SigmaWay Blog

78% of the companies have dedicated social media teams and only 26% of the companies have social media as the part of their marketing strategy. This shows that, they don't recognize the value of social media marketing and they don't trust social media data for decision making. Companies using social media for marketing or promotion mainly use two strategies for monitoring:

• Restrict to hash tag mentions: A strategy that leads to high precision at the cost of many missed mentions.
• Unrestricted keyword search: An approach that could generate numerous false positives.

But these strategies lead to false results. Now the question arises how to monitor social media accurately?

Rohini Srihari (Chief Scientist at SmartFocus and a contributor to Econsultancy) in her article “how reliable are social analytics?” talked about several ways for monitoring social media accurately. Some of them are:

• For comparison across brands and different content sources, you should consider the various features like share of voice, sentiments, sudden spikes etc.
• Sentimental analysis is best for analyzing trends like change in public perception.
• For location based analytics, a researcher should ensure that a sufficient number of samples have been obtained.

The Retail industry is very competitive. Retailers need to apply analytics to analyze consumer behavior and retain them. Predictive Analytics help retailers to predict the response of customers regarding new offer, discount or product. But barrier of culture and stage fright, stop them to apply big data analytics.

Leslie Dinham (Teredata) in her article "two ways retailers are overcoming barriers to analytics adoption," talks about solutions to these barriers or adoption blockers. They are:

Barrier 1# Culture is the culprit: Employees get rigid due to working in the same culture, performing same job or duties. They don’t want to change their decision making process and roles. It becomes difficult to apply data analytics in this culture. The solution to this problem could be informing employee about the benefits of using data analytics and provide necessary training.

Barrier 2# Stage Fright: Many times, retailers won’t get success while applying analytics in their organization because they won’t able to choose the right team, tool or technology, won’t able to integrate new analytical capabilities into operations or the culture of the organization is not innovative. Paying attention while applying analytics in these things can help organizations to successfully apply analytics.

To know more about these barriers and solution to them, read an article at: http://www.forbes.com/sites/teradata/2015/05/13/two-ways-retailers-are-overcoming-barriers-to-analytics-adoption/

In today's present scenario, retailers are facing threat from online stores. There is a fall in profit percentage. But to deal with this, retailers are increasing their customer's database as they can apply analytics on the data, predict and track customer behavior.

 In this context, the Future group's plan is to increase the database of customers so that they can fight ecommerce companies.

According to Punit Soni (CPO at online marketplace Flipkart), “Capturing a huge swath of pricing and things of the largest economies of the world, and becoming the default marketplace is not easily doable for offline players”.

To know more about Future Group strategy and analytics in retail, read an article link “Future Group banking on analytics to battle e-commerce companies” by Jayadevan PK (ET Bureau): http://articles.economictimes.indiatimes.com/2015-05-08/news/61947503_1_rakesh-biyani-future-group-data-analytics

The retail industry is B2C industry. In B2C industry, forecasting and planning future demand and supply is a very important function to improve operation's efficiency. But, consumer behavior is very unpredictable. To analyze this unpredictable behavior, retail stores need to analyze big data. In Consumer Goods Analytics Summit in Chicago, suggestions on applying Big Data Analytics in Retail Industry were discussed. Let’s have a look on some of them:

·        By using big data analytics try to find out actual problem and their solution.

·        Apply analytics in every possible way from making sales report to multi-structured data to understand and improve customer service.

·        Always Interpret big data.

·        Recruit persons who understand the value of data analytics. 

According to Burton White (Vice President of the Industry Supply Chains at Chainalytics), for retailers and e-commerce firms, developing an effective and efficient supply chain strategy is challenging. To make last-mile delivery as their core supply chain strategy, they have to provide right inventory at the right time at the right place in the right form.
Some tips to be considered while developing a supply chain strategy are:

• Never lose sight of what actually matters to the customer
• Explore innovative approaches, like to bundle product shipments.
• Explore non-traditional distribution capabilities.
• Optimize transportation solutions to meet last-mile demands.
• Consider the inventory’s form, function and placement within your supply chain.
• Focus on returns management efficiency.

Read more at: http://www.industryweek.com/last-mile

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. According to Gartner, the advantage of Data lakes is: helps in addressing the old and new problem by providing the relevant set of data for analyzing the situation. Disadvantages are:

• Lack of data quality.
• Security and access control.
• Data Lake requires proper infrastructure.

But using purpose built cloud systems security, access control and scalability problem can be solved, but data quality is not good.
 To know more about Data Lake and its advantage and disadvantages, read an article Data Lakes: Emerging Pros and Cons by Joe Panettieri. Link: http://www.information-management.com/news/Big-Data-Lakes-Cloud-Computing-Analytics-10026889-1.html

For any business, demand forecasting is an important function. Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase in near future. It helps you to order inventory and arrange staff for fulfilling customers need.

According to Peter Daisyme (Co-founder of Hostt), some ways to make demand forecasting more accurate are:

• Use the right set of data for making decisions.
• Consider the variables like the seasonal trend, random trend, economic conditions, etc.
• Know your customers and business.
• Each year, you should refine your demand forecasting technique.
Read more at: http://www.entrepreneur.com/article/244823

According to Sujan Patel (Contributor), companies before analyzing big data, must understand the company's goals and mission. In a survey by Price Waterhouse Cooper, only 44% of companies feel that they have the right talent to capitalize big data. When any company chooses tool for data analytics, focus should be on team needs and solution and the team must know how to use that tool. Read more at: http://www.forbes.com/sites/sujanpatel/2015/04/22/how-fortune-500-companies-are-building-big-data-teams-and-how-startups-can-too/

According to Rick Frascona (senior content manager with MadValorem), digital marketing is one of the parts of real estate marketing strategy. Event-driven marketing and predictive analytics can lower costs of direct mail marketing. A Real estate agent can find customers who have a maximum probability of buying or selling houses with the help of predictive analytics.  According to the National Association of Realtors, 92% of homebuyers in 2014 used the internet to search for homes. Read more at: http://www.inman.com/2015/04/21/can-predictive-analytics-breathe-new-life-into-direct-mail-marketing/

According to a survey, the Indian fashion industry is likely to touch $77 billion in a next five year. To understand the changing customer behavior and have a competitive advantage, e-commerce firms and retailers can use analytics. In a research from Technopak, the share of apparels and lifestyle in e-commerce is likely to increase by 30% and e-commerce will contribute about 6% of apparel sales. Fashion industry faces a challenge of understanding customers want and require quick access to market dynamics.  E-Commerce in retail is increasing, and all channels, firms are looking for advanced analytics to understand consumer behavior. Read more at: http://www.business-standard.com/article/pti-stories/data-analytics-can-help-retailers-know-consumer-behaviour-115042000358_1.html

According to Collen Jones (CEO of Content Science and co-founder of ContentWRX), big data help to identify new market opportunities and customers. But, most companies are facing problem with analyzing big data. Therefore, before analyzing big data, an organization also needs to analyze small data. • Understand your situation by collecting and analyzing the data.
• Interpreting the data you collected in a clear and compelling way
• Searching what to do next?
Collected Data should be focused on content and customers and should be accurate and reliable. And while searching what to do next try to find opportunities and threats.
To know more read at: http://www.cmo.com.au/article/573077/thinking-big-data-marketing-get-small-data-right-first/

According to Mary Shacklett (president of Transworld Data), organizations need big data and analytics capabilities for immediately transferring big data into actionable decisions. According to a Gartner September 2014 report, there is an increase in investment in big data by 64% from 2013. Jeff Kelley (Big data analytics analyst from Wikibon), says that “customers expect personalization when they visit websites, so companies need to develop analytical capabilities and in the long term apply real-time will grow as Internet of Things.” Preventive maintenance analytics can be developed, if data on the Internet to thing can be analyzed. To know more about real time analytics and Internet of Things read on:  http://www.techrepublic.com/article/surge-in-real-time-big-data-and-iot-analytics-is-changing-corporate-thinking/

Punk in Punk Analytics means working fearless and having an approach to ‘do-it-yourself way’. In the last few years with the rise of new technologies, we entered into the era of punk-style analytics. Some characteristics of punk analytics are:

• No barriers for information search, as you can download easily and free of cost.
• Mistakes are part of the process, but we can overcome by using analytics software and by having familiarity with the data.
• Fast and to the point is good.
• Idea should be transparent and it should have less processed time.
• Punk Analytics are basically concentrating on the issues of the moment, as it is the starting point for active exploration.
•   Working collaboratively is important to achieve a common goal.
To know more about punk analytics follow the article link of James Richardson (Business Analytics Strategist at Qlik): http://www.itproportal.com/2015/04/19/rise-punk-analytics/

According to Vincent Weafer (Senior Vice President of Intel Security), analytical capabilities will help you to be ahead of your competitors. Predictive Analytics help you to analyze the future trends more accurately and help you to realize your threats and opportunities which affects budget, purchase and staffing decisions. For prediction, a large amount of data is required from a range of activities which organizations perform, historical events and third party intelligence and to make predictive analytics more effective, you need to build foundational abilities like real-time hunting, prioritization and scoping of security incidents in their environment. You need to analyze your stakeholders for blocking decisions. Read more at: http://www.darkreading.com/partner-perspectives/intel/predictive-analytics-the-future-is-now/a/d-id/1319956

 According to Dave Blanchard (Industryweek), customer’s demands for lower delivered costs are seen to be a big challenge. But, by using predictive analysis, we can identify patterns and predict future events. An organization using predictive analysis can make better decisions. George Prest (CEO of MHI) says that companies that continue to use traditional supply chain models will struggle in the future.  According to the MHI/Deloitte study, it was found that less than 25% of companies have adopted predictive analytics though that number is expected to climb to 70% over the next three to five years.

 Read more at: http://www.industryweek.com/supply-chain/predictive-analytics-let-manufacturers-see-more-clearly-their-supply-chains

Predictive analytics is a “business game changer” that will separate the winners from the losers, according to Forrester. The better a company is at predicting what will happen in the future, the better positioned they are to do something about it. While data scientists will do the heaviest analytic-related lifting at big enterprises, the improvements that have been made to predictive analytic (also called advanced analytic) applications enables regular business people and developers to partake of the predictive bounty. With so many companies coming into the foray of analytics services, today the users have more options to choose from keeping the cost-benefit & need-value trade-offs in mind.  RapidMiner offers a “rock solid” enterprise solution with more than 1,500 methods that address all stages of the analytics lifecycle and has among the tightest integration with the cloud, Forrester says. There are also other options like SAS, SPSS, KNIME, sap, oracle to name a few.

To read more, visit:

http://www.datanami.com/2015/04/07/predictive-analytics-now-in-reach-of-the-average-enterprise-forrester-says/

Investing in talent management software can assist HR professionals in gathering and analyzing the data of employees. Applying big data analytics to measure employee performance can help organization to find strength and weakness of their employees e.g. many companies use wearable tech to improve communication within its stores, to track employees when they're at work. Employee satisfaction surveys, team assessments, social media, exit and stay interviews etc. can help HR to predict employee's attrition. Conducting performance appraisals and 360-degree performance reviews can help HR in better understanding the effectiveness of their professional development efforts. Big data analytics can be an advantage while hiring employees. Read more at: http://www.entrepreneur.com/article/244247

The investment banks are now looking at how they can use big data to do what they do better, faster and more efficiently. Senior executives at the banks want to enhance how they use data to raise profitability, map out markets and company-wide exposures, and ultimately win more deals. Big data is also a fundamental element of risk-profiling for the banks, enabling data analysts to immediately assess the impact of the escalation in geopolitical risk on portfolios and their exposure to specific markets and asset classes. Specifically, banks have now built systems that will map out market-shaping past events in order to identify future patterns. There lies the requirement of big data talent!

The banks are actively recruiting big data and analytics specialists to fill two main, but significantly different roles: big data engineers and data scientists. Data Scientists are responsible for bridging the gap between data analytics and business decision-making, capable of translating complex data into key strategy insight, while Data Engineers typically come from a strong IT development or coding background and are responsible for designing data platforms and applications. The competition between banks and fund managers to hire big data specialists is heating up. Data scientists are expected to have sharp technical and quantitative skills. They are in highest demand and this is where the biggest skill shortage exists.

To read more, visit the link given below:

http://www.computerweekly.com/opinion/Investment-banks-recruit-for-the-rise-of-big-data-analytics

Cricket and Analytics are hot topics today. With the help of technology Analytics, the interest quotient of cricket has gone up exponentially. High definition telecasts, data visualisation have really made cricket exciting to watch nowadays. Vijay Sethi, VP and CIO at Hero MotoCorp Ltd, speaks about how he sees his favourite game today that comes with a generous dose of analytics. To read more, visit the following link:

http://ibnlive.in.com/news/analytics-in-cricket-is-a-technological-marvel-hero-motocorp-cio-vijay-sethi/536201-3.html

Google Analytics is one of the best tools one can use for the purpose of analyzing the audience. With the right insights, one can modify the approach, anticipate customer needs, and create a superior user experience. It is also possible to measure a tremendous diversity of data on your web users, and using that data, you’ll be able to make meaningful change to your branding and marketing strategies. The acquisition insights and behavioural insights will help you explore more details and trend understanding. This information is especially useful in determining which of your marketing strategies is the most effective, but it’s also useful for determining what types of people are visiting your site and why. For example, if you find that the majority of your users are finding your site through content you’ve syndicated on social media, you could double your content writing and syndication efforts to attract an even greater number of users.

To read more, please visit the following link:

http://www.forbes.com/sites/jaysondemers/2015/03/19/how-to-analyze-your-audience-in-google-analytics/