At Home with Strategy, JDM & Analytics

I feel at home with three subjects: Strategy, Human Judgment and Decision Making, and Analytics (written in no particular order). Therefore, most of my blogs will be an intersection of these three subjects.

Analytics should lead to decisions based on reasoning. However, decisions are reshaped by not so apparent psychological forces such as overconfidence and emotions. Strategy is a set of actions programs that combines such psychological forces with analytics to unlock superior value.

Why Analytics? For better judgement and decision making?

Can you imagine of a $40 million dollar project going awry? Probably several do; but you may not have heard of any that could not be averted even after good analytics. A decade ago, a leading real estate developer had just finished an apartment project, a combination of three- and four-bedrooms, with landscaping, gymnasium, swimming pool, etc. However, the desired benchmark customer satisfaction was not achieved. The apartments were seen a little lacking in variety and not so adequate in value for money. But, the customers appreciated delivery time of the project and quality. The developer was contemplating a bigger phase 2 with $ 40 million in investment. The operations team read the phase 1 data as enough ‘evidence’ to support for reduced pricing and introduction of  two-bedroom & row-house variants in phase 2. I was asked to work on the data and the results of the statistical analysis were quite the opposite: delivery time and quality were impacting satisfaction significantly. And surprisingly, variety and value perceptions were not. The implications were clear (at least to me): ensure lesser time for project completion, improve perceived quality, not alter the pricing and shelve introduction of new variants. Statistical results do not find easy consumers in people who do not understand it. That is the sad aspect about business truth – it needs to make sense. The team went ahead with the original plan and suffered more customer dissatisfaction due to increased delivery time (and costs) incurred due to their decision to offer more variety. Worse, it could not sell the new variants easily at the price levels – reduced top-line. All in all, a double whammy knocking them down to much lower profits!  So, I keep wondering: How can one make reasoning, especially statistical reasoning, easily digestible?

It is not always the case that data and analysis are available for critical reasoning and decision making. Recently, we at, a matchmaking portal, wanted to know how many times a customer was touched by all channels in a period of her/his life time with us for any purpose. Channels included web, email, sms, mobile, telesales, customer support, service delivery, feet on street, retail, etc. We wanted to answer a simple question: is our number of contacts too many, just right, or too few? One may not appreciate the depth of the question without a feel for the context. We acquire about 8000 members a day; and, at any given point in time about 3 million members are active on site. Even after conquering the data-silos issue, the sheer humongous size of the data is daunting. A mere 15 days  member-contact history runs into several tens of millions of records. Imagine the size, if the average life time for any member at is a little over a year. The question stands unanswered for over two months now. Were we boiling the ocean?

Therefore, analytics (sieved in blogs “Big Data and Analytics”) for me is about my vision of aiding reasoning and prediction. It is also about ensuring data, information and intelligence is (1) available, (2) on time, and (3) easily consumable.

Judgement and Decision Making is not ways based on reasoning!

As said earlier, at the core of analytics is reasoning and prediction. The objective of analytics is to help us make the right decisions. Paradoxically, we rather fail several times in making right judgment and decision for ourselves and for our businesses. I was an analytics consultant to a medium-sized consumer durable firm. The management was facing a possibility of price war from a near equal competitor but with much larger cash reserves. Analysis suggested if we sustained the onslaught for just a month and did not match the moves then the competing firm would withdraw the price drops and have minimal impact on our top-line. Otherwise, if we reacted, then the price war would rage for longer period and it would inflict heavier damage on us.  “What Reason weaves, by Passion is undone” said the poet Alexander Pope. The temporary fall in top-line evoked extreme and visceral emotions. Fear and frustration lead to irrational decisions. The price war lasted for a quarter, wiping away much of cash and assets. Reasons re-throned only after the CEO was dethroned.

Therefore, Judgment and Decision Making (sieved in blogs with the same phrase as title) is about how our rational analysis, that ought to shape our decisions in future, is reshaped by not so apparent forces such as emotions, overconfidence, past references, social norms and other biases.

Strategy is value unlocking from Analytics and forces that shape Judgement and Decision Making!

A pattern in a stream of decisions is strategy says Mintzberg. If decision patterns have undesirable impact from not so apparent forces, then committing more to such strategy may get more of what we have always gotten; not significantly different ones. Increased value unlocking is possible only when such forces are used to our advantage and combining it with analytics.

Consider people’s reluctance to dispose old items. It hurts. The loss is unbearable to many. Televisions arrived in India during the middle 1980’s on a wave fuelled by Asian Games 1982 and two serial programs Ramayana and Mahabharata. About 15 years later, several firms were unsuccessfully eying the television replacement market. Akai, a TV brand offered to take away the old one for a price and started one of the run-away hits in business strategy: the exchange offer schemes. Akai had an answer for the psychological force, loss aversion, that shaped our reluctance to dispose such items. Amazon leveraged it online and is one of the best innovations in business strategy. Amazon trades-in old books, video games, etc. for gift-vouchers and later makes it available for resale. Success further depends upon Analytics: assessment of right price for the items traded-in and finding a new buyer at the earliest. So does the business model of Netflix.

One used to pay for each hire of a movie DVD and additionally incur late-fee charges for delayed returns. Considering entertainment being a money spinner, firms hoped to make big revenues from rentals from heavy movie-watchers and late-fees. But the forces that shaped consumption decisions were not demand for entertainment alone. There are several negative and powerful psychological forces at work. Consider two scenarios: the first one is a taxi ride with a fare ticking in the fare & distance meter. The second one is a fixed fare. Several ones choose the latter and also enjoyed the ride more having not to keep frequently gazing at the fare-distance meter. Netflix, nearly two decades ago, designed a business model using this understanding. It came up with fixed monthly fee, unlimited movie watching and no late fees! Rest is history. Of course, Netflix makes more money from infrequent movie watchers. It also uses heavy analytics for movie recommendation and motivate moderate usage.

Therefore, Strategy (sieved in blogs with the same phrase as title), is about the ability to create / unlock value from a synthesized view between analytics powered by reasoning, and the forces that shape our judgement and decision making.

Needless to say, all views expressed in this site are my own and not the views of the organisations I represent.