[MUSIC] People, welcome back to session two of introductory business analytics and digital media. Let's get rolling again. This time, our main topic is Toolscape. Now don't get me wrong, business analytics has a lot Toolscape associated with it. We are going to take a peek at two basic tools, two basic approaches that will be most used in this course. So, that's where we are heading in this session. So, let me motivate one of those two big ones. Now, we know that businesses can send promotional coupons directly to mobiles. You must have seen some version of this somewhere is my guess. Take a look at that. 20% off on pizza, enter your mobile number here and receive a coupon if you actually choose to redeem that coupon and each coupon has an identity. I mean, it's identified. So we know who we sent it to and who redeemed it and when, then it would mean that the coupon actually elicited a response. Here's a quick question for you. I'm going to show you two coupons. Take a guess on which one might actually be better at eliciting a response. So, one is offering a 5% discount versus another offering a 10% discount. Same product, same condition, same everything else. This is easy. Clearly, the 10% discount one will win, because it's a deeper discount. Businesses can also send what we call exploding offers. These are offers which come with an expiry date. Use within such and such time after which the coupon would no longer be valid. Here's another question for you. So, which of these two coupons that I will now show you that you think might have a better response? The coupon with the two-day expiry date or one with a one week expiry date. Now it's a little more involved, because why will a two day expiry date would imply greater urgency. And hence, perhaps a better response. Ordinarily, one week expiry might mean people have longer to plan and actually respond to it perhaps. It's a little more involved. It could go either way, perhaps. Businesses we know can also said, what are called geocoded coupons, geographical targeting. So within this radius of so many miles, anybody with a mobile and that will get this particular coupon. That can be done. It's getting done. Take a look at this one. So you are here, we want people within these localities neighborhoods to actually get a coupon. So, which gets a better response? One a coupon that is sent to people within a 2-kilometer radius or that, which is sent within a 30-kilometer radius. It depends there are more people in a 30-kilometer radius, so maybe there'll be more of a response from there. Maybe not two kilometers that's close by. Maybe you'll get a better likely hood of a response there. Let's actually make this a little more interesting. I'd say, this is easy so far. Let's make it a little more interesting. Take a shot at this one, which one gets a better response? A 5% coupon sent in a two-kilometer radius or a 15% coupon sent outside a three-kilometer radius. So what we are doing now is combining two stimuli, two conditions together and maybe the combination is going to release at a larger one. Take a shot at this one, 5% coupon. One day expiry within such and such kilometer radius versus another which again, combines these three conditions. And the question then becomes, how would we know how to know which will do better? It could go either way. Well, what factors might it depend on? There are plenty of factors, it turns out. Yeah, for instance, it might depend on a particular city. Calcutta might behave differently from Paris. It could depend on the product category. Pizzas versus apparel laminates. It could depend on a host of other things, the target segment, for instance, and so on. So a bunch of things that are possible and the only way to find out, the only way to be sure is to let data do the talking, which brings us to the field experiments which is where we are. One of the two big approaches in our Toolscape are experiments. The other being modeling, of course, predictive analytics and we'll get there in a second half of today. Let's basically see what actually happened. The field experiment was conducted [INAUDIBLE] in a management science paper published in 2014 and they answered all those questions that we raised in some sense. So let's go back to the easy questions, the first ones we'd seen. What happens when in some sense, you have, what happens to response rates as time varies, for instance? Clearly, same day had the highest response about 10% redemption. You had one day delay having a slightly lower response and the two day delay having an even lower response. Another simple example. So, this is the effect of mobile promotions in geographical targeting along expected lines. People who are nearby in neighboring geocodes had the highest response. We're looking at 10% there. Now people at a medium distance away from the store, the redemption location had a 7% of response and people far away had a much lower response, less than 5%. So what do we see, basically? The simple questions were answered in a fairly straightforward way. Figure one, figure two, we basically get some idea of the relative magnitudes to be had. So, two days prior is half that of a same day reduction. Figure two, for instance, we come to see that proximity rises, so does response. Again, we see that far is about half that of near in some instances. Now, here's what happened in the slightly more complex cases. The combination of stimuli and what do we get there? Interestingly, what we basically find is that when you combine the two stimuli near is good again for same day. However, near is not good in some sense as time increases. So basically, so when you come to far locations, same day is not good. I'd say, that's kind of expected. When you come to near locations, same day is good and medium sits in between those two. What we actually see? We'll have a look at that. So same day, which is so high for near is basically so low for far. One day prior interestingly is better than two day prior, even in the far location. So, a lot of interesting insights just coming out from data collected through a field experiment. To quickly summarize what we saw, combination of stimuli does tend to produce insightful results. The highest redemption condition is nine times that of the lowest redemption condition,18% versus 2%. Here are some notes from the motivating example. See, we were testing response effects of three distinct stimuli, the three separate stimuli. So, you have discount debt. You have geographical proximity and you have the time available. Consider the first two and recall in some sense, display this as a two by two table as you can see there. So you'll high, low there and you have near and far. The high and low, near and far. The exact numbers there can be determined by context. They usually are contextual. Now, what happens. How do I add the third stimulus condition? How do I add in some sense, a time available to this or in geographical proximity to this? The way to do that would simply be to replicate this for different levels of geographical proximity. High proximity condition, medium proximity condition and there would be one for low proximity condition, as well, right? Other interesting questions that could come about. What other outcomes could be measured about response rates? We were measuring response rates. You could actually measure things like say, value profits. If you're giving a discount, you want to know profit effects. Post trial retention. Will people come back? Does it work? Does try it? You will like it and you will come back work? Loyalty, for instance, and a lot of these things can be tested through experiments. So in some sense, what this example has aimed to do is motivate experimentation test and learn as one of the major approaches in our Toolscape which brings me to a primer in business experimentation. [MUSIC]
