Recommender systems have become the backbone of the ecommerce sector. They have helped companies like Amazon and Netflix to increase their revenue to as much as 10% to 25%.
And hence the need of the hour is to optimize their performance.
So, what are recommenders? Recommenders are the applications which personalize your customer’s shopping experience by recommending next best options in light of their recent buying or browsing activity. Recent developments in analytics and machine learning have let to many state of the art recommender systems.
Types of Recommenders: There are broadly five types of recommender systems, which are as follow:
1. Most Popular Item
2. Association and Market Basket Models
3. Content Filtering
4. Collaborative Filtering
5. Hybrid Models

In coming years, recommender system will be used by almost every organisation, whether it's big or small, and will become an inseparable part of the ecommerce world.

To know more read the article by William Vorhies at: http://www.datasciencecentral.com/profiles/blogs/understanding-and-selecting-recommenders-1

Data Science has become an inevitable charter in our everyday lives where every action of ours is measured, plotted, classified and logged. Businesses have also realized that they should adopt and embrace these changes now or risk being left behind in this fast moving digital world. Data Monetization is the new paradigm for organizations and slowly but steadily data is becoming their currency of trade.
Data Science is more like an art of turning data into actionable insights. Though we consume data regularly, we never cared to look behind the scenes on the rigorous processes, data preparation and machine learning algorithms that give us accurate data to devour. And this looks like some deep mystery but in reality it’s not a mystery, it’s just an intelligent use of data and various resources available to so called wizards: Data Scientists. To know more read the complete article by Prakash Pasupathy at: http://www.datasciencecentral.com/profiles/blogs/solving-the-data-science-mystery

In today’s digital age the world has become smaller.Gone are the days, when organizations used to load data in their data warehouse overnight and take decision based on BI, next day. Today organizations need actionable insights faster than ever before to stay competitive.With real-time analytics, the main goal is to solve problems quickly as they happen, or even better, before they happen. The lead role in revolutionizing real-time analytics is played by Internet of Things(IoT) . Now, with sensor devices and the data streams they generate, companies have more insight into their assets than ever before.
But it is so great as it looks , indeed it is as it helps getting the right products in front of the people looking for them, or offering the right promotions to the people most likely to buy using the real time recommender system.
are the days of waiting long hours to know the analytics of your data , now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!! You can read the full article at http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics

Customers love personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday It make them feel like they are your single most important customer. But in an online world, you can’t guide them through the product they may like. This is where recommendation engines do a fantastic job.

With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations make them feel like your shop was created just for them and hence they become your regular customers.

Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like and understanding the shopper’s behavior on different channels can increase the sale by over 30%.Ultimately most important goal for any organisation is to convert visitors into paying customers and hence product recommendations are extremely important in digital age.You can read the full article on Product recommendations in Digital Age by Sandeep Raut (Author) at: http://www.datasciencecentral.com/profiles/blogs/product-recommendations-in-digital-age

In a short span of five years the world of analytics has changed immeasurably. Now we see fast analytics, interactive experimentation with data and exploratory analysis of data.

But why ? The answer to this question can be summed in three simple points. First, with fast analytics, it’s easier to keep up in an ever-changing world and keep pace with customers and market forces and businesses can see a measurable value from running advanced analytics on their data. Second, due to low prices of analytics businesses must meet customers’ expectations or risk losing them to a competitor. Third, it has the ability to elevate a company to the next level and provide it with a competitive edge over its rivals through the real-time insights it can achieve.

And , hence every one in this competitive market is shifting to advance analytics. To know more you can read the article by Aaron Auld (CEO of EXASOL) at: http://www.datasciencecentral.com/profiles/blogs/the-rise-of-advanced-analytics .

Robo-advisers are automated advisers with provide financial advisory as low cost, so it’s available to everyone. The costs are as low as 1 euro. They open the door to the financial markets and give you the possibility to invest in stocks, bonds and other securities and keep their costs low by trading Exchange-Traded Funds.

But, how do they exactly work ?? Robo-advisors use algorithms based on mean-variance optimization, a mathematical framework to create a portfolio of assets such that the expected return is maximized for a given level of risk. Financial market data is used to estimate expected return, standard deviation and correlation for every asset class. On opening an account, you are asked simple questions about your age, income, savings and willingness to take risk. This data is collected to estimate your risk tolerance and fit their model to your current situation and preferences and give you the best advice to invest in the market. To know more read this article http://www.datasciencecentral.com/profiles/blogs/robo-advisers-and-the-future-of-financial-advice by Stefan.

Aim-Lever-Data-Implement (ALDI) is an approach to integrate marketing analytics with Data Science, i.e making data the primary object of various decisions. So is it something very difficult or some kind of rocket science , no it’s a simple paradigm which follow the following approach :

• Aim :

The aim of the analysis needs to be fixed by the strategy teams, before any data scientists gets involved, as they are are ones who know what exactly is needed.

• Lever :

It is very important for an organization to know what actions it is going to take as a result of the analysis, not what the organization’s strengths are.

• Data :

Once the objectives have been defined the next step is then to gather the appropriate data and then perform the analysis. This is where data scientists would really come in.

• Implement :

This is the final step of the problem where results from analysis are used to make further decision on how the problem is going to be tackled and what all needs to be done by the various departments.

You can read the full article by Srividya Kannan Ramachandran at http://www.datasciencecentral.com/profiles/blogs/aldi-a-new-paradigm-for-integrating-marketing-analytics-with-data

Yes, if companies need anything in 2017 they are Data Scientists.

But why , what is so special about them? And the answer is :

Data scientists tracks millions of data sets and provides concrete information for organizations looking to break their data into meaningful information that can be used at all levels in the organization.

As this is the data century , every company wants to recommend its users what they are most likely to choose and hence the need of Data Scientists to study the data and extract various pattern from it and hence creating a 360-degree view of their customers. This not only impresses the customers but also helps the companies in understanding their customers better and hence improving their services according to the customers.

So , in a nutshell yaa companies do need Data Scientists.

You can read more at  http://www.datasciencecentral.com/profiles/blogs/why-large-companies-need-data-science-experts-like-you

What is dark data ? The large amount of data collected by companies that goes useless due to lack of analysis(39%) or structure(25%) or even sometimes due to lack of proper tools(13%) is known as the dark data. If used/analyzed properly gives a new dimension to the companies.

But how can we harness the dark data. Well it’s no rocket science , just some simple measures and you have a whole new dimension of data.Here are just a few of them :-

• Keep a track of user logins and various checkout at different locations, this helps in creating a 360-degree view of the user.

• Mobile phone data, this will help to illuminate an array of new product and marketing opportunities, and hence improve marketing effectiveness.

• Free text input, such as feedback can be analysed to determine if general sentiment of the feedback is positive or negative.

Data when properly harnessed can be a powerful and can serve as a gateway to new insights, developing new opportunities and boosting your business into the data-driven century.

You can read more at http://www.datasciencecentral.com/profiles/blogs/dark-data-the-billion-dollar-opportunity

Data Science is one of the hottest job of the 21st century. But is it easy to be a data scientist , and the answer is it’s not hard. But here are some of the things that one need to avoid to be a bad data scientist :

• Focus on tools rather than business problems :

Yaa tools do matter, but what is even more important is the problem you are working on, it should be the basis of all your decisions.

• Planning communication last :

Communication helps in getting various insights about our ideas and hence help in improving our approach to handle a particular problem.

• Data analysis without a question / plan :

Data without a plan or motive is useless and we often end with more of the things we don’t need than the things we really need.

• Don’t read enough :

This is a mistake that everyone makes, to be updated with the recent development tends helps in constantly improving our skill set and hence benefits the organization.

• Fail to simplify :

Data Scientist generally fail to maintain the core idea behind their product and hence end upwith something which is much complicated for the end users.

• Don’t sell well :

The job of data scientist doesn’t end with creating the product , he must know how to sell it, how to reach to the end users.

You can read more at http://ucanalytics.com/blogs/6-worst-mistakes-for-data-scientists-and-how-to-avoid-them-explained-with-quotable-quotes/