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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