How does the constant sum technique works

The constant sum is a technique adopted to understand what a consumer prefers amongst a collection of attributes, and most importantly, by how much.

Constant comes in handy while computing percentages, handling finances, understanding customer preferences for specific criteria, or for any calculation which totals to a predetermined value, for that matter. For instance, when a respondent is asked to set 100 points for various features of a smartphone, he can assign a score of his choice for a feature he considers important. He can hit a null value for any attribute which is not charming enough. The constant sum technique is where all the allocated scores add up to a total of 100. The score which a respondent gives reflects on the value they give for the said feature.

An example of
Constant Sum

Consider a question that’s part of your survey, which involves asking respondents their preference while opting for a restaurant to dine out. The question and the responses go as, “What factors do you consider while dining out on a Friday night?” with the options “Cost, location, ambience, customer service.”
Most likely, you will receive a mix of different responses, with each option getting a share from the total percentage. Say the results are:

• Location(20%)
• Price(40%)
• Ambience (25%)
• Customer service(15%)

You sure do get an idea that people consider price as a strong deciding factor. However, do you know by how much, compared to other factors? For some, price is the deciding factor, while for others, it is merely a contributor. So how exactly can you analyze the situation? The constant sum is the answer to your dilemma. With constant sum, the question would be, “On 100 points, score points on the factors that mean the most to you; be sure to divide your score so that the total adds to 100!” and the responses you receive from a respondent would be:

• Price-60
• Location-10
• Ambiance-5
• Customer service-25

When you analyze the answers, the data gives you a clear picture of not just the deciding factors, but also how much a factor supersedes another one. Therefore, by placing respondents in this budgeting loop, instead of tagging every feature as important or randomly picking a feature, they give a more systematic response. Consequently, you get profound insights into their expectations & behaviours. Therefore, we can say that constant sum scaling can be used to analyze the comparative significance customers place on various factors of a subject.