Businesses can get recognized by various methods. But we need to consider the effectiveness and results of these methods. Social media when combined with right content can do wonders. There must be a good starting point at first. Then we create funnel method using the blogs. First, we set the goals and then do continuous evaluation at a regular interval. We must ensure that our blog posts create an impact on people. Then only we must publish the blogs and create a marketing collateral. These posts must be shared among the various social media platforms. The idea behind the funnel strategy is that the content should be apt and the targeted audience should be attracted towards it. Read more at: https://www.entrepreneur.com/article/296052

Mobile app analytics is used by professionals today to track their app. But there are flaws which needs to be fixed. One flaw is that the app focuses mostly on quantitative analysis and there are other general flaws. One must add quantitative analysis to the workflow in order to complete the picture. This analysis is further divided into heatmaps and user sessions recordings. Touch heatmaps gather all the gestures a user does in the screen and it also sees where the users are trying to interact. User session recordings enable to see what users are doing. Combination of qualitative and quantitative analysis has potential in terms of workflows and app optimization. Read more at: http://online-behavior.com/analytics/app-workflows

Data is an important tool which manages a lot of important activities in the business. And this is further enhanced by artificial intelligence and machine learning and by ease of collecting and storing data. Managers rely too much on data for the guidance which abdicates their knowledge and experience. In a big data project the manager connects the internal and external team to collect and process the data in order to solve the problem. Data must be in usable form and the algorithms must identify statistically significant patterns. The results are then presented to the manager through different visualizations. The main problem is the managers are not good with data science and the scientists are not good with the businesses.  Read more at: https://hbr.org/2017/06/the-best-approach-to-decision-making-combines-data-and-managers-expertise

This article link describes how to generate business from social media for B2B companies. The 4 steps are: 1) you should first see which channel they are engaged with then don't only show you follow the prospect but you should also engage with their content. 2) You should make your email signature interesting. There are many ways to do that for example you can add P.S. message in the end. 3) There should be a strategy to distribute your article, since there is no point of writing a book if nobody reads it. You can use the strategy of email signature, you should also set up a blog retargeting the social media. 4) Direct message via different social media programs should be sent this is equivalent to door-to-door selling. This is a cheaper and more effective solution. Read more at: https://www.entrepreneur.com/article/295649

According to a comparative study, Facebook shows a lead of 110% over the last few months. Dominance of Facebook videos over social media continues to grow. 167000 profiles were examined by the social media analytics and it was found that 47% of those profiles used videos of some sort in their campaigns. The study also said that the 90% of them used Facebook’s native video tool, YouTube was preferred by 30% and 9% used other sources. Some videos on Facebook are shared 4.5 more times than YouTube. Read more at: http://economictimes.indiatimes.com/magazines/panache/facebook-videos-dominate-social-media/articleshow/57686077.cms?from=mdr

One should search himself online and keep track of photos. There has been continuous warnings from the guidance counsellors. Research says that at the time of admissions the profiles of the students are checked by the institutes, which might lead to the revoke of admission. One also shouldn’t post false information about the wrongdoings. Posting forbidden information online is considered illegal as it may violate institutions rule. It should also be made sure that the information entered by the individual is appropriate about name, email address, etc. these information must not be made to look unprofessional in any manner. Also others should be treated nicely on the social media including comments and trolls. Read more at: https://mobile.nytimes.com/aponline/2017/06/19/us/ap-us-college-admissions-social-media-tips.html?rref=collection%2Ftimestopic%2FSocial%20Media&action=click&contentCollection=timestopics&region=stream&module=stream_unit&version=latest&contentPlacement=7&pgtype=collection&referer=https://www.nytimes.com/topic/subject/social-media

Data can be put to use in many ways and it should be explored. Data should be used to its full potential, this is not about technology but management. A team of data scientists employ a series of various analysis to look into the entire series from data insight to profit. There are 7 methods to put data into work. 1) Use data to make better decisions along the chart. 2) Use innovations in products, services and processes. 3) Making existing products more valuable. 4) Improve quality, eliminate costs and build trust. 5) Sell or license richer data. 6) Asymmetries should be exploited. 7) Connect providers and those who need the data. These help to provide greater value to others. Read more at: https://hbr.org/2017/06/does-your-company-know-what-to-do-with-all-its-data

Big data has now become old, organizations are very well familiar with it. But some of them are still struggling with data. Data lakes provides easy access of data and data mining. Due to management defaults data may turn into data swamps making analysis difficult. Data Lake has a lot of benefits, but the data growing in size becomes difficult to handle. To avoid this problem following steps are taken at the time of creation. 1) Too much data must not be collected at the beginning. 2) Data insighting cannot be done manually, so machine-learning capabilities should be enabled. 3) Businesses should keep an eye on changing data statistics and the employed models. To make it successful one needs to integrate it with business strategy and outcome. Read more at: https://www.readitquik.com/articles/elastic-computing/smart-ways-to-manage-your-big-data/

The difference between steam video and live casting s that live casting is uninterrupted steaming directly into the web. Livecasting offers a much better job at communicating. It allows the views to feel the experience as if they were alongside the participants. Also it lets the audience draw their own conclusions. Video take livecasting altogether to a different level. Livecasting creates an opportunity which adds that missing human element, traditional advertising is thus pushed away. Live video allows brands to communicate emotions in a way that is less likely to be misconstrued. The relationship between AI and livecasting is evolving and is focused around user-generating content. Livecasting creates a platform for brands to connect, build, trust and convey emotion. Read more at: https://blogs.adobe.com/digitalmarketing/social-media/hey-brands-busy-lifecasting-arent/

Machine learning can be used in our daily lives such as filtering the spams in our mailbox or for banks judging the credibility of customers before issuing credit cards to them. We can even deposit checks through our phone. Machine learning models train itself, gather inputs and generate output. Machine learning tools are used as a part of business intelligence. Through Natural Language Processing (NLP) machine learning can comprehend speech or written words and generate graphs and figures. It can correct any anomalies in business and find out when the demand for your product is high. The more inputs one feeds in faster it learns. Read more at: https://www.sisense.com/blog/beyond-hype-machine-learning-unlocking-power-bi/

In today’s scenario businesses need faster access to everything. Organizations keep looking for effective methods. Analytics can be of very much help in this. Organizations waste a lot of time, they should select a project that has a potential which does not require too much work or heavy investment. Analytics can start at any level in the organization. Executive should be involved to get the project succeeded as the executive level understands the value of the project. Analytics dashboard helps explore data. People new to analytics may overlook the data quality. There is a tradeoff between high quality and time and expense. An expert helps to find the balance. Analytics can demonstrate value quickly and cost=effectively. Read more at: http://informationweek.com/big-data/big-data-analytics/tips-for-getting-your-company-started-with-analytics/a/d-id/1329014

All organizations face pressure to improve performance. This is difficult as there exists risks which reshapes the businesses. As the risks become more intertwined, managing them becomes difficult and leads to chaos. GRC helps the businesses to achieve task of managing everything under one umbrella. GRC helps simplify the complex and huge data. Most businesses are implementing AI systems to speed up the investment decisions. Systems will be able to automatically collect data from various data streams and channels. Also analyze it against the company’s existing datasets and operations, making suggestions regarding the changes. As technology evolves, algorithms improves and probability of errors reduce. Cyber risk is a new threat. As companies face greater pressure a more advanced GRC technology is to be adopted. Read more at: http://www.itproportal.com/features/the-road-ahead-the-coming-rise-of-artificial-intelligence-in-governance-risk-and-compliance/

Consumer Intelligence is largely focused on improving customer behaviour and enhancing consumer products which are tailor made to match consumer expectations. AI helps industries to introduce new product features by finding patterns in huge datasets. There are two types of categories in consumer AI : front end bots and AI assisted human agents. Chatbots take care of customer text queries. AI in enterprises has been useful in Enterprise Resource Planning. Enterprises are conducting predictive analytics in developing AI applications. Enterprise AI can be of two types- Applied AI and Artificial General Intelligence. Though comparing these two enterprise AI is complex and requires much more expertise  than consumer artificial intelligence. Read more at: http://analyticsindiamag.com/enterprise-ai-vs-consumer-ai-understanding-two-differ/

This year was good for SaaS startups in the country. Indian markets adopted newer technologies into its systems. More opportunities for SaaS players are expected from the BFSI sector. SaaS companies use business model that provides software solution over the internet and they charge the customers according to the usage of software since most of the financial solutions have gone online. The software uses around 2000 data points to evaluate the credit score and corresponding interest rate. Many big data analytics startups provide solutions to industries. Read more at: http://economictimes.indiatimes.com/small-biz/policy-trends/digitisation-push-makes-bfsi-sector-attractive-to-saas-players/articleshow/56325400.cms?from=mdr

The computing power is growing by each day. Technological Singularity describes the transformation that could be noticed because of an intersection between humans, technology, and artificial intelligence. This concept has been modified over the years and has been a subject to research. Theories suggest that technologies will take over the humans. This is an era where human intelligence will become increasingly non-biological. Post singularity improvements are predicted and changes are expected to introduce machine speed and intelligence. Post human literally means humans and technology will become interlinked. In the upcoming years, humans will completely expand the capacity for intelligence of the civilization. Read more at: http://analyticsindiamag.com/technological-singularity-can-achieve/

The main responsibility of DevOps and IT operations teams include solving problems and facing challenges which is becoming tougher by each day. This is where real-time and centralized log analytics come to the rescue. It helps them in understanding the essential aspects of their log data, and easily identify the main issues. While Artificial Intelligence (AI) was a big thing a few decades ago, it is now being commonly used. As IT operations becoming more complex, AI is becoming a powerful and essential tool. One solution can be to have a platform that has collected data from the internet about all kinds of related incidents, observed how people using similar setups resolved them in their systems and scanned through your system to identify the potential problems. Cognitive Insights can be introduced, this technology uses machine-learning algorithms to match human domain knowledge with log data, along with open source repositories, discussion forums, and social thread. Read more at: http://readwrite.com/2017/05/15/artificial-intelligence-transform-devops-dl1/

6 elements to get a thorough overview of the business. 1) Mission statement which gives the overview of the company it should be precise. 2)  marketplace opportunity which explains the market for the product, includes both qualitative and quantitative research. 3) Product/service information which takes an in-depth look at the product and service and how does it work explaining its key elements. 4) Sales plan where high level analysis of sales and marketplace takes place. Also showcases high level sales plan. 5) Funding decisions are composed of 3 components- idea/concept, team and ability, this is why it is crucial to compile a team. 6) Lastly, financials this tells the story of the growth of the business and is crucial most. These elements if conveyed effectively will help investors to build an effective plan that will make the business successful. Read more at: http://thedec.co/2017/01/6-key-elements-to-crafting-the-perfect-business-plan/

Advanced data resources like big data analytics is required by every business. Major areas of sales and marketing getting help from big analytics are customer analytics, operational analytics, product innovation, etc. Relation between big data and mobility allows mutual growth. Mobile apps promotes growth of big data. Apps have all the materials to steer marketing initiatives. Reserve of mobile user data is used to optimize a variety of things. Data driven insights play a major role in mobile marketing. Specific user data along with location data allows more personalization in marketing. High volumes of data is giving more control to analytics and real-time analytics can provide more advantages. Most businesses use data driven marketing approach. Big data pushes the benefits of mobility with advanced approaches. Read more at: http://analyticsindiamag.com/mobile-apps-leverage-big-data-drive-sales-marketing/

Everyone wants to achieve highest connections in outbound dialing. Predictive dialers allow continuous connections. Tips to get most out of these are: beginning should be with a right list because it lays a foundations and there should be a connection between CRM and contact center, investment in automation is necessary like predictive dialing which minimizes downtimes, eliminates errors, etc. , rules and regulations should be highly adhered to, in order to avoid fines and litigations, outbound solutions keeps callers connected by giving a menu of telephone numbers to choose from and helps establishing a natural connection. Lastly, put inbound to work for outbound, all components of contact center must work in harmony. Outbond dialing solutions help in making quality connections, maximizing agent’s performance, improving customer satisfaction, etc. Read more at: http://www.incontact.com/blog/5-tips-for-a-better-outbound-dialing-strategy/

Business analytics help in taking decisions. Business intelligence involves analysis of prior analysis and is used to support tactical decision making. Machine learning involves processing historical data to make future predictions. There is scope to add applications to deal with consumers. Unless analysis is done in production application the business will not realize efficiencies. Such analysis can speed up daily activities. The prescriptive analysis makes best use of resources by establishing how processes should be executed. Artificial intelligence supports automation of processes and decision. Machine learning is under AI, but more advanced. The main issue is that the analysis and intelligence should be integrated into the working environment. Methods, culture and discipline will always be the key challenges. Read more at: https://www.gooddata.com/blog/key-strategies-for-profitable-business-analytics