Analytics was quite a buzzword. Then Brad Pitt made it even hotter with the Oscar-nominated Moneyball. The 2011 film tells the real-life story of Billy Beane, general manager of California based Oakland Athletics, whose team created an American league record by winning 20 consecutive games. Only the players on that team were picked by a computer based on an analysis of baseball statistics rather than just the collective wisdom of coaches, scouts and managers which was how teams were traditionally chosen.
Not only did his analysis create a winning team but also accomplished it at a third of the budget when compared to baseball superclub New York Yankees who spent $125 million dollars to go on the same unbeaten run. Naturally, every company wants to be Oakland Athletics. Fortunately, help is at hand as analytics is making it possible across a range of business objectives, whether it is higher revenues, increased profits, new product launches or better understanding of consumer behaviour. Analytics not only promises to change the way business is done but also rewrites the rules of competition.
Sure, jargon on analytics gets hurled about at the same speed as data explosion. Companies will talk you to about business intelligence, predictive analytics, and the latest catchphrase to hit this part of town — Big Data.
The jargon sounds intimidating but there are different stages in the analytics maturity model and they have the common objective of helping companies become more competitive. “Analytics is a journey and not a destination,” says Arun Gupta, group CTO, Shoppers Stop.
“Each milestone builds on the previous one. As the market evolves, so does the BI (business intelligence) capability of the company to keep pace with new challenges and opportunities.”
If BI provides hindsight, analytics is about looking ahead. “Predictive analysis ensures you meet your client’s expectations while reducing operating costs”, says Sudipta Sen, CEO, SAS Institute India. Imagine you are a telecom company trying to locate your most profitable customers. For Bharti Airtel, which has over 181 million mobile subscribers, trying to do that manually would be impossible but with the help of analytics, which basically builds systems that crunch billions of records or bytes of data through complex algorithms, it would take Bharti only a couple of minutes to zero in on its most valuable customers.
“We analyse usage and recharging patterns with the help of cutting edge technology,” says Amrita Gangotra, director-IT, India and South Asia, Bharti Airtel. “This helps us understand our customers better and allows us to cross-sell our products more effectively.” Airtel works extensively with IBM for its analytics requirements. Its latest campaign, ‘My Airtel My Offer’, is based on customer analytics — every day, the company comes up with a customised plan for its customers based on their usage. It has been most effective with users who hold dual SIM cards and who decide to go with Bharti based on the offer they get on a given day. “We have been able to increase usage based on the offers we’ve made,” says Gangotra.
Level next
Predictive analytics goes one step further. For instance, if you are a bank trying to find out which of your customers is most likely to default on a loan, predictive analysis finds patterns and correlations between data sets using statistics and complex mathematical models — customers who are always late on their credit card payments and don’t have a stable savings patterns are most likely to default and so forth.
“But prediction is not a pure science,” says N Veeraraghavan, senior vice president and global practice head, Data warehousing, BI and performance management, at Cognizant. “It gives you a probabilistic view of things. Sometimes, there could be 5-6 iterations (the process of repeating a sequence of operations, each building on the one preceding it, to reach the desired degree of accuracy) before we arrive at the solution.”
Interestingly, the speed of capturing transactions has increased dramatically. This is because companies want to know when their customers are transacting in real time so that they can run a promo right away. For example, every time you withdraw money from your ATM, your bank would want to run a campaign for fixed deposits or insurance products depending on the balance you have in your account.
“Companies have put a lot of work into systems and process engineering,” says KR Sanjiv, senior vice president-analytics and information management services, Wipro Technologies. “Analytics is the next lever with which companies can prop up their key performance indicators.”
On the bandwagon
However, to predict the future more accurately, companies have to build ‘information assets’ so that they are able to capture relevant data. Data is generated by the tonne outside the organisation as well. It’s gathered from sensors used to collect climate information, from posts on social networks, from pictures and videos posted online, from transaction records and even cell phone GPS signals.
This massive explosion of data has led to the era of, quite simply, Big Data, which involves analysing both structured data from within the company and ‘unstructured data’ like voice, video and texts to get better customer insights. “There is a lot of data being created by machines,” says Dhiraj Rajaram, founder and CEO of Mu Sigma, one of the largest independent pure play analytics firms based out of Chicago. “Companies now have the ability and technology to store and analyse this data in a way that was never possible before.”
Wikibon (the wiki for sharing business and technology) says the Big Data business, which currently hovers around $5 billion, is likely to grow over ten times to more than $50 billion in the next five years as more and more companies jump on to the Big Data bandwagon. “Consumer opinion is starting to matter more and more now thanks to social media,” says Jaskiran Bhatia, country manager-information management, IBM.
“So companies are looking to shape customers’ perception about the brand, sometime even before that perception is formed. Unstructured data is therefore becoming more important and that’s why Big Data is the next step for business analytics.” For example, more than 50% of travellers in the US are influenced by online ratings when they make their travel choices and they are even willing to pay more for a highly rated resort or hotel. Companies can judge the customer response to a new product launch, whether it is a mobile phone, an automobile or a consumer appliance, by using sentiment analytics on social media. Moreover, if there are early-stage product or service glitches, they can sort them out before they get out of hand.
Besides, companies have limited resources at their disposal and they are always looking to earn more bang for their buck. Analytics comes in quite handy for this purpose. “To do more with less is now the mantra for CIOs and CFOs and that will be one of the key drivers in the demand for business analytics,” says Prashant Tewari, country manager-business analytics, IBM.
Research firm IDC estimates that companies worldwide will invest more than $120 billion in analytics across hardware, software and services by 2015. The BI and performance management analytics revenue was pegged around $12.2 billion in 2011, a 16.4% increase from the 2010 revenue of $10.5 billion. The market for business intelligence software in India was around $65 million in 2011, and is growing at around 15-16% on an annual basis.
Hungry for more
Naturally, companies want to cash in on the opportunity. IBM and Cognizant have already snapped up analytics enterprises to increase their presence in the space. IBM has spent $16 billion on acquiring more than 25 analytics companies since 2005, and it’s projecting $16 billion in analytics revenue by 2015. Cognizant bought MarketRx in 2007 for $135 million, its largest acquisition till date, to get a headstart in the analytics segment.
MarketRx’s proprietary analytics software platform was helping pharma companies improve their marketing performance among other things, and came with revenues of around $40 million at the time of the acquisition. The acquisition gave Cognizant access to the top 20 pharma companies in the US at that time, and enterprise analytics continues to be a focus area. “Enterprise analytics will be one of the key growth drivers in the next few years,” says R Chandrasekhar, group chief executive, technology and operations, Cognizant.
To scale up its business, Mu Sigma raised $108 million from private equity firms General Atlantic and Sequoia Capital in 2011, which roughly put the value of the firm at around $500 million. The company has raised nearly $150 million over the past seven years. In the past six years, Mu Sigma has managed to scale up its revenues to $70 million and now has about 1,500 employees. “There were doubts about whether we can scale up this business, but we have shown that it’s possible,” says Rajaram. Mu Sigma expects to clock $110 million in 2012.
SAS is the largest independent vendor in the business intelligence market and has 2,100 employees. SAS is ploughing back 24% of the $2.7 billion revenue it made in 2011 to stay ahead in the analytics space. As for Wipro, business analytics and information management contributes 6.6% to overall revenues and has around 8,000 employees under this practice.
It recently snapped up Australian based analytics firm, Promax which specialises in trade promotion analytics for the food and beverage sector for $36 million. According to Wipro, consumer focused companies spend up to 15-20% of their annual sales on trade promotions and they are increasingly using analytics to improve effectiveness. Wipro plans to extend Promax’s product offering to clients in other industries like retail, banking and insurance.
Good math
Adoption of analytics causes culture challenges within the organisation, which is not always easy. Decision makers in companies can always ask: if we have managed to grow the business successfully all these years without analytics, why start using it now? “As we help companies transition from gut-based decision making to a more data-driven decision-making culture, we have to overcome existing biases that decision makers have,” says Deepinder Singh Dhingra, head-strategy, Mu Sigma.
Companies have been sitting on piles of data for years now but little has been done to use it to their advantage. But companies are now looking at data more closely and finding out how to use it more effectively. “Every business will use analytics to take data-driven decisions,” says Prithivjit Roy, CEO and founder, Bridgei2i Analytics, a start-up that specialises in analytic solutions by building applications.
“It won’t be just for a competitive advantage — it’s going to be the way you run your business.” For instance, a retailer in India didn’t have to worry about competition from online retail till some years back. But now, e-commerce and online shopping has changed the retail landscape. Not only do retailers have to grow their store sales, they also need to have a thriving online presence to take on competition from online retail companies. They are doing that with the help of analytics.
“When analytics is combined with actionable insights, it does provide a competitive differentiator to a company,” says Shoppers Stop’s Gupta. “At Shoppers Stop, we use a combination of conventional analytics and online analytics to stay ahead. We now use analytics for customer segmentation, merchandise strategies, and marketing campaigns.” Shoppers Stop is taking the help of social media to listen to its customers. “We use social media extensively to connect with our customers, to hear what they are telling us, and we use this information for our decision making. It is early days and the model is still evolving.”
Early birds
While there is a strong business case for companies to use analytics across industries, the banking, telecom, retail, pharma and consumer goods sectors have been early adopters. “Over 80% of our revenues come from industries such as financial services, healthcare, pharmaceutical, retail, consumer goods and media,” says Cognizant’s Chandrasekhar. “These sectors are the most aggressive adopters of analytics, and this naturally plays into the Cognizant sweet spot.”
Increasing competition amongst banks, particularly on the retail banking side, is eating into their margins. SAS Institute’s Sen says, “The biggest challenge for a banker today is: ‘how to create profitable growth?’ They have to choose the right client to upsell and cross-sell the right products rather than depend on low cost deposits. Thus, customer segmentation and profiling is important.”
SAS helped Standard Chartered Bank launch Diva — an international credit card designed especially for Indian women — when the bank realised that a significant portion of its business came from the upwardly mobile women of the subcontinent. The card gave discounts on brands in consumer durables, baby and leather products, healthcare, and airlines. It gave what was needed and proved to be an instant hit with its target segment.
In the Indian telecom market, which has one of the lowest ARPUs (average revenue per user) in the world, profitability is really a function of how well you can cross-sell value added products and retain your customers. “Data analysis is essential for telcos to run the business on a daily basis as it helps create a differentiation,” says Bharti’s Gangotra.
For instance, SAS helps Reliance deploy around 150 campaigns per day through SMSes targeted at relevant customer groups. The entire process is completely automated and pricing is done based on micro-segments across its 150 million customers. The marketing campaigns are generating significant ROI for Reliance, which has been acquiring 6 million subscribers a month with the help of SAS’ analytics.
Analytics also help companies optimise their costs and gives better insights into their performance. For instance, Wipro helped a retail company with over 100 stores in the US, which was under severe pressure to cut costs, save $50 million with just effective workforce allocation and scheduling.
Earlier, staffing was done according to historic monthly data and vacations. Wipro, through its advanced analytics solutions, factored in work areas within stores, the level of automation, and the criticality of ongoing campaigns, before scheduling the workforce. So, if there was an electronics sale, there would be additional workforce allocated for that aisle but if most of trailer unloading and backroom stocking was automated in a particular branch, that store would get fewer employees.
Analytics helps keep promotional spends of pharma companies in line. Excess promotional spend eats into their resources, which could otherwise be used for drug development. Cognizant helped a leading pharma firm cut its promotional channel spend by 70%, while maintaining its market share — this was done by helping the company optimise its spend across multiple channels through analytic solutions. Analytics also help companies detect fraud in healthcare. According to the FBI, healthcare fraud in the US alone runs up to the tune of $250 billion each year.
“We have saved money and time for our clients with our BI solutions,” says IBM’s Tewari. Take the case of Prudential Asset Management, which manages a corpus of around ₹70,000 crore. The process of data collection to measure its performance was time consuming because it had to derive the information from multiple sources.
For instance, preparation of monthly MIS required as many as eight to 10 working days. It could not generate daily sales reports either, which restricted the management’s ability to drive growth. IBM helped the company not only reduce the time taken to prepare MIS reports to just two days but made sales information available on a daily basis by implementing its BI solution, Cognos. Thanks to graphical dashboards generated on key performance indicators, the management could respond better to dynamic market conditions in a shorter time span.
Government agencies, too, can plan better with the help of analytics. According to SAS’ Sen, the Indian Government, especially the Ministry of Health and Planning, is also a big user of analytics — they are using analytics to decide where to set up hospitals and educational institutions, depending on the population spread. Wipro helped a leading transport authority in the APAC region with its public transport planning — it helped analyse 12 million records on a daily basis with its analytics solutions, reduced operation and fuel costs by 15% and distance covered by 20%.
While the opportunity remains huge, one of the challenges that companies are facing is the severe shortage of analytical talent. Developing algorithms that can process billions of records in minutes, and identifying patterns and insights which can otherwise get snowed under mountains of data, requires a different kind of skills set. A data analyst or a scientist must have a master’s degree in mathematics or statistics.
He or she must help identify data sources to be used, and find the correlation between data points before finally disseminating the findings to business users. They should also understand what drives certain customer behaviour so they are able to predict more accurately how the customer will respond or act in a particular situation.
According to a McKinsey report, by 2018, there will be a shortage of 140,000 to 190,000 data scientists, and about 1.5 million managers and analysts who can use Big Data effectively to make decisions. So companies like Mu Sigma are creating the talent by hiring and training engineering and mathematics graduatesat their Mu Sigma University.
The graduates once hired go through a course that is structured like a mini-MBA which covers coreanalytical skills like econometrics, structured problem solving and introduction to various domains.“There is paucity in analytical talent and we want to fill that gap,” says Mu Sigma’s Dhingra. Luckily for them, India has a large pool of engineering candidates of 350,000, around five times more than the US, which will further drive the outsourcing of analytical capabilities.
Finally, companies may not be able to keep pace with the growth in data though their ability to leverage it has definitely increased dramatically thanks to analytics and Big Data. “Data will always outpace your ability to control or analyse it,” says Wipro’s Sanjay. “But the good thing is the rate at which we can leverage the data has been increasing and that puts companies at an advantage.” Analytics and Big Data will continue to be much in demand as they will form the basis on how firms build their competitive advantage in the future.
“Consumption of data sciences or analytics, rather than the creation of analytics, will create the competitive advantage”, says Mu Sigma’s Rajaram. Many feel that what we are seeing today is just the tip of an iceberg and there is more to come. “Analytics is definitely at a tipping point,” says Cognizant’s Veeraraghavan. “It looks set to explode in the next 2-3 years.” More power to all the math geeks around the world. They just got hotter than Brad Pitt.
