Blessings from the Kailasha

The concept of Prakriti and Brahmanda is very beautiful. Prakriti is nature. It is the world as it is. Thus, it's definition is the same for everyone. Every human being has the power of imagination and believes the self to be special. Thus, one creates his own definition of the world. This perception gives rise to Brahmanda which is unique to each individual. Prakriti, therefore, resembles the objective reality whereas Brahmanda represents the subjective reality.

Shiva is the greatest tapasvin. He has liberated himself from the rules of Prakriti and does not try to control it. His control on his mind and his inward looking concentration also ensures he does not create his own Brahmanda. Thus, he does not own anything and detaches himself from all things - material or spiritual. Just as a surface looks white when it reflects back all colours in the spectrum, so does Shiva reflects back all things material. He is equally indifferent to objective and subjective reality.

The two shut eyes of Shiva embodies that nothing is rejected or selected by him. Nothing is approved or disapproved, nothing is included or excluded. Everything is the same. The third eye of Shiva embodies the absence of discrimination and choice. In doing so, Shiva remains aloof from the human world. His wisdom remains divorced from the world.

This wisdom, thus, does not benefit humans. The mythology is full of events that aim at getting Shiva to be involved in this world of humans. His consort, Gauri or Parvati, both aim to get him involved with the world.

His son, Ganesha, is more involved with the human world. Here I borrow from Devdutt Pattanaik's book "7 Secrets of Shiva". The pictures or statues of Ganesha often show two symbols of an axe and a noose. The axe represents analytical skills that enable one to separate objective from subjective reality whereas the noose represents the ability to outgrow this distinction and to unite the opposites.

There is an excellent analogy to the world of analytics in this mythological setup. The world of business is the world of data. This data is absolute and uniform across. The data forms the Prakriti of business. However, business leaders tend to form their own ideas and conclusions basis their  beliefs. This is the individual Brahmanda. Ignoring both the objective data and the subjective beliefs would make one aloof from the business. This is not a desirable state of affairs for the business stakeholders.

One needs the ability to separate the objective, analytical driven decisions with the subjective, gut feel based decision. However, it is not a choice of one over the other. It is required to have the noose in place that considers both option and uses one to reinforce the other with equal respect. Malcolm Gladwell advocates the importance of experience or gut feel. But he also goes on to qualify the subjective decision as those that arise out of 10,000 hours of repetitive experience. Tom Davenport occupies the opposite spectrum of analytical decision making. But neither approach tends to eliminate the other. Each needs to respect the other approach even if it means they are contradicting to each other.

The business that learns to gel the two approaches together as a process will benefit from analytics. But often, business leaders tend to look at analytics as a challenge to their business acumen, especially when the models throw up insights that contradict their belief. They would benefit if they look at analytics to validate their assumption and accept the results. Similarly, the analytical outfit tend to override business approaches without considering the situation on the ground. Data may showcase a certain behaviour but the true nature of the causal or correlated behaviour can only be known by considering the experience of the business leader on the field.

Ancient India has already given this lesson to us. They understood the difference and the combined importance of both the subjective and analytical approach. Modern business should learn from ancient knowledge to benefit from both approaches.

The blessings from Mount Kailash is available for those who are ready to consume and benefit from it.

The Karma of Analytics

This article has been published by the Analytics India Magazine. You can read it at http://analyticsindiamag.com/the-karma-of-analytics/ .

Lessons from Outliers

This article has been published by the Analytics India Magazine. You can read it at http://analyticsindiamag.com/lessons-from-the-outliers/ . 

Focus on the key moments of customer process

I was interacting with a person who introduced himself as a "customer experience expert". I see an increasing number of this title. I asked him what he does and what would the deliverable be. He defined it as someone who would design the customer interface along the complete process of customer interaction. For example, on a website, he would help design the page layout, the forms (one page or multi pages), menu items. On an in-store process, it would be the layout and the process of checkout.

There are an increasing number of companies that are now "walking the process in the customer shoes". Their aim is to make every step of the process as customer friendly and easy as possible. And this is exactly where they falter.

A Noble Prize winning psychologist, Daniel Kahneman, once stated:

"Human Beings only remember the peak and the end moments during an experience process."

And this is very true. Lets consider a queue for submission of, say, college admission forms. A typical process would be to obviously queue up early. Then await your turn. The person accepting the forms would check it and you would hope that everything is in order. If okay, then the form is accepted, else you need to get additional documents or information and maybe get back in the queue.

Now, let us evaluate this process. The peak moment is the relief from the anxiety of the comprehensiveness of the application docket. The end moment is getting a receipt of acknowledgement of submission. The college cannot do much about the number of people queueing up. But what it could do is address the peak moment early. So, a person could go down the queue checking the documents of each applicant and giving his advice. Thus the anxiety get eliminated much earlier. Then the wait is only for the queue to move up and submit the document. The end process is getting a receipt of acknowledgement. The college could hand over a bottle of water to the applicant along with the receipt. Well, he was in queue for say over an hour.

But what we find in reality is the college trying to rush up the queue by putting in more desks for acceptance. The security trying to get some sanity in the multiple queues that get formed. And the crowd experimenting with unruly behaviour in hope of jumping spots in the queue.

I, for one, have been through this scenario. The only thing I remembered was happily holding the submission receipt that confirmed my admission to the college. The 2 hour wait was forgotten. The anxiety was forgotten.

Businesses will do good to apply this analogy to their customer facing processes. The first step is to identify the peak moment of the process. Then address the same as early as possible. The next is to make the end moment or exit more pleasant.

For an online store, the peak moment would be creating the shopping basket. I have experienced web sites where once I click the "buy" button, I am taken to the shopping basket for checkout. For additional items, I am lost at this page. I need to go back or press the home page. There are websites that allow adding to the "shopping intention basket" from any page in the background. It stays on the same page and provides an uninterrupted experience of searching for additional buys. Finally, when I am ready, I get to review my basket and add/modify items in it. Also, the check out is often just a one page activity. With concepts like AJAX, this is a reality and I dont understand why websites dont adopt this approach. Often, checkout means atleast 4 pages of activity.

Focussing on the peak and end moments will make life simple and help the business focus on the key aspects of customer experience. The critical question here is "do we know the peak moment of our customer process?".

Campaign is not for the Wild Hearted

An amazing fact came to the fore while watching a documentary on National Geographic on the hunting and defensive skills of the wild animals. The key aspect of survival was not strength or size or venom ... it was patience. In one of the episodes, a group of three lioness laid seige on a watering hole for over 3 hours before the first zebra showed up. And even then, due to the impatience of one young lioness, they lost the hunt. If only the young lioness had waited a few more minutes they could have got the zebra trapped in the vicious triangle they had created. In this case the strength of the lions were not useful in achieving the success. Another episode showed a fish lying still under sand till its prey came close enough. Time to kill ... over two hours.

This was an amusing fact. The law of the wild rewards the one with most patience. But then nature has one resource which is unlimited ... TIME. Alas, we who live in the concrete jungle do not have access to unlimited stocks of time. There is always someone practicing to run faster, jump higher, become stronger.

Analytics was bought in to make organizations more nimble by using foresight or predictive insights. Knowing what is likely to happen in future gave businesses more time to adjust their business plans and approaches. But as more and more organizations are adopting analytics, the law of faster, higher and stronger is taking over. Already, the analytics vendors have started talking of automation and analytics factories. In-memory analytics is another subject area gaining popularity. These approaches are aimed at operationalizing anaytics much faster.

In light of this scenario, one cannot have a 3 month project plan for any analytical exercise. The secret mantra is to Fail Fast. This is epecially true in the marketing field. The campaign managers still make project plans that run into weeks for each campaigns. When the campaign is launched, a lot of time and effort and money has gone into the preparation. In order to justify this investment, the campaign managers then try to keep the campaign over the red line. This may involve additional efforts, more money or more precise analysis.

The catch is that while the campaign was in a planning phase, the world around the business was constantly moving. Things change very rapidly in the consumer business. So when the campaigns eventually get launched, it was a different world then the one that was referenced during the planning and analysis phase.

I was surprised when discussing with an ex team member who is currently implementing a "multi channel campaign management" product (I will refrain from naming the product now). He had run into some issues and had called up to check on some configuration. He told me he was too busy since this was a "go live" weekend for a campaign. I found out that this campaign was being planned for over a month. The customer had a one week UAT (user acceptance test). I was shocked and amused to know this. I am very confident that this customer had no idea of BTL campaigns. The best UAT is out on the field. He should have done a quick test campaign to maybe 100 or 500 or 2% of the customer base and checked out the result. This should have been done as quick as possible. Depending on the industry, even within a couple of hours. If it worked, he could have gone across the customer base. If not, then look for something else. There is a nice scenario that a colleague shared with me. He said a typical day in the life of the campaign manager should be "A new idea by 0800 hrs, a new campaign by 1000 hrs, a test campaign by 1200 hrs, evaluate results by 1400 hrs, reject or deploy campaign by 1600 hrs, track the campaign by 1800 hrs".

But everytime I present this case, the idea does not find acceptance. Maybe it creates stress on the campaign manager. Cause now he has to get up with atleast 10 new ideas that he will test during the day alongwith the campaigns of days past. Most probably 9 out of the 10 will get rejected in the test phase. 1 campaign gets rolled out along with other campaigns. The start of the next day needs another 10 ideas. Compare this scenario with the one where he takes a month to plan and launch one campaign and his rejection is understandable.

I have seen marketing departments with 7 or 10 campaign managers running maybe 5 times the number of campaigns. Some of these campaigns have been in force for over 3 or 6 months. On the other side, I have met companies that claim to run over 1000 campaigns daily. I seriously doubt how they calculcate the contribution from these campaigns. The world outside has changed a lot over the past 3 months, so how can a campaign perform uniformly over the same period. Let alone 1000s of campaigns. 

Somewhere, somehow complacency has set in the process. This is where a nimbler competition can overrun the business. Get your campaign department to run more finer and more multiple campaigns with shorter turnaround. If possible, with a turnaround of a few hours. That is a sure shot recipe to beat your competition. For a man of patience belongs to the wild world and not the business world.

Article: Relationship Based Pricing

Today I take a shortcut. An article, authored by me, that was published in the periodical issued by Tata Consultancy Services Limited's BANCS team. This article covers the steps towards making relationship based pricing a reality in the banking environment. Similar steps will be applicable to other industries where such pricing can bring unique customer relationship definition and enhanced commercial benefits. Industries such as hotels, travel, high end household, ecommerce can benefit from this approach. If anyone is interested in adopting the same for their industry, get in touch me with me at michaeldsilva@gmail.com.


TCS-BaNCS-Whitepaper-Making-Relationship-based-Pricing-a-Reality-in-Financial-Services

Do not forget to provide me your views / feedback / comments / critiques.

 

Reverse Deduction ... It is Elementary My Dear Readers

I have started re-reading classics over the past six months. The last time I read books such as David Copperfield and Jekyll & Hyde, was during my school days. During that time these represented more of a fantasy or science fiction. Today, after almost 3 decades of living, these same books now take a more philosophical tint. I have recently finished reading the complete works related to Sherlock Holmes. So expect a few articles influenced by Sherlock Holmes and Sir Doyle.

As an analytical person, the first curiosity was on how Holmes can solve the seemingly complex crimes. I managed to string together the theory based on the hints thrown by Holmes during his discussions with Watson. To the statistician in me, this theory connected very well. Today's post is on how Holmes solves his crimes and its relevance to modern day business.

A typical approach to analytics is to identify a particular event ... say customer attrition. The analytics consultant will go about collecting data elements that he believes may be influencing customer attrition. Then he starts building the predictive model to identify the data elements that are more significant to customer attrition. Once identified, the next step is to contextualize the significance of the data element. Refer to my post on micro modelling <<click here to read>>. Often, the analyst will find clusters of customers behaving differently. Hence, he starts segmenting and building different models for each segments. Eventually, there may be many segments and each segment will have different data elements defining the influence of customer attrition within the segment. Thus, though the end state is customer attrition, there are different sets of data elements or paths to the end state.

Sherlock Holmes' art lies in starting from the end state and knowing all possible paths to that state. For example, examining the state of the corpse, he would deduce the possible sequence of events that could lead to that state. Its like knowing all possible behaviour, across customer segments, that would lead to the attrition within the segment. The next step is to go back on the path and arrive at the source. That is arrive at the segment the customer belongs to. Apparently, in the earlier life of Holmes, which is not covered in the book, Holmes is supposed to have conducted a number of experiments to deduce various paths to the states of interest, whether this was the state of corpse, the footsteps on the ground, hand writing analysis, etc.

Once Holmes could deduce the various paths, he goes about the case of elimination to arrive at the surviving path. This logic is not hard to understand. We are exposed to it very regularly whenever we visit the doctor. The medical practice uses this logic of elimination. The doctor observes the symptoms and then asks some questions such as "did you have fever", "do you feel nausea", "hows your stool". Basis the answers, he starts eliminating illness till he has narrowed down to a few. Then he may prescribe some generic drugs to address them. If the illness does not respond, then he recommends detailed observation (read tests) to further target specific illness (such as malaria). Eventually, there is only one path left, and Holmes has the sequence of events leading to the crime. This is the same algorithm that has been adopted for Text Mining and Document Tagging. The author, Doyle, was a doctor by profession. He applied the medical approach to crime fighting. It sounds so obvious after a century.

If one is analysing customer attrition and assuming we are in the know of all behaviours that lead to attrition for each segment, we can analyse each customer and narrow down the customer to a specific segment and path to customer attrition. Once this is done, we can then plan our interventions in the customer life to prevent the end state.. ie customer attrition.

But, alas, we may seldom know every path to attrition and hence will keep building predictive models and testing them. Till then, we will always be impressed by Mr. Sherlock Holmes.
 

Statistics cannot play God

One of my earlier posts titled "Statistics hints at Existence of God" <<click here for the post>> detailed how the error component of any model hinted at a philosophy that is analogous to concluding the existence of God. While, this explicitly states that God exists, today's post is about why this God should not be omnipresent and should not be implicit in the statistical model.

A key element of the modern day God and his relation with humans is the fact that God endowed humans with the capability of free will. One definition of God is an entity that is "all knowing". This entity knows the past, present and the future. In fact, he defines the future. God gave human the capacity of free will. It is argued that this was done out of his love for mankind. But this also represents a paradox in the way we define and understand God. The free will of humans gave it the option of wrong choices. Thus, emerged the possibility that God cannot now know the future since he does not dictate the choices man makes which in turn defines the future. God, hence, took a big risk in creating humans as free thereby including the possibility for wrongful choices.

Can a statistical model embody that spirit of granting free will? Lets look at the not so past sub-prime crisis. Every financial institution that went bust or lost in the crisis were big users of statistics. There were numerous case studies published of how these institutions used statistical scores to take decisions and thereby improve business parameters. While the employees, or human kind, followed the dictates of the statistical decision, business was thriving.

Then some mortals realised that the asset prices are ballooning. So even if the creditor defaults, the bank could recover their money by forcing the creditor to liquidate the asset. Or the bank could attach the asset of a lazy creditor and auction it at a much high price than the outstanding loan amount. The default scores or credit scores were rendered powerless. Though the model scores showed that the customer has questionable ability to earn and service  the loan amount, the institutions still over rode this decision and went ahead with granting the loans. History knows the derivatives and the leveraging that was done on these loans. But our debate lies with the initial event and not the derivatives.

The statisticians or the analytics sponsors in these institutions, be they the Business Intelligence heads or the CxOs, decided to play God. They granted free will to the consumers of the statistical models. This was probably done for the love they had in the employees capabilities and in the potential profitability. But they did not account for the risk of wrongful decisions. Eventually, when the risk became a substantiated object, the Garden of Eden was lost. To some, it cost their very existence.

If only, the sponsors had not played a loving God but a tyrant one, forcing the business to follow the scores of the statistical models, they could have survived the crisis much healthier. And there have been some institutions, albeit a few in number, who stayed with the statistical models and refused to permit free will to the business consumers or employees.

This shows a key lesson for companies adopting analytical expertise. A vast knowledge of historical knowledge is accumulated in the final, adopted statistical model. This knowledge is much greater than any individual employee. Hence, it is critical that the business processes are designed such that exceptions or over-rides are minimized, if not eliminated. Every time employees or processes are allowed to override analytical models, business has faltered and eventually the statistical approach is blamed. In such scenarios, it is not uncommon for the enterprise to abandon its analytical approach completely. And we hear comments such as "we tried analytics in the past and it does not work". This statement in a time when there are numerous examples of statistical applications in the same scenario being referred.

So, note this good, if you are deploying analytical approach in your enterprise, do not play the loving God and grant free will to the consumers of analytics.

Statistics: Is it "Guilty" or "not Guilty"

In college, my statistics professor had an uncanny ability to link statistical concepts to mythology or philosophies. It also tended to make his lectures fun to attend and often sent us on a parallel track to read more about the event mentioned along with the statistical theory of the day. One of topics that often confuse statisticians-in-the-making is the right formulation of the problem or in statistical terms the right formulation of the hypothesis. In introducing the topic, he asked us to recall the court scene in every movie. The premise of all cases is that the accused is innocent unless proven guilty and it is the responsibility of the accuser to prove that the accused is guilty. The analogy to statistics is that a given series of observation is uniform unless it is proven otherwise. Thus, every hypothesis states that the series is closer to normal curve and the exercise is to prove that it is not. What is more interesting is that the conclusion in legal proceedings is  "given the circumstances the accused is not guilty". The law does not state that the accused is innocent. Again the analogy in statistics, the application of various theory eventually brings to the conclusion that the series does not deviate significantly. It does not state that the series is aligned to normal distribution or a derivation thereof.

It is important to understand this concept when applying predictive analytics to business scenarios. Let us considers the churn model or retention model. The premise of the entire engagement is to find customers who are likely to attrite. The data set is accordingly prepared such as one can define the population into one that attrited in a given period and the ones that continued. Accordingly the predictive model is built and the scoring rule applied to the target population.

The score is a representative of the likelihood that the customer will attrite or not. It is not a measure of the continuity of the customer being on books with the company.

The law states "in light of the known or presented evidence". Similarly, the statistical model is built based on the data variables (or information) fed as inputs to the model building process. The model only concludes whether the known variables indicate a attrition on the part of the customer. They do not indicate that a customer will continue the relationship. This understanding is very important in analysing and inferencing from the statistical models. One should understand that there are other factors which may not be known or could not be quantified. As such they constitute the missing information. And some of this information may influence some of the customers to attrite. Hence, we cannot say that a customer who will not attrite as per the predictive model will continue the relationship. In light of the known or input variables, the customer will not attrite --- that's the verdict.

One area of financial impact will be default modelling in credit lending business. The default model often predicts whether the customer will default on the loan taken. Businesses tend to wrongfully create an corollary that the customer who will not default is the good customer. As such, often these customers enjoy high ratings and the business tends to take additional exposure to such customers. Such is the belief that even the variable in the database is labelled as "good" and "bad" customers. The predictive model only states that given the variables analysed a customer will default (that is be a bad customer). It should not be construed that the others will be "good" customers.

This difference is not subtle and it is very critical that businesses deploying predictive analytics understand this difference. As long as there will be uncertainties in the business arena, the decision will be to segregate the target base into "guilty" and "not guilty". The statistics deployed aims to either "reject" or "not reject" the hypothesis. Maybe, the default status variable should be labelled as "bad" or "not bad" customers.

This is probably one of the toughest post for me. I had to explain a hard core statistical thought into layman language. It took some time to edit this post and I believe it is job well done. If you believe so then kindly let me know. If not, then I will be happy to get into a discussion to explain or further simplify the matter.

KISS and make up!!!

Most time, while presenting analytics, I get queries such as "do you use neural network?" or "do you use support vector?". Almost all models I have built have been with Linear Regression or Decision Tree. I have often found good fitment of the predicted values to the observed values. Whether it was for churn prediction, default prediction, offer uptake, next visit, next spend, etc. The accuracy (or classification rate) has ranged from 65% to 86%. A good enough accuracy considering the fact that these models where not mission critical such as the actuarial tables for life insurers.

So in all cases, my response to these questions was "No". This often upset the enquirer. Then we get into a debate on why did I not use these algorithms. Every point of the argument I bring everyone back to the uplift curve or the classification matrix. Irrespective of the algorithm, if I have achieved a acceptable accuracy in my prediction, that should be end of the argument. But, alas, it is seldom so.

All algorithms end up in generating a scoring logic and gives scores on the observed variable. In reality these scores are not much helpful on its own. Business owners want to know how such scores have been generated. This is where the simplified algorithms of regression and decision trees are very useful. The end result of the modelling exercise is a "human understandable" function such as:

For decision tree: if (age > 25) and (income < 15000), then (Y = 0.05)

For linear regression: Y = a + b(age) + c(income).

The Y denotes the score. Now these equations explains in plain human language the rationale behind the score. Since, it is understandable, it also gives some additional insights into the drivers of the score. Thus, two customers having the same score, may have different drivers of the score. For example, one would have the 'age' variable contributing significantly to the score while for the other customer, it would be the 'income' variable. Try getting this insight from a "neural network" algorithm.

Another reason, I go simple is because often such models are used to convince management to loosen the purse string for additional budget towards some activity. Maybe a new campaign, maybe a new campaign, etc. The management team are good business people but often not statistical experts. The simplified functions are easy to explain and to be understood by this team. Now try explaining a complex function and getting a budget sanctioned by the management team.

And finally, the adage Keep It Simple Stupid (KISS) is so very useful. The objective is not how complex the algorithm is but how good the model fitment is. The complex the model the more time taken for data preparation and for understanding the output and tinkering with the data for improvement in uplift. And often the uplift of the complex algorithms over the linear regression or decision tree are few basis points. It may not be worth the time and effort involved.

Remember, I am not talking mission critical applications here. If I was building actuarial tables or drug efficacy, then I would scout around for alternate algorithms and seek the best fit ones. But for marketing models, where the life is short for the model and the window of opportunity is opened for an even shorter time, it makes sense to keep it simple and run with the model. A 65%  accurate model is better than no model at all. And a 90% accurate model achieved after the window of opportunity closes is of no use.

So the next time I am asked if i used "neural network", I guess I will just KISS and make up with the interrogator.

An Opportunity Lost

India has been pretty gung ho about the Unique IDentification (UID) project. While the machinery has been churning out UID cards in thousands, there has been a whole series of debate on the use of UID numbers. One of the usage has been in the subsidy management process by the Government. See the article in Mint on the Finance Minister launching pilot of this scheme.

As per this scheme, a consumer of a government subsidised item such as cooking gas cylinder will have to pay the full market rate for the cylinder. The purchase activity will be passed on to the government agency who will then credit the subsidy amount to the bank account of the consumer. This bank account will be picked up from the bank account linked to the UID number.

India has a large number of items subsidised by the Government for its retail citizens. The public distribution system covers grocery items. Fertilizers are subsidised for farmers. Cooking gas cylinders are subsidised for retail/home consumption. When all consumers of these subsidised items are counted, the numbers will run into maybe 70% or more of the Indian population. The government is expected to pay the subsidy amount to the bank account of these consumers.

Each of these consumers will need to have a bank account declared attached to the UID number. Knowing how critical this bank account will eventually become, the chances of consumers changing the bank account will be minimal. Also, considering the cumbersome process of government agencies, consumers will be deterred form changing this account.

So now we are seeing a potential of a consumer sticking with a bank account for longer time since his subsidy amount is being credited to it. A excellent case in increasing the stickiness of the consumer.

Unfortunately, I dont see any bank realizing the potential of this. Banks cannot play a role in the UID registration process since the agencies are already appointed. But Banks can definitely facilitate the UID registration process. They could source the forms, provide address proofs (attested bank statements) and also get appointment tokens for the individuals. The catch --- the bank account registered on the UID number will be one that is opened in the same bank. For every customer who takes up this offer, the bank wins a highly persistent customer with a high life time with the bank.

Lets see which of the banks latch on to this idea. Till then, I wait for my appointment date for UID registration.

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CRM Bloopers...

This post is on a lighter note. The software industry and the manufacturing industry are proud of their Quality Assurance processes. But it looks like CRM processes lag behind on this front.


Today I renewed my vehicle insurance policy. This is my first renewal and I have had a year of no claim on the policy. So I enjoyed a "no claim bonus" on the renewal premium. After completing the payment of the renewal premium, I was presented with the usual "thank you" message from the insurer, Bajaj Allianz. While reading the message I was very amused by the following sentence:

"As a loyal customer you are covered Additionally for Accidental Medical Expenses Cover/Drive Assure protect and 24x7 spot assistance for sum insured of Rs. 0."

So for renewing the policy, I am termed as a "loyal" customer. Well, I am okay with that. I get additional services. Well, I am okay with that. For a sum insured of Rs.0. WHAT? Is that a benefit I should be happy about? I can make two guesses:

1. Either it is a typo error. In which case, I will wait for the detailed policy wordings document.

OR

2. The formula used for calculating the benefit resulted in a value of ZERO.

Typical, case of borderline defect as they say in the software industry. A case for Quality Assurance.


On similar lines, a couple of months back, I was withdrawing some money at the ATM. After the transaction, I was presented with the following screen.

Note the options given. It was like holding a gun to my head and stating that I have to take the offer. There was no exit option or refusal option. The first time it happened, I was perplexed on what to do especially since my debit card was still in the ATM machine. I did not want the bank to call me since my CA had already addressed all tax issues. While my mind was booting up to process this situation, the screen went away and the transaction terminated normally with my transaction slip and card being returned to me. The next time, I got this message, I knew I had to just wait for few seconds and eventually the message would go away. DEJA VU... I say.



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Integrating online and offline worlds

A few years ago I had written a post of my experience with a general insurance company. (Same Company ...Same Customer). This post highlighted how the company was giving different customer experiences across different channels. 

I have noticed a recent trend among my colleagues and myself included. Even when one purchases a product at the store outlet, one often visits online retailers to check options, comparisons and prices. In most cases, this online research also includes the web site of the outlet from where the product is eventually purchased. 

I have been working out on how the two worlds can be integrated for a retailer. The toughest format to integrate is the super market. These are stores with a high RFM value. I had a passing mention of this in my post on customer captivity (Aim for captivity .. not loyalty).  The relevant excerpt from this post is pasted below:

"Grocery purchases are often a chore rather than fun activity. A grocery retailer could allow a customer to define her basket of regular purchase. Then have an SMS facility wherein the customer sends in her request for a particular basket and have it delivered to her home. What a convenience that would be? Would this customer want to go over the pain of defining her baskets with another retailer... highly unlikely."

While the thought started with an idea, overtime I have fleshed out the model of integration between online and offline formats. The key aspect of this integration is unified customer experience across the channels. There should be a scenario wherein the customer feels that certain activity can only be done on the online or the offline format. I will attempt to highlight key aspects of the same in the post. 

• The customer gets registered over the net or at the store. The mobile number is used as the identifier of the customer.
• The customer can create shopping list over the net and store it. She can also give it unique and meaningful names..such as weekend, monthly, household, etc. 
• The customer can also create a shopping list by SMSing the receipt number to a predefined number. The backend system would retrieve the purchase basket of the receipt number and store the basked as a shopping list against the customer account. The customer can copy or modify the list as per requirement. Also, the feature to specify brands or leave it open for each item is available to the customer. 
• When the customer needs to shop for specific items, say household items on a monthly basis, the customer will need to send an SMS to the predefined number with the shopping list name or number. The customer can also order for the shopping basket over the net. Based on the delivery preferences, the basket is then delivered to the customer. 
• Over time, the purchase pattern can be identified for each customer. Using analytics, one can predict the likely basket and the time of purchase. As this time nears, the retailer can prompt the customer for upcoming need. This could also be a reminder call for the customer who probably just needs to confirm the basket and have it delivered to her. 
• In order to increase the basket, the retailer can used market basket analysis to recommended additional products to be added to the basket. Also, personalized offers could be presented to  the customer. 
• The customer can also order a shopping list over the web and opt to have it ready for pickup at a particular outlet. While at the outlet, maybe she wanted additional items which she wants to inspect before purchase. This also gives opportunity for impulse purchase while she is at the store. The customer saves time picking up items of regular purchase in her basket. 
• On lines of fast food joints, the above basket could also be in a drive-in lane where the customer can pick it up at the take-away counter and pay for it without leaving her vehicle.
• The retailer could also offer products on trial to entice purchase. This is corollary to the product association analysis that determines the next best product. In this case we look at products that are least likely to be bought by the customer but one that if enticed the product has best chance to be bought. For example, the profile of the customer shows that she has a school going youngster in her household. Our basket analysis shows the "school" related products that the customer has bought. The disassociation analysis also shows the products that the customer has not bought and is least likely to buy. A conditional modelling can show the product amongst this second set that has the best chance of being bought if the customer is enticed with the product. Such a product can be given to the customer on a trial basis to be returned within, say, 15 days if the customer does not want it. The customer can opt to purchase it and be billed in the next purchase basket.  
• Additional service, such as toileteries and personal effects can be made available in different cities to be delivered to the customer when she is travelling. 

If any retailer wants to run a pilot on this, kindly contact me at michaeldsilva@gmail.com. Together we can define an appropriate process for blurring that online / offline demarcation and provide a unified and enhanced customer experience.

Segmentation .... now or later?

Very frequently while building analytical models, I have had clients state "Let's start with customer segmentation." I have to spend a considerable time convincing the clients that segmentation is not mandatory as the first step of modelling. Unfortunately, a majority of the statisticians start with segmentation. They claim that the population need to be clustered into homogeneous segment. Every business user also is convinced that his customer base is a group of homogeneous clusters.

This thinking is very flawed for two reasons:

1. Segmentation is a relative activity. That is, one needs to do "statistical" segmentation towards some goal... whether it is to understand customer value or default or cross sell. Segmentation as a stand alone activity does not provide much value. This is the reason I always dissuade my customers from doing just a "segmentation" exercise. (see my post on "statistical segmentation" titled perspectives of segmentation).
2. Grouping customers into clusters induces biasness into the model building process. Let me elaborate further on this.

The primary reason in clustering is that the customer base consists of groups of individuals who behave in similar pattern. This also leads to the corollary that customers belonging to different clusters behave differently.

My econometrics professor in college had a wonderful way of explaining statistical concepts from real life scenarios or philosophies. He had stated that the basic premise of law is "a person is innocent unless proven guilty." He had told us to keep this premise in mind when defining the hyphothesis for model building. Going by this, we start with the assumption that the customer population is homogeneous unless proven otherwise. This proof can only come with model building.

By performing segmentation upfront, we are making an assumption that the customer population is not homogeneous. With this presumption we are inducing a biasness in the model building process.

From an effort perspective, I have a cost accountant view on this. A segmentation exercise typically leads to definition of 5 to 10 segments. The next step will be to build separate models for each of the 5 (or 10) segments. This multiplies the efforts required. And all because of an unproven assumption that different segments behave differently from one another.

An effective approach will be to first assume that the customer base is homogeneous. Then build a single model for the target variable. The next step is to find variance within the test population. The variance can be either on the decile dimension or we could look at the significant variable to idenfity the value that has the highest variance. This will then indicate a likely set of customers who behave differently than the rest of the population and hence provide the variance in the test population.

At one client where we adotped this approach, we found that the "product holding" was showing the highest variance. Evaluating the values, a particular product was found to be exhibiting the high variance. As a next step, we split the population into two segments -- one segment holding this product and the second for the rest of the population. Two separate models were built, one each for the two segments, and the scores were merged (after normalization). Since the population creating variance in the original model was now separated, the rest of the population was comparatively more homogeneous. The model for the variant population was custom for that population and hence a better fit. Thus, the combined scoring was more accurate than the first single model. This accuracy met the acceptable threshold of the client and we deployed the score in the business operations.

The entire exercise involved - 3 models and one filter based population split. Compare this with the "traditional" approach of one segmentation (leading to 5 to 10 segments) and one model for each segment (approximately 5 to 10 segments). We completed the exercise in 4 days against what would have taken us more than 10 days the traditional way.  

Downgrading an Upgraded Customer

A long time back I had written a post titled "The Power of Gold" which referenced to a customer being upgraded to Gold status basis his business with the bank. Within this post, I had promised to write one on downgrading the customer and its challenges. Many times companies make elaborate plans on segregating customers across value and promoting them to the higher levels. But very often, they forget to manage the downgrade of the customer when he does not meet the qualifying criteria.

A good case in point is Jet Airways. All airlines having frequent flyer program have graded memberships. Depending on the flights taken or mileage clocked a customer is upgraded to the next level. Then there are detailed rules on maintaining the tier level as well as on getting upgraded to a higher tier. Jet Airways has an additional feature whereby they track a customers flights with the airline. If they notice a increasing trend of using Jet flights then they upgrade the customer without waiting for the completion of the eligible number of flights. While this ensures a frequent flyer to get upgraded faster, Jet is also efficient to downgrade the flyer if his usage drops.

This was my personal experience. Due to some reason, for two months I had taken exclusively Jet Airways flights. Though my pattern of travel had not changed but since all the fligts where on Jet Airways, the airlines noticed a increasing pattern of patronage. Accordingly, they upgraded me to the Silver tier and explained the quick upgrade. As fate would have it, the next two months happened to be Kingfisher and Air India flights. Jet Airways probably saw this as a reducing trend of usage. After a couple of months, I got a letter from Jet Airways. The letter first reminded me that I was upgraded due to my increasing usage of Jet Airways inspite of me not having met the qualifying criteria of the upgraded tier and then went on to inform me that since I had not kept up with the usage patter, I was downgraded to the lower tier.

While I understood the logic and process followed by Jet Airways in this process. This incident did leave a bad taste for me. The next few months whenever the travel department presented an option between Jet and any other airlines, I always chose the non-Jet option. Normally, I would not have bothered and would have let the travel department select the best option within the time of travel.

Since then I have discussing with various experts on how to handle the downgrades of the customer in such programs. Till date I did not get any satisfactory reply.

Last week I was discussing with an HR consultant on employee promotions. He explained how he decides on employee growth. An employee who has outgrown his existing role is typically a candidate for promotion. However, he does not promote this person. The employee is encouraged to take up additional role or function. For the next 6 to 9 months, the employee is evaluated on his management of the increased responsibilities. When he shows good performance on the increased responsibilities, only then he is promoted to the next role. If he fails at the new responsibility, then the employee is offered a different set of roles or responsibilities. The promotion only happens when the employee is successful in the new role. The HR consultant explained that this process is beneficial to both - employer and the employee. The employer gets employees growing up the hierarchy who can manage the new roles and the employee grows into roles that are manageable by them. In other cases, since a employee is normally not demoted (since that causes problems with morale), quite often the employee's performance suffers and often he leaves the employer.

After my discussion with this consultant, I was trying to find how this approach can be applied to customer relationship (see my earlier post on Let HRD solve Marketing Issues). And I realized the solution was right here and very obvious. I could immediately draw up the analogy. Coming to my experience with Jet Airways. The airlines should have communicated that due to my increased patronage, the airlines is providing me a gift of facilities that are normally available the higher tier for a period of 2 months. After the end of two months, they should have reminded me of the additional service I enjoyed and enticed me to meet the qualifying criteria to continue to enjoy the additional services. If my usage has remained constant, the airlines could have extended the additional facilities for another two months, and so on till I met the qualification criteria for the next tier. If my usage had dropped, that was it, I enjoyed two good months of additional services. If my usage remained the same or increased, eventually I would qualify for the next tier and be upgraded as a normal course of action. There was no negative or demotivating message of being demoted to the lower tier.

Every company which has differentiated customer relationship based on tiers or grading can use this approach. Contact me on michaeldsilva@gmail.com and we can review your customer grading scheme and draw up similar strategies for customer upgrades and demotion.

Mr. Privileged Customer ... Please get in line

As a blogger I am really very happy with my bank ... one of the two largest private sector bank. It always amazes me and gives me great material for my posts on what not to do to your customer and how to improve your customer relationship. My relationship with this bank goes back more than a decade and a half. In fact my first salary was credited to my account in the same bank. I take pride in saying that my customer id is a 6 digit number whereas the bank has already moved way forward into allocating 7 digit numbers to new customers.

All along the bank has communicated to me on how important I am as a customer and how privileged they are to have me as a customer. It was happy - happy relationship.

My first brush with disappointment came when my employer, TCS, was launching its IPO. As employee I had access to the employee reserved quota which almost guaranteed my allocation of shares that would be applied for. Considering that the general public got a 1:7 allocation (that is one share for every seven shares applied for), a 100% allocation was a big bonus. During the period of the IPO, State Bank of India had setup a counter in the office premises. They were offering to fund 90% of the application money. The catch was that this money with some interest to be returned in 4 weeks time after the listing. Considering that the stock was expected to list at 25% premium, this was a sure shot win-win for both SBI and the employee. It was kind of short term financing for 8 weeks at about 6% annualized charge. If the money is not returned within 4 weeks, then the amount would convert into a unsecured loan on the employee.

I walked into my bank and met the manager. I presented the SBI offer to him and asked if the Bank would like to forward a short term loan of 8 weeks to me. He always reverted to standard clauses which said a minimum of 6 months Personal Loan. He left no stone unturned to tell me that he is doing this as a special case since I was an old customer. But all he was offering to me was the standard package which anyone walking into the branch would get. I told him he had to provide a better offer than SBI. I told him to consider the fact that I was not even a customer of SBI and yet was holding this offer from them. But all this was to no avail. Finally, I did not use finance offered by the Bank.

My second major disappointment was when I wanted to move my base branch from one town to another. I was told by the local branch that I will need to close the savings account in the old branch and open a new one. Seeing no option, I agreed to it and found all my automatic bill payment settings vanish. I had a lot of problems resetting the same and at the same time paying current bills by cash ensuring that the current due dates are not missed. Afterwards I found this was not required. The base branch had nothing to do with the savings account. Apparently, the branch manager made me open the new account in his branch since he was credited for opening new accounts and not on change of base branch.

My recent case is few months old. I had recently upgraded myself to the higher class of customer. Imagine my amusement when the first response I got that the bank needs to find a relationship manager who will agree to take me in her books. Anyways, eventually I did get upgraded. One of the eligibility criteria is the value of portfolio with the bank. A couple of months after the upgrade, I ran into a good opportunity of procuring a retail outlet at an upcoming residential township. I again approached the bank for commercial financing. Back came a prompt reply that the bank only does commercial financing for minimum of Rs. 25 lakhs. The property was for Rs. 20 lakhs and so the bank could not advance any loan. I again played the old customer and (now) a privileged customer card, but to no avail. Finally, I had to liquidate my portfolio with the bank to finance the purchase. This liquidation has brought my portfolio worth to less than the eligible balance.

Now I wait and watch what the bank will do. Will it demote me to the lower level or will it fine me for not maintaining the minimum portfolio. And all this when the portfolio could have been richer by another Rs. 20 lakhs had they forwarded the loan for the same.

Till further developments, fingers crossed.....

And what do I do with the outlet... maybe a franchise for cakes (since I love cakes) ... or a outlet for http://www.askforpets.com/ (a portal started by a friend for pet lovers.. do check it out).

Selling Product is Old Money

When was the last time you bought a product for the product? Customers buy tangible items for intangible solutions. There has also been high number of columns dedicated to how companies should sell solutions and not products. It is very prevalent in B2B industry. However, on the ground, companies still have a product approach.

Let's consider my experience with Godrej Interio a year back. We had planned to remodel our kitchen space. We wanted to maintain the basic platform and build modular storage around the structure. Among the vendors we contacted was the Godrej Interio center closest to our residence. After repeated calls, there was still not action or response from them. One day, Godrej Interio had an advert in the local newspaper asking for interests for franchisee setup. They had mentioned the franchise manager as the person to be contacted. My wife called up this person and blasted him on how his appointed franchisees show lack of response. The gentleman agreed to look into the matter and promised that someone would call back.

Sure enough, by the end of the day, we did receive a call from the nearest franchise outlet. They agreed to come visit the house and measure it. But they said I will need to pay them Rs. 500 for the visit. They said we will get a rebate of the same amount from the order, if given to Godrej Interio. We agreed to the payment.

That weekend a lady showed up representing Godrej Interio. After some basic discussion on our requirement, she proceeded to measure the kitchen. While we were finalizing the components of the storage cabinets, we realized that since the platform was to stay and was build not to the measurement of the modules, there were some areas where there were gaps or the space was slightly shorter. Also, in some cases, we needed some additional attachments. For example, near the overhead corner, we wanted two glass shelves instead of a closed cabinet. She said Godrej will not do it and we need to get it done with the local carpenter. Same answer was accorded for other areas where there was some gaps to be covered. She said that Godrej will not modify the pre-build modules. She mentioned that Godrej will provide the material and it will have to be measured and cut by the local carpenter. I wanted to cover the area beneath the wash basin with a matching cabinet door. Again, she said that is out of scope, but Godrej will give the panel and the local carpenter should be engaged to modify and build the cabinet door.

After the discussion, we realized that over 40% of the work was supposed to be done by the local carpenter. She said Godrej will only fit the standard modules and the final attachments and finishing will have to be done by the local carpenter. I told her that this does not solve my issue of having a functional modular kitchen. Anyways, I paid the Rs. 500 and decided to not speak to Godrej Interio again. Eventually, I got the kitchen done with the local carpenter. It has been over 15 months and I am very satisfied with the quality of work and the final kitchen.

In this case, Godrej Interio was so focussed on the catalogued items that they refused to see my need. Let me highlight another case, albeit one with a happy ending for the customer.

Our washing machine had lived past its life and one fine day stopped working. Since, we were contemplating purchasing a new one, we decided this event to be the best to get a new one. We exchanged the old washing machine for a new LG washing machine. Being a front loading machine requiring some installation, the retail outlet agent told us to wait for the technician to arrive at our house for setting up the machine. He picked up the old machine and promised us that the technician would visit us the same day. It was a Friday. We waited till 8 pm and nobody from LG showed up. My wife followed up with the retail outlet. The sales person informed us that the technician got delayed solving some problem and would be at our house first thing on Monday. Around 10 pm, my wife was getting very upset since there was a pile of clothes awaiting their wash. She called up the sales person and explained that she need to wash the clothes. The sales person said that he will try his best to help. After about half hour, the delivery person shows up at our door with the old machine. The sales person had got it temporarily fixed and sent it back to our house. He called up to say that we can use the old machine till the new machine is installed. He said he will take it back once we are satisfied with the new machine.

Now this person understood that we did not want a washing machine. We wanted a solution to wash our clothes when we want to at the convenience of our home. This sales person made us a loyal customer of the outlet. Eventually we bought our air conditioner, television, home theater from the same outlet. When he changed jobs to work for a competing electronics chain, we moved our purchase to the new outlet.

However, it is disappointing, that this event is a one-off rare case. It was probably a initiative or attitude of the sales person rather than the company or retail outlet chain.

'How' is more important than 'How Much'

A few weeks back, I visited WellHome, the retail outlet of Welspun, to get some bedsheets. They were running a promotion based on the amount of purchase. Our purchase entitled us to a rebate coupon and a holiday voucher. We convinced the store manager to let us consume the rebate coupon in the same visit since we stay about 40 km away from the store. The description on the holiday voucher sounded very exciting. But when I checked out the procedure to use the holiday voucher, all the excitement faded away. Of course, as a default, there was a blackout period. Weekends did not qualify. With two school going kids, this meant looking at vacation period. That was peak period for most places I intend to visit and as such not eligible for the voucher. The next clause really amused me. I was supposed to call the call-center two months in advance to book my stay. And the confirmation will come in only two weeks prior to the date of travel. Let me refocus your attention... the first period is two months (for booking) and the second period is two weeks (for confirmation). A trip is not just the hotel booking. One needs to plan for travel. Also, what if the confirmation is not received and the booking cannot be fulfilled.

This is not a one-off scenario. Airlines often give out additional flyer miles. But ever try to redeem them. Credit cards give out extra spending points. But are silent on their redemption. My Amex cards often has constantly running campaigns where at certian outlets I get 5 times the normal reward points.

Sometimes the redemption is so cumbersome that I often wonder if it was even intended that a customer should redeem some benefit. A lot of times there fine print is designed to severly restrict the customer from redeeming what is rightfully his.

I have seen marketing programs that define their success by the amount of 'increased' sale due to a promotion or the number of vouchers given away. Rarely have I noticed slides describing the redemption of these vouchers. The success of a discount / rebate scheme is not the amount of vouchers given away but by the amount of customers finding the deal valuable to actually consume the offer. There is a recent trend among credit card companies and airlines to expire the points accumulated by the customer. Maybe they want to force the customers to redeem the points. If that is the case, then it is a good intention. In one case, I actually saw a slide that showed how much money was saved because of lapsed points that dont have to be redeemed any more.

A customer who has registered for a point accumulation program, has accumulated points but not redeemed them should be an area of concern. The company should critically review the redemption process. Is it convinient for the customer? Are the fine prints mutually exclusive from the customer's perspective? See the holiday voucher case. Eliminating weekends for a customer with school going kids is mutually exclusive with black out periods during vacation period.

Some time back, Kingfisher Airlines has sent a communique stating that one can use the flyer points to upgrade. However, the fine print said that the request for upgrade should be received by the airlines two days before the travel. Now, in most cases of business travel, the plans are often flexible and subject to last minute changes. The terms were silent on what happens if the travel schedule changes after the points have been used for upgrade. The airline should have ideally provided this feature at the check-in counter. Technically, that is the most appropriate moment for a customer to decide if he wants the upgrade or not.

So the next time you plan your promotion, give more weightage to the redemption process. Make is much easier for the customer to redeem than it was to do the purchase that got him the redemption opportunity in the first place. If you can achieve that state then you have a potential winner of a promotion.

Don't Outsource Your HEART !!!

Recently I bought three pieces of furniture from House Full. They are a furniture retail chain with maybe over 5 outlets in and around Mumbai. They are probably the only chain currently present in Vasai area. Once I had selected and finalized the three furniture pieces, the attendants threw a surprise that the furniture come in a ready for assembly state and they will charge me Rs. 300 for sending over the carpenter to assemble the item. Since, I did not have much of an option (especially since my wife and daughter had selected the items), I reluctantly agreed to pay this amount.

Then started my travails. The delivery guys came over within two days and promptly dumped three cartons in the house. However, instead of a clothes dryer stand which I had ordered, they dumped a bean bag and left. I had instructed the outlet to send in the carpenter on a saturday. However, he arrived on Friday morning. The moment he arrived he started cribbing about how far the house is and the fact that he had to spend money on an auto-rickshaw to reach the house. He stated that HouseFull does not reimburse him the travel fare and he has to shell it out of his pocket.

He was pretty grumpy all the time. He assembled one of the furniture fine. For the next one, in his bad attitude he banged one of the panels to the wall. Thereby damaging both the wall and the panel. The panel had a chip off from one of the corner. He continued assembling the piece. Once done, he put the caps on the screws on one side and handed the rest of the caps to my wife and said to do it ourselves. Then he left abruptly still cribbing about the return fare he has to shell out.

I went to the retail outlet that weekend and complained against this behaviour of the carpenter, the damage caused to the furniture panel and the incomplete work with the screw caps. The attendant said that the carpenters are locals and the company has no control on them. He said he would take down the complaint and will have it attended to. Nothing happened after that. No phone calls ... no contact. They just replaced the bean bag with the clothes dryer stand after almost a week. The delivery person said he just delivers and is not concerned with any issues I had with the company.

Two weeks back, I walked in to the same HouseFull outlet looking to get a bookshelf. They had one which I liked. This time I told them that I will not pay for assembly and will do it myself. I said I will not pay the Rs. 300 they charge for assembly. The attendant said that I will still have to pay the delivery charges. He said the Rs. 300 includes delivery and assembly charges. Now this was not the same that was conveyed the first time. I was told delivery is free and Rs. 300 is for the assembly. I told him the same. Anyways, I asked how much is the delivery charge. He had no clue and told me that he is not sure about it. Next he changed his stance and told me that they will deliver but will not be responsible for any damage to the panels in-transit. I asked to see his manager. I told the manager that in this case what about the damage caused by the carpenter during assembly the first time. He again had no answer. He just took down my number and address. A carpenter came over to my place with no clue to what is expected out of him. I sent him back. That was it. No further interaction with HouseFull.

It is really surprising how companies outsource the customer contact activities. These are the interactions which create lasting impression and defines how the relationship will develop. If customer relationship is the heart of a company, it is like giving this heart in the hands of an outsider and expect him to pump it a the right interval.

At one of the B2C setup of a corporate house, I was studying the customer relationship process. I found a lot of problems with the call center. I raised this in my report to the operations manager. He said that he cannot do anything about it. The corporate house had decided to set up a call center and being of the same group, the company was forced to give its call center business to this setup. But while the call center was learning, the customers were leaving. It was a CRM harakiri.

Customers are the heart of any company. Any activity that has direct contact with the customer should be under complete control of the organization. Contracts and SLAs cannot bring in the customer ownership attitute. Companies must seriously rethink outsourcing their customer contact points. Its not a question of cheaper process with the vendor. A lost customer is much costly than the few rupees saved in servicing him with an outsourced vendor.

Have Data .. Will Mine

I recently recalled a very amusing episode. This occurred way in the past. I had spent the whole of the morning with a general insurance customer discussing claim analytics and claim prediction for automobile insurance. From there I rushed off to meet another client who operated in the life insurance space.

The client was busy with some worksheet data. I asked him what he was up to. He said he has received scores for a new model. I asked him which model is he building now and he told me it was claim prediction. Since I was with a general insurance customer, my mind was still oriented to the general insurance business. Instinctively, I asked him what his definition of claim was. He said with a smirk that claim is when the life assured dies. Then, it hit me that I am sitting in a life insurance business premises. We joked about the fact that we are actually trying to predict the death of a person. We laughed about what the output can be used for. One option was seeing that a person is predicted to die, the company can refuse to take his renewal policy and let it lapse. Imagine the call center interaction --- "Hello sir, since we see you are not likely to live over the next 18 months, we would like to terminate the life insurance policy. Thank you for being a good customer while you were alive."

I hinted that it was a pretty sadist model that he was building. Anyways, we got to the worksheet and I asked him who did this model. His outsourced analytics agency built this model. I asked him to show me which variable was most dominant in generating the claim score. I was not surprised to find that age was the dominant variable. It showed younger customers were less likely to die than older ones.

While the client understood that this was not the right approach, it was amusing that the analytics agency actually built a model for claim prediction. It was a true case of "since I have data, I will build some model". The agency did not question nor advise the client on the right approach to solve the business issue. The client wanted to arrive at expected expenses, including claim over the next couple of years.

In life insurance, the amount of data about the customer is very limited. Claims occur on termination of life (we are not discussing riders here). Length of life depends on quality of life, which in turn depends on various factors such as diet, lifestyle, etc. And not to mention homicides. Information which is not available in the life insurance database. Hence, the approach is to go macro or at a higher level. One should look at mortality of the target market and then draw a proportion of the policy base from the target base. This will give a good estimate of the number claims likely to come in. The analytics agency should have done a forecast of the deaths in a target location. They could have done this either (inside out) by taking the past experience of the life insurer and extrapolating it over the market for forecasting or (outside in) by taking the deaths registered in a target location and then adjusting the forecast for the profile of people buying policies with the insurer.

But instead, we had a typical mentality of a kid who when given a hammer thinks everything is a nail. Since there is data available, a model was built. On this topic, do you know "the salary of a product manager is inversely proportional to the unit price of the product." (findings from one of the models when I was a analytics infant).