Misusing Factor Analysis: How to make common errors?

This comes out of my personal experience. One of the most (ab)used statistical technique is factor analysis. MBA students use it at their leisure. I do not remember how many times I have used this myself. Following list of errors is obviously not exhaustive. But these were the errors I was about to make. Thankfully I was able to rectify my understanding. (very important from interview perspective)

Coolest Mistake to make: I want to find out which factors are important for customer. Ha!!! Do the Factor Analysis on the data, the Eigen values tell me the relative importance of the factors. Higher the Eigen value, higher is the importance.

The fact: High Eigen values do not represent importance. They represent the variance explained by that factor. Variance refers to variation from the mean or the expected value. So you see, importance does not come into picture. If you really want to know importance I would recommend other techniques like multidimensional scaling among others.

Another not so common mistake: Factor A got 10 variables, whereas Factor B has only 2 variables. Since factor A has more variables, customers have higher preference/significance/whatever! for factor A.

The fact: We must understand how factor analysis works. Let us say that there is a bag, it is called residual, and right now it is 100%. I take one variable out and start correlating it with other variables. Slowly I find a factor with one/multiple variables in it based on correlation. Let us say I got a factor which explained 30% variance. The bag now has 70% residual. The process continues until the bag is empty. So I think it is clear that we cannot compare two factors apart from how much variance they explain. The customer preference is also not shown here.

Gyan: Before I close, I would like to share one more thing I learnt from my current project in a telecom company. Though chances are very less, but clients with different sectors can come up with different factors/ different variables in different factors given same set of variables. So a researcher must always stay careful. If the sample size for each sector is good enough to do analysis, he must cross check taking sectors individually and finding factors. It may help in better understanding of the customer. That’s what factor analysis is all about..:)

Multidimensional Scaling and Cluster Analysis: Applying in Marketing

Doing a research project in my summer internship with a telecom major, and having collected data through surveys I’m now left with the most important step i.e. data analysis. With the objective to make the best sense of the collected data in a way that adds value to the organization, recently I was attracted to a number of statistical methods. To be honest the first few days were pretty intimidating with lots of technical words and relatively tougher language than I am used to. Having managed to read some part of it, I thought it would be a good idea to put a few things in a way that may be helpful for those who want to take a dive into these statistical methods.

A detailed analysis of each method needs quite a lot of extensive reading and this article in no way will help you in actually using these methods, they may only help to get a basic idea and how these methods can be applied to take marketing decisions.

Cluster Analysis:

Cluster analysis is a technique to group similar objects (an object is the entity whose attributes are being measured). A simple example to understand what cluster analysis does is to see the people around us. Take for example your city. There are several ways in which you can group people in your city. Age can be one, so you will have children, youth, middle-aged, old people as groups of people. Similarly you can have height, income, location (measurable parameters or variables); and degree of happiness, fashion-sense (non-measurable or categorical or nominal variables) on which you can group people. That’s essentially what clustering does for you.

You must be wondering that if cluster analysis is just grouping of objects on the basis of some criterion, what’s the big deal about it (even MS Excel can do it). Imagine a situation where you want to group people on the basis of three variables, let’s say income, age and preference to a particular brand. If you think this is complex then that’s the beauty of cluster analysis.

The idea is simple. Decide the number of variables on which cluster analysis is to be done. Then plot the objects in an n-dimension space with each variable as one of the dimensions. Using the distance (Euclidean) between two points, find out which points lie close to each other in the n-dimension space. Accordingly, cluster the objects into various groups. If the variables are nominal instead of distance, matching is used to determine the closeness.

The next important question is: how do we decide what is the ideal the number of clusters or groups? Well, there is no rule to this decision. It solely depends on the researcher’s discretion and the problem at hand. In fact there are two ways of doing it.

One way is to decide the number of clusters beforehand (k-means cluster in SPSS). For example, you are using income as the grouping criterion and you want the people to be grouped in only three groups, i.e. low, medium and high. In such cases, you fix the number of groups or clusters before doing the analysis.

The other way is to observe the groupings of objects within the cluster as the number of clusters increase (or decrease) and use your judgment to decide when to stop. The software also can be used to know when to stop, which is usually indicated in abrupt change in a “distance measure”.

A word of caution here is that there are various algorithms for cluster analysis and they give different results for the same data set, so as said you have to be careful to make sure that the results make sense.

For those who are familiar with Discriminant analysis and Factor analysis, there are reasons to think that these methods are similar with cluster analysis. Although all these techniques have a common impact and that is grouping of similar objects / variables, there is significant difference between them.

Discriminant analysis is a technique to group objects on the basis of a known criterion. It's objective is to find a relationship among variables to group the objects as per the criterion.

Factor analysis on the other hand, though being a grouping technique has the main objective of data reduction i.e reducing a large number of variable into a lesser number of factors .

Cluster analysis, on the contrary is about finding a criteria of grouping the objects / variable unlike discriminant analysis and the objective is never to reduce the data but view them as groups with similar objects / variables.

Now as we move ahead with multidimensional scaling, you may try to figure the ways in which this method can be used to simplify our marketing decisions. At the end, we will discuss these applications.

Multidimensional Scaling:

Multidimensional scaling (MDS) is a very powerful tool to graphically understand the differences / similarities or preferences of objects / variables. As the name suggests, it’s plotting of objects / variable on more than one dimension in a way that represents their differences. They are often called perceptual maps because they represent the perceptions of various objects graphically. Here dimensions are not simply axes in space but they are variables (attributes) on which the comparison is being made.

A simple example to illustrate this technique would be to make paired comparison between say 5 brands of soft drinks. Say we ask 45 respondents to take two brands at a time and comment which brand they prefer. As a result, we get 5 X 5 matrix; with each element in the matrix representing the number of times one brand is preferred over the other.

	Brand 1	Brand 2	Brand 3	Brand 4	Brand 5
Brand 1	-	23	12	15
Brand 2	22	-	34	33
Brand 3	33	11	-	19
Brand 4	30	12	26	-
Brand 5					-

The element in red color indicates that 23 respondents prefer brand 1 over brand 2.

Now what MDS does is that it converts the preferences of brands into distances and plots the various brands on a multidimensional space what we call as perceptual map.

I can understand that there are a lot of questions that must have come to your mind. We will try to answer each of them one by one.

The first question surely would be how we get a multidimensional space and how is the distance representative of the preference of one brand over the other. When a respondent prefers one brand over the other, subconsciously he uses some variables (attributes of soft drinks) on the basis of which he makes a comparison and chooses one brand over another. These variables sometimes may not be even perceived by the researcher. For example in case of soft drinks, sweetness can be perceived differently for differently flavored drinks by consumers though they may have the same content of sugar.

Whatever the variable (attribute) is, the bottom line is that the various variables over which the respondents differentiate the various soft drink brands become the dimensions of the perceptual map.

The next question would be how do we decide the correct number of dimensions of the perceptual map? In MDS we choose the minimum number of dimensions through which we can explain the 5 X 5 matrix completely i.e. when we plot each brand on the perceptual map, the position of each brand and their distances should confirm the results of the paired comparison of brands preferences. Generally more than 3-dimensions should be avoided as they are very difficult to interpret.

Ok! So how do we decide what each dimension of the perceptual map stands for? As we discussed each dimension stands for an attribute of the brand. To find out what these attributes are, the researcher has to use his judgment. In fact it’s a good way to find out what are the attributes over which a consumer differentiates the soft drink and forms a perception of that brand.

We can also use ordinal data (rank) of different brands to carry MDS and get similar results.

This is one of the basic applications of MDS. According to Green, Tull and Albaum in the book “Research for Marketing Decisions”, there are two more ways in which MDS can be used.

The other two are Unfolding type-joint space model and Projection type-joint space model.

Unfolding type, Joint space model does exactly what the basic model discussed just now do with an added feature. It also plots objects (in our example respondents) on the perceptual map such that the objects lie close to the brands of their preference. As you can make out, this exercise does two things for you. It not only helps you understand the image of your brand vis-à-vis the other brands on those attributes, it also helps you identify the segment of the market that prefers your brand and target them.

Projection type, Joint space model is one more step ahead of the two models. We add one more angle to our experiment of soft drinks. We ask the respondents to rate / rank the various brands on four attribute namely sweetness, tanginess, fizz and taste in the order of preference. Projection type MDS would result in a perceptual map which would indicate how preferred each brand is on the four attributes. Each brand would be a point in the map with the four attributes as vectors and the distance of each brand from vector would indicate the preference of that brand on that attribute.

Application in Marketing Decisions

I am sure by now you would have understood the beauty of these techniques in solving the marketing issues and taking decisions.

Let’s reinforce our understanding by a simple example which is very close to what I did for my summer internship. We are talking about the offering corporate solutions to SMEs and corporate by telecom players. Suppose we asked customers to:

1. Rank six parameters namely Network, Price, Services, Sales Executives, Company’s reputation and Product line in order of preference as drivers to choose a service provider

2. Rank the various telecom service providers in order of preference on these six parameters

Using the data collected from second question, we can apply MDS technique (Projection type, Joint space model) to draw a perceptual map describing how each brand fared on the six parameters discussed. We will be able to know how my company is perceived on these parameters vis-à-vis the other service provider. We will be able to know the parameters on which my company fares better than other service providers. We can also identify space in the perceptual map that is free and can be used as effective positioning of your brand. We can also identify those parameters on which our company is less preferred vis-a-vis other service providers and improve on those.

Now suppose my company has done very well in network and product line. We would definitely want to identify those customers that consider these parameters as important preference driver for choosing a service provider i.e. they consider network and product line as most important parameters while choosing a service provider.

There are two way of achieving this objective.

1. One way would be to use MDS (Unfolding type, Joint space model) technique on the data obtained from first question. Using this technique you would be able to plot the six parameters on perceptual map with the customers plotted in such a way that the customers are closer to those parameters they consider more important. We can identify those customers and then run a cluster analysis taking revenue, location, sector etc as variables to identify segment(s) that can be targeted.

2. Alternatively, we can run a Discriminant analysis straight on the data obtained from first question with variables “network, product line and sector” or “network, product line and location” to identify groups of customers who give most importance to network and product line and are in the same sector or location. We can target these groups to get successful results.

We see that Cluster analysis and Multidimensional scaling are very effective techniques for positioning and segmentation decisions. For those who want to take a deeper dive into the techniques can go through the following books:

1. Research for Marketing Decisions – Green, Tull, Albaum

2. Multivariate Data Analysis - Joseph F. Hair, Bill Black, Rolph E. Anderson, Barry J. Babin, Ronald L. Tatham

3. SPSS 17.0 Guide to Data Analysis- by Marija J. Norusis

Happy reading!

Telecom industry and customer service

My summer training is still on. Luckily, I got a chance to work for a telecom company. Service industry has its own challenges; the client expectations are different from other industries where products are tangible. Moreover, since telecom is an experiential product. Only after experiencing the product can client comment on it. This makes word of mouth publicity very critical in this industry.

The sales team is always concerned about the targets. The primary aim of the corporate sales team in any telecom company is to get a lead, convert it to sales order and insure timely installation of the setup. Maintaining relationship with the client helps the salesman because it helps in getting recurring orders from the same client. National tie up with between the two companies also becomes a possibility. In retail, the sales team’s effort ends when the product /service is sold.

One other very important aspect of this industry is the customer service. The term customer service is a very broad term. It includes service right from the installation of the setup to the issues which crops out of nowhere. Since telecom is a hygiene factor in most of the businesses, businesses do not want to waste time settling telecom issues. In case of retail, individual customers face issues and report them to companies, and every minute of delay in service adds to their agony. Therefore, the responsibility of the telecom company increases.

I had opportunity to speak many corporate clients and non clients during the training. I think that they consider customer service very important. Good products and cheap price can get a trial in a corporate to corporate scenario, but repeat purchase is not insured.

One bad experience can churn the client forever. I talked to one lady, whose company unfortunately churned out from my company to a competitor. When I was talking to her, I realized her company was earlier very loyal to my company’s brand. However, bad service led to broken alliance. She was so passionate about the incident that it was hard to say that she was talking about telecom vendor, and not a man.

I think of time after 10 years from now, when the telecom market will mature. All the telecom brands will have access to the latest technology; therefore there won’t be any point of difference in the product offerings. The network of all the vendors would be pan India. The price would be all time low and any further reduction will not be possible. Who will win the race then?

Obviously, the company with best customer service will win. The company who will position itself as best customer service right from today will have an edge, since with time the position will get stronger and it will become more difficult for competitors to dislodge the company from that position.

However, communication is not enough. The company has to take pain to be the best customer service provider. Because in corporate scenario, where marketing is minimal and in retail where there is more involvement of individual customer, long lasting distinct position along with good customer experience will ensure long term success.

From brand assets to brand equity.

One of the most important constituent of brand is its equity. Ever since I became a marketing student, I have come across many definitions of brand equity. According to Prof. Kotler, “Brand equity is the added value endowed on products and services. It may be reflected in the way consumer think, feel, and act with respect to the brand, as well as in prices, market share, and profitability the brand commands for the firm.”

However, I found the best definition in Kapferrer. According to him, brand equity or financial value of a brand is “the difference between the extra revenue generated by the brand and the associated costs for the next few years, which are discounted back to today.” The rate of discount and the number of years are derived from the market.
The definition takes care of the investment a company makes in a brand the extra revenue it generates from it. Before coming across this definition, I thought brand awareness, brand image, perceived quality etc as brand equity. The ‘added value’ factor was not clear in totality. These things are however, non monetary and these are called brand assets.

Referring back to Kapferrer, Brand Assets mean

· Brand awareness,

· The level of perceived quality as compared to competitors,

· The level of confidence, of significance, of empathy, of liking,

· The richness and attractiveness of the images conjured up by the brand, intangible values which are linked the consumption of the brand.

These factors determine the perceived value of the brand, compared to competitors becoming source of attractiveness and loyalty.

It is important that these non monetary factors can yield monetary results. I feel that there are companies which have high brand assets, like Ford, GM and Crystler. These three companies have invested a lot on their brand in the past. However, over last few years, these three big companies have been struggling to make profits (even before recession). I think one big factor is that these companies were unable to bring down the costs; therefore the difference between gains and costs of brand has been too low. People may argue that it is quite logical that if the costs are high, the profit will be low. So where does brand equity comes into picture? Well, that is why some brands are better than others. I am sure few will deny that Toyota has more brand equity than the other three. Moreover, everything ultimately boils down to profits.

Dhara is another classic example in Indian context. Despite having high brand asset value, the brand equity is very low.

So, the question is, how does a company moves from brand asset to creating brand equity? Well, I think brand assets can induce trial, however for repeat purchase the customer should not only feel added perceived value in the brand, but also that this is brand which satisfies my need in the best possible way. Here, non tangible ‘patents’ (product by one company), process compliance (helpful not only in manufacturing, but also in services) etc comes into picture. I believe these two are just indicators; there can be many other factors which contribute to brand asset converting to brand equity.

I feel that companies should keep evaluating their brand equity vs. their brand assets. If the correlation starts going down, companies must find out the reasons and take corrective measures, if any.