Motor Trend User Journey Analysis

Inspectlet Sessions Sep 2018

Methodology

We wanted to know:

How are users interacting with articles pages? What is the flow? What are the exit pages?

Hypothesis:

If we know how users interact with article pages then we will be able to iterate the flow to increase PPV.

Recorded 6,186 user sessions using Inspectlet.

  • 4,430 sessions where https://www.motortrend.com/cars/* was involved.
  • 672 sessions where https://www.motortrend.com/news/* was involved
  • 2,724 desktop sessions; 4,068 mobile sessions.
  • 49 sessions observed: MT – Inspectlet Recording (google sheets)

Executive Summary

The Buyer’s Guide initiated a significant number of user journeys (≈ 60%). Mostly MM and MMY pages.

User Experience/User Journeys appears to be working as designed. There were no obvious holes or broken experiences within the user journeys. Users were observed behaving as expected.

Some performance issues continue to hamper and confuse users. Observed slow page loads impeding users from fully understanding the experience (missing photos, etc.)

Users were observed intently looking for content to consume.

Modules and photos get more attention from users than text. Users slow down, stop and inspect modular/photo content while they skip or skim text content.

Common User Journeys

60% or more of the recorded flows started on a Buyer’s Guide index page. Mostly MM and MMY pages.

9% of the recorded flows started on an Article Page.

4% of the recorded flows started on the Home Page.

Observed User Journeys

Session 1

Looking for Content
Flow: Index Page > Article > Photo Gallery > Index Page

The user is browsing looking for something to read, watch.

Session 2

Looking for Content
Flow: Home > Car-Reviews

The user seems to be looking for something specific or just not satisfied with the article they started to read.

Session 3

Module Awareness
Flow: Buyers Guide MMY > Photo Gallery

The user scans index page slowly to thumbnails then returns to the top of the page, taps photo and goes into the photo gallery. Goes through several ads.

Session 4

Module Awareness
Flow: Buyers Guide Index Page > Photo Gallery

User pauses on the price module and then spends a long time on the competitors module, clicks on all tabs in the module and then moves down to the photo gallery module and clicks on it.

Session 5

Module Awareness
Flow: Buyers Guide Index Page > Photo Gallery > Buyers Guide Index Page

User pauses on specs module and then clicks on CTA “see all photos”

Session 6

Module Awareness
Flow: Buyers Guide Index Page > Photo Gallery

The user started on YMM. 2006 Buick LaCrosse page has a lot of empty spaces. Scrolls down pauses at prices, competitors and specifications module. Reads the screen one screen at a time instead of continuously. Scrolls down to photos and videos pause, then continue to the bottom of the page. Scrolls back up and then tap on the photo gallery. Seems to want to enlarge photo using 2 finger pinch.

Next Steps

  • Continue to improve performance. Users were consistently observed being thwarted by slow page loads.
  • Concentrate on MM/MMY pages. 72% of the sessions recorded involved buyer’s guide pages and more than 60% of sessions flows started here. Experiment with: Content order, modules, and design.
  • Double down on modules. Users seemed to pay particular attention to modular content (stop, pause, inspect) whereas they seemed to scan or pass over text content. Iterate current modules to increase PPV.
    • Experiment with graphics, photos and or video.
    • Design modules to increase user interaction. Reduce complexity. More links.
    • Look for ways to add additional modules.
  • Enhance users ability to find content.
    • Improve text scan-ability with more descriptive subheads.
    • Incorporate live chat in buyers guide and index pages? https://www.intercom.com/
  • Include aspirational purchase options. Observed users looking at more expensive MM and before looking to a more modestly price vehicle.
  • User Testing to flush out more context surrounding observed user behavior. There were a number of instances where understanding the users intent would provide more useful insights into what was observed.
    • Improve context around users looking for content
    • A better understanding of user interaction with modular content
    • Create a baseline for future iterations