applications of analytics in retail. It allows retailers to use the available data to get better insights about their customers in order to serve them better.
2. Leverage Supply Chain analytics in inventory management and forecasting.
3. Price optimization 
4. Fraud detection is a critical issue for retailers determined to prevent or minimize losses. Analytics is very useful in building effective fraud control strategies.
5. Localization and Clustering: Electronic product tags and internet stores are providing retailers with deep insights on local buying habits. This data can then be integrated with data on leases, costs, store performance, and maintenance to find an optimal location to open a store on. The offerings of the store will be unique to the preferences of the local customers. This will increase sales and prevent retailers from keeping too much stock.
6. Market Basket Analysis: You will be able to use affinity analysis methods to understand customer purchase behavior. If a customer is regularly purchasing cereal and milk together for example, offering discounts for both of the items is not very logical, but offering a discount for one of the items can drive the sales of the other.
7. Price Optimization:  is the practice of using data on operating costs, inventories, and historic pricing and sales to come up with the price of an offering that will maximize profits. Understanding the customer and how they will react to a specific price point is of vital importance in price optimization. You will be able to use analytics to determine the optimal pricing of products and services through their lifecycles. Price optimization leads to increased revenue and profits and has a direct impact on the bottom-line.
8. Marketing Mix Modeling: Marketing Mix Modeling is the practice of using historical sales and marketing data to understand the impact of different marketing tactics on sales and then forecasting the impacts of future strategies. This type of analysis is especially useful when preparing marketing and advertising mixes that have positive impacts on sales and revenue.
9. Real time Dashboards as the New Retail Battleground You will understand how Retail analytics makes it possible for brands and retailers to compare categories across multiple marketplaces. These insights are visualized on various dashboards throughout the platform to make for easy assimilation and quick action. Having access to retail dashboards is like subscribing to an exhaustive market report, the only difference being that it’s all in real-time.

YOU'LL WALK AWAY WITH:
1. An understanding of how data-driven models can improve your ability to make decisions in a fast-paced and uncertain world, and the ability to use modelling to predict future trends in Retail. 
2. Retail Analytics Box (RAB) and PowerBI data visualization and reporting skills, with which to clearly communicate your findings and business needs. 
3. A capstone project as proof of your ability to analyze, summarize, visualize, and report on insights extracted from your own dataset. (We will provide a dataset if you don’t have) 
4. A certificate of completion from Predictive Analytics Lab as validation of your data analysis skills and knowledge.

5. 2 weeks access to our Enterprise Retail Analytics Box Machine Learning Software.

COURSE CURRICULUM
Module 1
Digital Economy E-commerce and Developments, eCommerce Processes and Use of Data Analytics

Module 2
Retail Data visualization and descriptive statistics using PowerBI and Retail Analytics Box. 
Module 3
Click Stream Analytics and Leveraging Orthogonal Data sets including social media data to build marketing recommendation engines.
Module 4
Targeting customers using their demographic attributes and past purchase patterns

Module 5
Optimizing financial metrics for a retail company
Module 6
Market Basket Analysis for Recommendation Engine, SKU Optimization using Machine Learning and Big Data

PREREQUSITES 

A Marketing Business Problem to solve 

Motivation!

REQUIREMENTS 

Laptop

Dataset from work : Internal Data 

We will provide external data Demographic data, Economic indicators, Location Data etc