The value of data-driven Customer Value Management or CVM cannot be underrated. Data and other algorithms/analytics that shape data are an imperative part of customer value management in a telecom company. With enhanced customer expectations, it is up to the ability of telecom companies to provide customers with a seamless experience and to also ensure that they help boost revenue in the process.
To understand this concept in a more functional manner, I recently interviewed the chief of CVM at Mahindra Comviva, Amit Sanyal. With so much on hand to discuss, I got to the crux of the matter straightaway and asked Amit about the pillars he considered to be important for a customer value management program being driven by analytics.
The prodigy responded to my questions by commenting that all methods of CVM being driven by the force of analytics should be dedicated towards these three pillars.
Amit also outlined that one of the key challenges facing telecom companies globally is a drop in revenue. The drop in revenue is because of numerous reasons that are making growth a very difficult option to undertake for all protagonists involved in the market. While all telecom operators are looking out for newer options in the form of fresh customers, it is imperative to note here that fresh customers are rarely found. Most geographies have network connections than the people living in it or very close to that, so there is a real shortfall of new customers coming in for new connections. Other than the shortfall in garnering fresh customers, Amit also highlighted how the revenues from current customers were decreasing. These revenues have been decreasing steadily for a while now, due to the high amount of competition between the firms present. Most over the top or data content services are free. Margins have significantly dropped, since operators cannot risk selling at expensive rates considering how there are other operators selling at reduced rates.
The solution to this problem lies in reaching out to customers in a seamless manner. Since revenues in the market can only be increased through acquiring fresh customers or earning more revenue through current customers, reaching out to the customers and understanding their data is an inevitable outcome that needs to be followed.
Since it has been mentioned above that there are limited fresh customers in the market, growth can only be achieved by bringing in customers from other operators. Simply put, you need to acquire customers from somewhere else to show your growth.
Besides bringing in new customers, you can also increase revenues from existing customers by understanding the economic concepts of elasticity and inelasticity. Operators need to know just what customers will be willing to spend their dollars on. Match the products and services they want with a price tag that gets customers to buy them.
Moreover, you can also increase the quality of your service. Subscribers tend to stay longer with an operator who offers quality. Not only will they stay longer, but they will also bring in new customers from other brands by telling them about the quality of your service.
To do all of this, you need to know just what the consumers are looking for. This is where the concept of machine learning and real time analytics come in. You should comprehend how your typical consumer behaves, and should also have a basic understanding of their preferences. You can use big data in the network systems, and implement methods such as predictive analytics and targeted communications to get the data that helps you understand them. By understanding their behavior and their preferences through real time data visualization, you can know just what will be perfect for your customers. Implementation of this method could open doors to data-driven customer value management and machine learning. Some of the stats pointing in favor of these changes are:
Data-driven marketing is key for enhancing the customer experience. Data driven marketing can help connect data points and link them together to create a more actionable context. Cases that highlight this are:
The implementation of data-driven marketing calls for a mindset change in telecom operators. Operators need to understand what customers prefer, and then they should reach out to them on a personal level through data. “Everybody loves to talk about data science, it’s a cool thing – but only a few really move towards implementing it” said Amit before concluding the interview.
Originally published here.
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Online security has always been a major concern for individuals on the internet. Whether the general population knows it or not, there are issues here which destroy the goodwill of those who decided to give major online industries the benefit of the doubt. Hot off of the heels of Facebook’s recent drama concerning stored user text message data, this problem is finally seeing some changes within the EU. These changes adopt components that have long been key to the world of cryptocurrencies, specifically those surrounding the technology of blockchain. So, where do these changes come from, who will they effect, and what advantages can the end user hope to see?
Those Pushing for Change
As is often the case with developments in modern online systems and the way in which they integrate with the world, regulations are playing catch-up. The rapid development of technology means that laws and regulations can go out of date quickly. Unfortunately, this means that loopholes and similar security flaws can appear in unpatched systems, potentially exposing both customers and businesses to data theft.
Understand this was the key as to why the EU has decided to enact new regulations on big data. One of these methods comes in the form of the General Data Protection Regulation, or GDPR, which takes effect on May 28, 2018. In general terms, this law aims to require firms to first gain consent on the exact type of data which they will receive from users, and for them to clearly state the ultimate purpose for which this data is being collected.
“European Union flag P5132670″ (Public Domain) by kbrumann
Those Affected
Since this is regulation passing in the EU, many mistakenly believe that it only applies to those websites stationed in the EU, but this is incorrect. In actuality, these will apply to any organization placed outside of the EU which aims to collect information of people within the EU. While the current understanding is that these changes will cost businesses significant costs in terms of both money and manpower, the idea is finally to take a step on protecting the right to privacy of people over the internet and encourage other nations to follow suit along the way. Exactly how well adopted these series of consumer-level protections will manage to fight against corporate powers and lobbyists remains to be seen.
Why Blockchain?
Blockchain offers several significant advantages, which make it perfect as a means of security. The first of these components is that of decentralization. By removing a single point of ownership as a feature of security, the system instead becomes reliant on the blockchain network. The decentralized nature of this network means that threats and corruption face significantly more hurdles if they wish to take place. As each part of these systems are interconnected, and can tell if the others are being manipulated, it means that data theft or manipulation would require the simultaneous hacking of multiple systems, over multiple locations, in ways which perfectly trip each of the multiple security measures. Not a simple task, even for the most dedicated and professional hackers out there.
With these advantages, it might now seem obvious why blockchain based security is a positive choice but, for further examples, we can look at how other organizations have adopted the use of this technology. The most obvious and widespread example can be seen from the cryptocurrency market, Bitcoin specifically. As this type of transaction comes with such high levels of inherent security, even comparing favorably to fiat currencies in many areas, it has been adopted by many online stores. These include marketplaces like OpenBazaar, hotel bookings with Expedia, and gadgets through Newegg. Furthermore, the presence of Bitcoin and blockchain in the iGaming world has allowed casino websites to offer provably fair gameplay.
“Bitcoin and cryptocurrency” (CC BY 2.0) by stockcatalog
Long-term Advantages of Using Blockchain
By putting security power back in the hands of the consumer, the idea is to create a system which is both safer, and which is better able to function without the interference of or reliance on the organizations which have long taken advantage. This means a future with fewer surprises of actions which have hurt the user bases of many websites and services. To use the Facebook example again, with this regulation in place, the data and private information theft would not have been possible in any way. It would have had to agree to the regulations in the first place, setting themselves up for litigation in the case of dishonesty. Following this, they would still have to manipulate the blockchain, which would have been both detected and noticed before any actual damage could occur.
Regulations such as the one we are seeing with the GDPR have been a long time coming. To many, they are an inevitability which comes from the overreach of those with little opposition. By enacting these regulations, and relying on security measures such as blockchain to protect the end users, there are now effective safety measures in place, which means we no longer must rely on trust. With these changes and regulations, now and in the future, our data and information are safer, we are safer, and the online world is all the better off for it.
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R is considered as the de facto programming language for statistical analysis right? But In this post, I will show you how to easily implement statistical concepts using Python.
I will implement discrete and continuous probability distributions using Python. I won’t get into the mathematical details of these distributions, but I will mention some of the best resources to learn the math concepts involved in these methods.
Before we jump into these probability distributions, I want to give a glimpse of what a random variable is. A random variable quantifies the outcomes of a number.
For example, a random variable for a coin flip can be represented as
X = { 1 heads
2 if tails}
A random variable is a variable that takes on a set of possible values (discrete or continuous) and is subject to randomness. Each possible value the random variable can take on is associated with a probability. The possible values the random variable can take on and the associated probabilities is known as probability distribution.
I encourage you to go through scipy.stats module.
There are two types of probability distributions, discrete and continuous probability distributions.
Discrete probability distributions are also called as probability mass functions. Some examples of discrete probability distributions are Bernoulli distribution, Binomial distribution, Poisson distribution and Geometric distribution.
Continuous probability distributions also known as probability density functions, they are functions that take on continuous values (e.g. values on the real line). Examples include the normal distribution, the exponential distribution and the beta distribution.
To understand more about discrete and continuous random variables, watch Khan academies probability distribution videos.
Binomial Distribution
A random variable X that has a binomial distribution represents the number of successes in a sequence of n independent yes/no trials, each of which yields success with probability p.
E(X) = np, Var(X) = np(1−p)
If you want to know how each function works, you can use help file command in your I python notebook. E(X) is the expected value or mean of the distribution.
Type stats.binom? to know about binom function.
Example of binomial distribution: What is the probability of getting 2 heads out of 10 flips of a fair coin?
In this experiment the probability of getting a head is 0.3, this means that on an average you can expect 3 coin flips to be heads. I define all the possible values the coin flip can take, k = np.arange(0,11), you can observe zero head, one head all the way upto ten heads. I am using stats.binom.pmf to calculate the probability mass function for each observation. It returns a list of 11 elements, these elements represent the probability associated with each observation.
You can simulate a binomial random variable using .rvs. The parameter size specifies how many simulations you want to do. I ask Python to return 10000 binomial random variables with parameters n and p. I am printing the mean and standard deviation of these 10000 random variables. Then I am going to plot the histogram of all the random variables that I simulated.
Poisson Distribution
A random variable X that has a Poisson distribution represents the number of events occurring in a fixed time interval with a rate parameters λ. λ tells you the rate at which the number of events occur. The average and variance is λ.
E(X) = λ, Var(X) = λ
You can notice that the number of accidents peaks around the mean. On an average you can expect lambda number of events. Try different values of lambda and n, then see how shape of the distribution changes.
Now I am going to simulate 1000 random variables from a Poisson distribution.
Normal Distribution
The normal distribution is a continuous distribution or a function that can take on values anywhere on the real line. The normal distribution is parameterized by two parameters: the mean of the distribution μ and the variance σ2.
Normal distribution can take values from minus infinity to plus infinity. You can notice that I am using stats.norm.pdf as normal distribution is a probability density function.
Beta Distribution
The beta distribution is a continuous distribution which can take values between 0 and 1. This distribution is parameterized by two shape parameters α and β.
The shape of beta distribution depends on the values of alpha and beta values. Beta distribution is predominantly used in Bayesian analysis.
Exponential Distribution
The exponential distribution represents a process in which events occur continuously and independently at a constant average rate.
I set the lambda parameter as 0.5 and x in the range of
Then I simulate 1000 random variables from an exponential distribution. scale is the inverse of lambda parameter. ddof in np.std is equal to dividing the standard deviation by n-1.
Conclusion
Distributions are like blue print for building a house, and random variable is summary of what happen in an experiment. I would recommend you to watch the lecture from harvard data science course, professor Joe Blitzstein gives a summary of everything you need to know about statistical models and distributions.
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The universe of banking and payments is ever evolving. 2017 has seen a number of significant changes in the payments industry, thanks to advances in technology. Consumers now have access to a myriad of ways to pay. As a result, payment and shopping habits change. e-Commerce and m-Commerce methods such as in-app and one-click commerce are becoming increasingly popular. In addition, the exponential growth of IoT, one can foresee a wealth of new payment use-cases over the next few months. In this infographic, we present 10 key trends that will shape the payments industry in 2018.
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Are you searching for some best books to get acquainted with the basics of AI? Here is our list!
1. A Course in Machine Learning
Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
2. Simply Logical: Intelligent Reasoning by Example
An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material. A unique advantage to this work is the combination of AI, Prolog and Logic. Each technique is accompanied by a program implementing it. Seeks to simplify the basic concepts of logic programming. Contains exercises and authentic examples to help facilitate the understanding of difficult concepts.
3. Logic for Computer Science: Foundations of Automatic Theorem Proving
Covers the mathematical logic necessary to computer science, emphasising algorithmic methods for solving proofs. Treatment is self-contained, with all required mathematics contained in Chapter 2 and the appendix. Provides readable, inductive definitions and offers a unified framework using Getzen systems.
4. Artificial Intelligence: Foundations of Computational Agents
This textbook, aimed at junior to senior undergraduate students and first-year graduate students, presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture.
5. From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots
From Bricks to Brains introduces embodied cognitive science and illustrates its foundational ideas through the construction and observation of LEGO Mindstorms robots. Discussing the characteristics that distinguish embodied cognitive science from classical cognitive science, the book places a renewed emphasis on sensing and acting, the importance of embodiment, the exploration of distributed notions of control, and the development of theories by synthesising simple systems and exploring their behavior.
6. Practical Artificial Intelligence Programming in Java
This book has been written for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming techniques. In the style of a “cook book”, the chapters in this book can be studied in any order. Each chapter follows the same pattern: a motivation for learning a technique, some theory for the technique, and a Java example program that you can experiment with.
7. An Introduction to Logic Programming Through Prolog
This is one of the few texts that combines three essential theses in the study of logic programming: the logic that gives logic programs their unique character: the practice of programming effectively using the logic; and the efficient implementation of logic programming on computers.
8. Essentials of Metaheuristics
The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 70 figures and 133 algorithms great and small.
9. A Quick and Gentle Guide to Constraint Logic Programming
Introductory and down-to-earth presentation of Constraint Logic Programming, an exciting software paradigm, more and more popular for solving combinatorial as well as continuous constraint satisfaction problems and constraint optimisation problems.
10. Clever Algorithms: Nature-Inspired Programming Recipes
This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralised manner. These standardised descriptions were carefully designed to be accessible, usable, and understandable.
11. Clever Algorithms: Nature-Inspired Programming Recipes
Covers the mathematical logic necessary to computer science, emphasizing algorithmic methods for solving proofs. Provides readable, inductive definitions and offers a unified framework using Getzen systems. Offers unique coverage of congruence, and contains an entire chapter devoted to SLD resolution and logic programming (PROLOG).
12. Common LISP: A Gentle Introduction to Symbolic Computation
This highly accessible introduction to Lisp is suitable both for novices approaching their first programming language and experienced programmers interested in exploring a key tool for artificial intelligence research.
13. Bio-Inspired Computational Algorithms and Their Applications
This book integrates contrasting techniques of genetic algorithms, artificial immune systems, particle swarm optimisation, and hybrid models to solve many real-world problems. The works presented in this book give insights into the creation of innovative improvements over algorithm performance, potential applications on various practical tasks, and combination of different techniques.
14. The Quest for Artificial Intelligence
This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers.
Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications.
16. Virtual Reality – Human Computer Interaction
At present, the virtual reality has impact on information organisation and management and even changes design principle of information systems, which will make it adapt to application requirements. The book aims to provide a broader perspective of virtual reality on development and application.
This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field.
18. Machine Learning, Neural and Statistical Classification
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems.
Ambient Intelligence has attracted much attention from multidisciplinary research areas and there are still open issues in most of them. In this book a selection of unsolved problems which are considered key for ambient intelligence to become a reality, is analysed and studied in depth.
20. The World and Mind of Computation and Complexity
With the increase in development of technology, there is research going into the development of human-like artificial intelligence that can be self-aware and act just like humans. This book explores the possibilities of artificial intelligence and how we may be close to developing a true artificially intelligent being.
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People are able to process visual information much faster than textual content. For this reason, data visualization is an essential model of contemporary business intelligence. There are hundreds of techniques to present business-related information and sometimes it might appear challenging to find the best one for you.
In case you are running out of time, you should understand the basic data visualization tactics and keep in mind the catalog of charts, graphics, and diagrams. Luckily enough, you can count on our help in that regard. In this article, we will describe the top 5 best data visualization techniques.
Best Data Visualization Tactics
Data visualization can communicate complex information in a way that is easier to interpret by turning information into visually engaging images and stories. It enables you to highlight the most relevant conclusions from what would otherwise be considered a huge pile of worthless documents.
But what is the way to approach data visualization? What are the most efficient techniques? Let’s take a look at the 5 best models here:
The age of IT and Internet as we know it seems to last forever but don’t forget that it is still in its 20s. On the other hand, data visualization is even younger than that. A lot of people – even entrepreneurs – don’t know how to read more than a simple chart or pie. That’s why you need to understand the target audience and adapt the presentation so as to match their IT literacy.
Lucille Neely, a digital marketing specialist at Best Dissertation, explained it concisely: “If you are dealing with inexperienced clients, stay away from advanced solutions. But if you are meeting highly skilled professionals, going beyond pies and charts is mandatory”. Therefore, you must get to know the audience you face and give them materials they can digest successfully.
What you want to present is as important as who you are showing it to. There are 4 basic ways to approach data visualization:
- Relationships: Shows the connections and mutual impact between specific elements (such as education level and average income). Scatter plot is the best choice in this case.
- Timeframe: Line graphics suits perfectly if you want to show how certain phenomenon is developing over time.
- Composition: This technique is developed to reveal the structure of a single unit, showing its constitutive elements. A pie chart is the simplest way to do this but if you want a more distinguished data visualization, go for the 100% stacked horizontal bar graph or a slope graph.
- Comparisons: Bar charts are the usual suspect if you want to compare two or more values.
Although it seems irrelevant, the colors you choose will strongly impact the overall effectiveness of your data visualization model. You should keep 2 things in mind here: color consistency throughout the documents and the contrast.
First of all, you should make contrasts between the opposing elements, emphasizing the differences among these features. People mostly use red, green, blue, and yellow because they can be recognized and distinguished easily.
Secondly, you should not mix the colors too much because it creates confusion among viewers and interferes with already established patterns. For example, if you used red to mark negative trends and green to highlight positive outcomes, don’t change the style throughout the document.
Data visualization can become a source of valuable digital content, which demands adding interactive elements to the presentation. Interactive maps play the major role in that regard because they allow users to engage and look only for information that they really need.
Interactive maps enable users to wander around the chart, zoom in and out, identify special elements upon click, get a 360-degree overview, and many other interesting features. Creating such maps is a highly complex process but it will definitely leave a great impression on your clients or customers.
As the matter of fact, interactive maps had already become a standard technique for the vast majority of companies and websites, with the likes of Google, Booking.com, or National Geographic setting a good example for data visualization community.
There are dozens of incredible data visualization tools available online. We strongly suggest you use some of these programs to create custom tables because it’s the only way to impress your clients in 2018. Here are our recommendations:
- Microsoft Power BI: This app is easy to use and extremely intuitive. It is available in free version, so you can give it a try before deciding whether to conduct the purchase.
- Zoho Reports: The tool has tons of beginner-friendly features but also a lot of advanced possibilities, which makes it suitable for all levels of expertise.
- Chartio: Chartio is difficult to learn but very good for data visualization professionals.
Conclusion
Data visualization can help you to create better and more appealing business reports, maximizing the potential of your analysis. If you want the attention of your clients and colleagues, you need to learn modern data visualization models to improve the quality of your presentation.
In this article, we showed you top 5 best data visualization techniques for 2018. Give our tips a try and don’t hesitate to leave us a comment if you have more suggestions to share with our followers.
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