Constant sum is a technique that is used in market research surveys where respondents are required to allocate a constant sum of points or units on specified criteria or features. All these points add up to a constant total, which remains unchanged. Constant sum question type helps to get a better understanding of how respondents give value to specific attributes. When customers feel like certain features are more relevant to them than others, they tend to provide a higher score for that particular feature. Therefore, constant sum is similar to rank order, but it carries value or points, which helps analysts to translate the data into reliable information.
Create constant sum question type in your online surveys
Collect constant sum data from your respondents
Study the constant sum data and quantify user-preferences
Gather rich insights about your customers’ preferences
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 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, 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 while he can hit a null value for any attribute which is not charming enough. 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.
Consider a question that’s part of your survey, which involves asking respondents they 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, ambiance, 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:
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? 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:
When you analyze the answers, the data gives you a clear picture of not just the deciding factors, but also by 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 & behaviors. Therefore, we can say that constant sum scaling can be used to analyze the comparative significance customers place on various factors of a subject.
Implementing constant sum and getting responses can be quickly accomplished in two steps. The first is setting up the question while the second involves analyzing the results.
Select the constant sum question type, first and foremost. In the slider option, respondents can slide the answers to their desired value. You can set the minimum value and the range for the same. If you wish to keep the values within a particular number, you can limit the total and configure it. To make it easy for your respondents segment each scale into small units and for better precision, decimal points as well.
In addition to slider type, you can opt for the constant sum text question type to accept text input from your respondents. Just like the constant sum slider question type, you can set a range and total. Furthermore, you can introduce symbols in the constant sum text question type. You can input anything in this field- symbols like %, $, or text like ‘hours.’ Enter the corresponding numeric value from respondents.
Once you collect the survey data from your respondents, the next step is to analyze the survey results. Based on the points allocated by respondents for each feature, the various attributes are scaled and divided by the total number of respondents. This gives rich insights about how much a value respondents give for one attribute over the other. Constant sum makes the audience to think through carefully before picking a feature over the other. Hence, you can avoid getting responses that mark all features as ‘important.’
Constant sum derives data from respondents by making them distribute points to various options provided, which adds up to a predetermined total score. Constant sum offers the following advantages.
Constant sum enables an analyst to establish clear demarcation regarding the various attributes associated with the subject under study- if it’s a product, the features offered by it, for instance. While the rank order question type only gives an idea of the respondent’s preference, constant sum gives a ‘preference-by-how-much’ idea.
Constant sum question type is easy to implement and gives profound insights into customer preferences. You can not only understand which option is customer-favorite but also understand by what factor. To get this data, all you need to do is add the constant sum question type in your online surveys.
Constant sum prompts respondents to allocate scores to various attributes based on their preference. And, the sum of all the points must add up to a predetermined value. This gentle constraint makes respondents weigh their options carefully before making a choice. Instead of randomly answering all features as essential or important, they would take a moment to reflect on the factors that matter to them. Consequently, you get better results for your online surveys.
Even though constant sum enables you to get metric data for customer preferences, there are some challenges associated with it.
The respondent might miscalculate and set higher or lower points for the features.
If the number of attributes listed is many, the respondents are left with the burden of making cumbersome calculations, which leads to confusion and ultimately survey abandonment.
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