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.
Monday, December 19, 2011
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).
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