‘Big Data’ Analytics can quite easily help preempt calls into the call-centre. Next step, AI to mimic empathy?

http://www.ft.com/cms/s/0/fca8c484-e071-11e5-9217-6ae3733a2cd1.html#axzz41uxq5yop

This article is a fantastic piece on how technology can be applied. Basically, a bank is introducing AI to answer customer calls; the AI is an attempt at mimicking empathy.

One of the issues with using IVRs is that people used to complain that there was no human warmth, just cold efficiency and sometimes there were no relevant options, wasting precious time, worse still aggravating the caller further.

Technology has moved so much since the early days of IVRs. Initially, a caller was ‘faced with having a menu to navigate through by choosing options read to them, over and over, continually narrowing down the options until the ‘reason for the call’ is isolated and hopefully the issue could immediately addressed.

Simple usage statistics can help make the menu relatively more relevant, from simple counts to assign option priorities based on relative usage, sometimes based on the time or the customer demographics,  to slightly more complicated paths based on costs, expected probability of eventually having to go through to a call agent. 

Usually such menus are designed to try and resolve the issues before the caller resorts to the agent, or at least narrow down the issue to facilitate the work of the human agent. After all, human agents ‘cost more’ than machines.

The next step in the improvements to call agent systems is to preempt the calls. This involves finding out the ‘reasons of call’ and pro-actively contacting the customer with a solution before the customer picks up the phone to call into the call centre. 

While finding the events that usually lead to customer calling into a call-centre is relatively easy, I was initially skeptical whether there would be time to contact the customer before the call is made. But after working this issue with real customers, I was pleasantly surprised to find that even in these days of mobile phones and instant gratification, people would most often not immediately call into a call-centre for less than critical issues. For sure people would immediately call in if their card was swallowed by a machine or physically cut by a cashier, but even if their card was declined, people do not always immediately call to query the issuing bank. Most people having more than one card relationship helps; but this pattern also occurred in non card-related issues. People are more patient than I had expected.

Hence it is possible to anticipate who is likely to call into the call-centre and in many cases propose a solution before the customer calls in as illustrated above. This can be refined by adding an extra filter where the decision is made whether the organization wants to allow the call through, for cross-sell purposes for example.

The technological improvement highlighted in the FT article further minimises the chances of a call reaching a human agent that ‘costs more’ than the machines. The AI can mimic empathy and the customer can have his/her issue resolved without the coldness of a machine.

A combination of anticipating the call, deciding whether to preempt it, and possibly having the AI respond to the customer is a great way to control the customer experience and cost.

My question is whether mimicking emotions will be enough for customers, or would knowing that you are ‘talking to a machine’ albeit one that sounds human make you feel better.

 

“Self-driven/Autonomous car hits bus “

This is a headline that is not unexpected; accident happens, and whatever the hype, the autonomous vehicles are still at testing stage.  

“Google said the crash took place in Mountain View on Feb. 14 when a self-driving Lexus RX450h sought to get around some sandbags in a wide lane… The vehicle and the test driver believed the bus would slow or allow the Google (autonomous vehicle) to continue…But three seconds later, as the Google car in autonomous mode re-entered the center of the lane, it struck the side of the bus”

However, “our test driver believed the bus was going to slow or stop to allow us to merge into the traffic”; hence google agreed that they “clearly bear some responsibility”.

But what is, to me, scary is what google has learnt from the accident: “From now on, our cars will more deeply understand that buses (and other large vehicles) are less likely to yield to us than other types of vehicles”. It sounds like the algorithm will understand the type of vehicle approaching and allocate a different probability of the in-coming vehicle slowing down depending on the vehicle type/size. 

To me that’s not a brilliant idea.

I do not think it’s called safe driving, nor courteous driving, to cause an incoming vehicle to slow down to avoid an accident with you. Instead, you should at most assume the incoming vehicle will not accelerate and entering its lane will be safe for both vehicles (and their occupants). 

Assuming the incoming vehicle will slow down is a recipe for accidents. Refining that assumption based on the size of the incoming vehicle will only encourage people to buy larger vehicles.

This brings me to another question: who bears responsibility for vehicular accidents involving autonomous vehicles, especially one autonomous vehicle and one ‘traditional” human driven vehicle? Will the AI provider pick the tab? In this case it’s certainly based on a decision by the AI. 

So if this happens when autonomous vehicles are in production not just testing, who will pick the tab? If it is say google, would an individual (or an insurance company) try to sue google or would it just settle? In that case, would we end up with a two speed justice system?

I think there is a lot of potential in autonomous vehicles, but more thought has to be put in the legislation and implications (especially in the insurance domain because accidents will happen) around it, and we have to be very careful about what is being tweaked in the models of the autonomous drivers, about the behaviours we are creating.

At the risk of sounding like the NRA: “It’s not the technology, it’s the people using the technology.”

Source article:

http://www.reuters.com/article/us-google-selfdrivingcar-idUSKCN0W22DG

 

Some thoughts on what Uber/GrabTaxi/lyft can easily do with your data

“Exclusive: The ride-sharing company is conducting a trial in Texas using movement sensors in smartphones to track signs of erratic driving” http://www.theguardian.com/technology/2016/jan/26/Uber-monitoring-drivers-us-passenger-safety-houston

When I read the title of the article I wanted to laugh… It would be really dumb of Uber if this hasn’t been already happening for a while.

Imagine, as an Uber driver, you carry the Uber app in your phone; do you control what the app accesses? Your smart phone has in-built GPS (that’s what allows you to use the maps and other applications that give you directions, recommend stuff ‘near you’), gyroscope (that’s how you can play games that require you to move the phone itself rather than just use controls). 

Using these 2 pieces of equipment that most smart phones have in-built, it is very easy to know where you are, the speed and direction you are moving, and any sudden changes in direction. All you have to do is overlay a map that has data on the speed limits, and you will easily be able to tell whether speed limits are being exceeded, whether sudden lanes are changing are taking place…

So if this is an exclusive story, then I am really surprised. Uber can’t be that backward, afterall, remember the “rides of glory” where Uber classified the rides their customers took? (For example if you are picked up in an area with healthy nightlife in the wee hours of the morning and get dropped off at a new location, then depart from that location after a few hours later, it could be a one night stand…)

https://gigaom.com/2012/03/26/Uber-one-night-stands/

http://www.whosdrivingyou.org/blog/Ubers-deleted-rides-of-glory-blog-post

You cannot be doing this and not analysing what your drivers are up to, especially after the horror stories such as http://indianexpress.com/topic/delhi-Uber-rape-case/ . In fact, wouldn’t it be easy to simply track every Uber car, since you know the origin and destination of every trip a passenger takes, decide the most likely routes, and issue alerts when driver deviate from these routes, may be require driver and passenger to respond?

What is more even interesting is what is going to happen/happen to this data that Uber collects. 

Data is a resource, and a very useful one too, in the right hands.

MSIG recently announced it would be the first insurance company in Singapore to use telematics (http://www.asiainsurancereview.com/News/View-NewsLetter-Article?id=34686&Type=eDaily) . To be able to decide whether a driver is low or high risk, the insurer will have to collect the data on how that person drives. But for Uber drivers, Uber already has all this data.

Take a step further, think of passengers. 

When you fill up your application for insurance, you have to declare your habits, including the amount of alcohol you consume, and whether you enjoy sky-diving for example. It is up to you to inform the insurer if the response to any of the questions you answered in the past has changed. Else you could lose coverage at the most inopportune times (http://www.mcmha.org/never-lie-life-insurance-application/). What if your insurer knows you are often at a well-known nightspot until the wee hours? Did you declare alcohol consumption? This can impact your premium or seriously cut any pay-out from the insurance company. The insurance company would mostly likely gladly pay for information that would save them from large pay-outs.

What if your bank knew you often went to the vicinity of the casinos and add it to the fact that the bank knows that you do not spend on shopping there? How would that affect your credit rating and your ability to get loans at decent rates? The bank would also be most likely to be happy to buy data that ensures their risk is well covered (high-risk customers are charged ‘appropriately’ higher premiums).

What if the data on trips taken by Uber is sold in bulk? Nowadays with the proliferation of data, it takes less than 5 matches to be at least 90% sure of one’s identity and thus know who you are, where you were, and when. http://www.nature.com/articles/srep01376. Basically, all I’d need is 4 social media posts where you confirm your presence at certain locations at specific times that I can match with the Uber data (starting point or end point and time), and I can identify that all the Uber trips are made by you with 90% accuracy at least. 

Basically what I am trying to say is that there is a lot that can be done with data that Uber (or any other car ride company – I am just talking about Uber because the article that caused me to write this was talking about an ‘innovation’ by Uber), whether on its own to enhance Uber services (is the driver a safe driver? Is there a risk that the driver is up to no good?), or be used by third parties (is this driver safe and deserve a low premium or unsafe and merit a higher one to cover my insurance risks?) or merged with other data to reveal even more (is this customer a low risk customer or does he/she have less than prudent financial habits? Where does Mr XXX or Ms YYY go, where was he/she last Friday night, where does he/she visit often?)…

And this brings me back to my main concern; do we have any control over what happens to data we generate? Laws are required to force organisations that collect data that we generate to inform us of and get our permission for data collection and usage, and well as retention period and delete data upon request.

Think!

This sign on the left is found at a coffee shop in Singapore. A customer has a choice –after queuing for food – you queue again for drinks, or wait for an uncle/auntie who takes drinks orders and you pay upon delivery. The sign shows a productivity drive by the coffee shop owner.

Productivity can be defined as output per worker; so by encouraging customers to by-pass the uncles and aunties and going straight to queue themselves, there are less workers for roughly the same volume of drinks sold; abracadabra! Productivity goes up. The coffee shop owners might even gain even more: http://www.mom.gov.sg/newsroom/press-releases/2015/0819-leds

People who have lived in Singapore for a while will certainly understand that using the terms ‘productivity’ and ‘efficiency’ are words that push people to action. And there now is a long queue of customers at the drinks stall, and fewer uncles/aunties employed to collect orders.

As customers, what have we gained?

1.      Are the drinks cheaper? No.
2.       Is the waiting time for drinks shorter? No, on the contrary, instead of preparing 5 hot coffees,5 teas, and 3 milos the people manning the counter have to prepare them individually, increasing the time taken for each drink.

So we are paying the same amount for the drinks, only we are spending time queuing rather than spending the time with our friends and families and sharing a full meal together.

This is a simple example of an organization shifting costs to the end customer. The customer ‘pays’ more and the organization reaps savings. What savings? Some of the uncles/aunties are no longer seen at the coffee shop. I found one uncle, at a coffee shop in the next town, presumably further from his home.

So my question is, why do we as customers help these organisations fire people who obviously need the jobs?

I was having dinner with this friend last week and she was arguing that no one can stop progress automation…. Please, think!

This is not automation, no some soulless blind machine taking over the jobs of these uncles and aunties. We, as customers are actively taking hammers to the rice bowls of the uncles and aunties by reacting like Pavlovian dogs to the words ‘productivity’ and ‘efficiency’. We are choosing to take the extra effort upon ourselves to drive these people out of their livelihoods. And they might end up on the right side of the picture above, a cardboard auntie’s life is much harder than a drinks auntie’s life.

Think people, think!

I would have no problem paying an extra 5 or 10 cents per cup of coffee that would have meant the uncle/aunties could retain their jobs; I can’t be the only one right?