SigmaWay Blog

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

There are three modes of services available for customers, namely e-mail, live chat, and phone. Each of them has their own positives and negatives. E-mails are cheapest,\ and extremely useful for connecting globally. If solutions are not required immediately this can be the best platform with clear and written solutions, which are transferable. Customers do not have to deal with foreign accents or “on hold” waiting. Customers feel their importance and in this way a number of customers can be handled simultaneously. On the other side phone calls are better for people who want immediate support and are less technological forward. They get the solutions immediately and feel that their business is being appreciated. A helpful voice can calm and assure customers. Internet connection is not required. The type of medium selected depends on the type of client one faces. Usually sue to the fact that there can be multiple types of customers providing different forms of consumer support mediums is usually the best solution. Read more at:http://ezinearticles.com/?Email,-Phone-Or-Live-Chat-Support---How-to-Best-Serve-Your-Customers&id=3518663

It will take several years for banks to implement the necessary changes to become customer-centric. They need to assess their operations, identify what needs to change, and then implement the changes and the systems to support them. After the 2008 crisis, customers have eschewed “too big to fail” institutions in favor of local banks. Customers realize they have multiple banking options, often selecting different institutions for different services. A customer with a credit card from a multinational may keep savings and checking accounts locally. Attracted by competitive interest rates, the same customer may apply for a loan or mortgage with a regional bank – or even an online institution.To increase their share of wallet with customers, banks must address three fundamental areas: organization, processes and technology. However, once they crack the code on how to deliver a better experience to customers, they will position themselves to seize phenomenal opportunities to engage with customers. Ultimately, that translates to higher revenues. To know more on why banks should embrace a customer-centric model go to:

http://www.banktech.com/business-intelligence/banks-must-embrace-a-customer-centric-mo/240166955 .

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  .

Customer satisfaction is an important necessity for any organization to retain customers. Few basic rules of customer service includes firstly, integrity, the service provider should be honest and commit to the mistakes and also communicate ideas to correct such follies and such mistakes in future. Secondly, there is response time. It is essential that the customer service provider is able to explain the mistakes of the company through one to one communication or through e-mails in case of large customer circle in an effective way. Thirdly, there is realistic expectation. It is necessary that the consumers are satisfied with company's promises and delivery to retain customers in the long run. Thus, under promise and over deliver is the best policy. Fourthly we have respect. For customer satisfaction it is very important to respect and appreciate them and uphold their point of view. Hence these basic rules should be followed by any business house. Read more at :http://managementhelp.org/blogs/customer-service/2010/04/12/customer-service-basics/  

Service level, abandoned calls, productivity indicators, quality indicators, customer service are some of the key metrics typically used by a call center to measure data for customer complaints. For many businesses, retaining existing customers are cheaper than acquiring new customers. Customers become repeat purchasers usually because they believe that the services they buy are good value for money and the service they receive when contacting the firm is of optimal standard. Read more at:http://www.lieberandassociates.com/800_Number_Measurement_Metrics_Inbound_Customer_Service_Orders_Sales_Statistics.shtml 

The ongoing World Cup is expected to see most Social Media mentions of any sporting event. Since June 2013, there have been 19 million social mentions including the phrase “World Cup”! Soccer is the most popular sport in earth and social media is the creamiest option for fan & follower interaction and debate. However, for business houses, it is also the right time to explore this hype to turn the tides in their favour. With proper social media strategy, this world cup fever can help you reach 230 countries! According to FIFA secretary general Jerome Valcke, FIFA will earn $4bn (£2.3bn) in revenue from this tournament – ten per cent more than last time. About $1.4bn (£837m) will come from sponsorship deals, the balance from TV rights. Much of that increased investment will go into content and social media. "They will certainly continue to use conventional paid-for media, but owned and earned media are occupying a larger share of the communications mix as brands increasingly become publishers in their own right during major cultural moments," says Martin. The brands are at war! Brands like Nike, pepsi, Adidas all are trying to bank on this social media interaction Tsunami and beat competitors. While brands have long been publishers in one way or another, the latest iteration of brand publishing using social media to engage and magnify the content is really only now coming to maturity. There is even a tenuous strain of thought that suggests the 2014 World Cup could mark the high tide of this approach. Nancy Smith, President and CEO of New York-based marketing consultancy Analytic Partners, says her company has examined billions of dollars of marketing spend and has come to very different conclusions. She said that “Budgets are increasingly stretched and social media are a very good way to extend those dollars. It is clear that owned and earned media are a must. We have found that owned and earned engagement has boosted incremental sales from paid marketing by two to six per cent. The average is about five per cent, although we have seen effects from social media for smaller niche companies as high as four hundred per cent”. The question remains open: Brands are going full throttle on their online channels for this footy frenzy but could the 20th World Cup be the high watermark for social media? To read more, visit the following link:

http://www.prweek.com/article/1297965/20th-world-cup-social-media-strategies-brand-war-rooms

Text mining, which is sometimes referred to "text analytics", is one way to make qualitative or "unstructured" data usable by a computer. Also known as text data mining, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis etc. Text analysis involves information retrieval, analysis to study word frequency distributions, pattern recognition, tagging, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The main goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. To read more about text mining: http://www.scientificcomputing.com/blogs/2014/01/text-mining-next-data-frontier.

Today's big data and analytics efforts bring welcome relief to banks, insurance companies, healthcare agencies, non-profits, and other organizations that have habitually struggled with finding the most profitable customers and then selling to them. A new set of analytics reports can move these companies forward in connecting with their best customers. According to a research by Fractal Analytics, banking users are struggling in an industry where 40% of cardholders are inactive and 60% are unprofitable. Banks want to increase spending in its existing credit cardholder base, so that it can implement customer analytics framework that was once targeted at improving first-hand understanding of these customers' needs. Once the bank understood who its most profitable customers were, it developed a "genomic" understanding of how these customers spent their money and found that insurance and food expenses were among the leading "spend" categories. This enabled banks to plan and target promotions built around these major spend areas. By doing so, banks increased its value per customer while decreasing expenses on marketing campaigns, likely because the campaigns were better targeted. To know more about this go to: http://www.techrepublic.com/article/genomic-analytics-build-sales-by-finding-your-most-profitable-customers/ .

Mobile phones serve a dual purpose in the context of Big Data. Each mobile phone, non-smart phones inclusive, creates numerous types of data every day. These include call detail records, SMS data, and geo-location data. In case of smartphones, such devices also generate log data via the use of mobile applications, financial transaction data associated with mobile banking and shopping, and social media data from updates to Facebook, Twitter and other social networks. The volume of portable information and the velocity at which it is made is just going to build as both the worldwide population and cell phone infiltration rates ascent, and the utilization of online networking expands. When investigated viably, this information can give knowledge on client opinion, conduct and even physical development designs. Because of the sheer number of cell phones being used, Big Data specialists can tap versatile Big Data examination to better see such patterns cross over large population and sub-portions of clients to enhance engagement strategies and improve the conveyance of administrations. It gets to be especially valuable for examination purposes when joined together with outside information sources, for example, climate information and investment information, which permit experts to relate macro-level patterns to focused on sub-portions of clients. To read more: http://wikibon.org/blog/the-dual-role-of-mobile-devices-for-big-data/

Big models and big data have long been a feature of weather and climate modelling. Computer-generated global weather forecasts are initialized from millions of diverse observations from satellites, weather balloons, surface weather stations, ships and buoys. Data assimilation, the procedure of ideally mixing these perceptions into the estimate model, is the most computationally difficult part of making a worldwide conjecture, and is a basic component of forecast skill. The international climate modelling community has evolved interesting infrastructure and social institutions that enable a diverse community of interested users to obtain standardized results from leading climate models developed around the world, to capture aspects of climate modelling certainty and uncertainty and help inform decision-makers and the interested public.

Past the thriving information administrations industry, weather has huge monetary and well-being ramifications. Weather Analytics, an organization that gives atmosphere information, evaluates that climate affects more than 33% of overall GDP, influencing the farming, tourism, angling, amusement, and air transport commercial enterprises, to name simply a few. Dubious climate conditions likewise affect little entrepreneurs. Moreover, public safety is of vital concern when officials aim to understand the impact of extreme weather events such as hurricanes, tsunamis, or wildfires. To know more about this aspect go through Per Nyberg (Senior Director of Business Development at Cray)’s article link: http://www.informationweek.com/big-data/big-data-analytics/3-ways-big-data-supercomputing-change-weather-forecasting/a/d-id/1269439

The supply chain is a great place to use analytical tools to look for competitive advantage, because of its complex nature and also because of the prominent role supply chain plays in a company's cost structure and profitability. Data analytics is the science of examining raw data and drawing conclusions about information. It is used by many business houses to facilitate better business decisions and verify or disprove existing models or theories. Relying on traditional supply chain execution systems is becoming increasingly more difficult, with a mix of global operating systems, pricing pressure and increasing customer expectations. There are also recent economic impacts such as rising fuel costs, global recession, supplier bases that have shrunk or moved off shore, as well as increased competition from low cost outsourcers. All these challenges potentially create waste in the supply chain that is where data analytics steps in. To know more about the role of data analytics in supply chain visit:http://www.industryweek.com/blog/supply-chain-analytics-what-it-and-why-it-so-important .

At this point when individuals consider saving rare species, they consider remote jungles, researchers and individuals anchoring themselves to trees. The stereotyped thought is that creatures in the wild are extremely hard to track and that the main way that individuals do this is through a basic following framework with a little specimen making presumptions for the more extensive group. Big Data and the complexities of data analysis could not be further from this, with the collection of massive data sets combined with complex predictive models and algorithms creating insights. The idea that enough data could even be collected to make a useful analysis is hard to imagine.  However, this has changed as of late as HP have collaborated with Conservation International (CI) to make Earth Insights. This system has been intended to give an early cautioning framework for creature numbers amongst jeopardized species over the world. Through the utilization of cameras and atmosphere sensors, the framework can gather information from around 1000 of these gadgets and use it to group data on population numbers. To know more about this aspect go through Dan Worth (news editor of V3)’s article link:http://www.v3.co.uk/v3-uk/news/2318103/hp-big-data-tools-help-wildlife-charity-save-the-planet 

In recent times, the quickest and the easiest way to reach out to the huge market is through paid search marketing like advertising on Google AdWords or through other engines. Research shows that almost 75% of the North American population remain online. Marketing Analytics allows you to monitor campaigns and their respective outcomes, enabling each dollar to be spent as effectively as possible. According to a study in 2008 by the Lenskold Group "companies making improvements in their measurement and ROI capabilities were more likely to report outgrowing competitors and a higher level of effectiveness and efficiency in their marketing". In search marketing, one of the most powerful marketing performance metrics comes in the form of keywords. It is the keyword data contained within each click which can be utilized to inform and optimize business processes, monitor industry trends, provide customer support and identify the product design. A successful online marketing strategy relies on a winning AdWords campaign. The strength of your AdWords campaigns will dictate how well you rank in Google; without a decent ranking, your site will never be seen by prospective clients. As was published by E-Consultancy Research, it's generally well known that "a paid search campaign will add prestige and credibility to an organization. To know more about marketing analytics go to: http://www.wordstream.com/marketing-analytics .

Fraudsters are more competent, better made, and creatively excellent than whatever possible time in the later past. Their adulteration arrangements include complex frameworks of individuals, records, and events. The evidence for these schemes may exist on multiple systems, incorporate various data sorts, and deliberately represent hidden activity. So an analyst has abundant investigative focuses on these frameworks with no true approach to join data or results. To prevent and uncover deception, one needs a solution that is more exceptional and advanced than hoaxers. A basic venture in fraud detection analytics is visualizing the patterns in your data between people, places, frameworks, and events. These data mining and profound analysis capabilities provide more context and better information, enabling more accurate data segmentation and data labelling, which further improves pattern recognition. To read more about it: http://www.21ct.com/solutions/fraud-detection-analytics/.

When Kolkata Knight Riders (KKR) clinched their IPL victory, they must have been thanking the technology that powered some of the decisions right from team selection strategy to competitive analysis. The SAP Game Analytics solution helped KKR to analyze the strengths and weaknesses of each player competing in the IPL, and also helped KKR increase team readiness and performance against their opponents. SAP HANA based platforms - SAP Auction Analytics, SAP Game Analytics, and SAP Lumira enabled KKR to evaluate players during the auction, derive post-game analytics following each of the team's games, and drive fan engagement respectively. Read more about this aspect at:http://www.informationweek.in/informationweek/news-analysis/296068/analytics-helped-kolkata-knight-riders-win-ipl-2014

Professional sports organizations are multi-million dollar enterprises with millions of dollars spent on a single decision. With this amount of capital at stake, just one bad or misguided decision has the potential of setting an organization back by several years. With such a large amount of risk involved it requires a critical need to make good decisions, and hence it’s an attractive environment for data mining applications.

Sports Data Mining has experienced rapid growth in recent years. The task is not how to collect the data, but what data should be collected and how to make the best use of it. From players improving their game-time performance using video analysis techniques, to scouts using statistical analysis and projection techniques to identify what talent will provide the biggest impact, data mining is quickly becoming an integral part of the sports decision making landscape where managers and coaches using machine learning and simulation techniques can find optimal strategies for an entire upcoming season. By finding the right ways to make sense of data and turning it into actionable knowledge, sports organizations have the potential to secure a competitive advantage over their peers. To read more how it has been used: http://www.ukessays.com/essays/psychology/data-mining-in-sports-in-the-past-few-years-psychology-essay.php 

Companies can maintain customer support department to answer customer doubts and problems in social networking websites like Twitter and Facebook. Answers to customers should be quick and within few hours of posting. We can implement softwares that can be synchronized with Facebook, to manage private messages and posts, and to keep track of customer service questions & problems and also to serve requests. Some of these softwares are Sales Force Desk, Happy Fox etc. One can use the Facebook page to spread the problems associated with that particular product and other product issues. Social space provides a platform to make people aware of the product defects. It will demonstrate the business house's willingness to take responsibility for mistakes making fans aware of the present business scenario and maintaining transparency. Some brands often offer their fans monthly exclusive deals and some host frequent giveaways. To know about this aspect follow Jim Belosic (CEO of ShortStack)'s link: http://www.socialmediaexaminer.com/social-customer-service-facebook/ .

In today's era of competitive world, it is less expensive to provide "great" service over "good service". The customer service company relies upon listening, analyzing and acting on it. Many organizations underestimate the impact and damage of various customer issues. This usually occurs because they are using data only from a single customer channel or taking into account their vocal customers only, limiting data to a single customer channel. If your organization works in silos, you may not have the complete picture especially if customers complain across multiple channels. This means if a company only counts the cases that complain on a specific channel, your view is limited to a small subset. Even if your organization counts all the complaints across all channels on a specific issue, it is likely that you'll still see a small percentage of the cases where customers are encountering these issues, because   majority of customers affected don't complain to the organization directly. In order to estimate more accurately and prioritize the impact of each customer concern organizations follows the following process. To know more:

http://www.informationweek.in/informationweek/news-analysis/271903/steps-measure-impact-silent-customers/