Understanding Highlighted Times in the booking system
Highlighted Times help customers choose the most efficient booking slots by analysing travel time, schedule density, and availability rules.
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Highlighted Times help customers choose the most efficient booking slots by analysing travel time, schedule density, and availability rules. Instead of listing all free times equally, Hubhus intelligently highlights the best options first—while still allowing customers to pick any valid time.
This guide explains how Highlighted Times work and how you can configure the behaviour to fit your scheduling strategy.
What Highlighted Times do
Table of Contents
- Key configuration areas
- 1. Suggesting the most optimal time slots
- 2. Showing all slots if no clear winner exists
- 3. Suggesting optimal dates across multiple days
- 4. How Hubhus scans dates
- 5. Look Max. Days Into Future
- 6. Absolute transit threshold
- 7. Near-future preferences
- 8. Always suggest good transits
- 9. Relative transit threshold
- 10. Suggesting multiple days
- 11. Maximum suggestions per day
- 12. Optimal times after a date is selected
- 13. Daily vs. global transit thresholds
- 14. Allow less-optimal times (short notice)
- 15. CO₂ markers (optional)
- Summary
Highlighted Times are designed to:
Suggest time slots with the lowest travel time
Create compact, efficient working days
Prioritize nearby dates when possible
Avoid overwhelming the user with unnecessary choices
Promote sustainable, low-transit booking behaviour (optional CO₂ indicators)
The feature does not restrict bookings—customers can always select any available time slot.
Key configuration areas
1. Suggesting the most optimal time slots
The engine evaluates each available slot and highlights those that minimise travel time and keep the workday compact. If multiple slots are equally strong candidates, the system presents them without forcing a single priority.
2. Showing all slots if no clear winner exists
If the system cannot find a meaningful difference between options, all available times are shown so customers never experience a “no availability” situation when times do in fact exist.
3. Suggesting optimal dates across multiple days
When multi-day scanning is enabled, Hubhus looks across several dates to find the most efficient combinations of:
Travel time
Proximity to today
Overall workload patterns
Once the system identifies a set of suitable dates, they are surfaced first.
4. How Hubhus scans dates
The booking engine checks dates in chronological order and stops when it finds a set of “good” options.
If no strong match is found early, it will continue scanning until the best available low-transit day is identified.
This ensures predictable and fast performance—even with large calendars.
5. Look Max. Days Into Future
Defines how far ahead the booking form should search.
Short range → faster, more relevant results
Long range → more flexibility
6. Absolute transit threshold
A fixed rule:
Any slot with travel time below this threshold is considered optimal.
Example:
If the threshold is 15 minutes, anything under 15 minutes is highlighted.
Lower = stricter
Higher = more permissive
7. Near-future preferences
You can prioritise availability in the next few working days—even if they are not the overall most efficient options.
Transit tolerance for near-future slots
A higher tolerance allows slightly less-optimal travel time for short-notice bookings.
This balances customer convenience with operational efficiency.
8. Always suggest good transits
If enabled, the system continues searching until it finds at least one truly optimal slot—even if this is not the closest date.
If disabled, the first acceptable near-future match wins.
9. Relative transit threshold
A dynamic rule based on the day’s best slot.
Example:
If the best slot on a given day is 12 minutes, and the relative threshold is +10 minutes, anything up to 22 minutes is still considered “good”.
This helps surface a few more realistic options on days with uneven travel patterns.
10. Suggesting multiple days
You can define how many distinct dates should appear in suggestions (e.g., show the best 3 days).
This avoids presenting customers with a single recommended date.
11. Maximum suggestions per day
Set how many highlights appear per day.
Many businesses choose 1 highlight per day to reduce decision fatigue.
12. Optimal times after a date is selected
Even after a customer chooses a date, the booking form highlights the best time(s) on that day—usually based on lowest travel time.
13. Daily vs. global transit thresholds
Daily threshold
A same-day tolerance:
If the best slot on the day is good, slightly worse times are still acceptable.
Global threshold
A global baseline enforced across all days.
If travel is below this (e.g., 15 minutes), it is always good.
14. Allow less-optimal times (short notice)
Controls behaviour inside a short-range window (e.g., the next 3 days).
You can decide whether the form should:
Hide low-quality slots
Show only optimal times
Show everything but highlight good choices
15. CO₂ markers (optional)
You can display a CO₂ icon on optimal or low-transit slots to promote more sustainable choices without restricting customer behaviour.
Summary
Highlighted Times analyse travel time, workload patterns, day structure, and optional sustainability markers to help customers choose the smartest available slots.
By adjusting thresholds and look-ahead settings, you control how strongly the system optimises for:
Efficiency
Fast booking
Sustainability
User choice
The customer always remains free to pick any valid time slot, while the booking engine gently guides them to the best options.
? Common searches
booking setup • calendar setup • appointment scheduling • booking configuration
? Also known as
appointment • scheduling • reservation • calendar event
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