The latest buzz in Indian Analytics industry – March 2015

Analytics maturity in India climbs new rungs of opportunity, as it gets tied to cricket, beauty pageants and news distribution. Personalisation knocks on your living room with customised ads on your TV set and tailor-made news in your local language on your smart phone.

Takes you back to this quote;

The ladder of success is best climbed by stepping on the rungs of opportunity – Ayn Rand.

Acquisitions, Partnerships & Expansions

# Bangalore based regional news aggregator NewsHunt, has acquired the mobile analytics company Vauntz. This will help it to publish finely tuned, targeted and personalised content to users in local languages. NewsHunt also plans to develop apps that will distribute personalised videos and audios with regional content.

# British-Australian mining corporation, Rio Tinto, has unveiled its Analytics Excellence Centre in Pune, to improve equipment productivity across its global operations.  A world-first for the mining industry!

New launches – start-ups, services, vendor products, apps

#Ahmedabad based software company ElegantJ BI, has launched version 4 of its BI suite, with easy-to-use API integration for 3rd party applications.

An easy-to-use API through web services and Java script, embeds ElegantJ BI dashboards, reports, KPI and graphs into other vendor applications or in-house applications; for fast, affordable implementation and mobile access.

# TCS has launched a smart phone application that allows users to track, analyse and visualise Twitter conversations. Devised for the oncoming UK General Election,  the ‘ElectUK’ app will engage voters, their representatives and political commentators, turning the  smart phone into a Big Data social media analytics tool.

# Gurgaon based SilverPush, has introduced an innovation for ad analytics leveraging its cross-device expertise. It moves beyond the conventional impact measurement of TV commercials to retargeting the TV viewers on mobile.

“The company uses a sophisticated deterministic modelling to identify the multiple devices associated with a single user and maps his/ her demographics and behavioural property into a unique id. “

# Tableau Software is introducing six new features to its latest BI suite making data visualisation easier. Data preparation, query performance, admin views and a smarter, map functionality are the features introduced.

The Indian analytics landscape with a head start in IT, is all set to rock across verticals and application scenarios. As start-ups continue to make news, the analytics professionals can look forward to challenging and lucrative times ahead.

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Why you need to understand Digital Analytics

Before I do a blog on the latest craze in the hiring landscape – ecommerce analytics, I’d like to cover digital analytics in its new avatar.

As this article in Forbes magazine says, 2014 was the Year of Digital Marketing Analytics, when the ‘digital’ took over the marketing landscape. According to analytics pundits, the trend spills over to 2015, with Digital Analytics continuing to dominate the analytics space.

We have already covered web analytics, and explained marketing analytics. So let’s examine digital analytics.

What is Digital Analytics?

Digital Analytics is defined as

“the collection, measurement, analysis and reporting of data for optimizing channel usage…”

The most popular definition is from Avinash Kaushik.

Digital analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline).

So what it does, is collect, measure, analyse and report data residing in digital media for optimum usage, answer questions and offer insights for business actions.

In the larger picture,

web analytics < digital analytics < marketing analytics < e-commerce analytics

Where is Digital Analytics used?

Digital 1Why Digital Analytics?

 Digital 2


Why you need to equip yourself with Digital Analytics skills?

Today every activity, opinion or event is digitised. Social media, cell phones and the internet have greatly influenced consumer behaviour and the way businesses work. There is a seamless flow of information across the digital space, waiting to be harnessed for insights.

  • Every firm, commercial venture and store, has a digital presence – website, mobile apps, e-commerce, social media, content or comments on the web, and digital advertisements (video, TV and web).
  • Businesses are adopting a data-driven approach for maximizing ROI on the digital spend.
  • Social media, websites, cell phones, TV viewership and even store footfalls have become digitised points of data collection.
  • Digital analytics has become an integral part of every functional area – marketing, fraud detection, retail, social media, healthcare, banking and so on.
  • Organizations are moving their marketing budget to digital channels. Growth in digital ad spending is the fastest at 30%, expected to account for 9.51% of all ad spending in 2015. The e-commerce segment has seen the highest digital ad spends, growing at a CAGR of 59 % since 2011; followed by the Telecom and FMCG

Digital 3

  • 70 % of companies intend to increase investment in third-party social media services like Facebook.
  • Businesses like hotel booking and travel portals are tripling their spends on digital assets (investments in digital media management).

It is a ‘digital’ environment today.

So whether you are engaged in analysing the reason for the failure of a new product, or tracking repeat defaulters of a particular loan scheme, you will need to engage tools of digital analytics at some stage/s. It is no longer about digital marketing alone.

Bottomline – While web analytics may soon be passé, digital analytics is here to stay.  So it is time to hone your skills in digital analytics too.

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10 Tips to face and ace an Analytics Interview

The way businesses work is fast changing. We now have telephone interviews, skype sessions and of course, the conventional face-to-face. The entire process of getting interviewed starts with a telephone call or email from your institute or company. So at the very outset, what you need to do is gather information about the interview – the venue, time, whether you are expected to give tests or a presentation, any specific expectations, and so on.

With the above, you take the first step towards preparing yourself for the interview!

General, Behavioural (Soft skills) – How well you communicate, interact and present yourself

  1. First impressions do matter. So arrive before time. Dress appropriately. No strong perfumes. Walk in with a confident gait. And please, turn off your cell phone!
  2. Greet the members of the interview panel (“Good morning, Sirs and Madam”, Good afternoon, Sirs”). Make eye contact with each member. A firm handshake and a smile on your face. Wait to be asked to sit.
  3. Talk positive. Avoid any negative phrases (“I am not sure”, “I don’t think so”, “I will not be able to”). Talk slowly and clearly, but confidently. Clear communication is very important.
  4. Understand the question before you answer. If you are not sure, you can ask politely for the question to be repeated (“I am sorry I could not get the question. Could you please repeat it?”), rather than making a wild guess.

Technical Assessment – How sound is your knowledge

  1. Be prepared for questions that test your basic statistical concepts, and how well you can apply to different algorithms.
  2. Be prepared for questions that are solved using analytical tools like R and SAS, or whatever you have mentioned in your CV.
  3. Review the projects you have worked on – as a student or analyst. Be prepared to outline the analytical process, algorithm used, model validation / testing done, challenges faced and how they were resolved.

Skill Assessment / Situational Questions – How well you demonstrate your logical reasoning and business thinking skills

  1. Puzzle questions – Adopt a confident approach. Tackle the questions in a structured top-down manner, covering maximum aspects and situations. Ask questions. Call out assumptions if you are making them. Put it down on a paper for a methodical answer in the shortest time.
  2. Case Studies (Real World situations) – Address the problem clearly. Describe your solution and the action you would take. Make sure to include appropriate analysis and technical skill application.

Talk aloud as you work through your answer. An ideal situation would be an                           interactive session. Explain your suppositions and the factors that you think may                  have an effect on your estimate.

  1. Presentation – The key here is to think on your feet, as you will most likely get a time limit. Outline the problem in paper immediately. Use figures to illustrate. Circle solutions. If asked to use the computer or whiteboard, transfer your ideas to the screen / board.

Focus on logic. Present a structured thought process. Cut out the fancy.

Bottomline: Interviewing methods and questions depend upon whether you are applying for an entry level / mid level position. The interviewing panel may also adopt new approaches that test you for your logical reasoning and out-of-the-box thinking. The technique here is to think fast (ace/play that winning swerve), present what you know (all the aces up your sleeve), as best you can.


Useful resources from IVY PROFESSIONAL SCHOOL:

How to land a great job in Analytics

Top 30 Common Interview Questions

Latest Trends in Analytics Hiring

‘Net’ that job

How good is your personal AQ (Analytics Quotient)

5 Skills you need to become an Analytic Professional

Use Social Networking Sites to get a job

Be Prepared

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Why Data Dredging is trending

“If you torture the data long enough, it will confess to anything.”   – Ronald Coase, British economist

 If you thought DATA was only ‘mined’ and ‘extracted’ for analysis, take a look at this frequently used method of ‘data dredging’.

As we move over from traditional eyeballing of statistical data to dig deeper into machine based techniques, the entire process of DATA extraction gets more technique based.

One such DATA extraction practice is analysis of large volumes of data in the quest for ANY possible relationships. An example would be “fishing” in very large datasets to analyse crime clusters without understanding causation. Or say “snooping” into an App user’s habits for finding correlations.  That is, combing data for patterns without pre-established hypotheses or objectives. Which sounds absurd, but may actually throw-up significant unseen relationships (what does the App user do at lunchtime when in the vicinity of Connaught Place, New Delhi?).

With the evolution of Big Data a fundamentally different practice of experimental design has evolved. Formerly, the project / questions asked would decide what data to collect, for analysis of the same. Now, the low cost of data storage has caused a rethink with all kinds of data being collected first and then searched for significant patterns.

This practice of “data dredging” differs from traditional Data Mining practices.

Data dredgingData Dredging explained

Where the sample size is not truly representative, there is ‘confounding’ or ‘selection bias’, or there exists too many hypotheses for a given dataset, there may occur some highly correlated data that are statistically significant. Whereas, there is no effect between the variables and confidence level is .05 (5%). This is a typical case of “data dredging” with false positive findings, a result of looking at too many possible associations. One way to conquer errors  of “data dredging” is being stringent with “significance” levels, moving to P<0.001 or beyond.

Applications of Data Dredging

  • Forensic Analysis
  • Market Basket Analysis
  • Risk Analysis
  • Fraud detection
  • Medical Science
  • Public Health
  • Clinical Research
  • Digital Analytics
  • Social Media

When does Data Dredging occur?

  • Failure to make adjustments for statistical effects of search in large models
  • When there is statistical bias, confounding or misrepresentation of  the P<0.05 significance test
  • When there is suboptimal model construction
  • When there is ‘Overfitting’ of data
  • When too many hypotheses are tested without proper statistical control
  • When there is ‘Oversearching’ of relationships between variables
  • Overestimation of model’s accuracy
  • When Data Mining technique is explicitly used to prove a particular pre-established point of view!

So the next time you read such research findings like “Teens who eat lots of chocolate tend to be slimmer” – take it with a pinch of salt. Better, look at it as a possible consequence of distorted “data dredging”!

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Top 20 Analytics interview questions – Ecommerce

As analytics hiring increases across e-commerce companies, we decided to compile a list of interview questions to give you an idea what to expect.

At the very outset however, DO YOUR HOMEWORK. Research the company. Look at its site. Understand its mission statement and product offerings. Try to figure out what data is collected and how it is used.


These are questions aimed to test your analytics knowledge in digital marketing.

  1. What is your understanding of how e-commerce works?
  2. Differentiate between B2B, B2C, C2B and C2C.
  3. What is a Klout score?
  4. Explain the difference between attribution analysis and modeling.
  5. When is Structural Vector Autoregressions (VARs) favoured over the Bayesian approach?
  6. What method can help identify relationships and correlation between two elements?
  7. What techniques are used to measure campaign success?
  8. What are the KPIs (Key Performance Indicators) in supply chain operations?
  9. Which statistical method is best suited for predicting sales by product categories and forecast of margins?
  10. In demand forecasting, which techniques can help separate seasonal demand from trends?


These questions are for job roles that require a higher proficiency of digital analytics skills. Questions are aimed at gauging the level of involvement and analytic skills of the candidate. Some questions may also test vigilance to factors that skew results, and out-of-the-box analytical thinking.

  1. How would you use Analytics to help determine what CRO project will return the greatest ROI?
  2. What should you test if you have a decreasing open rate?
  3. How do you validate that your analytics is tracking correctly?
  4. How to know you have an attribution problem?
  5. If you could only choose one, would you choose A/B testing or segmentation? Why?
  6. Our client is complaining that their CTR is low. What would you investigate?
  7. * How do you approach deciding what to optimise first? (Ideally a candidate should ask questions about existing analytics and other factors driving business priorities).
  8. If the Google Analytics conversion reports do not match your multi-channel conversion reports, what do you infer?
  9. * What was one of the most surprising outcomes to a test you’ve run? What were the hypotheses that motivated their testing, and how did their perspective evolve over a sequence of tests? What did they learn from them?
  10. * How do you weigh the trade-offs between brand consistency and experimentation?


* These questions may have a series of follow-up questions. There is no single answer to these questions or a perfect solution. The aim is to test vigilance of the candidate, boldness in trying imaginative ideas and conversion savvy.

If you have interview questions you would like to add, please comment below, and I will surely include them.

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Why you need to upgrade your CV with skills and certifications

Today, a stand-alone degree or post-grad, is not sufficient to make you stand out in your chosen field of career.  As industries and businesses become more competitive, global and specialised, you need to keep pace with changing environments and IT systems.

If you are a fresh student, you certainly have the advantage of being familiar with emerging business models as they form part of your student projects. At the same time, mid-career professionals bring in their many years of work experience to their learning process.

Real-world knowledge is the way to go!

From understanding how to leverage social media analytics into your product upgrades or new model release to maximising the personalisation algorithm in an e-commerce model, there is plenty to learn and discover in real world scenarios.

Many softwares too have revolutionised the way analysis is done to suit business needs. While SAS and SPSS remained the dominant software in use, the flexibility of R statistical software has compelled many firms to adopt R.

What is all the hype about ‘Big Data’? Is the analytics process or modeling different for Big Data ecosystems? Do you need to re-learn your analytics for working with Big Data, or do you simply need to master the techniques of handling huge volume of data for insights in real-time?

To understand all of this and more, what you need is to upgrade your knowledge base in continuum. Much like Doctors and medical professionals do, keeping abreast of new viral diseases and outbreaks.

So how do you go about upgrading?

Check out professional career training institutes like IVY Professional School, which offers intensive campus training, as well as other modes customized to your company requirements. These are tailor-made to suit your timing and/or career. You can counsel / consult with the IVY team to find which training program would best enhance your learning curve.

What do you get?

  • You learn the software and statistical methods used in analytics.
  • You get a good grasp on the latest analytics models / techniques.
  • You are up-to-date with the current trends and practices in analytics.
  • You have excellent scope to land a good job with better salaries and entry positions.
  • If already working, you stand out in your workplace with the latest skills, software and applications. This puts you in the forefront for bigger and lucrative projects and of course, promotions.
  • With very less time and money invested, you get a certification that looks good on your CV and shortens the curve of your career prospects. You rise, shine and earn more in your chosen filed in a shorter time frame!

So what are you waiting for?

Get going and upgrade your skills to make the best of a fast-changing analytics industry.

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URGENT Job Opening Alert (Analytics) -

- Role : Data Analyst

– Domain :Analytics

– Education Background : B.Sc. / M.Sc. – Maths, Stats,  Econometrics , MBA , B.Tech.

– Work Experience : 0-2/3 Years

-Key Skills :Advanced Excel , VBA, SAS, R


– Should have undergone Analytics training at Ivy Professional School

Send your resume to at the earliest by 23rd February , 2015 by 9 pm. Or, call at 09748441111 should you have any questions.

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Urgent Job Opening Alert for R Analyst

URGENT Job Opening Alert (Analytics) -- Role : Analyst

– Domain :Analytics

– Education Background : B.Sc. / M.Sc. – Maths,Stats, Econometrics

– Work Experience – 0-1 Years

- Company : WNS

– Should have undergone Analytics training at Ivy Professional School

Send your resume to at the earliest by 20th February , 2015 by 1 pm. Or, call at 09748441111 should you have any questions.

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-Urgent Opening For R Internship-

-Urgent Opening For R Internship-

Location – Kolkata

Educational background – B.Sc. / M.Sc. In Econometrics /Statistics

Knowledge of R programming language is Mandatory

Should have undergone Analytics Training at Ivy Professional School

Send your resume to at the earliest by 20th February , 2015 by 1 pm. Or, call at 09748441111 should you have any questions.

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URGENT Job Opening Alert (Analytics) -

URGENT Job Opening Alert (Analytics) –

- Role : Analyst
- Domain :Analytics
- Education Background : B.Tech / B.Sc.In Computer Sc.
- Location : Bangalore
- Work Experience – 0-1 Years
- Desired Skills: R , SQL , RDBMS, Statistics;
Must Possess Good Knowledge in Programming
- Should have undergone Analytics training at Ivy Professional School

Send your resume to at the earliest by 4th February , 2015 by 1 pm. Or, call at 09748441111 should you have any questions.

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