Customer Relationship Management (CRM) software is important for any sales team as it gives 360-degree view of customers. But, sometimes they could face some problems which in turn prevent them from reaching their goals. This article by Susan J. Owens (content creator) explores the top challenges that are threatening the performance of your CRM system and how to overcome them. Read more at: http://it.toolbox.com/blogs/insidecrm/top-challenges-for-crm-marketers-in-2017-75446

Big Data Analytics and Business Intelligence is changing the way business interacts with customers. Modern big data solutions have enabled automated decision making in debt management systems for client handling processes. Correct implementation of these tools provides a more personalized experience to each customer and avoid infringements. Debt management automation has been proven a successful solution to maintain balance between meticulous efficiency and customer satisfaction. Such a CRM automates a lot of process and thus it requires a small team days to complete debt collection process. Analytics have not just accelerated debt collection, but also enhanced customer relations.

You can read more at: http://www.dataminingblog.com/what-could-big-data-mean-for-debt-management/

Global markets are becoming more complex each day, and therefore, it has become essential for business intelligence teams to apply advanced methods for data interpretation. They believe that only the decisions based on quantitative data can be justified. Although there are some ways quantitative research may go wrong, the truth comes out only when you meet people, talk to them, involve them in creative exercises.

Read more at: http://www.dataversity.net/science-big-data-art-interpretation/

Data is the backbone of analytics and machine learning and hence one of the most important tasks in analytics is to get the right kind of data and in the required format.The importance of data can be understood by the fact that around 60 to 80 percent of the time of an analyst is spent in preparing the data.
What exactly is data preparation? In a nutshell, it is the process  of collecting, cleaning, processing and consolidating the data for use in analysis. It enriches the data, transforms it and improves the accuracy of the outcome.
How is it done? It is mostly done through analytics or traditional extract, transform and load (ETL) tools. ETL tools include self-service data preparation tools, data cleansing and manipulation tools, etc.
Since data is the foundation of the analytics, right data will helps in analysing the situation better and help organizations in reacting positively to the market shifts.
To know more read the full article by Ashish Sukhadeve (business analytics professional) at: http://www.datasciencecentral.com/profiles/blogs/why-data-preparation-should-not-be-overlooked

Modern needs of Big data consumption require data integration before data actually hit the business intelligence tools. This includes leveraging complex and unstructured data and enables raw data to flow securely through business. Today, even the smallest companies produce huge amount of data across systems which need to communicate with each other and therefore requires a platform to pipe all these data sources into Data Lakes.

Read more at: http://www.dataversity.net/dont-put-cart-horse-comes-big-data/

It has been evident that a great customer experience is one of the signs of a healthy business model. Machine Learning and Data Analytics are playing a fundamental role in building consumer intelligence systems. It is important to capture data and there is no single magic source to collect data. Telecoms are making billions by selling data. You need to ensure that the data is relevant to business. Once you have the right data, you are ready to model, design and engineer and deploy your 360-degree customer view platform and achieve the enhance customer experience for your organization.

You can read more at: http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A508502

Content marketing is an important part of any business and is always changing from time to time. Every business house must follow the content marketing trends which helps you to know about the latest changes to the landscape, as well as what competitors might be doing to reach your audience. Roee Ganot (expert in the fields of SEO, analytics, social media and conversion optimization), writes in his article about the top five content marketing trends of 2017. Read more at: http://www.business2community.com/content-marketing/2017-top-5-content-marketing-trends-01758847#4KejdYfiMqB7AECp.97

Analytics is one of the of the most hot topic of the 21st century and it’s starting to become the second currency to  various organisation, but despite having so much knowledge we prone to create some blunders , they are broadly categorised as Data Visualization Errors (Erroneous Graphs) and Statistical Blunders.
Data Visualization Errors (Erroneous Graphs): This is one area that can give a nightmare to both the presenter as well as the audience. Incorrect data presentation can screw the intuition and can also lead to  misinterpretation of data by the audience and can leave the organisation with results which are practically useless for them.
Statistical Blunders Galore: This is probably a “no blunders zone” where one would not want to make false assumptions or erroneous selections and is easily one of the most error prone section. Statistical errors can be a costly affair to both the organisations as well as the audience, if not checked or looked into it carefully and hence must.
To know more read the full article by Sunil Kappal (author) at :http://www.datasciencecentral.com/profiles/blogs/the-most-common-analytical-and-statistical-mistakes

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

Today world has become smaller and faster, with increasing computation speed decisions are done in seconds instead of days. Product information and comparison is available on any device any time. Real Time analytics involve solving problems quickly as they happen or even before they happen. Companies now have more insights into their assets. Several industries are using streaming data and putting real time analytics. The big data revolution has further accelerated the demand of real time analytics to analyze customer behavior. Gone are the days when decisions were based on data stored on a disk , actions are taken on streaming data. Read more at: http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics

A traditional data science department is comprised of Data Scientists, Data Engineers and Infrastructure Engineers. This model has a drawback that one role is always dependent on other and likely to criticize them for task failures because they didn't do their job well. These conflicts may reflect in the quality of final data product. So, what went wrong? You probably don't have big data. Jeff Magnusson (Director of Algorithms Platform at Stitch Fix) suggested a clever approach of forming a "High Functioning Data Science Department" which involves building an environment which allows autonomy, ownership, and focus for everyone involved yet at the same time clearly distinguishing the roles of Data Scientists and Data Engineers. Data scientist can't suddenly become talented engineers nor is that engineers will be ignorant of all business logic, the partnership is inherent to the success of this model. You can read more at: http://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/

There is one traditional on-premises CRM and hosted CRM model. In traditional on-premises model, business owns and supports the software and hardware needed for the CRM solution and in hosted CRM model where CRM is purchased as a service for a monthly service fee. Both models have their own advantages and disadvantages. Generally, SME’s prefers hosted CRM and multinational companies prefer on-premises CRM system. Read more at: http://it.toolbox.com/blogs/insidecrm/hosted-vs-onpremises-crm-75321

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 .

According to a recent study, it was found that mobile CRM applications boosted productivity on an average of 15%, 30% of the respondents reported productivity boosts of 20% or more. In another survey, 81% of CEOs believe mobile technology is strategically important. Use of mobile helps in better communication which in turn helps in better customer service. It also helps the sales staff with more marketing insights on the go instead of being tied to the desktop while researching the prospect and updating on the status of the relationship between client and company. Read more at: http://it.toolbox.com/blogs/insidecrm/using-mobility-to-boost-crm-75304

In modern business, organization must sell in a smart way by using CRM tools as CRM lets you organize information, keep track of prospects, also helps predict what the customers will buy and when they are likely to purchase it. It also acts as a control to make sales efforts more effective and in turn gives valuable information from sales campaigns to individual customers. Read more at: http://it.toolbox.com/blogs/insidecrm/sell-smarter-not-harder-with-crm-75292

Customers have a life cycle like products. If marketers can understand customers, they will have better profitability. The stages are: Reach & Awareness – Customers become aware of the company, Acquisition - Here company can identify the specific prospect by collecting information such as name and address on them and entering it into the CRM database, Conversion – In this stage, a company converts a prospect into a customer which includes the initial sale, Retention – Here first time customers are converted into continuing customers , and Advocacy – this is the final step in the life cycle where regular customers  are converted into advocates for your company and its products. Read more at: http://it.toolbox.com/blogs/insidecrm/customer-lifecycle-and-crm-75251

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