Many basic concepts of mathematics are used in everyday life. But you will also find that many Elementary and High School math concepts come in handy if you have chosen a career in marketing and market research. To give you a sneak peek into some of the concepts that are learned in school and applied in marketing later, consider some that we have put together. These are beyond the basic concepts like addition, subtraction, multiplication and division, the application of which is fairly obvious.

1. Linear equations – While algebra may not entice you a lot with all the ‘x’ and ‘y’ variables, it is used to a large extent in everyday marketing. Whether it is plotting the sales of specific products and brands over a period of time or calculating the ROI of a campaign, if you have learned to solve linear equations in school and internalized them, you will not have to grapple with these calculations. While you may not use quadratic equations to a large extent, you will find that some complex situations may lead you into the use of simultaneous equations as well.
2. Percentage change – The most basic of all concepts and yet you will find that there are people who end up calculating the percentage change based on the wrong figure. Those who understand how to use the delta over the previous number and to convert it into a percentage do not have to struggle with calculating the increase in sales, revenues for a ‘year on year’ or a ‘percentage increase over last month analysis’. Calculating the CAGR (Compounded Average Growth Rate) also requires an understanding of percentage change in addition to interest calculations that are taught in high school.
3. Scales – For those in market research, a revision of the scale concept comes in handy when they have to devise and create scales. Only when you know about the nominal, ordinal, interval and ratio scales can you decide the right kind of analysis that can be performed on the data. For example, perceptual mapping requires that the data be captured in a nominal ‘yes/no’ or association data format. On the other hand, if the market researcher wants to use factor or cluster analysis, the data should have been gathered using an interval scale.
4. Statistics – One of the most used areas of mathematics in marketing and market research, concepts like statistical testing, hypothesis, probability and correlation and regression are used very often in analyzing data. T-tests and probability is used in assessing whether the response of one group is actually different from another group. Regression is one concept that is often used to understand the cause and effect relationship among various independent and dependent variables, like assessing the effect of salary, office environment, a good appraisal system, a friendly boss and other such attributes on overall commitment of employees.
5. Indices – Used less often but often in the development of models, indices and exponentials are often used to understand the growth patterns that are being observed in various parameters in marketing. This could apply to revenue, advertising spend or other data collected through primary research as well.

So before you start thinking about the futility of what you studied in school or have tots who question the relevance of the syllabus, remember these concepts and appreciate that you are using what you have learned.

Bias caused by the Respondent
Irrespective of what you do, there are times where the respondent will bring in a bias based on his or her own personality, reasons or past experiences.

• Social appropriateness – This is a bias that mainly creeps in face-to-face interviews where respondents give answers that they feel are socially acceptable. For example, stating that they tend to get angry with their children frequently is not something that any mother is likely to agree. Unless you have a rebel of a respondent stating that she does not like making friends is not a response anyone is likely to give. This phenomenon is sometimes also called the Best light Phenomenon and can be avoided by not putting respondents in a self effacing situation. Projective techniques can be used in such situations.
• Acquiescence bias – Also called the friendliness effect or the yea saying effect, this is a systemic bias that can occur if the interviewer or the respondent is over friendly and starts to agree with whatever that is presented.
• Halo effect – A bias that is caused by the respondent where a respondent carries an overall positive or negative impression about an element and colors all responses with regards to that positively or negatively. In consumer surveys, this often occurs in responses towards the brand used most often where all positive attributes are associated strongly with their own brand.

Response Bias – Bias due to Measurement Errors
The way in which the question is asked, the wording, the flow, the environment in which it is asked, other people present and the interviewer contribute to measurement errors.

• Leading questions – Questions that are leading give away the desired response in some manner. If you state what other people are saying about a new product and follow it up with a question with regards to the respondent’s opinion, chances are that the respondents will tend to agree with what you have just mentioned. Such a bias can also be caused verbal and facial expressions in a telephonic interview or a personal interview. Biased questions should not be confused with counter biasing, a technique that is used to increase the willingness of a respondent to answer honestly.
• Unbalanced scales – When you give options to the respondent to choose from in a close ended question, deciding on these is critical. If a satisfaction scale provides options such as – extremely satisfied, satisfied and dissatisfied, it automatically leads the respondent to choose the satisfied option more than the dissatisfied option. A bias can also creep in if the scale is not valid and therefore does not measure what it is supposed to measure due to the wording of the question.
• Implicit alternatives – When options are provided to the respondent to look at and then respond, leaving out some options can cause bias. For example, if specific brands are left out of the options for an aided awareness question, these brands are likely to get lower awareness scores, something that may not have been the case had the specific alternative been included in the list.
• Order bias – Also called position bias or sequential bias, this is caused when respondents prefer to choose specific options based on the position that they are in on the list. Mail interviews are most prone to this bias because personal interviews and internet surveys tend to resolve this issue adequately by the method of rotation.

The issue of leading question, unbalanced scales and implicit alternatives can be tackled by ensuring that you pilot the questionnaire with a set of respondents and fine tune the wording. Trying to keep these potential errors in mind and understanding the various ways in which a question can be interpreted also helps in creating questionnaires that are not biased. It is also essential that you use the funnel approach in deciding the flow of the questionnaire and start with general questions before getting down to the specific ones.