SigmaWay Blog

In the recent past, most companies have recognized the bottom-line impact to be gained through effective pricing. Tapping the full promise of pricing requires an infrastructure to drive real and sustained pricing performance. With such a foundation, a company can establish and strengthen pricing activities by creating deliberate decision processes, a specialized pricing organization, mechanisms that appropriately measure and reward pricing excellence, and vigorous support tools and systems.

A pricing infrastructure can be difficult and costly to create. It requires investing appropriately, empowering the right people, articulating clear targets and goals, and managing risk. Yet the benefits of realizing true pricing excellence are worthwhile: a one-percentage-point improvement in average price of goods and services leads to an 8.7 percent increase in operating profits for the typical Global 1200 company. 

Every company should have a set of pricing metrics that measure the financial and operational health of pricing across the business. These metrics may include simple data, such as the average selling price, discount, and margin for key products; operational data, including the number of pricing exceptions and win/loss percentages; and special measures to track the progress and impact of specific pricing initiatives. While the manager of a single product line may see metrics only for that, the general manager of a business unit sees those same metrics across the operation and can drill down to the level of individual products to understand the root causes of pricing performance.

Without uncovering and acting on the opportunities big data presents, many companies are leaving millions of dollars of profit on the table. The secret to increasing profit margins is to harness big data to find the best price at the product—not category—level, rather than drown in the numbers flood. To read more visit: http://www.mckinsey.com/insights/marketing_sales/using_big_data_to_make_better_pricing_decisions

The two important ways to redirect a user agent client-side are meta refresh and JavaScript redirect. Either method of client-side redirection has potential for causing significant issues with both analytics and SEO in spite of having flexibility in choosing whether or not to redirect after the page is rendered and portability in having code. Essentially there are two issues of client-side redirection with Analytics: 1) page view bloat 2) unclean referrer. The first issue occurs when the client-side redirect fires after the analytics code has already discovered the page view. The second issue, unclean referrer, refers that the final result has an unclear referrer. Regarding SEO, the problems are more extreme. There are at least two key issues: 1) page rank devaluation 2) content mismatch. The use of a meta in short explains that most crawlers support a meta refresh of 0 and treat it as a redirection of server-side, and a JavaScript redirect is simply ignored as crawlers don't read JavaScript.Read more at: 

http://thesimplesynthesis.com/post/effect-of-client-side-redirects-on-analytics-and-seo-meta-refresh-and-javascript

To boost manufacturing and entrepreneurship in the country, Flipkart (Indian e-commerce company)  announced its tie up with Small and Medium Enterprise (SMEs) promotion bodies. Strong data analytics that forms the base of e-marketplaces will help the sellers to improve their products easily and attract more customers. According to the Executive Director of NCDPD (National Center for Design and Product Development), analytics and market intelligence provided by Flipkart will assist NCDPD in improving their products and R&D and also enable the craftsmen to create better saleable products. The objective of this tie-up is to continue helping entrepreneurs to create products according to buyer requirements and grow significantly by expanding their business so that they may become manufacturers not only at a local but also at a national level. Read more at: http://articles.economictimes.indiatimes.com/2014-06-18/news/50678912_1_data-analytics-flipkart-market-data.

According to Tali Yahalom, consumers want to be treated indivually and not like any other consumer you deal with every day. Companies should realize that changing rules once in a while is less costly than losing a customer. Moreover, social mediums like Facebook, Twitter, and Yelp have become extremely useful in tracking one's reputation online. One should be considerate towards a customer's emotional state of frustration and listen calmly to him/her instead of becoming defensive. Nowadays, companies have tools for collecting consumer feedbacks and suggestions which are extremely important. A company should be optimistic towards feedbacks and try and learn from it. To know more, follow this link: http://www.inc.com/guides/2010/12/the-new-rules-of-handling-customer-complaints.html

Enterprises hire  lots of people, but in a world where change happens fast and often, they can’t anticipate every need. One solution is to hire contractors for new or temporary projects. But that involves recruiters finding people—but they won’t already know the company’s systems and culture. A better way is to find someone in-house, but in a company with hundreds of employees, that can be difficult—unless you let big data do the heavy lifting.

Progressively, organizations are doing simply that: Big Data is helping them match positions to existing representatives' profiles. Organizations are utilizing HR programs that change the representative profile from ignored comfort into an effective instrument giving them a chance to discover abilities that don't match a specialist's set of expectations or even their delineation toward oneself. They work by scouring social-media profiles, forums, blogs and comments across the Internet, to unearth talent that’s under their own roof—but they just didn’t know it. For a detailed article on this topic visit: http://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/.

In India, banking industry plays an important role in ensuring the sustainable growth through more credit flows and reaching out to more people by financial inclusion. Thus, the retail banking systems has to handle several issues like customer identity, managing credit risk, fraud detection and prevention, customer relationship maintaining etc. Data analytics helps in this way by ensuring organizations in achieving their growth objectives. It also enables them to manage and automate large volumes of day-to-day decisions. Data analytics draws inferences from large amount of data and use these inferences within banking processes transparently. Data analytics (DA) has an effective use in the following stages of customer lifecycle- Customer targeting -DA helps to develop right offer to right customer by understanding their behavior. Customer Acquisition- DA benefits banks by acquiring profitable customers at low cost and also helps to understand the affordability status of customers. Customer Management- DA helps banks to ensure responsible lending and to undertake effective risk management measures which in turn provide better customer services. Collection- Banks can use DA to reduce delinquency, manage cost of collections, and reduce wasted time by knowing the right value of the customers according to their worth retaining for the future. Read more at:http://www.informationweek.in/informationweek/perspective/287791/indian-banks-improve-customer-lifecycle-analytics?utm_source=rss&utm_medium=rss&utm_campaign=how-indian-banks-can-improve-the-customer-lifecycle-by-using-data-analytics

Big data is a popular term used to describe the exponential growth of structured and unstructured data. Despite of such an understanding by business organizations, according to a survey, second annual analytic usage, trends, and future initiatives report, 75 percent of businesses have yet to reach big data production. However, just 12.6 percent of respondents said their company has completed several big data projects that are now in production. Drew Rockwell (Lavastorm CEO)said “It’s organizations that go the next level by removing complexities from the analytics process and empowering others in the organization, namely business analysts, who are going to be able to turn data insights into actionable business enhancements for long-term success.” According to another survey, one concern for executives who are experimenting with big data is a shortage of expertise in that field. With a lack of big data skills, organizations are reluctant to take the plunge since a clear ROI immediately available. Without being open to a little disruption, organizations will have a much more difficult time adapting to changing consumer mindsets. Businesses should conduct their own research and see what options best fit their needs. Read more at: 

http://www.pymnts.com/in-depth/2014/is-big-data-just-a-big-problem/#.U6KBPZS1Yn7

An investment banking firm  has developed a ‘stochastic model’ its predictions to generate distribution of results for the each of the 64 matches to be played during WC.The predictive model is part of the the World Cup and Economics 2014. Stochastic modeling is used to forecast outcomes, even in the field of sports. This mathematical model predicts the probability of outcomes. According to the model, predicting the outcome of the WC is based on predictions for each match on “regression analysis”. This analysis used 14,000 observations to approximate the coefficients of its model. The dependent variable is the number of goals attained by each team during a match. Based on this analysis Brazil fares as the winning team. The next are Argentina and Germany, but these two teams have inferior probability. Read more at:

http://blog.arekibo.com/world-cup-2014-the-use-of-analytics-to-predict-the-winning-team/

Rapid Miner had an existing partnership with Radoop - an analytics company that optimizes the big data platform known as Hadoop. Now, after successfully acquiring Radoop, Rapid Miner will be able to provide access to many other Hadoop features to its customers which will in turn build a larger presence in the Hadoop ecosystem for RapidMiner. The acquisition also brings partnerships with Hadoop platforms Cloudera and Hortonworks, and adds 20 new clients to RapidMiner’s customer base. The powerful combination of RapidMiner and Radoop will allow applications of advanced analytics to big data. Apart from providing scripting and advanced predictive analytics for experts, it will also help non-technical people to access, analyze, and visualize big data.

To read more, Visit the following link:

http://betaboston.com/news/2014/06/17/rapidminer-acquires-big-data-analytics-company-radoop/

Business intelligence is certainly a good area to be in right now. It is expanding faster than any other area of the enterprise application landscape. According to Gartner (an American information technology research and advisory firm), the business intelligence market (including data warehouses and CRM analytics) is growing nine percent per year. While it was worth $57 billion at the end of 2010, it will surge to $81 billion by 2014 and as high as $136 billion by 2020. Because analytics is the "combustion engine of business," organizations invest in business intelligence even when times are tough. Gartner predicts the next big phase for business intelligence will be a move toward more simulation and extrapolation. Gartner surveys show most users still focus on measurement of the past, with only 13 percent of users making extensive use of predictive analytics. Less than 3 percent use prescriptive capabilities such as decision/mathematical modelling, simulation and optimization. "This trend is changing as organizations express an interest in increasing their use of advanced styles of analytics," said Sallam (analyst at Gartner).To know more go to:

http://www.enterpriseappstoday.com/business-intelligence/gartner-taps-predictive-analytics-as-next-big-business-intelligence-trend.html

Online social networks give vital information. If someone posts something on Twitter, Facebook, they generate a digital footprint. If someone reads it, or watches a YouTube video, they add to the data trail. We can analyze it to take business decisions. The information that we look for depends on business goals. Some companies use social media to increase sales, some concentrate on brand consciousness, others are focused on brand trust, or increasing customer satisfaction. An analyst records keywords which customers are discussing, use them into status updates or tweets to be more relevant to customers. They check which messages attract readers most so that they can reach new customers. There are different social media analytics tools. Some are Hoot Suite; span multiple channels, Twitter etc. Some tools include workflow; they allow social media managers to connect information to others. Customer feedback raises sentiment analysis. Read more at: 

http://www.theguardian.com/technology/2013/jun/10/effective-social-media-analytics

Big Data does not necessarily mean good data. And that, as increasing number of experts are saying more insistently, Big Data does not automatically yield good analytics. As everyone realizes, bad data equates to bad intelligence, which equates to bad decision-making and thus equates to bad things happening in your business. If the data is incomplete, out of context or otherwise contaminated, it can lead to decisions that could undermine the competitiveness of an enterprise or damage the personal lives of individuals. So how do we detect that? Firstly it's important to understand where your data originates. Has it been captured by your own work force? What measures were put in place to ensure that the very best job has been done and that the data being captured lives up to expectations? What are the requirements for the data your business needs and uses daily? Do you enhance your data from other sources (external or internal)? An example of how out of context data can lead to distorted conclusions comes from Harvard University professor Gary King, director of the Institute for Quantitative Social Science who was attempting to use Twitter feeds and other social media posts to predict the U.S. unemployment rate, by monitoring key words like "jobs," "unemployment," and "classifieds." To read more about the episode visit: http://www.infoworld.com/d/business-intelligence/big-data-without-good-analytics-can-lead-bad-decisions-225608.

Government today is suspected to be unworthy by most of us. And it is totally justified because of the increasing corruption and selfishness creeping in the society. US president Barack Obama has agreed to keep full transparency between him and the people and prove his credibility. He has signed an 'Open Data Policy' to create an open government. The idea behind is simple. All the government departments are required to update their expenses on the website USASpending.gov. People have full access to this website and can mine the data available for finding any expense that is unnecessary or excessive. Only some areas covering the expenditure of military and covert operations are unlikely to be revealed. Read more at: http://analytics.theiegroup.com/article/538f58943723a83b6f000205/Obamas-Open-Data-Policy

Big data is extending the range of data types in banks that can be covered beyond those common transaction data, and it helps to address problems. Some important areas in banking like fraud analytics, customer analytics and web analytics are also enhanced by big data. Today's improved technologies and frameworks enable banks to get customer data, graph data and geo-location data easily from customers, other banking channels etc. which in turn yields significant insights that can be used in customer marketing, risk management and infrastructure optimization. Big data projects are beneficial as they enhance areas like web security, compliance checks and customer analytics and thus cause the banks to make relevant investments in it. Banks need to know and understand the characteristics of the data they need and need to capture more information beyond risk and marketing data. If the users have sound idea of the nature of the available data, their strategies of making a rough analysis and then use the results to guide them in refining the analysis, will be more effective. This approach helps banks to analyze more data and gain insights that were previously difficult to achieve, without changing the current analytical infrastructure of the banks. Read more about this in Jaroslaw Knapik (Senior Analyst, Financial Services Technology)'s article link:http://www.cloudcomputing-news.net/news/2014/jun/16/big-data-set-to-boost-the-effectiveness-of-analytics-in-banking/

The biggest fallout of the big data revolution -- where every type of business gathers and analyzes data -- is a massive human resources shortage. Across the globe, thousands of data analytics jobs are not filled up because of a shortage of qualified manpower. Data analytics is not coding work but thinking work, described Dinesh Kumar, a professor of quantitative methods and information systems at the Indian Institute of Management in Bangalore. "The potential is huge, but we are behind in creating a talent pool," he said. Quality is a worry, and companies are finding it difficult to recruit top-class people, Kumar said. Data analytics as a job discipline became mainstream almost a decade ago, and the demand for trained professionals has been growing steadily since. Given India's reputation for the availability of professionals in varied disciplines at reasonable costs, global banks and financial services firms were the first to migrate their analytics work to India, followed by pharma and life sciences companies. Global retailers, consumer firms, logistics firms, consultancies, and engineering firms have all begun routing their data analytics work to IT services providers and specialized analytics service providers in India. In India, which has long been a hub for outsourced technology services work, the scarcity of analytics talent is particularly acute, as global companies send increasing numbers of data-related tasks to the country. To know more about this go to:

http://www.techrepublic.com/article/indias-high-demand-for-big-data-workers-contrasts-with-scarcity-of-skilled-talent/ .

According to Gartner, more than 80 percent organizations will fail to develop a consolidated data security policy which will result in potential non-compliance, security breaches and financial liabilities. As big data is transforming the way of storing, processing and accessing data, so to avoid uncoordinated data security policies and security chaos Chief Information Security Officers (CISOs) need to develop and manage an enterprise data security policy which will define data residency requirements, business needs, stakeholder responsibilities, data process needs and security controls. At first they need to evaluate current implementations of Data-centric Audit and protection solutions against data security policies, then they need to find out the gaps in the current implementation and finally review the risks with business stakeholders against those potential DCAP solutions. To do so, CISOs also need to build a good partnership with those stakeholders to develop a new management structure. Read more at:http://www.informationweek.in/informationweek/press-releases/296090/-centric-security-focus-gartner/

Big-data analytics impacts any organization economically, but often data scientists hope for benefits.The reality of where and how data analytics can improve performance varies across industries. Customer-facing activities- the greatest opportunities lie in telecommunications. Here, companies benefit by focusing on analytics models which optimize pricing of services, maximize marketing spending by predicting on where product promotions will be most effective, and identify ways for withholding customers. Internal applications- In industries, like transportation services, models focus on process efficiencies-optimizing routes. Hybrid applications- Some industries need both. Retailers use data to influence next-product-to-buy decisions and to choose the best location for new stores or to catch flows of products through supply chains. Companies operate along two horizons: capturing quick wins to build momentum while keeping sight of longer-term. Open data- swelling reservoirs of external data. Models are often improved combining these data with the existing ones for better business outcomes.. Read more at: 

http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution

There are significant opportunities to make use of big data techniques. Unlike technology and consumer retail sectors in which advanced analytics has been implemented, it can also be used in other industries like insurance, health care, banking and public sector. In insurance, data can be aggregated from public sources and specialist data providers, allowing companies to better target customers and frame policies accordingly. Banks are increasingly using big data to generate a much deeper view of their customers, combining the information collected from all of customer's interactions with bank with selective third-party data like paying patterns for mobile phone bills, tracking trends on social media platforms such as Twitter. Read more about this aspect in Dominic Barton (global managing director at McKinsey & Co.)'s article link:http://blogs.wsj.com/experts/2014/03/28/sectors-where-big-data-could-make-an-impact/?KEYWORDS=analytics 

For the very first time in this computing era, it is made possible for machines to learn from experience and penetrate through the complexity of the data and identify associations between them, collectively known as cognitive analytics. This innovation works in a similar manner as of human brains. It processes information, draws conclusion and codifies behaviour and experience into learning. Cognitive analytics has the ability to process and understand exploding volumes of data in real time including data that may contain wide variations of format, structure, and quality. Instead of depending on predefined rules and structured queries to mine answers, cognitive analytics relies on systems that draw from a wide variety of potentially relevant information and connections to generate hypotheses. This process differs from traditional analysis in the way that more data is fed into a machine learning system, the system learns, which results in higher-quality insights and more accurate hypotheses. Read more at:http://deloitte.wsj.com/cio/2014/05/13/human-brain-inspires-new-cognitive-analytics/?KEYWORDS=analytics

Today, companies have the option of collecting information about consumers which was never available in the past. This information is collected through internal sources, such as company websites and sales records, and external ones, such as social media, smartphones and tablets. This vast amount of information on consumers is increasingly referred to as big data. When a consumer visits a website for the first time, cookies are sometimes uploaded on his browser containing a unique ID, making it possible for the company to identify him during his next visits. Once identified, it will be possible to link the consumer to all information the company stored about his profile, which makes personalized marketing possible. Today, because of prescriptive analytics models embedded into their operational systems, websites and apps can analyze consumer information in real time in order to offer personalized travel options instantly. In the next few years, we will witness a gradual move to 1-to-1 marketing in the online travel category, with each consumer treated in a different way in terms of the whole marketing mix. To know more about this visit:

http://blog.euromonitor.com/2014/05/big-data-unique-ids-and-prescriptive-analytics-revolutionising-online-travel-marketing-part-1.html  .