SigmaWay Blog

Domain Level Metrics has released a beta version of its tool for mining aggregate metrics and provides an API (Application program interface) aggregate data from marketing analytics provider Moz, search analytics firms SpyFu and SEMrush and web stats provider Alexa. The benefit is that one does not require subscription to access the data on these platforms. To use this data it can be exported to a .csv file as the domain metrics itself is not available via an API. To read more: http://marketingland.com/new-dashboard-offers-metrics-selected-search-web-analytical-tools-184464

Many companies, over the years, have made significant investments, from data warehouses to analytics programs and have pursued the promised benefits of big data and advanced analytics. Data-analytics investments significantly increased value-added or operating profits, the simple revenue impact for consumer companies was considerably lower. The time frame of the analysis is important, since broader performance improvements from large-scale investments in data-analytics talent often don’t appear right away. B2B companies appear to confirm the intuition of executives struggling to uncover simple performance correlations. Read more at: http://www.mckinsey.com/industries/high-tech/our-insights/big-data-getting-a-better-read-on-performance

Satisfying the customers mean retailers needs to be more accurate on the business context, needs more granularity in terms of data and insights, and the ability to respond closer to or in real time. This is where big data technologies provide practical solutions that deliver performance and economies at scale. Understanding customer behaviour and its impact on shopping decisions is a relatively underutilized science, therefore only a combination of big data, machine learning and predictive & prescriptive analytics can tap into the real potential of how organizations understand and respond effectively to customer needs today. Read more at: http://www.computerworld.in/interview/big-data-machine-learning-and-analytics-can-help-us-understand-customer-needs-seema-agarwal-manthan

Jacob Baadsgaard (Founder & CEO of Disruptive Advertising) shows how to determine and track the contribution margin in order to make the marketing initiatives profitable. Basically contribution margin is the difference between the amount of money made from the sale and the variable cost associated with that sale. The easiest way to predict the success of the marketing efforts is by following the rule of thumb.

§  1X contribution margin:- making extreme loses

§  2X contribution margin:- making losses

§  3X contribution margin:- breaking even

§  4X contribution margin:-  starts making profit

To make the data presentable, data visualization is of utmost importance. According to columnist Paul Shapiro data visualization plays a crucial role for marketers because by just looking at large datasets, we can’t get an idea about the pattern of the data. This can be easily done with a help of a scatter plots, bar charts, pie charts, etc. This data visualization makes our data analysis more effective. Here comes in the concept of preattentive attributes. These attributes are those aspects of a visual that our iconic memory picks up, like color, size, orientation, and placement in a few milliseconds. To read more, follow: -http://marketingland.com/brief-introduction-data-visualization-theory-marketers-184112

Implementation of self-service Business Intelligence (BI) can help you reap many benefits. At times non-technical professionals can make better, faster and efficient decisions by generating their own reports and conducting analyses without any sort of assistance from IT staffs. However, these self-service BI environments must be very user friendly in order to be effective. Agility, bandwidth and personnel are some of the factors that should be taken into considerations by the company before implementing self-service BI. Such self-service BI brings along independence and autonomy from IT with it. Few capabilities that these BI tools must possess are anti-hacking facilities and non-stop operation. This technological tool depends on the Enterprise Data Quality. Data Governance on the other hand helps to provide accurate information using the self-service BI tools. Read more at:  http://www.dataversity.net/self-service-bi-success-depends-upon-data-quality-governance/

Machine learning is a type of Artificial Intelligence that provides a computer with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach them to grow and change when exposed to new data. Artificial Intelligence is supposed to be the core of Big Data, producing exponential growth in the volume of data used for scientific research. Out of all two most popular machine learning techniques are, supervised and unsupervised learning. Nowadays, learning algorithms such as Bayesian networks and support vector machines is being used more extensively in daily commercial systems. To learn more read at: http://www.dataversity.net/machine-learning-now/

Accessibility to big data is becoming difficult nowadays. So, here comes in the concept of Indent data. Information collected about a single entity of a company is called intent data. There are two types of indent data:- 1.   Internal indent data: it is also known as first party data as it is the action which company captures on its own website. 2.   External intent data: It is also known as third party data which is collected by publisher networks. This data is collected at the IP level or through registration of users and cookies. To predict prospects, internal intent data is more useful than external indent data. Internal intent data is commonly used for prioritizing, strategically nurturing programs, campaigning etc. while external intent data is commonly used for targeting advertisement and account list, etc. Read more at: http://marketingland.com/intent-data-mean-data-driven-marketer-177773

Scott Rayden (Chief Revenue Officer for 3Q DigitalHere), writes in his article link about some ways for marketers to collect data and use them. They are:

1. Collect first party data which mostly includes name, address, gender, age, etc.

2. Third party data also plays an important role as it gives a deeper insight of customer behaviour.

3. Try to keep the marketing strategies simple, attractive and measurable.

4. The third party data is not very expensive and nowadays typically follows pay-as-you-go option.

A customer relationship management (CRM) is a database which keeps all the data about the customers, their name, what they like. It determines which factor is driving the customers and which makes them angry. CRM works efficiently, when the customer calls, gives all the information like customers' name, age, if he has some disability, his all transactions with the company. All this information comes up on a single screen. CRM also has many key features which will help to know about the system. Read more at: https://www.callcentrehelper.com/an-introduction-to-customer-relationship-management-crm-systems-88296.htm

 Customers get to know about any product from the sales professionals, and sales people use certain key tips to attract the customers. Now, as the technology is advanced, people get to know about all the products, specifications, alternatives and the reviews from the customers who already used it through social media. Sales people use big data have to research on customer behavior. Big data have transformed customer behaviors. Nowadays, sales and marketing professionals also take advantage of these conversions to study the customer behavior and plan accordingly for the promotions and advertising events. Read more at: 

http://bigdataanalyticsnews.com/big-data-has-proved-to-be-the-absolute-enhancer-of-business-sales/

Traditional structured databases are being replaced by databases which are often used in clouds. Well, the problem that such databases bring along with them is that it's very difficult to back up these databases. However, these databases can be protected through multisite replication. To run a node-level backup of the data on one node of the multi-node system is one of the most common methods used to back up these databases. One can also backup their database using a snapshot. Data IO helps to make this process of backing up databases easy. Read more at: https://storageswiss.com/2016/06/07/backing-up-massive-cloud-databases/

 Instead of cracking your head on the entire data available it is of utmost importance to target the data which actually matters. Here are 5 ways to do so:-

1. First, find out the KPI (key performance indicator) of interest.

2. Drain out the noise that is creating useless buzz. This includes activities like likes and retweets graphs.

3. Filter the data patterns that make sense and avoid irrelevant or accidental patterns.

4. Use these data patterns to generate meaningful conclusions.

An extra push in today’s world is a must to withstand your product from the rest of the products available in the market. Marketing is the only sector in a firm that earn revenues for the firm, so any tricks that can provide that extra edge to your marketing is always an add on. Especially, today in a generation of social media, something as relevant as Buzz Analytics can be used to take your business to another level. Buzz Analytics make the use of free and abundant data available on websites to give you the positive or negative sentiments of customers which can always be incorporated while developing a product. Apart from this, Buzz Analytics helps you to keep a note of your competitor’s strength and weakness by analyzing your competitor’s offering. To know more, follow: http://www.mckinsey.com/business-functions/operations/our-insights/using-buzz-analytics-to-gain-a-product-and-marketing-edge

Predictive analysis is now getting more popular as most B2B companies are using it to expand their businesses. But what is important here is to target the right people/accounts. The best way to do this to look at the CRM (customer relationship management) but there is a dearth of optimal databases. To expand their databases, companies are coming up with new marketing ideas, prompting people to view their websites, generating leads and opportunities. But this method may be tedious and costly. Thus we can say that predictive analysis should be used to identify appropriate a/c targets as well as increase the no. of contracts from cost effective marketing programs. To read more:

http://marketingland.com/predictive-data-abm-move-account-lists-account-contacts-181446

According to Nasscom (National Association of Software and Services Companies) the expected rise in the Indian business analytics sector is 8-fold (from $2 billion to about $16 billion). It targets to make India among the top three big data analytics market in the world. To achieve this target Nasscom is partnering with its members to build a multi prolonged approach that encompasses skill development, thought leadership, products and platforms. India is an emerging hub for analytics solutions across the globe. The witnessed rapid growth is due to increased demand for cloud based and predictive analysis solutions by industries like BFSI, retail, telecom and healthcare. Even the requirement of manpower in this sector would increase magnificently in the next 5 years.

It is the rapid advancement of artificial intelligence and deep learning algorithms which enabled the development of machines which can do tasks that requires deep expertise and skills. Read more at: http://economictimes.indiatimes.com/tech/ites/big-data-analytics-to-reach-16-billion-industry-by-2025-nasscom/articleshow/52885509.cms

In the task of predictive analysis, predicting the unknown itself is a challenging problem. Moreover, the entry of an unknown variable in the equation makes the task all the more troublesome. Summary-level data are generally inaccurate and lack deep insights, because of which sometimes such unknown variables manage to creep in. Buyer life cycles generally vary in length in spite of which analysts generally tend to work with smaller cycles, which is dangerous because sometimes important marketing decisions are taken based on flawed information. B2Bs are also depending on real-time insights and are scrapping linear prediction models. It is noticed that, combining Big Data with traditional CRM information is also not sufficient because data science involves lot of research and experimentation. Hence we can conclude that predictive analysis derives its success from data governance and collection. Read more at: http://www.marketingprofs.com/opinions/2016/30118/predictive-analytics-has-a-scaling-problem-and-bad-data-is-to-blame

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

Machine Learning algorithms are classified as –
1) Supervised Machine Learning Algorithms
2) Unsupervised Machine Learning Algorithms
3) Reinforcement Machine Learning Algorithms

Top 10 Machine Learning Algorithms --

1)    Naïve Bayes Classifier Algorithm
2)    K Means Clustering Algorithm
3)    Support Vector Machine Algorithm
4)    Apriori Algorithm
5)    Linear Regression
6)    Logistic Regression
7)    Artificial Neural Networks
8)    Random Forests
9)    Decision Trees
10)  Nearest Neighbours

To know more: https://www.dezyre.com/article/top-10-machine-learning-algorithms/202

Thomas C. Redman (Ph.D),  in one of his article, 'Data quality should be everyone's job', published by Harvard Business Publishing, mentioned that correction of errors in the data is an expensive and time consuming process. Moreover, at times even after correction of the data, some flaws remain which leads to bad credibility of the firm and angry customers. However, if companies welcome provocateurs - individuals concerned with addressing data proactively with the help of their teams, departments, and companies, then errors can be prevented at their source itself. Most of the data revolutionists while exploring with their work found out that to eliminate the root cause of the error and prevent future error, was the best way to ensure high quality data. No matter how innovative an idea is, a Provocateur is a must for the first step in a company dealing with data. So people concerned with data should take up this role actively, which will lead not only in the creation of innovative ideas but will also ensure high quality data output. Read more at: https://hbr.org/2016/05/data-quality-should-be-everyones-job#

According to a new industry study, demand for embedded analytics is increasing. The study has found a growing number of users who wants analytics integrated with applications. The study also suggests that the trend toward embedded tools is being driven by the view that an application's value is tied to the data and the analytical tools available to an application. The goals of embedded analytics include moving beyond the traditional business intelligence approach of extracting insights data and differentiating platforms in a market being flooded with analytic approaches. Read more at: http://www.datanami.com/2016/04/18/analytics-increasingly-seen-embedded-apps/