Session 4 Experiment in Restaurant Section B Group 6_Chandan Parsad(13FPM002)


Experiment in Restaurant

In 2000 Ivon Enterprise set up a chain of Restaurant in Delhi NCR. These Restaurants serve south Indian food.  While the Restaurant in Delhi was doing well, but NCR was showing a stagnant growth of 3-3.5% per annum.  The GM (sales) was concerned and was thinking of ways to boosts the sales. A meeting of senior marketing officer was called to discuss the issue. Many suggestions came up including the increase the advertisement budget for Restaurant, reducing the price, and giving a discount to the loyal customers. One of the suggestion was to introduce Ivon food card and offer 5% discount to customer who bill  Rs 1000 and above only for cardholder. It was argued that it gradually increase loyalty of customers which leads to increase sales and profit of the Restaurant. However, a market researcher who was the part of the team argued that the sales increase depend upon  a multiple factors like as location, the layout , seating arrangement, competitor’s price and competitor’s advertisement  besides the others variables. The controlling of many these was beyond their control. The GM thought to design a study in order to examine the impact of the idea and the sales and profit of Restaurant.

What is an Experiment?

Experiment, is a study or research design in which we MANIPULATE variables and measure the effect on the dependent variables. Since any change in the dependent variable may be caused by number of variables, the relationship between cause and effect often tend to probabilistic in nature.

Basic principles of experimental designs: Randomization and Local Control. Each of them is described below in brief:

Randomization: This is the first principle of an experimental design. This process randomly assigns treatments to the experimental units. It implies that every allotment of treatments ends up with the same probability.

Random assignment ensures that every participant has an equal chance of being assigned to both the experimental group and the control group.

Local Control: Randomization do not remove all extraneous sources of variation. A more refined experimental technique is required for that. A design should be chosen such that all the extraneous sources of variation come under control. For this purpose, local control, which refers to the amount of balancing, blocking and grouping of the experimental units, is used. Balancing implies that the treatments should be assigned to the experimental units such that the result is a balanced arrangement of treatments. Blocking means that, similar experimental units should be collected together to form a relatively homogeneous group. The main purpose of local control is to increase the efficiency of an experimental design by minimizing the experimental error.



Pre Experimental Designs

One-Shot Case Study

X          01

ž  A single group of test units is exposed to a treatment X.

ž  A single measurement on the dependent variable is taken (01). 

ž  There is no random assignment of test units. 

ž  The one-shot case study is more appropriate for exploratory than for conclusive research.

If Ivon Restaurant offer the discount only for card holder (X) and measured the impact on revisiting (O). The problem is that no prior study conduct on the discount and revisit, hence no valid conclusion is made.


One-Group Pretest-Posttest Design

01              X        02

ž  A group of test units is measured twice. 

ž  There is no control group. 

ž  The treatment effect is computed as 0201.

ž  The validity of this conclusion is questionable since extraneous variables are largely uncontrolled.

Consumer revisiting pattern in Restaurant before (O1) the discount(X) and same consumer revisiting pattern after offering a discount (O2)

Treatment effect is computed by taking a difference before and after the discount.


Static Group Design

EG:                  X          01

            CG:                 02

ž  A two-group experimental design. 

ž  The experimental group (EG) is exposed to the treatment, and the control group (CG) is not. 

ž  Measurements on both groups are made only after the treatment.

ž  Test units are not assigned at random. 

ž  The treatment effect would be measured as 0102.


True experiments

In a true experiment participants are randomly assigned to groups. Usually these groups are 1) the treatment group and 2) the control group. The treatment group receives the stimulus (the treatment; such as exposure to a message, some kind of training, or a new drug), but the control group does not receive the treatment. The two groups are compared to see if significant differences exist between them (that resulted from the treatment). In order to see if any changes have taken place based on the existence or nonexistence of the treatment, measurements are taken before and after administration of the treatment. Unfortunately, sometimes the pretest sensitizes people to the issue, and thus it is difficult to see if the treatment had an effect or not. For example, if I am interested in measuring people’s attitudes toward abortion after exposure to a Public Service Announcement about abortion, the pretest in which I ask a lot of questions about abortion may stimulate some people to go out and find out more information about abortion on their own. So what caused the change, the treatment or the pretest? If this is a worry, then one can use a posttest only design, or best yet, a Solomon Four design in which all possibilities are included. Of course, a Solomon Four design is expensive, so it might not be feasible to use a Solomon 


BASIC ASSUMPTION of Experimental Approach: Human behavior is NOT Random.


Three requirements necessary for establishing causal relationship between

IV and DV

1. IV must precede DV 

2. IV and DV must be shown to covary

3. Changes observed in DV must be result of changes in the IV (and not some other unknown variable) 



1. Manipulating exposure to an Independent Variable

2. Observing exposure to an Independent Variable

3. Ruling out initial differences between the conditions (creating equivalent experimental groups; similar types of people in similar settings tested at similar times)


Section B Group 6_Chandan Parsad(13FPM002) 

Other Member:

  • Apurva Ramteke(13PGP068)
  • Komal Suchak (13PGP086)
  • Rohan Kr. Jha (13FPM004)
  • Silpa Bahera (13PGP107)
  • Sushil Kumar (13FPM010)
  • Vivek Roy (12FPM005)
  • Vaneet Bhatia (13FPM008) 

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