Customer World

As I reflect back over the year, this is one conversation that has left some deep impressions on me. The challenges that are there in rewiring our skills, learning from different streams and applying them in our day-to-day working is becoming more and more critical.

Marketing today is on the threshold of change. In the past, marketing as we knew it was largely dominated by 30-second TV spots and other mass media such as print, outdoor, radio and so on. The number-crunching only came into play while deciding which medium to back in the advertising campaign and for what price to buy the media.

But, look around today and there are the likes of Google, Facebook, Twitter and others who apply complex algorithms such as Page Rank, Adsense, marketing mix modelling, content marketing and so on along with technology (analytics, digital marketing, search engine optimisation (SEO) and search engine marketing (SEM) to make marketing a lot more data-driven. Similarly, in music the magic of maths plays a huge role.

As a musician, Padma Vibhushan Umayalpuram K Sivaraman has an accomplished career of nearly 71 years – he debuted at the age of 10 and has been seen and played with the best musicians across the years. His son has been one of the earliest adopters of marketing analytics in India. cat.a.lyst brings you a conversation between the legendary mridangam vidwan and his son S Swaminathan, Co-Founder & CEO of Hansa Cequity, a major customer marketing & analytics firm, on the cross-learnings from music, maths and marketing.

Here's the full link to the conversation

Recently, I read a very provocative and interesting article in HBR - 'AI may soon replace even the most Elite Consultants'

As I read thro' the article, the key question that came to my mind was- really, how close are we to this reality? Leave alone consulting, there are several industries like legal, medical, design, fashion, movies, media & creative fields where human mind, intelligence & experience plays an important role in discovering, exploring ideas and making decisions.

I felt AI may support and  aid decision making more & more not just replace everything that humans do, mostly replace repetitive tasks that may not need human intervention and improve efficiency but will be used in areas to help people take better & informed decisions. AI will be successful only if there is a strong human collaboration between AI tools & platforms. As I read a little more about this, I came across a lovely interview with MIT Media Lab's Sandy Pentland who talks of complementary relationships between man and machine for higher level results! Here's the video link:

Would love your thoughts & feedback!

I loved this article by Jeff Leek on how the era of data has evolved over time.

1. The era of not much data This is everything prior to about 1995 in my field. The era when we could only collect a few measurements at a time. The whole point of statistics was to try to optimaly squeeze information out of a small number of samples - so you see methods like maximum likelihood and minimum variance unbiased estimators being developed.
2. The era of lots of measurements on a few samples This one hit hard in biology with the development of the microarray and the ability to measure thousands of genes simultaneously. This is the same statistical problem as in the previous era but with a lot more noise added. Here you see the development of methods for multiple testing and regularized regression to separate signals from piles of noise.
3. The era of a few measurements on lots of samples This era is overlapping to some extent with the previous one. Large scale collections of data from EMRs and Medicare are examples where you have a huge number of people (samples) but a relatively modest number of variables measured. Here there is a big focus on statistical methods for knowing how to model different parts of the data with hierarchical models and separating signals of varying strength with model calibration.
4. The era of all the data on everything. This is an era that currently we as civilians don’t get to participate in. But Facebook, Google, Amazon, the NSA and other organizations have thousands or millions of measurements on hundreds of millions of people. Other than just sheer computing I’m speculating that a lot of the problem is in segmentation (like in era 3) coupled with avoiding crazy overfitting (like in era 2).

What is interesting to me is that how this will impact the world of analytics thro' application of new methodologies like AI and Machine Learning is one thing. The other one that I see is that how does this 'mass of data that is being generated' represent the right population that one is developing insights on. There is a lot of potential biases that can happen given the kind of people who have access to the net.

So, the future is about an era of mixing lot of samples offline along with a lot of data that is being generated online. The power of data fusion techniques will be required to build meaningful insights and predictive actions by various industries across the world.