ANALYSE, PLOT & RECOMMEND

Talking about our everyday take away from the class, there may be certain things that you tend to miss out on. Well, if you have missed the presentation which one of my group members had given, here it is.

The main objective of this analysis is to infer the relationship between the randomness levels in behavior of the phone users in a cellular network. While individual phone user’s calling behavior is random, some users might be more predictable than others. Being more predictable can also mean being less random. To quantify the randomness or amount of predictable structure in an individual calling pattern, the information entropy can be used. The information entropy or Shannon’s entropy is a measure of uncertainty of a random variable.

The calling pattern is understood from the Calling Time, Interconnected Time, Talk time & Location of the call. These calling patterns and the factors affecting them are studied using correlation coefficient and factor analysis. 

If a factor has a low Eigen value, then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important factors. If a factor has Eigen Value of more than 1, then it is considered as effective component and is not redundant. Scree Plot helps in selecting the number of factors to be retained in order to account for most of the variation. The Scree Plot denotes the relationship between the factors of variance. It helps you determine which factors to retain. 

• What we understood from the analysis is that User’s randomness level based on location has high correlation to the randomness level in time of making phone calls and vice-versa.
• Randomness level based on user’s inter-connected time has a high correlation to the randomness level in time spent talking on each phone call

Author: Mohamed Sahle

Group: Marketing 5

The Curious case of Scree Plots

While working on our last assignment i.e. Analysis of the behaviour of cellular phone users and identify behaviour signatures based on their calling patterns by using Factor Analysis. We came across a term called ‘Scree Plots’ .

The Cattell scree test plots the components as the X axis and the corresponding eigenvalues as the Y-axis. As one moves to the right, toward later components, the eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep decline, Cattell's scree test says to drop all further components after the one starting the elbow.

We drilled in further to understand how Scree plot is plotted and what are the different factors considered for plotting.

In other words It helped us understand how many factors should be retained for analysis? There is no clear answer but a couple of rules of thumb.

One rule is to consider only those with eigenvalues over 1.

Another rule of thumb is to plot all the eigenvalues in their decreasing order. The plot looks like the side of a mountain, and "scree" refers to the debris fallen from a mountain and lying at its base.

So the scree test proposes to stop analysis at the point the mountain ends and the debris (error) begins.

In this instance, that point coincides with the eigenvalue criterion.

Author: Navdeep Kumar

Roll No : 13150

Marketing group 5

SOME MORE NUMBERS

And I give you a 100% assurance that this would be better than the last. Hope so. Fingers Crossed.

Class just got over and I glanced to look at all my peers. BA got over. Some gave a sigh of relief as they got done with another “subject with numbers” and some wished the subject had more lectures. There was an intense discussion as to what all one learnt over the last week. 

Well, on my part, I learnt a lot. Reflecting back to my SI, I just wished I had known SPSS before the internship. It would have probably helped me a lot and I would have produced a better quality work.

So today I will elaborate on Conjoint Analysis.

Conjoint analysis:

Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.

Area of Application:

Conjoint analysis is appropriate when a researcher wants to measure preference for a product or service, the source of that preference, or the impact on preference caused by product design changes.

  

While there are a wide number of uses for conjoint analysis, four of the most common will be discussed below.  

They are:

• Product Design Research

• Market Segmentation Research

• Brand Equity Research

• Price Sensitivity Research

Conjoint analysis takes these attribute and level descriptions of product/services and uses them in interviews by asking people to make a number of choices between different products. 

Let’s demonstrate the use of conjoint analysis in a quirky example:

For instance who would Mr.X choose Angelina or Megan?

Age       36    25

Height                5’8’’             5’5’’

Intelligence      7/10                 2/10

(Hypothetical assumptions; sorry if someone’s sentiments got hurt .... NOT)

In practice you can see how difficult some of the choices can be... :p.  

Mr.X’s thought process might be: 

“Angelina is older, but has the height and intelligence, but Megan Fox is younger and dumber. But for me Intelligence is more important so I’d rather focus on getting Angelina.”

By asking for enough choices one can work out how valuable each of the factors is relative to the others around it – this value is known as the ‘utility’ of the level.

In this instance we can see that for X, the optimum age is 24. 18 is too young and above 24 is too old. We can see that there is no benefit in spending exorbitant amount of money on make-up and amazing clothes, to attract Mr.X if the age is above 24years.

However, we can also compare across attributes to see which attributes may have the greatest impact in his decision. We can therefore say which attribute is most important and measure importance by taking the relative impact of one attribute compared to the others. For example:

In this dummy example, being intelligent is more than twice as important as the height of the person and is the most important variable. So that would be a more important criteria.

So, we come to the end.

Till we meet again..... 

Mudit Bhandari 

Marketing Group 5

13023

Factor analysis

Factor analysis is a statistical method used to describe variability among observed variables. It is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables. 

There are basically two types of factor analysis: Exploratory and Confirmatory.

• Exploratory factor analysis (EFA) attempts to discover the nature of the constructs influencing a set of responses.
• Confirmatory factor analysis (CFA) tests whether a specified set of constructs is influencing responses in a predicted way.

Factor analyses are performed by examining the pattern of correlations (or covariance’s) between the observed measures. Measures that are highly correlated (either positively or negatively) are likely influenced by the same factors, while those that are relatively uncorrelated are likely influenced by different factors.

Scree plot: The Cattell scree test plots the components as the X axis and the corresponding Eigenvalues as the Y-axis. A scree plot shows the sorted eigenvalues, from large to small, as a function of the eigenvalue index.

A Scree Plot is a simple line segment plot that shows the fraction of total variance in the data.

The Scree Plot has two lines: the lower line shows the proportion of variance for each principal component, while the upper line shows the cumulative variance explained by the first N components. The principal components are sorted in decreasing order of variance, so the most important principal component is always listed first.

Group: Marketing 5

Author : Sanandan Atrey

A Joint with Facebook and Conjoint analysis

Usually we used write our learning’s for the day in this blog, for a change i am gonna write a prelude to what we are going to learn tomorrow.

Tomorrow we are going to learn about conjoint analysis for which we had taken a case which deals with preferences that people consider while choosing their spouse. Each and every student in the class had provided the data with his/her own preferences and the final data was generated. The preferences were based on looks, education, family background,  experience (this is my favorite attribute :)), religion... etc. In tomorrow’s class we are planning to analyze this data, having said that i still have a doubt on the data that was generated today. As per my understanding which is as unclear as a Lokpal bill, the data generation was only partially right. The reason is as follows

The best way to understand a person is to analyze his Facebook profile.

Both guys and girls don’t have similar requirements

Please note this is just a humorous take on the task that we did today, no offences meant on any particular gender....(going by the rate at which i am commenting on the female audience, i might even get sued by some women activists)

The point which i am trying to emphasize is, no matter how many kinds of analysis we do and no matter how many statistical tools we use, Girls will be girls and Guys will be Guys.

Cheers,
Ragu


Group: Marketing 5
Author: Ragunathan A
Roll no: 13032

Conjoint Analysis

In today’s class we learnt about Conjoint analysis, which is also called multi-attribute compositional models or stated preference analysis. It’s basically a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement.

Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors. The principle behind conjoint analysis is to break a product or service down into its constituent parts then to test combinations of these parts to look at what customers prefer. The exercise can be administered to survey respondents in a number of different ways. Traditionally it is administered as a ranking exercise and sometimes as a rating exercise (where the respondent awards each trade-off scenario a score indicating appeal).

Analysis is traditionally carried out with some form of multiple regressions, but more recently the use of hierarchical Bayesian analysis has become widespread, enabling fairly robust statistical models of individual respondent decision behaviour to be developed.

For example, a television may have attributes of screen size, screen format, brand, price and so on. Each attribute can then be broken down into a number of levels. For instance, levels for screen format may be LED, LCD, or Plasma.Respondents would be shown a set of products, prototypes, mock-ups, or pictures created from a combination of levels from all or some of the constituent attributes and asked to choose from, rank or rate the products they are shown. Each example is similar enough that consumers will see them as close substitutes, but dissimilar enough that respondents can clearly determine a preference. Each example is composed of a unique combination of product features. The data may consist of individual ratings, rank orders, or preferences among alternative combinations.

As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data is available for statistical analysis, resulting in a carefully controlled set of "profiles" for the respondent to consider

There are three phases in the analysis of conjoint data:

• Collection of trade-off data through a questionnaire.
• Statistical analysis of the data, and
• Market simulation.

Advantages:

• estimates psychological tradeoffs that consumers make when evaluating several attributes together measures preferences at the individual level
• uncovers real or hidden drivers which may not be apparent to the respondent themselves
• realistic choice or shopping task
• able to use physical objects
• if appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation

Disadvantages:

• designing conjoint studies can be complex
• with too many options, respondents resort to simplification strategies
• difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
• respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to poorly designed studies may over-value emotional/preference variables and undervalue concrete variables
• does not take into account the number items per purchase so it can give a poor reading of market share

Conjoint analysis also forms the basis of much pricing research and powerful needs-based segmentation. From the understanding obtained from the class, conjoint analysis is one of many techniques for dealing with situations in which a decision maker has to choose among options that simultaneously vary among two or more variables. The problem facing the decision maker is how to trade off the possibility that option X is better than option Y on attribute A but worse than option Y on attribute B, and so on.

Group : Marketing 5

Author : Mihir Sikka

Saga of Factor Analysis in Marketing

Saga of F(Find) A (Analyze) C (Correlate) T (Tabulate) O (OLAP) R (Rotate) Analysis Continues….

In marketing factor analysis helps in studying the effect of changing one marketing variable and its outcome. Eg the change in sales also affect the bottom line of the company , so factor analysis in marketing helps companies determine which marketing efforts it should pursue , which efforts need work and which marketing efforts may meet the cutting room floor. 

Significance of Factor Analysis: 

It is used to get small set of variables (preferably uncorrelated ) from a large set of variables  ( most of which are correlated to each other ). It also helps to create indexes with variables that measure similar things (conceptually). Identify salient features attributes consumers use to evaluate products in this category, use factors to construct perceptual maps and other product positioning devices.

Principal Component analysis:

PCA is a mathematical procedure that uses an orthogonal transformational to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. Mainly it is used for image compression without loss of any information. 

Steps in factor analysis

Problem Formulation, Construct correlation matrix, Select method of factor analysis , determine the number of factors, rotate the factors, interpret the factors, calculate factor scores and determine the model fit.

The appropriateness of using factor analysis can be checked by looking at the correlation matrix of using Bartlett’s test of sphericity,

Source: http://anthony.liekens.net/index.php/Computers/DataMining

Group : Marketing 5

Author : Apoorva Bhalerao

Roll No : 13009

Factor analysis

Factor analysis was invented nearly 100 years ago by psychologist Charles Spearman, who hypothesized that the enormous variety of tests of mental ability--measures of mathematical skill, vocabulary, other verbal skills, artistic skills, logical reasoning ability, etc.--could all be explained by one underlying "factor" of general intelligence that he called g. He hypothesized that if g could be measured and you could select a sub population of people with the same score on g, in that subpopulation you would find no correlations among any tests of mental ability. In other words, he hypothesized that g was the only factor common to all those measures.

It was an interesting idea, but it turned out to be wrong. Today the College Board testing service operates a system based on the idea that there are at least three important factors of mental ability--verbal, mathematical, and logical abilities--and most psychologists agree that many other factors could be identified as well.

Factor analysis is a multivariate statistical technique that is concerned with the identification of structure within a set of observed variables.  It’s  appropriate use  involves  the  study  of  interrelationships  among variables  in  an effort to  find a new  set  of  variables, fewer  in  number  than  the  original  variables,  which express  that  which  is  common  among  the  original variables.

Factor analysis is used to study the patterns of relationship among many dependent variables, with the goal of discovering something about the nature of the independent variables that affect them, even though those independent variables were not measured directly. Thus answers obtained by factor analysis are necessarily more hypothetical and tentative than is true when independent variables are observed directly. The inferred independent variables are called factors. A typical factor analysis suggests answers to four major questions:

1. How many different factors are needed to explain the pattern of relationships among these variables?

2. What is the nature of those factors?

3. How well do the hypothesized factors explain the observed data?

4. How much purely random or unique variance does each observed variable include?

Factor analysis is a means by which the regularity and order in phenomena can be discerned. As phenomena co-occur in space or in time, they are patterned; as these co-occurring phenomena are independent of each other, there are a number of distinct patterns. Patterned phenomena are the essence of workaday concepts such as "table," "chair," and "house," and--at a less trivial level--patterns structure our scientific theories and hypotheses. We associate a pattern of attitudes, for example, with businessmen and another pattern with farmers. "Economic development" assumes a pattern of characteristics, as does the concept of "communist political system." The notion of conflict itself embodies a pattern of elements, i.e., two or more parties and a perception of mutually exclusive or contradictory values or goals. And to mention phenomena that everyone talks about, weather also has its patterns.

Types of Factor Analysis:

1. Exploratory: It is exploratory when you do not have a pre-defined idea of the structure or how many dimensions are in a set of variables.

2. Confirmatory: It is confirmatory when you want to test specific hypothesis about the structure or the number of dimensions underlying a set of variables (i.e. in your data you may think there are two dimensions and you want to verify that).

Importance of Factor Analysis in Marketing:

Factor analysis in marketing is important because it reflects the perception of the buyer of the product. By testing variables, it is possible for marketing professionals to determine what is important to the customers of the product. For example, if a product is only available in black and the sales reach $150,000 in one year, but when the company adds colour options of red, blue and silver, and sales reach $300,000, then the company can conclude through factor analysis that colour options are important to the customers of the product. Ultimately, it is imperative to use factor analysis in marketing to create the ideal product for customers, which in turn, increases the sales of the product.

Generally, companies test variables with factor analysis in marketing using tools such as focus groups and surveys. Since making changes to the product itself in order to test variables can be expensive, surveys and focus groups allows companies to gather pertinent information without increasing the cost to manufacture the product. Focus groups and surveys allow companies to gather perceptual information from current and potential customers of the product. This information is important because it allows the company to see the product from the vantage point of the customers and determine which factors in marketing are the most important to the customers. For example, a focus group may see four different package versions of a product and then ask the focus group participants to choose which package they like best and explain why. Companies can use this information to alter product packaging to attract more customers and sell more products.

Group : Marketing 5 

Author : Krupa Vijura

Roll no: 13083

FACTOR ANALYSIS

Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables.

Factor analysis is used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of variables. 

The variables should be quantitative at the interval or ratio level.  Z-score is used as a method of standardization between the variables. Variables are reduced on the basis of correlation. The variables converted into Z-score have a Mean of zero (0) and standard deviation of one (1). 

Communalities table in the output table shows the common variance which each component can extract from other components. The higher the correlation, the better it is. Variables having extraction value less than 0.5 are not considered for analysis.

Difference between Factor Analysis and Component Analysis:

Component Analysis takes in account all variability in the variables whereas factor analysis estimates how much of the variability is due to common factors (communality).

Scree Plot:

Scree plot: The Scree test plots the components as the X axis and the corresponding eigenvalues as the Y-axis. As one moves to the right, toward later components, the eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep decline, Scree test says to drop all further components after the one starting the elbow.

Author: Mohit Agrawal

Group: Marketing Group 5

Roll no:13022

CUBES, BUBBLES & WEBS

Graphical representation of data through SPSS & Excel

Today was a day filled with graphics, trying to make it look interesting and easy to analyze. Initially we made some really sad graphs like the 2D bar graph, with data from the retail data set. Then we moved up slowly to 3D graphs and then the whole thing got really interesting when the 3D graph could be rotated. It is called the Interactive Graph where we take two attributes and then plot them on the graph.

OLAP Cube – Online Analytic Process

Like sir explained in the class, Imagine that the OLAP cube is literally a cube and u cut a small portion of that cube for analysis. Here also we do the same wherein we segment the data into smaller cubes and analyze it from various angles. The process is called Online Analytic Process because we can quickly check up the details and get instant online results.

In order to make OLAP cubes, we should understand summary variable and grouping variable.

Summary variable is also called as scale. It is discrete i.e. round it off (if you are of the age 14.4 you tend to say 14). Here, age, monthly bill, SMS bill etc are considered. Grouping (Category) variable include gender, level of education, name of the service provider etc.  You take both of these on the x axis and the y axis.

Although it was a disappointment to know that OLAP cube was not really a cube, it is a table which represents the data in a classified manner. Once the attributes are selected, a report will be generated which can be filtered according to the choice of comparison that the analyst requires. Along with this we also learned to analyze Bubble Graph, an example to this was the ICICI bubble graph analysis regarding Hedge funds, liquid funds, income etc. Then the report from a government website showed moving bubbles representing the no. of people employed and the trends in productivity and value added.

SPYDER

This type of graph is mainly used when we have more number of attributes. This is like Perception Mapping. In order to analyze and understand we should look into different attributes, as they are differentiated using different colors, from the closeness of it from the center to the outside. The further it is from the center the better it is. It helps in looking at the attributes from different perspective and interprets the data deeply

Author : Mohamed Sahle
Group: Marketing 5

Threat levels of Spiderman's enemies

Today's topic in BA classes was regarding the graphical representations of data through SPSS (with a much required help from MS Excel). Topic was easy, and hence participation was all-time high. And only because of some men like Ragu I'm writing this post (he started it). Because, roughly, 86% of people expected me to simply copy and paste some stuff from the net.

So here I am, representing graphically, my research results.

The research topic was "Threat levels of Spiderman's enemies". I would not go into the details of the research, since it is quite likely to gulp more than 100 pages of Arial font-size 12. But I'll try to explain the process in as easy a way as possible after the GRAPH!

Methodology:

By Delphi method, 

First, the six most important attributes of super villains were found out (intellect, extraordinary powers, goal/motive, psychopath, antisocial and solipsism);

Second, weights were assigned to each of the six attributes again by expert opinions (which were calculated in percents respectively, 17.241, 11.724, 20.690, 19.310, 12.414 and 18.621);

Third, Spider-man fans rated ten of top Spiderman’s enemies out of 10 for each of the six attributes. And then the scores were given on the basis of assigned weights.

The result was a much expected one (apart from a few surprises like Scorpion and Dr. Octopus for entirely different reasons).

The following data is represented in the above graph, and the threat level is calculated out of 10:

Lizard	4.290
Electro	5.228
Hobgoblin	5.317
Dr. Octopus	5.752
Kingpin	5.903
Mysterio	5.979
Rhino	6.386
Scorpion	6.497
Venom	6.903
Green goblin	8.883

(Green Goblin is the most threatening of all Spiderman’s enemies, and Lizard is least threatening.)

P.S. So, why represent graphically?

Because:

1. Graphical representations make it easy to understand and interpret data at a glance.
2. Such representations help to do comparisons among many things. 
3. Moreover it makes data easy to recall.

Post P.S. If you didn’t like it, maybe you never read comics


Author: Jitesh Sharma
Group: Marketing 5

A Simple perspective on Discriminant Analysis

Discriminant Analysis allows a researcher to study the difference between two or more groups of objects with respect to several variables simultaneously, determining whether meaningful differences exist between the groups and identifying the discriminating power of each variable.

For example, a research company may want to investigate which variables discriminate between customers who decide:

(1) To buy branded products,

(2) To buy non-branded products

For that purpose the researcher could collect data on numerous variables. All the shoppers will naturally fall into one of the two categories. An explanatory use of discriminant function analysis can provide insight into which variables are most significantly associated with shoppers who go to market. The variables may be age, sex, profession, earnings, etc. Based on these, the companies can use the information and apply it to predict the buying behaviour of customers to price their product, design product according to their needs.

Therefore, Discriminant Analysis is a very useful tool

(1) for detecting the variables that allow the researcher to discriminate between different groups, and

(2) for classifying cases into different groups with a better than chance accuracy.

Author:Mohit Agrawal

Group:Marketing -5

Discriminant Analysis

Three birds are pecking at the ground while Garfield pretends not to notice them. Some feathers are scattered on the ground & Garfield still pretends not to notice the remaining two birds. More feathers are scattered on the ground. The last remaining bird says scornfully to Garfield, "You must think birds aren't very observant," to which Garfield replies, "On an average, two out of three of them aren't."

Welcome to the world of numbers, stats, figures (no, not the one you’re fantasizing about right now) and god knows what all. Yes, it will be useful in the future. Yes, you probably will still not take it seriously. 

What is Discriminant Analysis? 

Discriminant analysis is useful for situations where you want to build a predictive model of group membership based on observed characteristics of each case. The procedure generates a discriminant function (or, for more than two groups, a set of discriminant functions) based on linear combinations of the predictor variables that provide the best discrimination between the groups. The functions are generated from a sample of cases for which group membership is known; the functions can then be applied to new cases with measurements for the predictor variables but unknown group membership.

Eg. :  Whether people recovered from a coma or not based on combinations of demographic and treatment variables. The predictor variables might include age, sex, general health, time between incident and arrival at hospital, various interventions, etc. In this case the creation of the prediction model would allow a medical practitioner to assess the chance of recovery based on observed variables. The prediction model might also give insight into how the variables interact in predicting recovery.

Use of Discriminant Analysis ?

Discriminant analysis analyzes the dependency relationship, whereas factor analysis and cluster analysis address the interdependency among variables.

In marketing, discriminant analysis is used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data.

It can also be used for market segmentation and predicting group membership because of its ability to classify individuals or experimental units into two or more uniquely defined populations.

Product research – Distinguish between heavy, medium, and light users of a product in terms of their consumption habits and lifestyles

Perception/Image research – Distinguish between customers who exhibit favourable perceptions of a store or company and those who do not

Advertising research – Identify how market segments differ in media consumption habits

Direct marketing – Identify the characteristics of consumers who will respond to a direct marketing campaign and those who will not

(Source: http://www.cob.sjsu.edu/webb_k/B231A/Discrim.htm)

Author: Mudit Bhandari 13023

Marketing Group 5

A Crazy Experiment on Perceptual Mapping !!!

Something very strange happened to me today, after nearly 10... No, 11 years... nope its 12... Forget it doesn’t matter, the point i am trying to make is, i happened to sit in the first row of a class after nearly so many years. And that too for 3 hours in a business analytics class.After sitting for 3 hrs now i know why these first benchers get so many marks in exams, u get to hear everything professor says and you don’t get distracted like last benchers like sleeping or staring at a girl, or doing any other better thing in life. But being last bencher all my life i couldn’t stop myself to implement my learning`s in a typical back bencher style. So here it goes.

The topic for the day was Perceptual Mapping which was done using software called Permap. If u ask what perceptual mapping is its simple “It’s a map where we can map the perceptions”. It’s used to make some of the tough decisions in marketing (!) and in some other industries which you can see here in the other blog posts. But what i am trying to do here is to map the perceptions of something that is so tough to predict in the whole world. Any guesses?  Storm, stock markets, Sreesanth...Nope It`s a woman... they are one of the most toughest to predict and understand, their mind is so fast and complicated which will make even a higher end Intel processor  look inferior. To solve this problem and make every guys life easier let us consider that some jerk like me(!) has found a mobile app which will generate a Perceptual map on his phone about whichever girl he`s currently speaking to. And based on the map the app gives some recommendation on what the girl is thinking and what the guy is supposed to do. Here is the conversation 

Girl: I`m planning to go shopping, you coming?

The guy looks at the app, where the girls perception is plotted as a star and the recommendation below

He thinks he`s safe (we guys are so stupid): 

Guy: Actually I’ve got some work tonight?

Girl: Is it so important?

The guy looks at the app

He`s terrified: “We`ll i can mange some time where are you planning to go?”

Girl: What are you looking in your mobile? Are you looking at someone other than me ?

Guy (Doesn`t look at the app this time): Come on its nothing, u know me better, tell me where are we going?

Girl: I was thinking we can go to brigade and buy some stuff, and a movie perhaps? But looks like u have no time for me.

The guy looks at the app again

He replies,”Movie, that`s gonna be a lil tough ? Can we go this weekend?”

Girl doesn’t reply anything. The guy looks the mobile

He presses the “S pls” button, the app crashes and a spark comes out of mobile hurting the guys hand.

Girl: Oh shit!!! What happened to your phone...? Your hand is bleeding, Come on we`ll go the doctor

The girl takes him to the doctor, the guys picks up his phone and to his surprise the app flashes in the screen 

Hope you found this entertaining, I’m kind of sure that guys would welcome such an application. 

If i had hurt the sentiments of any of the female audience, i am not apologizing, because i don`t give a damn :)

If our professor reads this post he will never ever call me to the first bench.

References : Based on a true event from every guys life :))

Cheers

Ragu

Group: Marketing 5

Author : Ragunathan A

Roll No: 13032