At present, it is becoming less and less challenging to identify and target specific audience segments more effectively, optimize performance of social media ads and to create personalized content due to the emergence of Predictive Analytics. Predictive analytics, driven by Machine Learning (ML), allows digital marketers to predict future trends and user behavior, make smarter decisions and improve ad performance.

What is Predictive Analytics?

Predictive Analytics uses current or historical data and predicts plausible future trends, events and patterns. Such models have been in use for quite some time now, such as predicting the sales of tickets for a movie, understanding future staffing needs of a hospital or even forecasting a business’s financials at the end of an upcoming quarter. However, today this practice has evolved from simple manual predictive analysis to complex ML systems that are faster, way more effective and can be implemented on a much larger scale.
ML, a type of AI, uses algorithms to enhance prediction accuracy by analyzing data and making informed judgments. ML algorithms analyze datasets to find patterns and characteristics among users. In the context of social media marketing, this helps marketers to segment their audience accurately and effectively. It can also be used to customize ad content based on individual user preferences. Through analyzing data, it can predict which ads are likely to give the highest ROI.
According to the Crowdfire website, 57% of businesses that used machine learning to improve customer experience notice a 100% boost in customer loyalty, over 100% rise in brand awareness, 70% improvement in fraud detection and 28% increase in acquiring new customers. Therefore, using ML and AI, offers great benefits to a business in leading to higher growth, increasing loyalty and enhancing market position.
Every business wants to be a part of the AI movement, especially implementing in all business systems at the earliest, but do not do so as they have no idea where to begin. We can help you with that! Learn more and consult us today!

One of the most trending and look for technology, Predictive Analysis is a powerful tool that can help us to forecast and predict what lies ahead us. However, it is usually accompanied by few issues that user encounters while using it. They might not be visible during early stages of development but they can become great concern when they will not be able to deliver results to customer. Prevention is always better than cure and thus it is recommended to study the technology well before use. 

Following are few tips that one should use to avoid and resolve common project challenges:

1. Create and execute a formal strategy
2. Ensure data quality
3. Manage data volume
4. Respect data privacy and ownership
5. Maximize usability
6. Control costs
7. Choose the right tools

   To read more about them visit: https://www.cio.com/article/3287937/predictive-analytics/7-tips-for-overcoming-predictive-analytics-challenges.html?upd=1532674958240

    

Prescriptive analytics is considered the third wave of the Big Data revolution after descriptive analytics and predictive analytics. Prescriptive analytics add a third layer of technology by evaluating possible actions in response to the data and produce a desired outcome. Combination of predictive and prescriptive analytics can be helpful for organizations which wants to do more in less time. Combining prescriptive and predictive analytics add more features into the equation, such as the responsiveness of a prospect to your outreach. Effective prescriptive analytics deliver more transparency and control. For more read: http://insidebigdata.com/2016/03/03/how-prescriptive-analytics-puts-complex-decision-making-on-rails/

Nowadays, computers solve crimes, drive cars, cure sickness and accurately predict political races. But the problem is, it's not enough to just store, access and process data. Machine learning and artificial intelligence algorithms are divided into two factors: huge quantities of timely and accurate data. There are four typical methods to be used to acquire data. They are -1. Required self-declared data,2. Self-declared or observed data 3. Volunteered self-declared data, 4. Crowdsourced observed data. For more read the article link written by David Elkington: http://techcrunch.com/2016/03/01/getting-predictive-about-politics-and-everything-else/

Investment in shares requires analysis of huge historical data. Analysis is the primary phase and it forecasts the future investment process. Broking house plays a clinical role in this context and charges a percentage of hike in shares. But these days such information is frequently available in different websites and are updated on a regular basis. The usage of technology for predictive analysis is hugely correlated with big data. These are limited to institutional buyers. The automation in predictive analysis requires a huge precision which is cost effective, but its implementation could be a game changer.

To read, follow: http://www.thehindubusinessline.com/markets/stock-markets/big-data-robo-analytics-to-drive-next-phase-of-growth/article8249330.ece

Through predictive algorithms, text analytics in Big Data can discover a wide range of happening things about a business. On the positive side, text analytics can be fed a lot of unstructured text. Customer experience management, brand monitoring, compliance and brand management can be done with the help of text analytics. This was of great use during the US election as it found out reasons behind interested voters.  Though on the negative side, text analytics tool get confused for words which has a dual meaning in the same culture or for different cultures. Hence, it should be made such that it can stand up successful for different sentiments too which many are trying to establish now. Read more about this article by Elliot Pannaman (International Events Director) at: https://channels.theinnovationenterprise.com/articles/maximizing-business-performance-with-text-analytics

Planning before working out is the most basic idea. A planner successfully merges both aesthetics and practical planning. But a strong design comes from the collected data. Combining user and sales data, Ford F-150 has been designed to reduce weight and increase sustainability which are made keeping in mind both environmental and business sense. Though A/B testing was a popular method of creating effective marketing choices due to its manufacturing constraints, companies nowadays use data gathered by observing public interest to create new products or make changes to earlier products. With increasingly complex algorithms, architectural design is now getting more accurate. With faster computing power, various tests can be done to establish strength and quality. Read more about this article by Chris Towers (Big Data Divisional Head) at:  https://channels.theinnovationenterprise.com/articles/designed-by-data

With the help of predictive analytics, marketers can predict future sales. With this prediction information, companies can now decide on their campaigns. Analytics is mainly used for correlation and causation. A lot of vendors pay maximum attention towards correlation but causation underlying a pattern is important to predict a customer’s purchase behavior. Thus predictive analytics analyzes customer behavior and offers them promotions according to their behavior so as they intake those. Hence if used properly, it can be of great importance. Predictive analytics can help marketers across the entire customer lifecycle, said Fern Halper, director of TDWI Research for advanced analytics. Read more about this article by Katherine Noyes (IDG News Service) at:  http://www.computerworld.com/article/2934086/business-intelligence/marketers-are-betting-big-on-predictive-analytics.html 

Faced with challenges of bureaucratic vices and realities, government agencies often let things slip through the cracks, including fraud. It is disheartening to learn that huge losses are incurred by many public facing agencies due to fraud and such losses are regarded as expected operating costs. Yet, no measures are being employed. Thankfully, investments are being made in predictive analytics tools by agencies and some progress has been achieved in detecting preventing and prosecuting fraud. However, to tackle crimes effectively the tools need to be comprehensive, flexible and affordable. For example, dynamic case management solutions can be applied to tackle the mammoth challenges faced by the agencies. Read more: http://gcn.com/articles/2015/06/10/fraud-control.aspx

Companies can use data scientists to prepare data sets, business analysts to develop models using both statistical and machine learning algorithms, application developers can be used to deploy and manage predictive analytics life-cycles, and tools. There are many vendors in the categories of customer analytics, cross-selling, smarter logistics, e-commerce etc. Open source software community is driving predictive analytics into the mainstream. Many Business Intelligence platforms also offer “some predictive analytics capabilities."  Rapid Miner’s predictive analytics platform can also be integrated into the cloud. Read more about this article at: http://www.cmswire.com/cms/big-data/3-vendors-lead-the-wave-for-big-data-predictive-analytics-028684.php?mkt_tok=3RkMMJWWfF9wsRomrfCcI63Em2iQPJWpsrB0B/DC18kX3RUnJb6Wfkz6htBZF5s8TM3DVlJGXqlI4UEKTLE%3D 

Fascinating, the first word that comes to the mind. Drones and big data are bonding pretty well these days and can be a successful recipe for innovations. 

Jonathan Buckley, senior vice president of marketing at Qubole, gives us a glimpse of this promising combo but not without the challenges. 

Agriculture, disaster relief and anti-poaching are some area where the combination has worked wonders. Needless to say more such wonders are in pipeline. But, yes there is a ‘butt’ of challenges attached here. The data gathered by drones is immense, it will require real time big data analytics and ad-hoc analysis to improve. But, one can feel optimistic about the future.

Have a look at  http://smartdatacollective.com/jonathanbuckley/316611/growing-relationship-between-drones-and-big-data

According to Gene Pease, CEO of Vestrics, "The power of predictive analytics is you can understand where your investments are working". In the last few years, the idea of hiring, retaining and training employees has been a foreign concept for many companies due to the downsizing. Thus, predictive analysis in collaboration with HR is proving to be one of the ways managers are modernizing with their hiring and engagement practices. Organizations can thus create the profile of workers that should fill a particular position. According to writer Anne Loehr, "The biggest benefit is that predictive analytics can help remove human bias". Thus customized on-boarding and leadership development programs may evolve out of predictive analytics in the near future.  Read more about this article at:  http://fieldservice.com/2015/05/26/predictive-analytics-a-magic-bullet-for-field-service-hiring-managers/

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/