Use of Analytics in Business verticals.


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In the last posts we saw how Analytics has become mainstream & how it is different from the Business Intelligence.

Let us see how businesses are using it for competitive advantage. Today businesses are more worried about survival than profitability. The very purpose of any business to exist is to be profitable & sustain it. This can happen only when there are good loyal & profitable Customers attached to the business.

Hence Customer analytics has become very prime importance in the current time and it is valid across all the business segments.

Customer Analytics:

* Customer Life time value – group Customers on high, medium, low value & take actions to increase revenue
* Customer Segmentation – grouping of Customers based on demographics, or profitability, or life time value
* Customer Churn/attrition – predict which Customers are likely to leave you & take suitable actions
* Customer Retention – identify most profitable Customers & then retain them
* Campaign management – selective campaigns based on segmentation or Customer’s likely behavior
* Cross-sell and Up-sell – increase the revenue proposing other products or high end products

Apart from these I am mentioning below some of the areas in business verticals, where Analytics is applied for foresight.

Banking & Financial Services Analytics:

* Anti money laundering – identifying suspicious transactions to alert investigation officers
* Credit scoring – score the customer based on various parameters to arrive at certain number and if that is above a threshold then approve the credit
* Credit Risk – predicting the risk involved due to non payment by borrowers in case of credit cards, loans etc
* Fraud detection & Prevention – predicting suspicious transactions which are likely to be fraud in all the transactions of card, wire transfers, online transactions etc
* Price Optimization – Debt collection agency can predict the optimal price for the portfolio & forecasting the probable recovery from defaulters

Insurance Analytics:

* Claims Fraud detection – predicting the claims which are likely to be fraudulent
* Policy Lapse prediction – predict which are the policies that are going to lapse before completing the tenure
* Underwriting rate optimization – predicting the best price for the insurance products based on Customer profile
* Agent performance prediction – how agents are going add revenues to the organization, improve customer satisfaction & retention
* Agent Lifetime Value – how best an agent is going to serve the organization throughout his/her tenure

Healthcare Analytics:

* Healthcare Claims Fraud detection – predicting the claims which are likely to be fraud
* Financial recovery – predicting the payments from healthcare insurance payer which are overpayments to service providers
* Health plan analytics – allows organizations to compare & predict different benefits & risk options in terms of coverage & costs
* Condition Management – predict which of the people are likely to develop diseases like blood pressure, cholesterol etc

Retail Analytics:

* Discount or Price optimization – predict the optimal prices of discounts & normal prices of merchandizes for today’s sensitive shopper
* Cross-Sell & Up-Sell – propose other products depending on various factors such as color, fashion, choice, location, earning patter & Customer buying behavior etc.
* Forecasting – based on demands from Customers predict how much stock is required to avoid stock-outs & excess inventory

Manufacturing Analytics:

* Predicting the parts failure – based on the history data predict which of the machinery parts are going to fail & when
* Issue detection – predicting the issues before they occur so preventive maintenance can be done on the parts
* Warranty Analytics – identify issues across production period to reduce warranty costs
* Forecasting – based on demands from Customers predict how much stock is required to avoid stock-outs & excess inventory
* Inventory Optimization – to reduce inventory carrying costs & increase order fulfillment by predicting optimal inventory to be stored across warehouses

Text Analytics:

* Discover & extract meaningful patterns and relationships from the text collection from social media site such as Facebook, Twitter, Linked-in, Blogs, Call center scripts
* Understand Customer sentiments – positive & negative. Used for Product & Customer service improvements. Also for knowing what competition is good or bad at

Analytics is used in every area of life to get better insights about what is going to happen & what we can do so that a best outcome is expected !!!

Republished with author's permission from original post.

Sandeep Raut
Sandeep Raut is Founder and CEO at Going Digital.He is ranked in top 10 global influencers and thought leaders in Digital Transformation.


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