All you wanted to know about Analytics

WHAT IS ANALYTICS?

Analytics involves the application of advanced quantitative techniques on enterprise and third-party data to help you make the best possible decision in a given situation.

Analytics helps you how:

Act on Customer Insight

"Connect the dots" in your customer relationship data to understand complex patterns of behavior
Identify the best course of action to take at every decision point during your interactions with customers
Guide managers at all levels to achieve deeper understanding of their current and potential customers

Enable Data driven Decision Making

Take the "guesswork" out of decision making - tactical, strategic or operational
Create an agile organization by enabling intelligent decision making at every touch point with the customer
Automate operational decision-making leading to more consistent decisions and better compliance

Reduce Costs and Achieve Efficiency gains

Reduce costs due to fraud, by detecting abnormal patterns of behavior and flagging it in real time .
Reduce write-offs from bad debts by identifying potentially risky customers and accelerating them through various delinquency buckets for proactive collections action
Increased return on marketing investments through fine-grained segmentation and precise targeting of prospects and customers

Analytical solutions combine insights from diverse disciplines, including statistics, machine learning, artificial intelligence, data mining and operations research. Across industries, companies have used the power of analytics to forecast customer behavior and make better decisions everyday.


What are the different types of Analytic Solutions?

Risk, Marketing, Pharma, Retail, CPG,Supply-Chain

What are different types of Models in Analytics ?

Analytic models can be used for a wide variety of problems. Here’s a list of three of the most common uses of analytics.

Segmentation

Helps you divide your customers into different homogeneous groups or segments, and used for defining marketing or risk strategies for each segment
These segments are often “discovered” from the data, using techniques such as clustering

Classification


The most widely used purpose of analytic models. For predicting customer behavior (such as retention, defaults, response to a marketing offer, etc.), and develop interventions based on the same
Involves techniques such as decision trees, logistic regression, neural networks, etc.

Forecasting

Used to project the future business metric based on historical data. For example, Sales forecasting by SKU’s and store (sales), or loss forecasting for a loan portfolio (risk management)
More complex, typically involving handling Longitudinal data, using Time Series, Regression or a combination of both

Generalizing these further, Analytic solutions can be classified into three different types – explanatory models, predictive models and decision models.

Explanatory Models

Explanatory models are developed to discover complex relationships between various phenomena.
For instance, a risk manager at a consumer lending institution would like to understand the underlying drivers of their customers' ability and willingness to pay. The factors could include demographics, past behavior, econometric factors, product characteristics, etc.
The output of an explanatory model is a set of graphs, charts or tables that clearly help managers identify key drivers and validate or disprove hypotheses

Predictive Models

Predictive models are developed to classify customers, accounts or prospects or predict their behavior
For instance, a lending institution could use a predictive model (or a series of models) to estimate the propensity of an applicant or a customer to default on a loan, at the time of origination, and at different points in the course of the relationship
The output from a predictive model is a set of one or more scores that is used for decision making in the business process, or fed into a decision model

Decision Models

Decision models help identify the best possible decision in a given situation
For instance, a direct marketer in a retail financial institution would like to make decisions regarding: who to target (the target customers), what offer to make (the products and features), what channels (telephone, direct mail, email), what message to use (creatives). These decisions need to be made within time, budgetary constraints and other considerations. The direct marketer is likely to use optimization techniques and simulators to identify the right set of decisions to make

The output from the decision models help identify the outcomes associated with certain interventions that are being planned by the decision maker

More to come soon.....

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