How Analytics is unlocking the power of data in India

World over there is a lot of excitement today, around “Big Data”. It’s become buzzword of a sort. And rightly so in India as well – because of the sheer volume of data that we are witnessing by the hour if not less. However, what we need to understand is, that it’s not about how big the data is. It’s about what you are going to do with it. In fact big data has always been there – what seemed to be a lot of information yesterday, in comparison to what gets amassed today, seems small. With that same logic, what seems to be big data today, would neccessarily appear to be ”not big” tomorrow. But what is exciting is high performance “Analytics”, because it’s the analytics that will help you do something with all of that data today and tomorrow.

Let’s look at some examples of how analytics is helping enterprises in India become high performance organizations:

Power & Utilities: Power distribution is a regulated business in India – a power hungry economy. By regulation, the utilities are required to plan, estimate and report their subsequent day demand to the load dispatch centres which in turn schedule the available power to them for the next day consumption. Any shortfall/ excess in estimating demand incurs either penalties or losses due to wastage. Today, Analytics solutions like load forecasting are helping leading power & utilities companies forecast short-term & long-term power demand with accurate results thereby minimizing losses.

Stock Exchanges & Regulatory bodies: The magnanimity of transactions that happen on a daily basis on exchanges brings with it surveillance challenges. To analyze the behaviour of investors and scrips over a period of time & how to uncover frauds including insider trading, circular trading etc. becomes a challenge. On the other hand, the key role of regulatory bodies is to protect the interests of the investors and citizens. New regulations, increased scrutiny, and scandals have increased the need for sophisticated analysis & monitoring of mal-practices. Analytics is helping exchanges & regulators in India identify investors, relationships between them, fradulent behaviour, manipulation patterns & unknown patterns for investigators to identify & detect fraud.

Automotive Manufacturing: India has a huge base of indeginious auto makers as well as foreign players. Add to that a huge customer base with varied tastes, preferences and a mindset that majorly revolves around pricing. Analytics is helping leading players in India in the areas of accurate demand forecasts of various vehicle models, warranty analysis, campaign management and social media marketing; to having a single view of customer enabling them to have targeted marketing  campaigns thereby optimizing spend and improving sales. 

Government & PSUs: For a country of over a billion customers (citizens), government of India can be seen as an organization that neccessarily needs to cater to each of them in various ways. Add to that the complexities of multiple regions, central policies, state policies, large number of ministries & departments. Governance in such a scenario becomes a herculean task. Analytics is playing a pivotal role in the Indian governance enabling the policy makers to implement citizen welfare policies in areas of healthcare, inland security, defence and income tax to excise & customs and census.

BFSI: The banking industry in India has a huge canvas of history, which covers the traditional banking practices to the reforms period, nationalization to privatisation of banks, scheduled and cooperatives to foreign multi national banks. The opportunities & challenges run hand in hand – with the quest for acquiring more & more customers, providing various services to the issues such as risk, fraud & economic uncertainties. Analytics is helping Indian banks & insurance companies in the areas of integrated risk management, identification of customer characteristics that lead to deliquencies, cross-selling to existing customers to areas like rate making, claims fraud, campaign management etc.

Retail: Indian retail sector is amongst the largest in the world and is experiencing rapid growth & sophistication to address swelling market demand and ever-changing customer preferences. Growth in the sector has created a highly competitive environment with foreign players opening shops. In such a market place, retailers are under constant competitive pressure for customer wallet share, improving customer experience & loyalty, meeting growing customer aspirations, increase its breadth of merchandise & expand store operations in to new markets, while maintaining profitability. Analytics is helping leading retailers in India with insighful analysis of merchandising, assortment & inventory management, loyalty, campaign management, shelf-space optimization etc., including real time knowledge about sales & store performance.

Telecommunications: Telecom industry in India has a big market potentiality and is a fast growing sector. India has the world’s second largest mobile subscribers with over 900 million as of beginning this year. With ongoing price war, high government taxes, and with a hyper-competition bringing in other challenges like eroding revenue and decreasing profitability; communication providers are focussed on data and content services to increase revenue and profitability. Analytics is helping these organizations to effectively address issue like churn, cross-sell/ up-sell, price plan optimization and network optimization to areas like identifying potential high value users, campaign management and nurturing profitable relationships.

 Jaydeep Deshpande

Essentials of Marketing Analytics

Everybody is talking about customer analytics and how it can help companies market more effectively. But for many marketing professionals, there is a gap between theory and execution – and it’s getting wider every day. Marketers know in theory, that gaining insight from the incredible explosion of new digital data being generated, collected and stored requires analytics. And it’s this insight that marketers desperately need to keep up with today’s tech savvy consumers as they comparison-shop online, blog and tweet reviews that can influence millions – and use spam filters to avoid marketing they don’t want to receive. If marketers don’t deliver product and service information to consumers that’s personally relevant, timely and delivered via their preferred channels, they’ll ultimately drive them away. 

To be effective in this environment, organizations must base their marketing processes and strategies on an analytical framework. Here are 3 essential components you need to get started:

1) Analytically driven customer segmentation - Customer segmentation is a basic component of modern marketing strategies. All marketers know what this process involves so I will directly discuss two types of segmentation to discuss – “foundation segmentation” and “targeting segmentation”. Foundation segmentation creates core segments that enable marketers to deliver consistent marketing treatments as a part of a long term customer strategy. All customers must be included, but each can fall into only one segment. Some of the key attributes of foundation segments include: value, profit, attrition, risk and demographics. Targeting segmentation identifies customers with specific needs and preferences. Not all customers may be included and customers can fall in to multiple segments. It is focussed on short term marketing activities that deliver highly relevant messages and offers to recipients. Analytics enables you to go beyonf foundation segmentation to targeting segmentation, allowing you to execute more effective, sophisticated campaigns with messages and offers that are highly relevant to recipients.

2) Predictive modeling -  To know what customers will do in the future, marketers have to understand what they did in the past. Predictive analytics provide insight in to the behaviour patterns of a company’s best and worst customers. By having insights in to customer attitudes, behaviour, profitability and risk, marketers can make better decisions to improve marketing outcomes.

3) Marketing optimization technologies -  Issues such as competing divisional business goals, managing multiple marketing programs against constraints – such as channel capacity, budgets and customer contact policies – and internal politics can make decisions about which campaigns to send to which customers very difficult, especially for multiproduct companies. Marketers need optimization, a technology based solution that applies mathematical techniques to maximize economic outcomes by making the most of each individual customer communication. In addition, optimization analytics can help increase organizational efficiency by quantifying where changes in staffing and budget will really pay off, where money is being left on the table or where there is any unused capacity.

Given the rapid changes occuring in the world of marketing, companies can’t afford not to employ analytically driven marketing strategies and tactics. With an integrated analytical framework for customer intelligence, marketers can make smarter decision, solve more business challenges and, ultimately, get more insight from customer data to drive optimal marketing performance.

Jaydeep Deshpande

Customer Analytics

Moving from BI to Advanced Analytics

Business Intelligence (BI) provides companies with valuable historical information, keeping many organizations competitive during tough times. Purchasing departments, for example, use Business Intelligence to monitor, choose and negotiate with suppliers. Customer service departments use it to identify problems. Airlines use BI to monitor the status and performance of their fleets and personnel.

Though BI provides many advantages, it has limited ability to predict, forecast and make inferences on unknown facts and relationships – for instance, predicting customer behaviour, the probability of fraud, or suggesting the next best offer during an online transaction. For these reasons, most companies are enhancing their BI practices to include predictive analytics and data mining. This combines the best of strategic reporting and basic forecasting with additional operational intelligence and decision-making functions. By developing the capability to move from insight to action, leading businesses are combining historical and predictive analysis to determine what immediate actions to take.

In today’s economic environment, “good enough” is no longer enough. For example, in fraud analysis, knowing what happened yesterday and stopping the same thing from happening tomorrow is only step one. With advanced analytics, organizations can now identify fraud before they write a cheque, refund money or settle a claim.

This seems simple enough, but even companies with sound enterprise data management practices, processes and infrastructures that were built for traditional BI reporting cannot always handle the complex requirements and unpredictable workloads of operational analytics. The good news is that much of the work that establishes enterprise-class business intelligence – creating consistent data, rigorous systems governance, and sophisticated data integration and data quality – can serve as a sound foundation for advanced analytics.

Jaydeep Deshpande

Big Data Analytics

What used to be a serious problem just a few years ago has today taken the shape of a business opportunity. We are referring to “Big Data” here. When data deluge started skyrocketing, storage technologies were overwhelmed by the numerous terabytes of big data – to the point that IT faced a data scalability crises. But then storage and CPUs not only developed greater capacity, agility and intelligence; they also became cost competitive. Enterprises went from being unable to afford or manage big data to lavishing budgets on its collection and Analysis.

Today, organizations are exploring big data to discover facts they didn’t know before. This is an important task right now because the recent economic recession forced deep changes in to most businesses, especially those that depend on mass consumers. Using advanced analytics, businesses can study big data to understand the current state of the business and track still-evolving aspects such as customer behavior.

Big Data Analytics is where advanced analytic techniques operate on big data sets. Hence, big data analytics is really about two things – big data and analytics – plus how the two have teamed up to create one of the most profound trends in business intelligence (BI) today.

What exactly do we mean by big data?

The world’s production of data has been running ahead of our capacity to store and amount of data is expected to grow in rapid progressions every year. This data comes from different sources such as sensors used to gather climate information; posts to social media sites, blogs, public forums; digital pictures and online videos; transaction records of ATM machines and credit card readers; and cell phone GPS signals, to name a few. This data is collectively referred to as “Big Data.”

Big Data represents a new era in data exploration and utilization. More than a challenge, it is an opportunity to find insight in new and emerging types of data, to make the business more agile, and to answer questions that in the past were beyond reach. Today Indian markets are witnessing three major trends. Firstly, as more MNC’s set up big bases in India to harness the volume potential in the Indian market, we will see an increased in focus on the usage of Big Data and Analytics. Secondly, as industries mature and get more competitive (for example, telecom), having an accurate data and the ability to use it smartly will become the next competitive advantage. Thirdly, striker regulations could even drive more conventional businesses like banking to use more and more Analytics. The Unique identification Authority of India (UIDAI) project is one such example where huge amounts of data is being collected and collated by the government of India.

Organizations should look at big data in terms of the opportunities inherent in a value shift from simply collecting large amounts of information to clearly understanding what that information can do for you. It’s not just about how do you collect data and store data, but how do you share that data and create visualization for it? How do you do Analytics with it? How do you search for it? … The kind of information and the volume of information are changing; therefore, the value becomes “what do you do with that information?” It’s being able to analyze that in increasingly real-time, collaborative manners. Not just to ‘cloudify’ data sets – i.e., transform IT – but truly to transform business.

 

Jaydeep Deshpande

Alison Bolen from SAS says “Analytics i

Alison Bolen from SAS says “Analytics is the hottest career for today’s graduates and jobseekers” – get the upper hand with her top ten.

The new world Marketing

Today’s marketing executives are witnessing a watershed moment, the convergence of unprecedented access to customer data and the emergence of highly empowered customers who demand highly personalized offers and service.

And ’Analytics‘ is the new game that virtually all marketers must eventually embrace if they’re going to be successful. Analytics was once just the province of a few direct marketers and market researchers, but now the entire field of marketing is being transformed by this capability. Whether the focus is consumer or industrial marketing, and whether the customer channel involves a sales call, contact with a call center, a web click, or an e-mail; marketing is becoming increasingly analytical. There is no longer any excuse for not knowing whether your advertising or your promotions are working. It is possible to test and analyze any campaign or any change in how you go to market with analytics.

This is of course a major change in culture for many marketers. Many marketers will therefore need considerable reorientation and may be even retraining for this new world. It isn’t that the old creative and intuitive capabilities will go away, but they will have to be supplemented by a new set of analytical skill sets. Of course the best marketers will continue to be those that have an intuitive and emphatic understanding of customers, but that cannot be the only perspective. It must be complemented by empirical, quantitative analyses of the behaviours customers actually exhibit and the impact of marketing activities on those behaviours.

It is an exciting time for the field, and one in which careers and reputationsof marketers will be rebuilt or newly established. In other words, it’s a great time to be in marketing, but only if you embrace analytics.

 Jaydeep Deshpande

 

Effective data management is now a boardroom discussion

For years, board members could safely regard data as an operational issue requiring little discussion. This is no longer the case. The financial crisis exposed the shortcomings of data in a large number of institutions and highlighted the need to pay greater attention to this valuable asset.

Research conducted by the Economist Intelligence Unit (EIU) on behalf of SAS has consistently highlighted data as a barrier to effective risk management for many financial institutions. In the most recent survey, just 39 % of respondents believed that their organizations were effective at collecting, standardizing and storing risk data. Respondents also considered insufficient data among the top 3 shortcomings preventing more effective risk management.

Why has data been elevated to a boardroom discussion? For one thing, key stakeholders want assurances that boards are providing robust oversight and making well-informed decisions based on solid data. For another, data enables institutions to respond quickly and effectively to crisis. In the hours leading to Lehman Brothers’ collapse in 2008, some institutions were unable to calculate their aggregate exposures to Lehman and its subsidiaries in a timely manner because data was distributed inconsistently across multiple divisions, and even when it could be gathered, the data had to be aggregated so that overall exposures could be measured.

In addition, regulatory changes are forcing boards to focus on data issues. One aspect of this new regulatory intervention is likely to be a greater requirement for financial institutions to make risk exposure data available. In combination, these trends are encouraging a broad review of the board’s role & responsibilities toward data. There is a growing realization that data should be viewed as an asset with fundamental strategic importance.

Leading financial institutions now recognize that data can be a major source of strategic advantage. By asking the right questions at the right time, understanding the strengths and weaknesses of their institutions’ data, and helping to prioritize investments and initiatives, boards can play a vital role in raising the quality of data and, by extension, their own decision-making.

Jaydeep Deshpande

Analytics in Action

How are key industries deriving value from their Analytics implementations?

Banking

In a challenging economic and regulatory climate, bankers must be especially vigilant. Two key indicators of a bank’s health are net charge-offs — the value of loans written off as uncollectable – and non performing loans that are in default or delinquent. So how can financial institutions improve their collections and protect their bottom line?

Analytics can provide the insights that institutions need to reduce both loan writeoffs and the cost of collections activities. First, models created within an Analytics framework can identify likely candidates for workouts and loan modifications. Second, Analytics can optimize collections activities to improve the probability of success and maximize self-treatment among debtor segments.

Manufacturing

From diapers to jet engines and almost everything in between, manufacturing expertise is a competitive differentiator for companies that follow optimal practices and methodologies to attack inefficiencies and eliminate waste.

Analytics is essential in these settings to improve production and sales planning, enhance the supply chain, reduce inventory, optimize warranty, streamline logistics and much more. For example, with demand forecasting, Analytics can be a key contributor to a manufacturer’s success. Better forecasts deliver ROI by: Reducing inventories, Improving order fulfillment rates and Shortening cash-to-cash cycles.

Telecommunications

You have likely experienced it before – your cell phone network drops one too many times. And now with MNP in place, you may not think twice to change the service provider. Low barriers to churning mean providers must vigilantly and carefully invest to maintain and increase their service quality and customer satisfaction rankings. After all, your satisfaction keeps them in business – more so with the birth of MNP in India.

Analytics and approaches such as predictive fault analysis help network managers analyze performance to pre-empt failures. They can analyze trouble tickets and optimize corrective services, shortening times you are without coverage. Analytics help providers prevent churn by allowing them to gauge the potential customers who are most likely to churn, and those who are not adding to their revenues.

Healthcare

According to world health organization, global health spending totalled more than US$ 4.1 trillion in 2007, with $639 as the total health expenditure per person. That number will only grow in ways that affect businesses and citizens.

Healthcare quality today is uneven and resistant to changes and improvements. How can we enhance health care delivery while controlling those costs? It starts by carefully measuring and monitoring the quality of that care – a complex task perfectly suited for Analytics. 

Jaydeep Deshpande

SAS Analytics

See how Shoppers Stop is poised to furth

See how Shoppers Stop is poised to further grow their business with Business Analytics. Watch the video: http://ow.ly/3hWLk

What is your business analytics style

Today’s Business Analytics technologies are capable of so much more than simple reporting. Organizations are using predictive analytics to forecast outcomes, optimize processes and build what-if simulations. Automated or semi-automated decision making is built in to business processes. Decision makers have self service access to information that allows them to navigate, visualize and find patters in data.

Organizations are recognizing the value of business analytics, and the need to exploit the full range of capabilities is increasing. The challenge is melding the elements your organizations needs in to a cohesive business analytics architecture.

There are 5 styles of business analytics that are being embraced by organizations in all industries:

1. Classic Business Analytics
   A system that supports only query & reporting. This basic level of data sourcing provides information via data exploration or predictive analytics techniques and shares information with usersat various organizational levels using generated reports. This type of reporting provides important information that helps decision-making particularly if it provides backward-looking and forward-looking information.
   But a change is occuring in the traditional information distribution method.
2.  Classic Business Analytics with Data Quality
   In many ways this style is similar to classic business analytics except that it recognizes the need to cleanse the sourced data. It integrates data quality in to the data sourcing process. The adoption of this style is driven by the need to increase the trust in information delivered to end users.
3. Business Analytics with feedback loops
   Business analytics with feedback loops supports cyclical business processes. For example, you might want to provide specific recommendations into a procurement workflow. On a regular basis, depending on the purchasing cycle, you would forecast sales of your inventory to determine what you need to replenish. In this scenario, data is extracted, cleansed and analyzed using advanced forecasting techniques.
4. Real-time business analytics
   Real-time business analytics can aid decision making in customer-facing situations. Each customer touch point represents an opportunity to make a real-time decision to create additional sales or reinforce behaviour – for example, the real time credit scoring of bank customers to see if they qualify for a loan, or in deciding what additional offers to make to a customer placing an order. This approach requires triggering analytics or data collection and delivery in real time from an application.
5. Business activity monitoring
   Many companies have a host of operational systems that are critical for day-to-day operations. Automation is the standard, as human monitoring & decision-making is impossible given the large number of possible events or transactions. Take, for example, credit card fraud detection. Rules exist to determine whether a transaction is approved, rejected or routed to a service agent for follow up with the cardholder. It is not just the single transaction that is considered but the pattern of behaviour linked to the transaction that is important.

No matter which style, or styles, you choose; by sourcing the data, discovering what the data is telling you, and sharing the information with the people who need it, you will support better decision making in your organization.

Jaydeep Deshpande

SAS Business Analytics