Infographic: India – The Hotbed of Analytics Talent

Over the past decade or so, I have been fortunate to be a part of some intense employee performance evaluation discussions. It was fascinating to watch supervisors’ lock horns with each other in gladiatorial like contests to grab the biggest slices for their employees.

“Flying high in the blue sky” is the most beautiful dream that has haunted the heart of the man since…

It’s been an extremely interesting experience trying to do whatever it takes to stimulate the adoption of analytics by enterprises. Typically, service providers dangle the carrot of a POC or a “proof of concept”.

In the relentless pursuit of competitive advantage, companies have adopted data driven decision making in a big way. A big manifestation of this is the proliferation of predictive models that are being deployed to lift business outcomes in a whole host of applications.

These range from trying to understand customers, segmentation and targeting, propensity to buy, forecasting, etc. etc. A typical firm would use 20 – 400+ models depending on portfolio size, number of products and analytics maturity.

The Pursuit of Happyness is a 2006 American film about Chris Garner’s struggle, very beautifully portrayed on screen by Will Smith and a cherubic Jaden Smith. As much as I was moved by the movie itself, what has stayed with me the most, is the title.

EARTH HOUR is a fascinating spectacle. It is quite astonishing to see such a powerful symbol showing synchronization of intent on such a massive scale to conserve what’s left of our beloved planet.

Maruti Peri, Vice President (Americas Region) at BRIDGEi2i Analytics Solutions, shares an interesting take on companies investing in big data and analytics solutions.

Before the era of supermarkets hit our country, the ration of the house would come in from the neighbourhood mom-n-pop store right around the corner. Our grandparents would hand us a ten rupee note and send us off to buy a pack of biscuits, or milk,

Brands, no matter how big or small they may be, a common concern they all share is maximizing their campaign effectiveness. For the sake of illustration, imagine a Mercedes Benz Ad targeting a female aged 22-27, will it be effective in any sense?

We bring to you a 3 part interview series with the BRIDGEi2i Leadership. In the first of the the series, we talk to the CEO and Co-Founder of BRIDGEi2i, Prithvijit Roy.

One of the most interesting features of Tableau is its capability to understand your geographical data like Longitude & Latitude, Country, Stage, Country, Post Code etc. and plot data as filled maps (or choropleths) or symbols on a map at a desired (and available) level of geographical granularity.

“Above all else show the data,” quoted visualization guru, Edward Tufte, way back in 1983. While Tufte might have been referring purely to visualization, we can extrapolate his concern very easily to the metrics we use to “show the data” as well.

Marketing Analytics 101! Learn how defining the right set of marketing metrics will facilitate the growth of your enterprise.

This blog discusses the applications of Monte-Carlo simulation methods by modeling real-world situations, explaining those using well known and often researched statistical distributions such as the Poisson distribution and then applying optimization models…

Gone are the days when a customer was identified as: married, has children, lives in an upscale neighborhood, reads Time magazine and wears a Rolex. Now, customer profile entail aspects such as no. of daily customer visits to the site for the last two months, items purchased by the customer in the last one week etc.

Let us start giving names to the types of customer profiles. Identifying a customer by their attributes is called as “Demographics Segmentation”; to be precise, demographic segmentation consists of dividing the market into groups based on variables such as age, gender, family size, income, occupation, education, religion, race and nationality, whereas identifying customers by their behavior is behavioral segmentation.