According to a research, it was found that the Human Capital Management market will grow by 10% annually through 2020. Another research found that 1. Real-time feedback and analytics will boom, and it will improve employee productivity. 2. A new generation of performance management tools will emerge to improve feedback-based approach. 3. A focus on human performance and well being will become a more critical part of HR, as it will emphasize employee wellness and engagement, and a healthy work-life balance, and 4. The employee experience will become a primary focus of HR to provide integrated, high-value experiences that excite, engage, and inspire employees. Read more at: http://it.toolbox.com/blogs/insidecrm/mobilefirst-cloudfirst-crm-75930

CRM has come a long way from a contact management system to a tool that organizations can use to create and build a meaningful customer relationships. But, the question is that how will it is how’s that going to change during 2017? This article explores the CRM trends that you should watch this year. Read more at: http://it.toolbox.com/blogs/insidecrm/crm-in-2017-4-trends-to-watch-75879

Data science platform has become a buzzword of the decade. So, what is it? The sole purpose of a data science platform is to encapsulate all off-data science work by incorporating tools required to visualize, deploy, collect, analyze data, build models, generate reports. This toolkit makes it convenient to maintain, reproduce and scale up the project and produce results dynamically. Adoption of data science platforms is expected to grow almost double by 2018 as more companies realize its potential benefits. Many data driven business faces the challenge of effectively utilizing data science tools and lack integrated approach to their data science technology stack to find value in the data. While on the other hand, companies who have already established data science platforms are excelling in the field.

Read more at : http://dataconomy.com/2017/02/tech-wave-data-science-platforms/

In a recent research, it was found that email has the highest conversion rate (66%) of any marketing message type (social and direct mail) to become buyers. Information is important for creating intelligent, successful and profitable email campaign.  Hence, gathering information is very critical. It was also found that personalized mailings have 29% higher unique open rates and 41% higher unique click rates than generic mailings. This is where the power of customer resource management is important as CRM can give you the critical customer metrics needed to create email campaigns that will increase your email’s open and click-through rates. Read more at: http://it.toolbox.com/blogs/insidecrm/use-crm-to-optimize-your-email-strategy-75867

The three critical steps involved in deployment of machine learning algorithm and exposing it to real world are :

Define a goal based on a metric : Decide if you want human level intelligence or an acceptable one as this decision will affect time and engineering cost of your system. Also define a metric to measure performance of your model.

Build the system : Build a minimum viable system without worrying much about accuracy. Then build an incremental strategy to improve your system by solving problems you face in each iteration.

Refine the system with more data : Initial metric values are not the indicators of real life, your data and users might change , so regularly monitor the system performance. Update it with new data and fine tune the model accordingly.

Read more at : http://www.erogol.com/short-guide-deploy-machine-learning/

Sometimes even the Machine learning algorithms behave so dumb that an image recognition model can be confused by generating an adversarial instance, i.e. by changing few pixels by either taking derivative of model output or exploiting genetic algorithms. Adversarial instances lie in low probability regions which is in contrast with limited instances of high probability regions from which the model was trained. A possible approach to solve this problem is ensemble training - To let multiple models back each other. As we look forward to developing more artificial intelligent systems it would become common to encounter such problems.

You can read more at: http://www.erogol.com/ensembling-against-adversarial-instances/

A business hypothesis is a foundational theoretical concept whose good understanding helps you to achieve business goals. For instance, it provides a mathematical way to answer questions like whether you should spend on advertising or whether increasing a price of a product will affect your customers. Data collection is one part of the game, but correct data processing and interpretation is the final stage of your decision-making process. Hypothesis testing is used to infer whether there is enough data to support evidence . There are various test methods : Parametric Tests - z-test, t-test, f-test. Non Parametric Tests - Wilcoxon Rank-sum test, Kruskal-Wallis test and permutation test.

Read more at : http://www.datasciencecentral.com/profiles/blogs/importance-of-hypothesis-testing-in-quality-management

The emergence of massive unstructured data sources like Facebook and Twitter has created a need to develop distributed processing systems for Big Data Analytics. Hadoop (A Java based programming framework) has become the first choice of developers and industry experts mainly because its: Highly scalable, flexible, and cheap. An application is broken down into various small parts which runs on thousands of nodes to achieve fast computing speed and reduce overall operation time. Hadoop architecture continues to operate even if a node fails. Its incredible design allows you to process large volumes of data and extract computationally difficult features of users/customers.

Read more at : http://www.datasciencecentral.com/forum/topics/how-to-use-hadoop-for-data-science

Email marketing is the best way to turn one-time shoppers into loyal, long-term customers and that’s the reason why 80% of brands rely on it to drive customer retention, and another 56% of them say email marketing is the most effective way to reach retention goals. It is proved that email marketing is perfect for driving retention and the following reasons support that. They are: 1. Welcome emails, Cart Recovery emails, Loyalty emails and post purchase emails. Read more at: http://www.business2community.com/email-marketing/drive-customer-retention-email-marketing-01779908#mkYEibx0bsjCD70R.97

You can’t be a good data scientist unless you have a good hold on statistics and have a way around data. Here are some simple tips to be an effective data scientist:
Statistical Methods Should Enable Data to Answer Scientific Questions - Inexperienced data scientists tend to take for granted the link between data and scientific issues and hence often jump directly to a technique based on data structure rather than scientific goal.
Signals Always Come with Noise - Before working on data, it should be analysed and the actual usable data should be extracted from it.
Data Quality Matters - Many novice data scientists ignore this fact and tend to use any kind of data available to them, if always a good practice to set norms for quality of data.
Check Your Assumptions - The assumptions you make tend to affect your output equally as your data and hence you need to take special care while making any assumption as it will affect your whole model as well as results.
These are some of the things to keep in mind when working around with data. To know more you can read the full article by Vincent Granville athttp://www.datasciencecentral.com/profiles/blogs/ten-simple-rules-for-effective-statistical-practice

The CRM screen of any organization must help. But, most of the time, it doesn’t function properly. The problems arise because of lack of foresight and aging system. The basic CRM system should be updated to keep up with the changes in organizations. Read more at: http://it.toolbox.com/blogs/insidecrm/screen-organization-with-crm-75717

Things get corrupted over time. In the world of CRM, it is called bad data. Organizations must start by cleaning the data. In a research, it was found that CRM databases, corrupt at the rate of at least 10% a year. Organizations must regularly maintain their database, both on a day-to-day basis and through cleaning at regular intervals. Sometimes, databases accumulate outdated or irrelevant data. Know more about the bad data and how to clean them at: http://it.toolbox.com/blogs/insidecrm/crm-cleaning-75709

Marketing automation helps CRM to build business. Market automation automate workflows and other tasks associated with the marketing function and also include automating marketing campaigns and routine marketing functions. It doesn't replace CRM, but it supplements it by working very closely with the existing CRM system. In a nutshell, a complete market automation package provides a range of features for supporting the marketing function. Read more at: http://it.toolbox.com/blogs/insidecrm/complementing-crm-with-marketing-automation-75560

Companies nowadays are integrating social media seriously. They are also integrating social media efforts with your other sales and marketing team. The first social media strategy is to define the goals, and then define the objectives. It's important for companies to take a broad, integrated, approach to social media efforts. Read more at: http://it.toolbox.com/blogs/insidecrm/getting-serious-with-social-media-75572

Often the outputs of data models developed by data, scientists end up in a report which summarizes the state of business and used by stakeholders to make decisions. But it is necessary to achieve a system that can predict the future outcomes in real time. This can be done by integrating the model in a production environment, however, it requires advance engineering skills and data scientists cannot do it alone. The process of deployment follows broadly 7 steps :  1.Refactor the model code

2. Walk through the code and determine how it slots into the engineering cycle

3.Re-write into a production stack language or PMML

4.Implement it into the tech stack

5. Test performance

6. Tweak the model based on test results

7.Slowly roll out the model.

Today many companies are adopting tools to make this process faster to reap the benefit of data driven decision making.

Read more at : https://www.datascience.com/blog/navigating-the-pitfalls-of-model-deployment

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

 2016 has been the year of deep learning, some big breakthrough were achieved in 2016 by Google and DeepMind.Some of the most significant achievements are as follow :

 AlphaGo triumphs Go showdown : AlphaGo the google’s AI for the game Go to everyone’s surprise was able to beat Go champion Lee Sedol.

 Bots kicking our butts in StarCraft : DeepMind AI bots were able to outperform some of the top rated StarCraft II players.

 DIY deep learning for Tic Tac Toe : AlphaToe a AI bot was able to outperform most of the people that played with it.

 Google’s Multilingual Neural Machine Translation : Google was able to make a model which is capable of translating text b/w languages, reaching a new milestone in linguistics and NLP.

 Hence , in a nutshell , 2016 was the year for Deep Learning and a lot of unachievable milestone were conquered during the annual year.

 To know more you can read the full article by Precy Kwan at http://www.datasciencecentral.com/profiles/blogs/year-in-review-deep-learning-2016

Machine Learning is the backbone of today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms. And hence algorithms are the next most important thing in data science after data.
Hence , the question which algorithm to use ? Some of the most used algorithms and their use cases are as follow :

1) Decision Trees - It’s output is easy to understand and can be used for Investment decision ,Customer churn ,Banks loan defaulters,etc.

2) Logistic Regression - It’s a powerful way of modeling a binomial outcome with one or more explanatory variables and can be used for Predicting the Customer Churn, Credit Scoring & Fraud Detection, Measuring the effectiveness of marketing campaigns, etc. ,

3) Support Vector Machines - It’s a supervised machine learning technique that is widely used in pattern recognition and classification problems and can be used for detecting persons with common diseases such as diabetes, hand-written character recognition, text categorization, etc. ,

4)Random Forest: It’s an ensemble of decision trees and can solve both regression and classification problems with large data sets and used in applications such as Predict patients for high risks, Predict parts failures in manufacturing, Predict loan defaulters, etc.

Hence based on your need and size of your dataset , you can use the algorithm that is best for your application or problem.
You can read the full article by Sandeep Raut at http://www.datasciencecentral.com/profiles/blogs/want-to-know-how-to-choose-machine-learning-algorithm

 The first block to build a winning business strategy is to create a map based on business value of the question and approximating how much time would it take to get high quality answers to that question. The idea is to break the business questions into groups that corresponds to real time data systems. It allows you to focus on a specific system at once to build a strong strategy and optimize the sequence in which each sub question needs to be answered depending upon its current business value. A pattern of actions for data strategy begins with a hypothesis and collection of relevant data followed by building models to explain the data and evaluating its credibility for future predictions. The entire process is achieved on an enterprise scale digital infrastructure using Industrialized Machine Learning (IML). This approach can have a huge impact on natural resources and healthcare industries as well.

Read more at : https://blogs.csc.com/2016/07/05/how-to-build-and-execute-a-real-data-strategy/

E-Book business communities generate a lot of revenue everyday but sometimes it is difficult for author(s) to earn decent amount because of lack of preparation and research. No matter how unique and interesting your content is, if it doesn't appear on the first or second page of search results, it's highly unlikely that a visitor would ever read it. The story doesn't end here, one must cleverly select the title and cover which attract the reader as it changes the way we think. A neural network approach for the determination of most titles using Doc2Vec can be adopted to increase revenue. It involves training a thin two-layer neural network, which operates in unsupervised mode and form clusters of most similar words (using cosine similarity metric) based on context.

To read more about the technical implications here: http://www.datasciencecentral.com/profiles/blogs/use-neural-networks-to-find-the-best-words-to-title-your-ebook