How can Analytics solve specific business problems?

How can Analytics solve specific business problems?

Ever wondered what made Uninor, Aircell, enter into Indian market when there already were 12 mobile operators in India, including two government-owned companies?

It is expected that the Indian mobile market will double in the next three to four years and there is nothing called one-size-fits-all for a 1.2 billion India. Uninor, Aircell followed customer segmentation strategy and are targeting only those customers who will benefit from its services.

Now it’s a bigger challenge for other leading GSM telecom operators in India namely Airtel, Vodafone, Reliance Communications, Tata DoCoMo, and Loop Mobile to retain existing customers and grow their customer base amidst stiff price war posed by the new entrants in the market.

Let’s take a critical business decision of retaining high-value customers. If we knew which customers of ours were likely to quit to a competitor, and which of these are really worth keeping, we could intervene in a timely manner and take steps to retain them. This will ensure ongoing revenue from them, reducing costs of replacing them with the new customers and maintaining market share.

So what has analytics to do here? Analytics will help build predictive models to identify high value customers at every point of interaction. Once the high value customers and potential quitters are identified, one can devise strategies to retain them.

Telecommunications companies hold rich data on their customers. Personal information, demographic information is gathered while subscribing to a new connection. Call detail records like Call volume, Usage, Calling Patterns, Roaming Behavior can be used to study customer behavior.

A statistical model can be built that evaluates customer and classifies as loyal or a potential quitter and a likely time period in which the connection can be withdrawn. Quitters can be identified based on the complaints raised and demands posed and subsequently strategies devised to make them loyal.

Telecom operators operate in highly dynamic markets; customer preferences change quickly based on fashions, trends, competitors’ actions and the products, services and packages the provider offers hence ongoing market research and analytics becomes paramount to the success.



Mukta Phadnis-Joshi

Analytics in Business , Marketing

Though not among the toppers, I was an above average student right from my school days. It would sound a little cliché’ – I fell short of few marks to secure a free seat in some of the renowned Engineering colleges. I didn’t get the grapes hence obviously they were sour – a thought was lingering in my mind, there are so many Engineers in the social strata I come from; in fact if I pick up a stone and throw in a crowd I am 95% confident that it will hit an Engineer. Well, the next question in my mind was - aren’t there any other choices? I had aptitude for Mathematics and Statistics sounded similar then… I thought of exploring further. I spoke to one of the professors of a local college.

He asked me a simple question – Do you eat Mangoes? Well, I said “yes”. The next question was – Do you eat Mangoes in winter? I denied. Then he asked what the reason was. I said “Because we don’t get mangoes in winter”. He further asked “Would you like to have mangoes in winter as well? I promptly said “yes, of course”. Then he explained that might be waste if the farmers preserve and stack up mangoes without knowing the actual demand. To know the actual demand will I go to the 1 billion people and ask them if they want to cherish Mangoes even in the winter? The answer obviously was “no”. He then mentioned that there is a statistical method called sampling that will serve the purpose here. By circulating a well defined questionnaire one can get an idea on the demand. Statistics can be further used to analyze the quantities that need to be stacked, the consumer behavior, forecasting for the next year to decide the quantity of produce to meet next year’s demand. This was my first introduction to the expanse field of Analytics.

Find below some information that I gathered on the Net.

Analytics is a science of analysis that helps drive optimal decision making. Analytics closely resembles statistical analysis and data mining. Some of the disciplines under Analytics are Business Analytics, Marketing Analytics, Econometrics, Clinical Research, Actuarial Science, Customer Relationship Management, and Psychometrics…

Analytics may be used as input for human decisions or may drive fully automated decisions.

Business analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.

Marketing Analytics is the science of analyzing the customer data covering marketing research, marketing strategy, and marketing plans. The objective of a marketing analysis is to identify the general interest of the buying public and understand the growing changes and public threats of the company as they relate to the strong and weak points of the business in the industry.
Econometrics is a branch of economics that applies statistical methods to the empirical study of economic theories and relationships.
Clinical research is a branch of medical science that determines the safety and effectiveness of medications, devices, diagnostic products, and treatment regimens intended for human use. These may be used for prevention, treatment, diagnosis or for relief of symptoms in a disease. Studies performed in humans that are intended to increase knowledge about how well a diagnostic test or treatment works in a particular patient population.
Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in the insurance and finance industries.
Psychometrics is the theory and technique of educational and psychological measurement of knowledge, abilities, attitudes, and personality traits.

By Mukta Phadnis-Joshi

BPO and KPO Analytics.

It is required to use analytics in both BPO and KPO companies. BPO is Business process outsourcing where the skills of the people are average to moderate for the working knowledge for a standardized routine processes. Graduates could be trained in the relevant business processes and it is training service personnel for business transactions. These transactions could be invoice processing, credit card processing, order processing, support help desk for any kind of activity, simple maintenance of the applications without involving the coding part, any kind of document processing, giving user awareness or limited technical help.
BPO are guided by Service level agreements (SLAs) and these are to be analyzed periodically to show performance improvement over time that need to optimize operations, reduce repeated tickets. Revising the SLAs require the usage of analytics techniques balancing the various factors like team size, shifts, skill levels, attrition or transition of people, tools and technologies, load variations with time and maintenance schedules of the applications, correlation studies of resolved tickets, maintainability availability and scalability of existing knowledge base, shifting trends etc., .


Whereas KPO is Knowledge process outsourcing requiring high knowledge intensive skills to work compared to BPO. It is like a exploratory research work to be done so highly qualified multi skilled persons are required involving specialized domain expertise as well as business sense. KPO processes demand advanced information search, usage of right analytics techniques depending upon the problem under study and data, interpretation and with proper judgment and decision making. Naturally, the compensation package also will be different.
Some of the examples of KPO are R&D in Pharma or biotechnology areas like new drug discovery, exploring high yield seeds, competitive research, financial modeling and equity research, data mining and database creation , specialized industry reports using the basic and advanced analytics techniques like hypothesis testing, correlation and regression, logistic regression, discriminant , cluster and factor analysis, times series analysis, design of experiments, conjoint analysis for product design, structural equation modeling etc.,

by Srilakshmi & varanasi Subrahmanyam

Analytics- Analytics- Analytics -everywhere Analytics

Analytics is gaining momentum in every industry, be it at company level, competitor level, and supplier level, like manufacturing, Telecommunication, Health care, retail , Utilities, BFSI, Technology forecasting, real estate, investments etc., Analytics is a data exploration method to study and tap the full potential of the existing and growing markets in various geographies from east to west and south to north. Any Business organization needs to have a vision which is nothing but what it intends to become and achieve at some point of time in future. So it is a forward thinking futuristic statement which ultimately needs to be converted to goals and actions. As per a Proverb, Vision without action is a daydream and action without a vision is a nightmare. To set the target goals along with time lines, analytics is of great help. It allows achieving actionable decision making in the organization. Every organization is looking at the fast reliable decision making analytics tools and techniques that empower their staff that gears itself as market leader out beating the competition. Analytics helps the organizations to constantly evaluate the existing market offers to drive, better value added products/services to boost the business operations by providing overall customer oriented value proposition. This is derived logically by the data based decision support model with strong actionables for actualizing the vision. Analytics techniques provide good understanding of customer key trends, enable to forecast various business parameters like sales, revenue, cash flow, Inventory, market share, restraints balancing locale critical market drivers.

Why people are crazy about Analytics

Why people are crazy about Analytics
Analytics is study of business data and playing with numbers for quantum growth of business. Now everybody interested in the Analytics because they are vexed with the routine and non meat jobs, people are looking at new challenges and big career growth opportunities. Especially those who are in love with number manipulation and forecasting based on data, analytics is really a fascinating field of their interest. If your intricate innate is not for the field, thought oriented, if you get kick out of number forecasting,closely want to work with management and change the business dynamics, people look towards analytics.
Most of the MBA’s to get further leverage of their skills and proceedings for the better career opportunities.
The employees from BPO’s want to find new ways of life overcoming the monotony for fresh water in their careers.
Placement assistance is also vital factor as it is developing fast and now every organization want some or other modeling and prediction based on the current available data.
The incentive are fast as you will be working closely with management.
Some of the students with B.Tech back ground they wanted to play with numbers and as they have interest in mathematics and the future strategies and secrets uncovered by these data analysis.

By Srilakshmi V

Learning Analytics

Effective techniques for learning / imparting the analytical skills require rational reasoning, common sense and enthused to learn statistics. These should be decorated with the technical skills on statistical concepts. These are implemented nicely using the tools like SAS, SPSS, Minitab, Statistica and various addins for Excel like EZ-R Stats.
When you are exposed to statistics, you will be invigorated to learn complete techniques in some cases whereas you will be astray and confused by too much of math in some cases. The learner should keep in mind the purpose of the learning and question himself is it for the learning pleasure or is it for the application to a problem? When you are using the screw driver, will be interested in learning what types of screwdrivers are there and what are the characteristics and strength of the material used for the screw driver bit how much is the length from the handle? You do not think of the technical details right.. Your only objective will be to use it for unscrewing the screw you are focusing but not anything else. Similarly when you are applying the statistics, you should be focusing only on the data to be collected, the right tool to be used depending upon the type of the data. Once results are the there, what & where to look how to interpret your solution. In general, a business problem is converted to statistical problem and solved statistically; interpret these statistical results onto your business problem without defocusing in the ocean of statistical amazement. The Analytics person should have the right focus in applying this statistics.

Analytics in Retail

Retail markets are heavily focusing on the use of Analytics for data analysis and classifying the customers. Essentially to understand the customer better for the overall business improvement. In general, majority of the retails are pushing Loyalty cards and these are the best sources for knowing your customer in a batter way. Once you know the customer, you are better equipped to know pulse and influence the target segment of customers by proper sales touch and relevant promotion schemes. The most important of all is who is willing to buy your product or service so that you deploy matching schemes that meet the customer needs and wants. You must understand the buying Patterns and trends. Based on Demand forecasting, you need to manage the inventory so that when customer asks, he is not denied. With proper demand equations, you can manage the vendor relationships and plan your cash flows too. You can leverage these demand by promotions. The analysis tells you the effect or control action of the promotion leaving aside the assumed organic growth. Operational issues and costs could be planned accordingly for optimum levels. Operational efficiency effectiveness is one of the big thing for all the sectors in this tough times and sure way for better bottom lines by reducing the wastes and inefficiencies. Once you understand the customer needs, you can even evaluate the life time value of the customer so that time changing needs are satiated by tweaking the offers for sustained as well as incremental growth prolonging the life cycles. Major retails like Pantaloons, Shopper stop etc., are using for better net profits quarter on quarter.

By Srilakshmi & Varanasi Subrahmanyam