Surveys collect people’s opinions. In addition, mostly to segment opinions, we collect objective socio-economic criteria like income or gender. Opinions are subjective by nature. With representative samples, average opinions represent groups of people and become facts to describe this group. These results can be used to benchmark companies, products and services.
French survey to benchmark the performance of French sports stores
Although the average result is often used to describe a group’s opinion and to benchmark different research objects like products, services or companies, the „experience horizon“ of respondants needs to be factored in. Different groups of people, e.g. users of different products or customers of different companies, have differing experiences. A customer from an insurance company with a 24h-telephone hotline has a different experience horizon than one from another company with a hotline available only 8 hours a day. The scale (like 1 to 10) to evaluate an opinion may be interpretated in different ways by different groups of respondants. Hence, customer survey benchmarks can only give an indication of the potential performance.
Quality of Survey Design is Key
On the one hand, the quality of surveys strongly depends on the design of the questionnaire. The more precise the questions, the better the answers can be used. However, there is no scheme to ensure that respondants all understand the same question the same way. One can only reduce sources for errors.
On the other hand, the quality of surveys depends on the sample of respondants. Respondants need to fulfill certain screening criteria, before they are admitted to participate in our surveys. If respondants fail to meet the screening criteria, they are excluded. A typical screener of ca CGG study is, whether the respondants are customers of a certain company or have used a certain product or product categorey in the past.
A French survey on sports stores with a screener „Did you visit a sports store in the last 12 month“
The size of the sample is the other important factor. There is no uniform sample size that meets all potential requirements. There is also no 100% representative sample size, except the universe (population). But with logical assumption concerning the distribution of results (the standard is normally distributed), standard deviation, confidence interval and estimate error a fair guess of the sample size can be derived.
CGG Samples normally > 1.000
Our CGG-institutes normally strive to fulfill a sample size of 1.000 in population surveys. Given much smaller universes, sample sizes can be correspondingly smaller.
Our surveys reflect the opinions of users or customers of comparable products, services or companies. Carried out on the same scale, they allow to benchmark the performance of the research objects and give an indication, which choice could be best for other users.