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5 Best Tools Transforming Predictive Analytics in 2018

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5 Best Tools Transforming Predictive Analytics in 2018

Gone are the days of sloppy decision-making techniques. Competitive businesses are embracing predictive analytics and associated tools.

Predictive analytics is a real game changer that allows companies to implement marketing strategies and serve customers with renewed efficiency. Customers in their natural interactions and networking with a company leave behind huge amounts of data. Predictive models extract valuable information from all this data, thereby helping enhance the performance of products and services, promoting better customer retention strategies, and improving core business competencies.

‘’The capacity for predictive analytics to learn from experience is what renders this technology effective, differentiating it from other business intelligence tools and analytics techniques’’, says the predictive analytics experts at Quantzig.

From thoroughly examining data, to figuring out correlations and patterns in data, predictive analytics tools effectively manage the entire business process. In this blog, we talk about some of the latest predictive analytics tools that are all the rage in 2018! So let’s dive in!

SAP Business Objects:

  • A powerful Business Intelligence platform that provides techniques to make swift and informed business decisions.
  • Offers a novel perspective on forming scalable solutions
  • SAP Business Object helps develop insights that encourage real time actions.
  • Enables users to visualize data in a self-serving manner

Image Source: Technosap

IBM Predictive Analytics:

  • Offers predictive analytics solutions that are simple to use and meet the requirements of different types of businesses.
  • Two important softwares, namely IBM SPSS Modeler and IBM SPSS Analytics, allow all users to implement predictive analytics and improve their businesses, irrespective of their skill levels.
  • The platform helps prevent frauds and maximizes profits.
  • It transforms extraneous data into predictive insights that steer key business decisions.
  • It is built with abilities to perform geospatial analysis and text analytics.
  • Runs on open source platforms with optional coding
  • Secure and private

Image Source: SlideShare

QlikView:

  • Flexible and easy-to-use business intelligence platform
  • Created by QlikTech
  • Allows enterprises to pull out relevant information from a given data set, which in turn helps design guided analytics applications.
  • Platform adopts a user-driven approach towards building charts and creating dashboards
  • BARC’ BI Survey 10 recognizes the ‘Agile BI’ ability of QlikView

Halo:

  • Ideal pick for an uninterrupted supply chain management system that aids in business forecasting
  • A smart platform with a dependable data repository, where cases can be run over and over again in order to perfectly match predictions with results.
  • Accessible through all kinds of browsers and available for cloud or hosted.
  • Self-serving nature of supply chain management allow organizations to increase customer satisfaction.

Image Source: Software Advice

Dataiku-DSS:

  • Dataiku is capable of transforming raw data into predictions.
  • Allows users to employ analytics appropriate algorithms
  • Allows users to leverage available libraries and apply custom codes in R and Python
  • Permits integration of external libraries by means of code API’s
  • Equipped with 80+ in-built functions that help investigate and clean raw forms of data
  • The best feature is a visual data profile at each step of analysis.

Image source: The Dataiku Blog

Want to learn SAS Predictive Modeling? Contact DexLab Analytics. The industry-experts at DexLab offer excellent SAS predictive modeling training. It encompasses theoretical understanding of core concepts and hands-on experience.

 

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Refugee Migration: How Predictive Analytics Coupled with Big Data is Developing Urgent Solutions for Countless Refugees?

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Refugee Migration: How Predictive Analytics Coupled with Big Data is Developing Urgent Solutions for Countless Refugees?

In total, 65 million people are currently displaced or live refugees – owing to the Syrian Civil War. Each day, thousands of refugees are fleeing their homes and seeking asylums in foreign countries. Many countries have opened their borders, countless UN agencies have come forward to help and handle the ongoing global crisis – but how bad is the current situation? What are the chances of working out a satisfactory solution?

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Predictive Analytics is the key. It’s a raw form of statistical science that mines through available data for future prediction of outcomes. Though we agree to the potentials of predictive analytics, we can’t turn a blind eye to the political and financial roadblocks it poses in front of us, which keeps us from addressing the current crisis with same gusto.

For excellent SAS predictive modeling training, DexLab Analytics is your go-to learning destination in Gurgaon. 

The Power of Prediction

Past data helps! They help the algorithms to anticipate the challenges even before they arise. Also migration data… gathered from a plethora of sources, including World Bank data, population censuses, sample surveys, population registers, and other administrative sources. Such treasure troves of data could be groundbreaking, especially for representatives working on forefront of the ongoing crisis. Armed with meaningful data, officials using advanced analytics could chart out most likely locations, where the refugees are about to head next. Spotting the possible signs of influx, government and respective policymakers might reroute the refugees to different locations, where better assistance is possible and expected. This kind of real-time data helps respectable authorities to transfer money and goods to locales that need them the most.

Nevertheless, predictions are not always on-point or don’t lead to the best guesses, all the time, yet in many cases, refugees could benefit – remember refugee crisis is not only a serious humanitarian crisis but also a development issue for countries that accept the asylum seekers. Thus, the authorities should refrain from bottling up hundreds and thousands of refugees from bottling them up in overcrowded camps, without food, water and other basic amenities. And for that, they need adequate data, which could help them make the best possible decision in such situations of distress.

A Hope in Sight

Technical challenges are soaring; if the world is resilient to solve the ongoing international crisis, predictive analytics has to be embraced, but make sure you give adequate importance to data security. Accidental data breaches and releases are happening all around, which could result in triggering targeted violence in specific, highly-populated, vulnerable areas.

Addressing the growing concern, hefty financial investment is the best play. Several private players and multinational organizations, including UN till now have given undue attention but devoted limited resources to tackle the challenge. That needs to be changed now. And fortunately, change is in motion; recently two key players in the humanitarian aid and development area of work signed a partnership to formulate innovative solutions for refugee crisis using far-reaching claws of big data and technology. The striking partnership between the World Bank and United Nations Refugee Agency is the first stepping stone towards improving the quality of data about refugees, prompting an improved smarter assistance for refugees across the globe.

No longer are such initiatives a distant concept; the phenomenal rise of big data hadoop and predictive analytics technology has stepped up the quality and speed of data resulting in tailor-made sophisticated assistance, perfect for refugee crisis. In a nutshell, the new dimension is going to make a lot of difference, and technology is going to be a game-changer in this.

For more interesting blogs and data-related stuffs, follow us on DexLab Analytics. We are a leading SAS Predictive Modelling training institute in Delhi offering high-in demand certification courses. Reach us today!

 

The blog has been sourced from —

sisense.com/blog/refugee-migration-where-are-people-fleeing-from-and-where-are-they-going

mashable.com/2018/04/24/big-data-refugees/#8md_gh7p2iqr

theconversation.com/millions-of-refugees-could-benefit-from-big-data-but-were-not-using-it-86286

 

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5 On-Going Predictive Analytics Applications That Marketers Should Be Familiar With

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5 On-Going Predictive Analytics Applications That Marketers Should Be Familiar With

Predictive Analytics is a very popular concept, and increasingly often, the marketing strategies are tied to the very idea of predictive analytics. However, businesses seeking insights about the future aren’t satisfied with whatever happened in the past. They want to know more, and that’s where the promising role of predictive analytics comes forth.

In this article, we will talk about some modern applications of predictive analytics that are deriving great results in the marketing sphere:

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Five On-Going Predictive Analytics Marketing Applications

Customer behavior models

Once only bigwig companies, like Amazon and eBay had the ability to do customer behavior prediction, but with increasing expansion of technology, even smaller companies have adopted the practice. Developing a comprehensive catalogue of predictive models is for sure a challenging task, but fortunately a number of relatively easier model types are plying assistance in the marketing scope. Amongst them, three most significant predictive models are Cluster Models, Propensity Models and Collaborative Filtering.

Assessing and prioritizing leads

With 3 B2B marketing use cases, here’s how we can predict early success and prepare the base for more complex use of predictive analytics:

  • Predictive Scoring – Prioritizing known leads, prospects, and accounts based on how they are probable to act upon.
  • Identification Models – Determining and acquiring prospects with characteristics akin to existing consumers.
  • Automated Segmentation – It’s crucial to segmenting leads for customized messaging.

The above-said groundwork helps prepare sales team to better apply the strategies and prioritize leads that can be converted into buyers. But of course, the techniques mentioned above need a high sales volume to perfectly prepare a robust predictive model.

Assessing which products or services to introduce into the market

Data visualization is crucial. It is not only visually effective, but is also ideal in guiding actions, based on customer behavior. It notifies which type of customers lives near a particular store, what is their average age-density and what kind of products they buy: do they purchase hard or soft goods, who goes for grocery shopping, the aged ones or the younger ones, and so on.

Content is the king

Targeting the right customers with the right content at the right moment boosts customer segmentation. It’s one of the most common predictive analytics marketing tricks because it’s simple, effective and directly impacts ROI. Some of the most popular predictive analytic models for content targeting are response modeling, affinity analysis and churn analysis – all of them can foretell you whether it would be fruitful to combine digital and print subscriptions or keep them separate or help you differentiate between a content that should levy a subscription fee from a content that has a one-time sales price.

Enhancing marketing strategies with predictive analytics

 Apart from the above 4 uses, few other drilled-down uses of predictive analytics in the marketing domain are as follows:

  • Gaining access to internal structured data
  • Accessing social media data
  • Employing behavior scoring on customer data

These applications could ascertain whether a social media- based marketing campaign would meet a raging success or a mobile marketing strategy would be more beneficial for the target audience.

For readers interested in making predictive analytics your career option, opt for SAS certification for predictive modeling from DexLab Analytics. At present, SAS predictive modeling training is the go-to course program you need to give wings to your analytics career.

 

The article has been sourced from —  https://www.techemergence.com/predictive-analytics-for-marketing-whats-possible-and-how-it-works

 

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6 Predictive Analytics Use Cases Proven To Ace Marketing Efforts

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6 Predictive Analytics Use Cases Proven To Ace Marketing Efforts

Predictive analytics includes a set of functions that leverage customer information to construct interesting assumptions about future potential customers. They play an integral role in determining a customer’s lifecycle.

What’s more, predictive analytics influences company strategy even before any prospect gets converted into a lead. That’s the way it functions, and as the leads are converted into customers, the new data collected impacts the next-generation marketing activities. The process is almost cyclical.

In this post, we will discuss about 6 use cases for predictive analytics that shows a significant impact on marketing ROI.

And here it starts:

Better Lead Scoring

With predictive analytics, lead scoring becomes a whole data-driven process that targets customers. It helps you leverage the actions of existing customers to build better future strategies. No more it remains an anecdotic listicle of measures from sales, instead it points out the ‘hot’ leads that can be pushed down through the funnel of sales.

Improved Lead Nurturing

Remember, one-size-fits-all doesn’t apply to lead nurturing.

For converting prospects into leads, a definitive plan for lead nurturing should be adopted. Predictive analytics is the hands-down tool to consider: take cue from behavioral and demographic data to push leads towards the sales funnel.

Smart Content Distribution

Today, every company invests in quality content creation. Content marketing has the power to fetch measurable ROI for your company. And this is where predictive analytics play a vital role: it analyses the type of content customers would find interesting, based on certain behavioral and demographic data and then automatically distribute them to the prospective leads.

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Protecting Baseline Becomes Easy

Predictive analytics help learn from the past mistakes. Past behavior is a testimony of future behavior, and it holds true for customers, as well. A clear analysis of behavioral patterns of previously-churned customers in your company would help identify the red flags your present customers are showing, thus results in protecting your baseline for a better, secure future.

Develop Products Fit For Customers

Understanding what customers need becomes a tad too easier when you are armed with a set of behavioral, demographic and psychological data of your customers. Again possible with predictive analytics! Leverage customer data and figure out what they are looking for.

Design Successful Future Campaign

Past performance analysis always leads to constructing better future campaign designs. As more and more new customers seem to enter your business, you need to leverage your data more precisely and curate content based on their preferences and requirements. And may even have to target specific audiences! So, treat past data as a treasure!

As parting thoughts, these six strategies for predictive modeling are perfect for transforming your business. Today, data is the power. Clean, high-quality data have the potential to take your business venture to unforeseeable heights of success.

And for that and more, DexLab Analytics is here to help. Our skilled consultants can crack the toughest data management problems and provide solutions to make predictive modeling using SAS better. For SAS predictive modeling training, peruse over our course section on the website.

 
The article has been sourced from – https://blog.reachforce.com/blog/8-use-cases-for-predictive-analytics-in-marketing
 

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Why B2B Marketers Should Use Predictive Analytics in 2018?

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The Risks are indispensable. In any business.

But what if you can nullify the risks?

Or find a suitable solution to limit them?

Why B2B Marketers Should Use Predictive Analytics in 2018?

That’s probably where Predictive Analytics ring the bell. Right from logistics and inventory to marketing initiatives and sales to applications in hiring and HR, predictive analytics is the ultimate tool that impacts business decisions across every domain of a B2B enterprise.

Continue reading “Why B2B Marketers Should Use Predictive Analytics in 2018?”

Researchers Peer into the Hood of Computational Linguistics

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Researchers Peer into the Hood of Computational Linguistics

 

To start, give a look at these two sentences:

“This house is in a detestable location.”

“This detestable house is in this location.”

 

Well, these two sentences have virtually similar words, but owing to their structure, they exude entirely two different meanings. Understanding the true meaning of the sentences just by having a look at the words was something only reserved for the human intelligence, until now. Breakthroughs in Natural Language Processing (NLP), also known as computational linguistics have blazed a trail in this domain, which was once dominated by humans.

Continue reading “Researchers Peer into the Hood of Computational Linguistics”

Here’s why SAS Analytics Is a Must-Have IT Skill to Possess

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Here’s why SAS Analytics Is a Must-Have IT Skill to Possess

Without the great Analytical surge, everything was looking fit and fine. The economy was performing well. The IT industry was looking stable. The tech honchos were playing fine. And then IT happened! Data Analytics snatched the dazzling limelight all to itself.

It’s true once in a while, our market needs a good shaking, or else things tend to get sluggish and slow. Over time, the industries start decreasing in efficiency and business houses crumples. Therefore, the change induced by Big Data Analytics is one for good: it started pulling back the market to its former position. From medical science to military to security, the reach of Big Data Analytics can be witnessed everywhere.

The evolution of analytics is largely consistent and covers a wide span of industries. It’s not like it suddenly came into a lot of focus, its advancement was slow and steady. Now, it has strived to become extremely important to store, interpret, analyze and develop crucial insights – social media is deriving maximum benefits out of analytics, while customizing their products to make more money from advertisements. On the other hand, the service-oriented companies love to manipulate data that is generated through myriad social channels to trigger customer base.

The ABC of Summary Statistics and T Tests in SAS – @Dexlabanalytics.

Today, SAS certifications are extremely rewarding and scores high for both employee and employer. Analytics is a big word, encompassing a whole array of job roles, such as Forecaster, Market Researcher, Data Miner, Operations Researcher and Statistical Analyst – so when are you choosing this career gateway for a better future! DexLab Analytics is here with its state-of-the-art SAS training courses, help yourself.

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3 key benefits of becoming SAS analytics professional:

Increase marketability and reach

SAS Analytics professionals possess higher marketability skills and enjoy a certain edge over competitors. Their job is to deliver nothing but the best, and they are very focused in doing that, leaving no scope for complaints.

Expand credibility for being the right technical professionals

As the SAS certified professionals have a thorough know-how about using SAS Software the employers stay relaxed and trusts their predicaments, hence, enhancing their credibility quotient.

Enhance skill and expertise in SAS area of specialization

No doubt, SAS Analytics professionals are extremely good in their field of work. Owing to their professional nature they tend to attract more lucrative job opportunities.

Data Preparation using SAS – @Dexlabanalytics.

Apart from SAS, R programming is rapidly gaining popularity. Small and large companies have realized the growing the importance of these two tools. SAS combined with R language training in Delhi opens a whole gamut of striking opportunities. Having said that, companies that have stayed traditional, through its very core, have now embraced SAS and R skills, and for the right reasons.

At DexLab Analytics, we increasingly focus on making students totally data-ready. Opt for R programming certification, and give new data-hungry souls the drive to enter the world of analytics. After all, to excel in the analytics career and sail high you need to be well-equipped with SAS and R – they are the tools of combat for the future IT domain.!

 

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How Many Category 5 Hurricanes Have We Had in the Atlantic?

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With Hurricane Irma battering the pretty state of Florida while ripping through the Caribbean like a mammoth buzzsaw blade, we start wondering how often such rare Category 5 hurricanes occur. We know hurricanes of such magnitude are rare, but how much rare?

According to Wikipedia page information, hurricanes having wind speed greater than or equal to 157 mph are termed as category 5 hurricanes, enough to wreak havoc around. Using SAS Analytics, let’s start digging some data to unravel how many hurricanes of such great magnitude have hit the Atlantic coastal towns and cities with such dangerous wind speeds..

After indulging in a bit of research work, we came across weather.unisys.com website that contains exhaustive data about all the past hurricanes formed out of Atlantic. It turned out to be a good repository of data – we jotted down a bit of code and parsed the data into SAS data set. Next, we marked all the Atlantic hurricane paths on a map, and highlighted the line segments in bright red, where the wind speed fell under the Category 5 tab. So, come know how often they have taken place, along with their accurate position.

sas certification

Hit the above image to view the full-size interactive version of the map with HTML mouse-over text displaying the hurricane names in red for category 5 descriptions.

Take a look at the technical details of the code we used to draw the map:

  1. The map is created using SAS/Graph Proc GMap.
  2. We projected the map with the help of Proc GProject. Followed to that, we saved the projection parameters using the brand new Parmout=option. It was only then that we can project the hurricane paths individually, using GProject’s parmin=option. (Before the innovation of parmout/parmin parameters, we used to combine the map and hurricane paths, compile and project them together, and then divide the results into two separate datasets – of course the new functionality eases the things out).
  3. The paths of hurricane were plotted using regular ‘move’ and ‘draw’ Annotate functions.
  4. We first plotted the land areas (choropleth map), then covered (annotated) the hurricane tracks (while doing so, make sure the red lines lie on top for better visibility), and finally overlaid the country border contours on top again so as to make them prominent.
  5. As lines are incompatible with mouse-over text, we annotated circles using mouse-over text along the red hurricane paths. We outlined these circles at the very beginning (using when=’b’), hence they would become invisible later.

Have a look at the table we presented below. The table comprises of 34 Category 5 Atlantic hurricanes, derived from 150 years of data. You can also run your eyes through a snapshot image – click on the image to see the entire interactive table. And if you are interested in knowing more, hit each hurricane name and ask Google to give you information.

sas training institute

Nota Bene: There might be some hurricanes under Category 5 domain we missed out. Kindly excuse us there, but we think we have nicely hit our main point of discussion. If you have anything to say us, scroll down and comment!

DexLab Analytics is reckoned to be the best SAS analytics training in Pune. The courses are a collaboration of intensive subject matter research and industry experts’ relentless dedication towards their students. For state-of-the-art SAS training courses in Pune, drop by DexLab Analytics.

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The Basics Of The Banking Business And Lending Risks:

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The Basics Of The Banking Business And Lending Risks:

Banks, as financial institutions, play an important role in the economic development of a nation. The primary function of banks had been to channelize the funds appropriately and efficiently in the economy. Households deposit cash in the banks, which the latter lends out to those businesses and households who has a requirement for credit. The credit lent out to businesses is known as commercial credit(Asset Backed Loans, Cash flow Loans, Factoring Loans, Franchisee Finance, Equipment Finance) and those lent out to the households is known as retail credit(Credit Cards, Personal Loans, Vehicle Loans, Mortgages etc.). Figure1 below shows the important interlinkages between the banking sector and the different segments of the economy:

Untitled

Figure 1: Inter Linkages of the Banking Sector with other sectors of the economy

Banks borrow from the low-risk segment (Deposits from household sector) and lend to the high-risk segment (Commercial and retail credit) and the profit from lending is earned through the interest differential between the high risk and the low risk segment. For example: There are 200 customers on the books of Bank XYZ who deposit $1000 each on 1st January, 2016. These borrowers keep their deposits with the bank for 1 year and do not withdraw their money before that. The bank pays 5% interest on the deposits plus the principal to the depositors after 1 year. On the very same day, an entrepreneur comes asking for a loan of $ 200,000 for financing his business idea. The bank gives away the amount as loan to the entrepreneur at an interest rate of 15% per annum, under the agreement that he would pay back the principal plus the interest on 31st December, 2016. Therefore, as on 1st January, 2016 the balance sheet on Bank XYZ is:

dexlab-01

Consider two scenarios:

Scenario 1: The Entrepreneur pays off the Principal plus the interest to the bank on 31st December, 2016

This is a win – win situation for all. The pay-offs were as follows:

 

Entrepreneur: Met the capital requirements of his business through the funding he obtained from the bank.

Depositors: The depositors got back their principal, with the interest (Total amount = 1000 + 0.05 * 1000 = 1050).

Bank: The bank earned a net profit of 10%. The profit earned by the bank is the Net Interest Income = Interest received – Interest Paid (= $30,000 – $10000 = $20,000).

Credit Risk Analytics and Regulatory Compliance – An Overview – @Dexlabanalytics.

Scenario2: The Entrepreneur defaults on the loan commitment on 31st December, 2016

This is a drastic situation for the bank!!!! The disaster would spread through the following channel:

 

Entrepreneur: Defaults on the whole amount lent.

Bank: Does not have funds to pay back to the depositors. Hence, the bank has run into liquidity crisis and hence on the way to collapse!!!!!!

Depositors: Does not get their money back. They lose confidence on the bank.

 

Only way to save the scene is BAILOUT!!!!!

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The Second Scenario highlighted some critical underlying assumptions in the lending process which resulted in the drastic outcomes:

Assumption1: The Entrepreneur (Obligor) was assumed to be a ‘Good’ borrower. No specific screening procedure was used to identify the affordability of the obligor for the loan.

Observation: The sources of borrower and transaction risks associated with an obligor must be duly assessed before lending out credit. A basic tenet of risk management is to ensure that appropriate controls are in place at the acquisition phase so that the affordability and the reliability of the borrower can be assessed appropriately. Accurate appraisal of the sources of an obligor’s origination risk helps in streamlining credit to the better class of applicants.

Assumption2: The entire amount of the deposit was lent out. The bank was over optimistic of the growth opportunities. Under estimation of the risk and over emphasis on growth objectives led to the liquidation of the bank.

Observation: The bank failed to keep back sufficient reserves to fall back up on, in case of defaults. Two extreme lending possibilities for a bank are: a. Bank keeps 100% reserves and lends out 0%, b. Bank keeps 0% and lends out 100%. Under the first extreme, the bank does not grow at all. Under the second extreme (which is the case here!!!) the bank runs a risk of running into liquidation in case of a default. Every bank must solve an optimisation problem between risk and growth opportunities.

The discussion above highlights some important questions on lending and its associated risks:

 

  1. What are the different types of risks associated with the lending process of a bank?
  2. How can the risk from lending to different types of customers be identified?
  3. How can the adequate amount of capital to be reserved by banks be identified?

 

The answers to these questions to be discussed in the subsequent blogs.

Stay glued to our site for further details about banking structure and risk modelling. DexLab Analytics offers a unique module on Credit Risk Modelling Using SAS. Contact us today for more details!

 

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