Website Behavior & Booking Funnel Analysis

Analytics Objective 5 | Google Analytics 4 | Jan 1 – Apr 21, 2026

Author

Kathleen Juarez

Published

May 14, 2026

Project Overview

This report is part of a larger consulting engagement examining how digital marketing activity translates into customer bookings for a small service-based business. This objective focuses on website behavior and booking funnel optimization. Despite traffic arriving through multiple acquisition channels, the business lacked visibility into where visitors were dropping off and which channels were actually converting. This analysis uses GA4 event-level data to diagnose that gap.

⚠️

The Problem

No visibility into where users were leaving the site
Traffic volume treated as a proxy for success
Booking form drop-off untracked and unaddressed
Channel quality vs. volume not distinguished

✅

The Analytical Goal

Map the four-stage booking funnel using GA4 events
Identify the largest friction point in the user journey
Segment funnel performance by traffic channel
Validate patterns through subgroup analysis

Methodology

The method selected is a Descriptive Funnel Analysis using GA4 event-level exports. The funnel maps user progression through four sequential behavioral stages, using total users (not event count) throughout to avoid inflation from repeat-event firing within a single session.

Funnel Stages

Sessions

Entry point — all website visits; denominator for overall conversion rate

→

Page Views

Users engaging with site content; surface-level behavioral signal

→

Form Start

User begins filling the booking form — highest-intent observable action

→

Booking Complete

Form submitted — confirmed booking intent; the conversion event

Derived Metrics

Metric	Formula
Page View Rate	page_view users ÷ sessions
Form Start Rate	form_start users ÷ page_view users
Booking Rate	booking_complete ÷ form_start users
Overall Conv. Rate	booking_complete ÷ sessions

Traffic channel (GA4 Default Channel Group) is used as the primary segmentation variable, enabling comparison across six acquisition sources: Direct, Organic Search, Organic Social, Referral, Unassigned, and Paid Search.

Statistical Summary

Metric	Value
Total Sessions	563
Page View Users	470
Form Start Users	38
Bookings Completed	5
Page View Rate	83.5%
Form Start Rate (of page views)	8.1%
Booking Rate (of form starts)	13.2%
Overall Conversion Rate	0.9%
Largest Drop-off Stage	page_view → form_start (91.9% drop-off)

Key Findings

The funnel narrows sharply and unevenly. The dominant friction point is the transition from page viewing to form initiation — a 91.9% drop-off rate that holds across every traffic channel.

Funnel: Most Users Exit Before Starting the Booking Form

Show figure code

funnel_long <- tibble(
  stage = factor(
    c("Sessions", "Page Views", "Form Start", "Booking\nComplete"),
    levels = c("Sessions", "Page Views", "Form Start", "Booking\nComplete")
  ),
  count     = c(totals$sessions, totals$page_views,
                totals$form_starts, totals$bookings),
  highlight = c(FALSE, FALSE, TRUE, FALSE)
)

ggplot(funnel_long, aes(x = stage, y = count, fill = highlight)) +
  geom_col(width = 0.52) +
  geom_text(aes(label = comma(count)),
            vjust = -0.4, size = 3.8, fontface = "bold", color = "#1A1A1A") +
  scale_fill_manual(values = c("FALSE" = "#66ada9", "TRUE" = "#C0392B")) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
  labs(title = "Most Users Exit Before Completing Any High-Intent Action",
       x = NULL, y = "Users") +
  theme_ao5

Figure 1: Unique users at each stage of the booking funnel. Red bar indicates the largest drop-off point.

Conversion Rate by Traffic Channel

Show figure code

ga4_data |>
  mutate(
    channel   = fct_reorder(channel, overall_conv_rate),
    above_avg = overall_conv_rate > site_avg
  ) |>
  ggplot(aes(x = channel, y = overall_conv_rate, fill = above_avg)) +
  geom_col(width = 0.52) +
  geom_text(aes(label = percent(overall_conv_rate, accuracy = 0.1)),
            hjust = -0.15, size = 3.8, fontface = "bold", color = "#1A1A1A") +
  geom_hline(yintercept = site_avg, linetype = "dashed",
             color = "#888888", linewidth = 0.5) +
  annotate("text", x = 0.65, y = site_avg + 0.0025,
           label = paste0("Site avg: ", percent(site_avg, accuracy = 0.1)),
           size = 3.2, color = "#888888", hjust = 0) +
  coord_flip() +
  scale_fill_manual(values = c("FALSE" = "#7aa496", "TRUE" = "#66ada9")) +
  scale_y_continuous(labels = percent_format(accuracy = 1),
                     expand = expansion(mult = c(0, 0.28))) +
  labs(x = NULL, y = NULL) +
  theme_ao5 +
  theme(panel.grid.major.y = element_blank(),
        panel.grid.major.x = element_line(color = "#edf0f7"))

Figure 2: Booking conversion rate by traffic channel. Dashed line = site average.

Booking Funnel Performance by Traffic Channel

Table 1

Traffic Channel	Sessions	Page Views	Contact Us	Form Starts	Sched. Appts	Conv. Rate
Unassigned	32	29	2	3	2	3.1%
Organic Search	86	61	4	4	4	2.3%
Organic Social	207	203	2	12	3	0.5%
Direct	213	167	9	18	6	0.5%
Referral	21	6	2	1	2	0.0%
Paid Search	4	4	0	0	0	0.0%
TOTAL	563	470	19	38	17	0.9%

High-Intent Behavioral Signals

Schedule_Appointment and contact_us are link clicks to the page hosting the booking form. They are reported here as behavioral context rather than sequential funnel stages.

Table 2

Traffic Channel	Sessions	Sched. Appt Clicks	Contact Us Clicks	Form Starts	Bookings
Direct	213	6	9	18	1
Organic Social	207	3	2	12	1
Organic Search	86	4	4	4	2
Unassigned	32	2	2	3	1
Referral	21	2	2	1	0
Paid Search	4	0	0	0	0
TOTAL	563	17	19	38	5

Recommendations

Redesign the Mid-Funnel Experience

The 91.9% drop-off between page views and form starts is the highest-leverage fix available. Strengthening calls to action, sharpening the value proposition, and reducing booking form friction can increase appointments without requiring additional marketing spend.

Prioritize Organic Search & SEO Investment

Organic Search converts at 2.3% — more than twice the site average and more than 4× the efficiency of Organic Social. Shifting content and budget investment toward this channel is likely to yield stronger returns than broad social reach alone.

Audit Channel Tracking & UTM Configuration

Unassigned traffic converts at 3.1% — the highest rate on the site — but its source is currently invisible. Auditing UTM tagging and GA4 configuration to properly classify this traffic is a critical step toward understanding what is driving the best results.

Deploy Purchase Event Tracking via Google Tag Manager

Current tracking measures booking form submissions — not completed purchases. Configuring GTM to capture purchase completions will allow future analyses to connect marketing activity to actual revenue outcomes, enabling true ROI measurement.

Appendix

R Packages

Package	Role
tidyverse	Data wrangling and visualization (dplyr, ggplot2, readr)
scales	Percent and comma formatting for labels
knitr / kableExtra	Table rendering with custom styling