Many studies utilize calculated variables, either for use within the survey or for analysis. In this example, we'll walk you through how to create variables used to measure lift in an ad study.
Measuring the change in brand interest before and after exposure to a specific stimulus (like an advertisement) is a common research objective. This involves asking respondents about their level of interest in a brand before and after they view an ad and then calculating the difference to quantify the ad's impact. Below is an example of how you could structure such a survey using the MX8 platform, including storing the lift in brand interest.
Step 1: Measure Pre-Interest
First, ask respondents about their initial level of interest in the brand:
pre_interest = s.rating_question(
"How interested are you in {brand} before viewing the advertisement?",
number_of_points=5,
style="slider",
labels={1: "Not interested at all", 5: "Extremely interested"},
brand=media.brand,
)
Step 2: Show the Ad
After uploading the ad to the media library, insert it into the study.
s.multi_choice_question(
"Here is a recent ad for {brand}. Have you seen it before?",
options=["Yes", "No"],
image=media,
brand=media.brand,
)
Step 3: Measure Post-Interest
After the respondent has watched the ad, ask them about their level of interest in the brand again:
post_interest = s.rating_question(
"After seeing this ad, how interested are you in {brand}?",
number_of_points=5,
style="slider",
labels={1: "Not interested at all", 5: "Extremely interested"},
brand=media.brand,
)
Step 4: Calculate and Store the Lift in Interest
Calculate the difference between the post-interest and pre-interest scores to determine the lift in interest. Then, use `store_value` to save this lift for later analysis or use within the survey:
s.store_value("interest_uplift", post_interest - pre_interest, brand=media.brand)
Step 5: Putting it All Together
from survey import Survey
s = Survey(**globals())
age = s.numeric_question(
question="How old are you?",
min_max=(18, 65),
recodes={"0-17": "Under 18", "18-34": "18-34", "35-54": "35-54", "55-120": "55+"},
)
gender = s.multi_choice_question(
question="What is your gender?", options=["Male", "Female"]
)
for media in s.media:
pre_interest = s.rating_question(
"How interested are you in {brand} before viewing the advertisement?",
number_of_points=5,
style="slider",
labels={1: "Not interested at all", 5: "Extremely interested"},
brand=media.brand,
)
s.multi_choice_question(
"Here is a recent ad for {brand}. Have you seen it before?",
options=["Yes", "No"],
image=media,
brand=media.brand,
)
post_interest = s.rating_question(
"After seeing this ad, how interested are you in {brand}?",
number_of_points=5,
style="slider",
labels={1: "Not interested at all", 5: "Extremely interested"},
brand=media.brand,
)
s.store_value("interest_uplift", post_interest - pre_interest, brand=media.brand)
s.complete()
After creating the survey in the platform, simulated reports are available to check and ensure the calculated variable is stored correctly.
This survey flow captures a respondent's interest level in a brand before and after exposure to an advertisement. By calculating the lift in interest, you gain valuable insights into the effectiveness of the ad in enhancing brand interest among the target audience. Storing this lift value enables further analysis within the survey or post-survey reporting, helping inform marketing strategies and ad effectiveness studies.
This approach can be customized for different brands, products, or services by adjusting the questions, the ad presented, and the labels used in the rating questions to suit your specific research needs.