The course provides a framework within which one can develop his or her thinking on any key analytic business challenge with a view to identifying the right solution. There is nothing comparable to the Retail Analytics course anywhere and anyone looking to enter into retail should go through this.
·You will build a scorecard for the merchandising function-head which will provide a drilldown business view of all the categories sold. The methodology applies retail business logic to come up with the appropriate metrics to view the business. It uses the built scorecard to arrive at a set of business-level recommendations for the merchant.
·You will arrive at a customer value analysis for a seafood-retailer to help him identify his most profitable customer segments to target. The analysis applies RFM methodology on the customer data. The results offer the retailer a set of choices based on the choice of retail strategy he wants to take.
·We will demonstrate, case by case, the building of association rules, and discriminating good rules from bad, followed by a look at a real-world case study to understand the impact of market basket analysis on a retailer's strategy.
·A step-by-step introduction of the various marketing variables arising in the retail-marketer's context, followed by an exposition on the typical market mix modeling technique on these variables, and its impact on the marketer's decisions
·You will build a promotion program for a food-retail chain by segmenting the stores on the basis of their propensities for different categories under promotion. The methodology employs clustering technique, covering attributes from historical trade and site-characteristics of the store
Retail Analytics ltd delivers scalable, flexible, advanced and cost effective analytics solutions for optimizing merchandizing and marketing decisions.
Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.
Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, arenas within analytics include predictive analytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.
The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities.
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