Trim Placement
A study on model page and the impact of where trim information is placed on the page.
Background
A small observation sparked a thought. The trim section of model pages, the section that provides greater detail about the specific vehicle of interest, has different placements on the RZR and RANGER pages. This led to a few questions such as does the placement influence our users seeing this information? Does the placement relate to user interaction with the element? And, does the location of the trim element impact the page’s lead conversion rate?
Questions like this can easily be answered through statistical regression tests that explain the relationship between different sets of data. By conducting correlation calculations, we can determine the direct relationship of a page’s trim element placement with the completion of a lead form.
Research Objectives
The goal of this exercise is to examine how users behave on the RZR and RANGER model pages and whether or not their experience on the page, based on element placement, impacts the user’s rate of lead form completion.
Potential Benefits
Understand if the element location leads to users leaving a page or not
Arrange our model pages that showcase the trim element that drives users to explore that information
Promote higher lead conversion rates for Polaris
Methodology
In order to determine the relationship between the trim placement on the page and the lead conversion rate, we need to gather data on user behavior.
Using Google Analytics, we can find which model pages are visited the most and pull the number of sessions a page has as well as the number of completed lead forms for a page. Additionally, we can determine page bounce and exit rates. Contentsquare statistics, however, has exposure and click rates available that help aid in the discovery if location impacts conversion rates.
Following the Pearson Correlation formula, that measures a linear dependence between the x and y variables, solves for r, the correlation coefficient, that will determine whether or not a relationship exists and how.
The correlation coefficient, r, reveals where the relationship exists on the spectrum of -1 through 1. -1, being a negative correlation, to 0, no correlation at all, to 1, a positive correlation.
The Pearson Correlation Formula
Pages
The subjects of this exercise are the brands RZR and RANGER. The focus for the study is on the pages with the top views which equate popular interest and host lots of data to rely on. For each brand, the top five most viewed pages from June 23, 2020 – June 23, 2021 are selected. In addition, the pages user behavior statistics are collected across Google Analytics and Contentsquare.
RZR Pages
RZR PRO XP
RZR PRO XP 4
RZR PRO 1000 EPS
RZR TURBO S 4
RZR XP TURBO EPS
RANGER Pages
RANGER 1000
RANGER XP 1000 NorthStar Edition
RANGER 500
RANGER XP 1000
RANGER 570
Below is a chart displaying all the user behavior data used for this study.
EX = Exposure Rate; CR = Click Rate; BR = Bounce Rate; LCR = Lead Conversion Rate; XR = Exit Rate
LCR is the ratio of number of page lead completions over the number of page sessions.
It is important to notice RZR’s trim section has a higher exposure rate, 44.29%, on average in comparison to RANGER’s trim section which has an average of 28.43% This is because RZR’s trim information is placed higher and seen quicker into the user’s experience on the page.
The following layouts are all inputs plugged into the Pearson Correlation formula. The coefficient solving for r indicates whether or not a relationship exists between the given variables and how.
The closer r is to -1 indicates a negative correlation meaning as x increases, y decreases.
The closer r is to 0 indicates there is no relation.
The closer r is to 1 indicated a positive correlation meaning as x increases, y increases.
Exposure Rate to Click Rate
The correlation coefficient is 0.42, showing there is a positive relationship between the exposure rate and the click rate. Meaning, as the rate of the element’s exposure increases the user interaction with the element increases.
Having the trim information placed higher on the page, on average, has a higher exposure rate. The results from this correlation show the exposure rate results in a higher interaction rate. In turn, the higher the trim section is on the page, the higher the user will interact with the information given.
Exposure Rate to Lead Conversion Rate
The correlation coefficient, 0.22, shows there is some relationship between the element’s exposure rate and the page’s lead conversion. Although there is a slight correlation, it suggests the correlation is positive indicating as the exposure rate increases, the lead conversion rate also increases.
Having the trim information placed higher on the page, on average, has a higher exposure rate. The results from this correlation show the exposure rate results in a higher lead conversion rate. In turn, the higher the trim information is on the page, the higher the user changes from a browser to a lead.
Exposure Rate to Bounce Rate
The correlation coefficient, -0.43, indicates there is a negative correlation between the trim elements exposure rate and the page’s bounce rate. The graph illustrates, as the exposure rate increases, the bounce rate decreases.
Having the trim information placed higher on the page, on average, has a higher exposure rate. The results from this correlation show the exposure rate results in a lower bounce rate. This is understandable because the higher the trim placement, the higher the interaction is from the user. When interaction is detected, the bounce opportunity disappears. So, the higher the trim information is on the page, the lower users bounce from the page.
Exposure Rate to Exit Rate
The coefficient, r, tells us that there is a relationship that exists between the element’s exposure rate and the page’s bounce rate. The negative correlation, -0.51, explains as the exposure rate increases, the bounce rate decreases.
Having the trim information placed higher on the page, on average, has a higher exposure rate. The results from this correlation show the exposure rate results in a lower exit rate. The takeaway is that the higher the trim element is on the page, the lower the user leaves their session.
Click Rate to Lead Conversion Rate
The correlation coefficient, -0.30, explains the relationship between the element’s click rate and the page’s lead conversion rate. The graph, interestingly, illustrates as the click rate increases the lead conversion rate decreases.
This correlation shows the higher users interact and explore the trim information actually lowers the relationship with the lead conversion rate. Why could this be? Perhaps as the user explores the details isn’t interested in what the vehicle has to offer and doesn’t want to continue forward. Another reason could be that users are looking for specific type of information about the vehicle that isn’t presented at this level leading them to not move further.
Click Rate to Exit Rate
-0.55, r, indicates there is a negative relationship between the element’s click rate and the page’s exit rate. This means as the click rate increases, the exit rate decreases.
The relationship between the click rate and exit rate explains users who interact with the trim element are not leaving their session.
The calculations indicate an interesting result.
In researching the trim information being placed in different sections on the RZR and RANGER model pages, regression tests, using the Pearson Correlation formula, calculated the statistical effect of a model page’s trim information placement with the page’s lead conversion rate. The calculations indicate an interesting result. The data shows that the higher placements does not exactly move the user forward in the process but rather prevents them from moving backwards in their experience.