Daily Dose of Analytics: Indian Politics

543 loksabha seats. 28 states (and 7 union territories), 2 major parties and several small to mid-sized ones. Reflect on 2009 elections and what you may remember is tonnes of analysis and political commentary that the front pages and editorial pages of a newspaper or the jarring voice of Arnab Goswami would’ve shove down our throat and mind. And in there, not many real recommendations. Lots of critique and counter critique. Almost passively taking sides. Yeah, part of the problem is that media can’t be seen taking sides too blatantly.

The limiting thing about the political commentary that we read everyday in newspapers and tabloids is that its too political and too top-down. Worse still, the best political analysts are sitting by the sidelines working for media houses and focusing on “what is happening?”, and in some cases trying to reinforce the biases that some of the media house leaders may have in favor or against some parties. The “hypotheses” get passed around as insights. If the result lines up, we are prone to saying “I said so”. And if it does not, we are smart enough to reengineer the explanation.

Where am I headed with all this? Recently, people have been really excited by the use of analytics in predicting the results of US Presidential Elections 2012. However, the next game changer could be using analytics to drive political results. Transforming the business of politics using analytics.

Why not approach the whole scene ground up? What does BJP need to do to win a seat in Kerala? Can it? The answer is always a yes, right? Given the resources, costs and commitments required, should it? Maybe not.

For instance, let’s take BJP’s predicament. A party which has been the second fiddle for too long now, and had a go at power once. Has a strong national recall, but low/moderate national appeal. A strong brand which stands for something, which probably the party isn’t playing to. Or is afraid of playing to. Strong foothold in a few states, swinging foothold in some, and no foothold in many.

The question to ask – Is there a way to become the party of choice for at least 60% of the Lok Sabha seats? (I have selected seats and not population. Because the eventual result talks about seats, and not the percentage of people who voted for you, or the voter turnout or some such metric).

Political analysts look at the problem in totality. Or, in complete isolation. None of them has ever tried to or would most likely be able to put together a draft success/growth strategy for BJP. If it were a consulting gig, there are far too many frameworks (opportunity assessment, market entry, investment planning , blah blah) that consultants would reuse/create. But then, most consulting gigs are also top down. That’s where analytics could score by being bottom up in such scenarios. Analytics is special in its ability to not lose much by switching from being top down to being bottom up. Though you know that the effort is much higher for bottom up recon in this case.

In your first series of interviews, you’re bound to encounter significant amount of experiential and tribal knowledge– “This is how it happens. I know it” or “That’s how that community has always been!”, “It’s a strong Dalit foothold”, “BJP needs to get away from its non-secular image”, “… find a strong young leader”, etc.
But, once you’re done with these discussions (and they are important for understanding the issues and perceptions and hypotheses), you will need to understand the voters, what may make them change their existing decision in favor of BJP or what they may like their next MP/MLA to do, etc. The answers, not surprisingly, will still be simple and basic. Some practical, some impractical. And a lot of data already exists to support most hypotheses including this one simple hypotheses – most elections fought on the back of strong infrastructural or social development go favorably for the incumbent. But there are many more triggers that influence consumer choice. And like in business, in politics too, customer can be the king.

This is the point where you’d ask me to shut up because I don’t know jackshit about all this. And of course, my political awareness is not top of the charts. Like that first quiz by Rambo at IIMB, you’d start looking for my name from the bottom of the list.

Maybe, you are right. Or, maybe, I don’t care. Because my final question remains – doing what you’re doing right now, what hope do you really have of changing the game in the coming elections? And focusing on what you’re focusing on right now, do you think you will get 272? And lastly, if not, then would you rather lose the next one too instead of focusing on something that can get you there, rather than hope for more idiocy from Congress leaders? For Congress, on the other hand, the question is, how long are you going to keep hoping that you are the best amongst a confederacy of dunces?

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Daily Dose of Analytics : Coffee Shops And The Personal Touch

I have always found coffee shops to be a shared yet extremely personal space. Swarming with people, but you always get your quiet space, or the space to discuss the biggest and the most profound of topics.

Much before I started working, as a student with an insignificant pocket money, a friend and I would save just about enough money in a week or a month to the go have a cup of cappuccino at the newly opened Barista at Vasant Place market in Delhi. It was an aspirational act for us. Back then, a cup of coffee costing 30 bucks was a luxury that middle class students like me could not afford every day. I survived a week on bus passes and about 100 bucks. With chole bhature in college canteen costing 5 bucks, it wasn’t too difficult, in case you are wondering. However, But for the coffee shop manager, I was somewhat of a regular.

During my MBA days, I welcomed the opening of the Café Coffee Day inside the campus. While the poor guys had stiff competition from the legacy Nescafe machine serving super sweet desi coffee for 5 bucks or so, there were loyalists who would go to the café regularly. I would do that sporadically (continuing financial constraints). Yet, while at the café, it was a personal experience. Reason – the fellow at the counter knew me well enough by my third visit, and my order as well.

The phenomenon continued with me and Tushar playing “Jaadu Hai Nasha Hai” on the jukebox of the CCD at Ispahani Center in Chennai, or the string of coffees (mostly with biwi, TG, Shumeet, Shilpa, NehaG, Sulabh, Aziz and/or several others at Inductis) at the CCD at Solitaire Plaza on MG road. At these places, the old age touch of the coffee shop team knowing you, smiling, understanding what you’re going to order, and gradually establishing a personal connect with you was a part of the reason why I would go to the same coffee shop over and over again, even as the very cup of coffee became a standard output from one outlet to another. And more outlets, maybe closer to where I was, popped up at regular intervals. Ajay (at CCD Solitaire) even invited us for his marriage, even though he was really confused about who’s dating who for a very long time, given the NC2 combinations of coffee-ing!

Over the last few months, there are two coffee shops that I have frequented with great regularity. The Di Bella at BKC, and Gloria Jeans at Powai. However, these two are regulars because they are convenient. Whenever I am in BKC (was the norm when I was still working and continues even now with the people I meet there), it’s the only half decent option. CCD’s coffee quality has become despairingly bad in the last year and a half. GJC in Powai is also close to home, half decent coffee, has power connector points for me to work uninterrupted for some time, and enough quick bite options close by. And is open till about 1AM.

Now, in both these cases, I don’t think the folks would recognize me from one visit to another. I would probably need to strip and dance before they’d start recognizing me on my subsequent visits. Like the coffee, the customer is becoming a standardized product, is it?

The answer is no, in all likelihood. And that’s where Retail/POS Analytics should help do the job that the friendly neighborhood stores were doing so effortlessly. All the nearby stores would know me and my parents, back in the days, because of several factors – smaller/closer communities, repeat visits, continuity of the people who managed the same store over years, and lastly, a general culture of taking interest (which the modern world can called nosy as well). Retail Analytics can make this very easy for most.

An example that comes to mind – the small touches that Amex customer care often adds. For instance, last year, I called them for a query in February and they knew that my birthday and my anniversary are around the corner. How? B’day is easy. But the year before, I had some purchases around those dates, the address on my file and my wife’s file are the same, and lastly, my wife had purchases around the same date. Someone inferred it to be an important date. Not exactly what, but most likely, the flag of an important went up. The customer care executive promptly asked me if I had any vacation plans and if I needed any help. To the extent of suggesting that I could redeem some points against some of the travel options because I had a very healthy point balance.

Earlier, a lot of these required manual effort. Like that branch manager at your bank, or the store owner at the nearby store, or Ajay continuing at the same CCD for two years on the go. Now, data quality (better organized and cleaned data being made available in large volumes), and simple analysis can make it very easy for the POS person. If nothing else, a simple name to call out for and the last four digits of the credit card being swiped could start establishing the relationships, right? Next swipe, bring out that 10% discount coupon for registering – bingo – name and address collected. Follow it up with Coffee Clubs/ Loyalty – wonderful retention. Especially, for GJC, in an area where there are approx 5 or 6 coffee shops close to each other. Will occasional errors happen? Yes. But as long as the touchpoint is consciously used as a positive reinforcement, the impact cannot be negative. Analytics should take care of the machine so that the touchpoint can continue to be more human.

On that note, why hasn’t any coffee chain thought about organizing coffee evenings for groups of friends? Movies, Coffee and Sandwiches. ☺

 

[The Daily Dose series could evolve into a series of stray thoughts on analytics in daily life)