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.
Monday, January 02, 2012
Monday, December 19, 2011
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.
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.
Monday, December 05, 2011
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).
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).
Tuesday, October 18, 2011
Analytical Data Mart -- is a Myth
Over the past two years, I have observed an increasing number of RFPs which include setting up of analytical data marts as part of the scope. This is a disturbing trend. It shows that the analytical projects are driven by non-analytical expertise.
I dont blame the IT guys for the way the RFPs are designed. They take a typcial data warehouse / reporting approach. In this case the requirements are known and the data warehouse is expected to maintain and provide the data elements for the reporting needs. Analytics too follows similar analogy. We have an end requirement -- which may be lapse prediction and the predictors, say. And this requirement needs data elements. This is where the similarity ends. Before the model is developed, one does not know which of the data elements are needed for the end report. In fact, one of the objective of the model building is to identify the data elements that are significant contributors towards the event under consideration, the lapsation of a policy.
Vendors in the market strut analytical data models. It basically consists of 100s of variables and derived variables which are likely to play a role of a contributer to the observed event. The IT team following the sales pitch of such vendors often include a scope of creating an analytical data mart containing all the 100s of variables listed.
If a company has enough budget and time (and patience), it will be okay to create an analytical data mart of over 800 variables from multiple sources of data and involving complex transformations. But this is never the case.
Now consider the models built on this data mart. Any model used in business will hardly have more than 20 variables (both direct and derived). So the rest of the 780+ variables was wasted.
An ideal way would be to let the statisticians use data dumps to do the modelling activity. Once a model is developed and tested and found to be useful for business deployment, the need for productionizing is to make the 6 to 8 to 20 variables available for the scoring purpose. Compare this with creating a 800+ variable data mart -- the former is a much more practical approach.
I have not even dwelt on the process of modelling and data prepartion. Based on the objective being tested, the data preparation will differ hugely from model to model. Often the analytical data marts get ignored and the analysts goes back to data dumps for creating the analytical data set for modelling. See my earlier post on time stamped data sets for modelling (http://crmzen.blogspot.com/2010/03/time-factor-in-modeling.html) to understand the complexity in creating data for statistical modelling.
It will be good for the IT department and the data warehousing personnel to understand this difference in the analytical process. Especially since this difference is not subtle. It is not needed to wait for 12 to 18 months (or more) till the data warehouse is set up and populated for the analytical activity to begin. And, the return on investment is much higher with predictive analytics. When compounded with the quick turnaround, the returns multiply.
I dont blame the IT guys for the way the RFPs are designed. They take a typcial data warehouse / reporting approach. In this case the requirements are known and the data warehouse is expected to maintain and provide the data elements for the reporting needs. Analytics too follows similar analogy. We have an end requirement -- which may be lapse prediction and the predictors, say. And this requirement needs data elements. This is where the similarity ends. Before the model is developed, one does not know which of the data elements are needed for the end report. In fact, one of the objective of the model building is to identify the data elements that are significant contributors towards the event under consideration, the lapsation of a policy.
Vendors in the market strut analytical data models. It basically consists of 100s of variables and derived variables which are likely to play a role of a contributer to the observed event. The IT team following the sales pitch of such vendors often include a scope of creating an analytical data mart containing all the 100s of variables listed.
If a company has enough budget and time (and patience), it will be okay to create an analytical data mart of over 800 variables from multiple sources of data and involving complex transformations. But this is never the case.
Now consider the models built on this data mart. Any model used in business will hardly have more than 20 variables (both direct and derived). So the rest of the 780+ variables was wasted.
An ideal way would be to let the statisticians use data dumps to do the modelling activity. Once a model is developed and tested and found to be useful for business deployment, the need for productionizing is to make the 6 to 8 to 20 variables available for the scoring purpose. Compare this with creating a 800+ variable data mart -- the former is a much more practical approach.
I have not even dwelt on the process of modelling and data prepartion. Based on the objective being tested, the data preparation will differ hugely from model to model. Often the analytical data marts get ignored and the analysts goes back to data dumps for creating the analytical data set for modelling. See my earlier post on time stamped data sets for modelling (http://crmzen.blogspot.com/2010/03/time-factor-in-modeling.html) to understand the complexity in creating data for statistical modelling.
It will be good for the IT department and the data warehousing personnel to understand this difference in the analytical process. Especially since this difference is not subtle. It is not needed to wait for 12 to 18 months (or more) till the data warehouse is set up and populated for the analytical activity to begin. And, the return on investment is much higher with predictive analytics. When compounded with the quick turnaround, the returns multiply.
Monday, October 03, 2011
Statistics hints at Existence of God
At the onset of this post let me make a few things clear. I am not an atheist. I have faith in the bible. But I do not hesitate in questioning facts about the bible. Now, according to some, that makes me an atheist. Atleast, it does not make me a fanatic. So I leave it at that.
My professor of economics, Mr. Sakhalkar, once made a statement that when one reads he should not be selective. That creates a bias and restricts ones circle of influence. Going down that path a couple of years ago took me down the path of the origins of religion.
A very interesting fact came to the conscious. "God" was an invention of man to blame someone for things that man did not understand and could not control. In the old days, the most frightful element for man was fire. He could not understand it, he could not control it and it was most destructive force. So a Fire God existed. And among all the gods, the Fire God was the most powerful one. There was Water God, Land God, Wind God, Sun God, Star God and so on.
As man started understanding the elements of nature, the importance of that God diminished. Until the God itself was abolished. Eventually as we sit in the twenty-first century, almost all of the element Gods are extinct. When the Gods started disappearing, man found reason to blame other men for various elements. So a forest fire was because some fool dropped a lighted cigarette on dry grass. Understanding man and its nature became a prime topic of importance. God now starting taking form of man. A convenient person to blame when things go beyond explanation.
A statistical model is a function which describes the observed behaviour based on identified independent variables. But what most sales personnel (call them consultants or statisticians) leave out is the "epsilon" (E). Every function in statistics has the epsilon attached to it. Consider the following function which forecast the amount of sale at a retail outlet.
Y = aX + bZ + ε
where Y is the amount of sale, X is the average salary of store visitors and Z is the fact that it is raining. ε represents the epsilon or the error component. This is statisticians' way of keeping themselves legally safe (yup... statisticians are smarter than lawyers). It implies that though the function predicts the amount of sale, there is an error component which explains the deviation in actual amount against the predicted amount. So if the actual sales is different from the predicted amount, blame the error component and not the statistician.
There is a whole lot of effort expended in trying to understand and explain this error component. New variables found, new algorithms applied, but the ε still lives on. Till date there has not been any statistics model that does not have the ε in it.
Even a statement that "All crows are black." will be stated by a statistician as "It is with 95% level of confidence that 99% of the time all crows are black." This is with the understanding that if someone sees a crow with is not all black, the statistician is safe with his statement.
So now we have a EPSILON which is the unexplained factor and responsible for all the deviations in the statistical model. In some cases, like predicting the likelihood of a patient surviving a critical operation, this EPSILON also represents a dangerous and frightening probability. A play in the equation that is unexplained and blamed for any deviation in our predictive capabilities. EUREKA ---- we have found the "STATISTICS" GOD.
My professor of economics, Mr. Sakhalkar, once made a statement that when one reads he should not be selective. That creates a bias and restricts ones circle of influence. Going down that path a couple of years ago took me down the path of the origins of religion.
A very interesting fact came to the conscious. "God" was an invention of man to blame someone for things that man did not understand and could not control. In the old days, the most frightful element for man was fire. He could not understand it, he could not control it and it was most destructive force. So a Fire God existed. And among all the gods, the Fire God was the most powerful one. There was Water God, Land God, Wind God, Sun God, Star God and so on.
As man started understanding the elements of nature, the importance of that God diminished. Until the God itself was abolished. Eventually as we sit in the twenty-first century, almost all of the element Gods are extinct. When the Gods started disappearing, man found reason to blame other men for various elements. So a forest fire was because some fool dropped a lighted cigarette on dry grass. Understanding man and its nature became a prime topic of importance. God now starting taking form of man. A convenient person to blame when things go beyond explanation.
A statistical model is a function which describes the observed behaviour based on identified independent variables. But what most sales personnel (call them consultants or statisticians) leave out is the "epsilon" (E). Every function in statistics has the epsilon attached to it. Consider the following function which forecast the amount of sale at a retail outlet.
Y = aX + bZ + ε
where Y is the amount of sale, X is the average salary of store visitors and Z is the fact that it is raining. ε represents the epsilon or the error component. This is statisticians' way of keeping themselves legally safe (yup... statisticians are smarter than lawyers). It implies that though the function predicts the amount of sale, there is an error component which explains the deviation in actual amount against the predicted amount. So if the actual sales is different from the predicted amount, blame the error component and not the statistician.
There is a whole lot of effort expended in trying to understand and explain this error component. New variables found, new algorithms applied, but the ε still lives on. Till date there has not been any statistics model that does not have the ε in it.
Even a statement that "All crows are black." will be stated by a statistician as "It is with 95% level of confidence that 99% of the time all crows are black." This is with the understanding that if someone sees a crow with is not all black, the statistician is safe with his statement.
So now we have a EPSILON which is the unexplained factor and responsible for all the deviations in the statistical model. In some cases, like predicting the likelihood of a patient surviving a critical operation, this EPSILON also represents a dangerous and frightening probability. A play in the equation that is unexplained and blamed for any deviation in our predictive capabilities. EUREKA ---- we have found the "STATISTICS" GOD.
ε
Subscribe to:
Posts (Atom)
