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Steps in Segmentation, Targeting and Positioning

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Hello everybody! Welcome to Marketing Analytics course. This is Dr. Swagato Chatterjee fromVinod Gupta School of Management, IIT Kharagpur who will be taking this course for you. Weare in module three now and we are discussing segmentation, targeting and positioning.So in the last video, we have discussed at length about what is segmentation and howto do it, what is targeting and what is the utility of targeting and how to position yourproduct when you target a market. (Refer Slide Time 00:45)So here, in this presentation, we will go ahead from whatever we have done in the lastclass.(Refer Slide Time 00:52)So first thing that we will discuss about the steps of segmentation, targeting and positioningand these are the five steps that are there. Behavior of consumer and we do it throughfactor analysis – that is the first step. And then we do some…. And factor analysiswill have this kind of methods. Sorry, this is the second step. This is cluster analysisstep. So first we do factor analysis and then...Why we do factor analysis? Because there are lot of behavior that are involved and sometimesthose behavior has to be clubbed together so that some similar kind of behavior comesup. And the second step is cluster analysis where based on the behavior of the customerwe try to find out which customers have similar kind of behavior and which customers do nothave similar kind of behavior. And some of the classic methods that we applyto see that which kind of customers are coming close to each other and which type of customersare going away from each other, we use, these are some of the methods like: Hierarchicalclustering. Ward’s is one of the part of hierarchical clustering method and there isanother algorithm called K-mean algorithm and then model based algorithms as well.So second step is, so first step is to combine the behaviors to find out certain behaviorwhich is, for example let us say price sensitivity. Price sensitivity is a behavior or probablysometimes the attitude of the customer towards the price. Now that can be seen as the behavior.You cannot actually ask all the customers what is their price sensitivity but you cancapture that ok, this is the customer who only buys during the sale season or he buysonly when there is a particular, some amount of offs are going on or he always uses coupons.He cuts down coupon from newspapers or probably certain kind of coupon codes from differentsources and he spends time on that, finding out the sources of coupons and etcetera andthen he makes the purchase. Or he is not very happy to give let us saydelivery charges in an ecommerce setup. He actually looks for options how to get awaywith the delivery charges. Now all of this behavior sometimes are related to a similarpattern of that particular consumer or similar attitude of that particular consumer whichis expressed in their behavior which is let us say price sensitivity.In the other case, there can be let say brand awareness. So a guy who only purchases majorbrands and who search for those brands or who generally, even when the sales promotionof the non-branded product is going on or some other brand is going on, he will be loyalto that particular brand. So all of these things might come together as some singlebehavior as well. So there can be so many behaviors, consumer makes so many behaviorsin a retail store or in ecommerce setup or different places. So we have to do first factoranalysis and we will discuss about that in a different part of the course – how tocombine them and create certain meaningful characteristics of the customer.Now after we found out different meaningful characteristics of the customer, we are tryingto club those customers based on whether the meaningful characteristics are matching witheach other or not. And if they match with each other, if customer A and customer B havesimilar meaningful characteristics then they will be close to each other and they willbe kept in the same bucket and if this one set of customers, one bucket of customersis very away from the other bucket of customers then that is a good segmentation. That issomething that we try to achieve. And the methods are hierarchical clustering,Ward’s method, K-mean and say other methods also and a combination of them. So once youhave created, still now you remember that we have not talked about demographics. Wehave created all these clusters based on similar characteristics which is behavioral characteristics.So we are not talking about demographics. But, you have to, once you have created twosegments, three segments, you have to name them, you have to name what that segment is.So let us say, I found out there are certain people who are let us say price aware or priceconscious and then there are certain people who are like some other kind of behavior comesup. Now who are they? What is this person? If a new customer comes in my retail store,how will I identify that whether this guy is segment A or segment B or segment C? Sofor that, we have to name the segments. So for that, we use something called definingthe segments, that is the third step. Defining the segments which is the third step and thenrather than defining, we actually try to find out how I can predict the segments. And generally,we use methods such as let us say, we use methods such as LDA – Linear DiscriminantAnalysis and there is a method called Multinomial Regression.So at this moment, if you are with me, we will be discussing various methods, but youcan go pause the video and probably search, I will share certain links also, you willbe finding it in the description or additional materials where certain links will be givenwhere you can read about what factor analysis in general is, what linear discriminant analysisin general is or what multinomial regression is. These are the kinds of topics which willbe covered in a which should be covered in a business analytics kind of course. So Iam not covering the nitty-gritty of the topics, what these things are, what is the algorithm,what are the findings and etcetera, but the part that we will be needing in our analysis,we will cover that. So, now given that, what we will do, LDA andmultinomial regression is nothing but trying to predict the segment of a customer basedon certain demographics. So, this is the first part where demographics come in when you aretrying to target, when you are trying to create a profile, create a name, create an identityof a particular segment. You are creating a human being’s, how the human being willlook like, that is when you are trying to create a segment and that is where the demographicscomes in. So what you do, you use age, gender, income,domicile and various other places, various other things as your ‘x’ variable, asyour independent variable and ‘y’ variable is a categorical variable which is like….itcan be two categories – segment A and segment B – for that you can directly use logisticregression, binomial logistic regression. If there are more number of segments, segmentsA, B, C, D, we use something called multinomial logistic regression. Multinomial means thereare multiple names, binomial means there are two names, so we use multinomial logisticregression. And we can also use Linear Discriminant Analysiswhich is another method by which we can probably predict that whether a customer will be insegment A, and segment B or segment C, based on the demographic factors. And based on that,I will create my product features and etcetera and I will target certain market rather thantargeting all the customers. So this is something that is important.So, here in today’s class, what we will do is, we will not do the factor analysispart. Let us say, that we will have some data points and based on that, we will create segmentsand using those segments, we will also try to target those segments using the multinomialregression method. So I will also give certain inputs or certain information about how to,on the same dataset how to use LDA, linear discriminant analysis and what is the meaningof that in a different class. So, today, we will talk about customer segmentation.So far these are the three steps that I have talked about, the four steps – hierarchicalclustering, one of them is Ward’s method and K-mean and model based. So we will majorlyfocus on the first three – Hierarchical, Ward’s method and K-mean.(Refer Slide Time 09:15)So hierarchical clustering looks like this. So let us say, you have five people – A,B, C, D and E and you start from below, so at the bottom if you see, that A, B, C, D,E, everybody are, A, B, C, D, E are all separate. So all the five people are in five separatesegments. Then what is my job? My job is to join them, to create, to reduce the numberof segments and to join them and to put them in the same segment. So I find out let ussay A and B are very close. So I join A and B and in step two, I have four segments insteadof five. A and B gets joined and C, D, E. Now for further analysis, I have to find thedistance of C, D, E with the joint AB as a segment rather than A individually or B individually.So next, I see probably C, D are the guys who should be joined. So I join C, D. In thethird step, I have three segments and then again I join C, D and E and then I join A,B, C, D. So I slowly go on joining. So if I keep on joining, the maximum possible numberof segments is all five but each of segment consist only one customer. So all five hasonly one customer each. So that is the maximum possible number of segments. What is the minimumpossible number of segments? Yes, everybody is in the same segment. So there is only onesegment available. So you can do many things – you can eitherstart from the bottom and go to the top, that we call as agglomerative clustering and Ican also do the opposite one – I can start from the top and go to the bottom which wecall as divisive clustering. Both of them is hierarchical clustering because they arecreating an hierarchy of that. And how to do that?(Refer Slide Time 11:11)So there are several methods of finding out the distance between two groups or two peopleand etcetera. So let us say, A, B, C, D, E, F, G- I have in this case, seven customersand I have created two major variables which is brand awareness and price sensitivity andthese are the scores given for brand awareness and price sensitivity. So what happens isthat I find, I try to find out Euclidean distance between person A and person B. So what isan Euclidean distance? (Refer Slide Time 11:54)So the Euclidean distance formula will be, let us say, if A has five brand awarenessand price sensitivity as 5 and 7 and for B, it is let us say 3 and 4, so the distancebetween A and B is 5 minus 3 square plus 7 minus 4 square and then square root of that.That is an Euclidean distance. So we can try to find out Euclidean distance. So what isthe Euclidean distance formula? The Euclidean distance formula is something like this:(Refer Slide Time 12:34)So the formula is like this: that if A has x1, x2……..up to xk number of characteristics,so person one, rather than A and person two has, so x11, x21…….xk1, number of characteristics.Person 2 has x12, x22………….xk2 number of characteristics, then what is the person‘i’? The ith person has x1i, x2i…………….xki, number of characteristics.So let us say, there are two persons i and j. The distance between i and j is basicallyx m i minus x m j, square of that. Now m varies from 1 to k and then I take a square root.So that can be the formula of Euclidean distance. So each of the two, same characteristics,you take it up – let us say brand awareness of A, brand awareness of B, you take it up,find out the distance, square it up. Then again you find out price sensitivity, it’sof A, it’s of B, do a subtraction, square it up.And then let us say something else, let us say brand loyalty of A and brand loyalty ofB, subtract it and square it up. Then you join them, add them up and take a square root.So that is how Euclidean distance is calculated. So, once we calculate the Euclidean distance,we can calculate actually 7C2 number of Euclidean distance. So each of the guys will have Euclideandistance with each of the people. So that is there.(Refer Slide Time 14:37)And then, what we do is, see, the diagonal elements are all zero and it is only a triangularmatrix, that means, nothing above the top, why? Because whatever is above the top, abovethe diagonal is actually same to whatever is below the diagonal. So if the distancebetween A and B is 3, the distance between B and A is also 3, so that is why we onlyfocus on any one part of the whole matrix. Now you see, which two guys are of least distancein this particular this thing. In the matrix that you can see here which one has the leastdistance? So you can see that the least distance iscoming up for C and E and for B and C. So C is the person who has the least distancewith both B and E. So if that is the case, then what will you do? So if that is the case,then you can either join B and C or can join C and E. So any two guys you can join.(Refer Slide Time 15:55)So I randomly started, I started by joining B and C. Now BC becomes one single segment.Now there are different ways to find out, how I can find out the distance from B andC to A, B and C to D, B and C to E and so on?….so which one?So that is how single linkage, complete linkage, average linkage and centroid linkage are different.So you either find out the average of these two and then find out the distance of thatfrom each of the observation. So you find out the centroid or sometimes you find outthe complete linkage. So, you take each of them distance from, so let us say BC is yoursegment, and in complete linkage, you might find out B’s distance with everybody andC’s distance with everybody and then take an average of that. So there are differentways of calculating the distance. The easiest way of calculating the distance is actuallytaking the mean. (Refer Slide Time 17:00)So I would suggest that if that is the case, if you have two guys, let us say B and C gotjoint, so B is let us say 3, 2 and this guy is 1, 5. Then BC when it got joint, the correspondingvalue becomes 2 which is the mean of 3, 1, 5 plus 2, 7 by 2 is 3.5. So you take this,the mean deal value, the average value of the two guys, then you go ahead and try tofind out the distances. So you find out the distance and then the next distance whichone is lowest? (Refer Slide Time 17:29)The lowest is BC with E. So here I think they have not taken the average. Here they havetaken the single linkage which is the lowest distance. So BC and E. So BC and E is still1.414. So then if I join them, it becomes A. So right now, you have how many? Five segments.Now I have five segments. I can still join, which one will I join? The lowest distanceis A and D, so I join A and D. Now which one will I join? I will join either AD with Afor AD with BC. So let us say I join AD with BC because thesize of the segment goes up by doing that and then what will I join? I will join ABCDEwith F. So ABCDEF, I join. So I can keep on joining. Last one will be, everybody willbe in the same segment. Now this does not make any sense. If I keepeverybody in separate segments, it does not make any sense because I cannot create productsfor each of the people individually and if I join everybody in one segment, that alsodoes not make any sense because as we discussed that not everybody likes lukewarm milk, lukewarmtea. Some will like hot tea and some people will like cold tea and you have to find outwhich one you will target. You cannot actually create a lukewarm tea. So putting all of themin one segment does not matter. So then there has to be a point when I will stop. So shouldI stop at four segments, three segments, two segments? Where will I stop? That is somethingthat is important. (Refer Slide Time 19:15)To do that, what we do is we plot. So we see that, when we started with from six clusters,from seven to six we jumped, initially everybody was in separate clusters or segments. Whenwe jumped from seven to six, the distance covered was 1.414 and then when we jumpedfrom six to five, again the distance covered was 1.414. The lowest distance was 1.414.And then when we covered from five to four, the lowest distance, the distance coveredwas 2. From four to three, it was 2.236 and from three to two, it was also 2.236.So if I plot that, generally what we find is a kink. What is a kink? It’s an elbowlike situation. What is this elbow like situation? Or always you will get such good looking elbowlike situation. What is situation? That means that before that, if you actually furtherincrease the number of segments, you do not get much information but before that the information,extra information that we are getting is much higher. So this you can assume as a measurementof information that you get and this is where the elbow suggests that after that, furtherbreaking does not matter, does not make any sense. So in this case, we have found fiveas our classic segment, so we can actually choice this.One – A, D, F, and G are in separate segments and B, C, E are in same segment. So that ishow you do. Now this is called Hierarchical method. Here we have taken Euclidean distanceas our measurement.(Refer Slide Time 20:59)Ward’s method is Agglomerative clustering. So that means it is a hierarchical methodand does not consider distance matrix. It does not consider distance matrix, not applicablefor, sorry, it is mostly applicable for quantitative variables and gives almost equal sized clustersif there is no outliers. This is some of the characteristics of Ward’s method which isa special type of hierarchical clustering. So here, they actually measure the ‘r square’.How much is the, instead of distance, they measure r square and check that which oneI will join that will actually improve the r square.What is r square? R square is similar to what we have measured in case of regression. Ifyou remember the multiple r square, it is similar. So if you see that the first one,the ESS is the error that is still left. So X ijk, what is the thing? The thing is that,so for all people who are in ith group, so ith group, so for all the people I find outthe mean of that group and then divide it from that particular group’s individualobservations and then add them up. So that is one part which is the still error thatis left. And TSS is, when I had no segment. When I had only one segment, when everybodywas put in one segment, what was the situation? So that way, that is TSS.And r square is just nothing but, it actually tries to find out whom I will put in whichsegment such that this r square maximizes. That means TSS minus ESS by TSS maximizes.It is similar to, so TSS minus ESS is what? Which is the part of the variance that hasbeen explained. TSS is when there was no model, what was the error left? ESS was when thereis some kind of clustering, what was the error left?That means TSS minus ESS is what, the numerator of this r square is what, the percentage,or the amount of variance, not percentage, the amount of variance that has been explainedby these clusters. So if I want to see in percentage form, that by TSS gives me thepercentage of the variance that has been explained by these clusters and as r square goes high,we join people. So we try to find out whom to join such that the r square improves. Sothat is something that is also agglomerative clustering, that means, you start from allof them at separate segments and then you try to keep on joining.And here we measure, we actually plot this within sum of square as the measurement whenwe are measuring this. (Refer Slide Time 23:54)We also plot Scree plot, we call it a scree plot here, so we also plot something likethis but instead of distance covered, we measure within sum of square. That is Ward’s method.(Refer Slide Time 24:10)And the third method that is very easy probably is something called K-means method. So K-meansmethod, so once we have decided through Ward’s method or Hierarchical clustering method thathow much will be the, how many segments you want and etcetera, you try to do somethinglike, let us say you decided that I want to have three segments. Now you will try to useK-Mean to give a better application, sometimes K-Means works better than hierarchical clusteringmethods to find out who will fall in which segment, whether the segment is stable enoughor not. So what they will do is, let us say that Ihave two observations here also. So in a two dimensional plane, you plot the points. Sofor example, let’s say, if you remember, A was 3 comma 7, so brand awareness and pricesensitivity is 3 comma 7, so what we did here is A here, you see, this is 0, so 1, 2, 3and then this is 7. So 3 comma 7 is A. Similarly, for every point, I position themin this x, y plane. If it is a multi-dimensional observation, if there was let us say, insteadof price awareness, I had some other variables, so the next method we will talk about is hierarchicalclustering method. So sometimes in comparison to Ward’s method or in comparison to the,sorry, hierarchical clustering method and Ward’s method, sometimes the K-Means methodworks better. So the next topic is K-Means method.What happens? Let us say, through Ward’s method and Hierarchical clustering method,we have decided that we should have, through the Scree plot, we decided that we shouldhave two segments or three segments. So what we do is then, we put the individual valueslike let us say, here we had A, B, C, D, E, F, G, there were seven observations and Awas 3 comma 7. If you check it, A was brand awareness, 3 and price sensitivity as 7. Soyou plot A, here you plat in x axis, you plotted A as brand awareness in the x axis and inthe y axis, it is price sensitivity. If there were more number of variables, thenyou create a multi-dimensional space where one axis is brand awareness, another axisis something else. So sometimes we cannot visualize a multi-dimensional space. For simplicity,that is why I have used a two dimensional plane. But in the multi-dimensional space,what you do is, you actually put these points and then you close your eyes and randomlyput two points in the plane and see that how these two points, because you have two means,K-Means, that means two clusters will have two means.If you have more clusters then more number of means, and how many clusters will comefrom Ward’s method or the Hierarchical Clustering method. So you find out that, ok, I will havethree or four clusters and you put those means and whoever has lesser Euclidean distancefrom the cluster mean, you just put that in that particular segment. So that is the firstjob.(Refer Slide Time 27:29)So what we do, I have positioned CC1 and CC2, two means and ok, so A, B and C are closeto CC1 then to CC2 so I put them in cluster 1 and the rest D, E, F, G are close to CC2then CC1, so I put them in cluster 2. But who says that this is something that, thenwe have to check how much this clusters are stable. So I shift CC1 and CC2 a little bit,delta x in each of the lines I would say, each of my dimensions.So I shift CC1 and CC2 to new position, CC1 dash and CC2 dash. Now I see that whetherthe number of segments and I would say where the segment falls is same or not. And I seethat all of a sudden, this E guy has come to CC1 instead of CC2. So that is somethingthat I generally keep on repeating and I check that why it will become stable. The placewhere it becomes stable that is my final segmentation method. So if I keep on doing that, keep ondoing that, at some point of time, I will reach stability and that will be my segments.Now a segment will be a good segment only when it is stable over all these three methodsthat we have discussed. So we will continue on this particular videoin the next part where we will do the coding with the dataset and we will use these threemethods of clustering and then we will try to see that how targeting can be done. Thankyou very much. I will see you in the next video.