Database mining and analytics are defined as using the power of hidden information locked in databases to reveal consumer and product insights, trends and patterns for future tactical and operational strategy. A key differentiator between analytics and market research is that analytics relies on data which is existing within a database while market research generally involves collection, collation and tabulation of the data.

Business intelligence is defined as the seamless dissemination of information throughout the organization and is a broader term, which involves and includes analytics as well as reporting systems.

The field of data analytics is vast and it comprises the following types.

Types of Analytics

1. Reporting or Descriptive Analytics – Each organization relies on series on management information systems (commonly called MIS) to gather the current state of business as well as any emerging trend. This typically involves sales, finance, customer and competitor data, which is presented within spreadsheets and presentations. Reports tend to be either regular (like monthly and quarterly) or ad-hoc (for special investigative analysis). This is known as descriptive analytics simply because it describes the data which is present. While reporting or descriptive analytics is often the starting point in analytics careers, a proper grounding in this domain is necessary both to build an eye for detail in dealing with large amounts of data and for polishing the presentation skills for presenting insights from the data.

2. Modeling or Predictive Analytics – Predictive Analytics refers to the art and science of using statistical tests, hypotheses and methods to build up predictive recommendations. These recommendations can range from which type of customer to call by phone for a credit card or insurance, to which type of mobile scheme to offer to a cell phone customer by a short message (sms) or to what kind of customers are likely to default on the loans they have taken. Predictive analytics includes techniques like segmentation, and regression modeling. It is generally considered both high value and a background in statistics helps in preparing for predictive analytics careers.

3. Data-Driven Strategy – This is also called test –control or champion-challenger testing. This is done by segmenting the data population into test (on which a new strategy called the challenger strategy is to be tested) and control (which uses existing strategy called champion strategy). Building association rules which describe which parts of the product or customer data are clustered or co-related together are also part of analytics.

Basic Domains within Analytics

Data driven analytics by definition thrives in industries, which have large amounts of data, and high volume transactions, which need systematic and scientific analytics to cut costs and grow sales. The following domains offer employment opportunities to both new comers and experienced analytics professionals. These can be both in domestic firms, captive outsourcing firms or third party business process outsourcing companies.

1. Retail Sales Analytics

Retail sales analytics deals with the handling of vast amounts of Point of Sales data, inventory data, payment data and promotional data to help increase sales in retail stores especially in organized retail. The use of RFID’s, Electronic Payment and Bar Scanning helps capture the data better and store it in vast databases. An example of this is the famous Thursday baby diaper-beer sales phenomenon. A big retailer found that on Thursday evening sales of beer and baby diapers were highly co related. It then found that is was due to young couples preparing for the weekend by buying supplies of diapers and beer. Thus by placing diapers and beer closer together sales could be boosted up. This is an example of market basket analytics in which a large amount of data is scrutinized to see which products sell well together. Wal-Mart, the American Retail Giant established a competitive edge over its rivals by proactively using data driven analytics to cut costs and thus offer goods cheaper than others. Another example of a big retailer is Target which has it’s own captive back end analytics in India.

In India, since Reliance Retail, Future Group, Walmart-Bharti have started setting up shop this is a sector that is bound to grow even within the domestic sector, as these high volume retailers need data driven decisions to squeeze the maximum from their retail stores.

2. Financial Services Analytics

Financial services use analytics extensively. This is because they are in a very competitive field, have millions of customers and a lot of transactions. It is extremely important for them to store data for billing purposes and to recover the money they lend out as well the deposits they collect. An incremental gain of a few basis points (one hundredth of 1 percentage is a basis point) in profitability can lead to millions of dollars in aggregate profits. Within financial services analytics the broad sub categories are –

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   1. Risk and Credit Analytics – Risk and Credit functions measure the ability of a customer to pay back loans or debt owed by them. Delinquent customers are those that have fallen behind in paying back debt as per agreed schedule. Debt can be fixed installment like EMI’s for a personal loan, and debt can be revolving as in variable amounts that can be paid for credit card outstanding including the minimum balance. Debt can also be secured debt as secured against houses, consumer durables ,two wheelers, automobiles as collateral or it can be unsecured as in personal loans or credit card debt that have no collateral or backing. A risk analyst develops scorecards that help measure the risk worthiness of both new and existing customers. As financial service instruments are priced against risk, the riskier the customer the more they are charged in terms of interest rate. But this has to be balanced with total repaying ability of the customer, including sources of income and current leverage .In addition the income of customers especially in India is changing rapidly and there is also

un-declared income as black money. Doing the analysis for millions of customers is what make risk and credit analytics one of the hottest sectors to be in as credit analysts are in demand with all banks, outsourcing corporate and finance companies. ICICI has a big analytics unit (called Business Intelligence Unit) and Citigroup has both domestic analytics (in Chennai) and international analytics centers (in Bangalore).

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   2. Marketing Analytics- Marketing analytics helps in customer acquisition and retention. It does so by helping choose more responsive customers and selling through a wide variety of channels like call centers, direct mail, sms through mobiles, and email. It is marketing analytics which helps to bring in new customers by giving inputs to the marketing team and feedback to sales and distribution channels.

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   1. Collections –Collections Analytics focuses on recovery from delinquent customers using optimized efforts like telephones, direct mails, emails, or visits. Its aim is to maximize recovery at minimum costs.

   2. Fraud –Fraud Analytics seeks to build in triggers or automated alarms if there is any unusual trend or behavior in spending by the customer especially in credit cards.

   3. Pricing – Pricing Analytics tries to give the most optimized price, adequately compensating for risk as well as the competition. Pricing Analytics is a vast field and is also a part of financial services analytics especially in products like insurance.

2. Telecommunications – Telecom Analytics has the fields of marketing analytics defined above, but an important part is also attrition modeling or churn analytics. It also analyzes the wide variety of pricing schemes and options and the customer response to them. In addition it has delinquency analytics as well.

3. Pharmaceutical or Clinical Analytics –Clinical trials depend on test and control of thousands of patients on new drugs. Clinical trial analytics focuses on large number of variables that may or may not affect the drug response.

4. Supply Chain Analytics – Supply chain analytics comprises inventory optimization, tracking turn-around time, multiple reports, and how to minimize the distribution costs.

5. Transportation Analytics- Transportation analytics while covered more extensively in the field of operations research seeks at minimizing route length or fuel costs, or pricing of fares.

6. Online or Website Analytics – Website analytics focuses on analyzing traffic to the website from sources, and how to retain them on the website for longer time or purchase more goods. It also involves a bit of search engine optimization to make sure the website is relevant in searches by search engines.