Personalisation Engine Increasing Travel Upsell Revenue by £2.1M Annually
Challenge
Solara, a mid-market package holiday operator, had a 6.3% ancillary attachment rate, well below the industry benchmark of 18–22%. Post-booking upsell emails had a 2.1% click rate, and customer data was fragmented across four disconnected systems. The business had no capability to personalise offers or timing.
Solution
We built a unified customer data platform (CDP), a behaviour-based segmentation model, and a real-time personalisation engine delivering individualised upsell recommendations across email, web, and mobile channels.
Results
Ancillary attachment rate increased from 6.3% to 16.8% within 9 months. Email click-through rates rose from 2.1% to 9.4%. The system generated £2.1M in additional upsell revenue in the first year, with airport transfer attachment increasing from 11% to 34%.
The Revenue Gap Hidden in Plain Sight
In travel, ancillary products such as transfers, seat upgrades, insurance, and excursions often account for 30–40% of total gross profit.
Solara was significantly underperforming. Not because demand was low, but because the experience was generic. Customers received identical upsell communications regardless of:
- destination
- travel behaviour
- purchase history
- booking context
A customer travelling to the Maldives was receiving the same follow-up messaging as someone booking a short-haul city break. In many cases, offers were irrelevant or redundant. The result was predictable:
- low engagement
- low conversion
- lost revenue
Fragmented Data, No Decision Layer
Customer data existed, but it was disconnected. Four separate systems operated independently:
- booking engine
- CRM
- email platform
- customer service tools
There was no unified customer profile. No shared context. No ability to act in real time. This meant:
- duplicate or irrelevant offers
- no visibility into customer intent
- no optimisation of timing or channel
The business had data, but no system to use it effectively.
Building the Data Foundation
We started by creating a unified Customer Data Platform (CDP). This consolidated data across all systems into a single, continuously updated customer profile, including:
- booking behaviour and travel details
- historical purchase patterns
- engagement signals across email and web
- customer service interactions
Each profile maintained a multi-year history and updated in near real time as new interactions occurred. This layer became the foundation for all downstream intelligence.
From Segments to Predictions
With unified data in place, we introduced behaviour-driven segmentation. Customers were grouped dynamically based on:
- destination type (beach, city, long-haul, cruise)
- travel party (family, couples, solo)
- booking behaviour (early booker, repeat, last-minute)
- historical ancillary purchasing patterns
On top of this, we deployed a recommendation model that:
- scored each ancillary by purchase likelihood
- excluded already purchased products
- ranked and selected the most relevant offers per customer
Instead of broad campaigns, each user now received a shortlist of highly relevant options.
Timing as a Conversion Lever
Relevance alone was not enough. Timing proved equally critical. We analysed 18 months of behavioural data to identify when customers were most receptive to specific offers. Clear patterns emerged:
- Transfers convert best 48–72 hours after booking
- Seat upgrades peak 6–8 weeks before departure
- Excursions perform strongest 3–4 weeks before travel
- Insurance must be offered within 48 hours due to both regulation and declining intent
These insights were operationalised into automated triggers. Every recommendation was now delivered:
- at the right moment
- through the right channel
- with the right context
Multi-Channel Personalisation at Scale
The personalisation layer was deployed across:
- post-booking email flows
- customer portal experiences
- mobile application journeys
Each touchpoint was connected to the same recommendation engine, ensuring consistency across channels. Customers no longer experienced disconnected messaging. They experienced a coordinated journey.
Measured Impact
Within 9 months, the system delivered measurable, sustained improvements:
- Ancillary attachment rate: 6.3% → 16.8%
- Email click-through rate: 2.1% → 9.4%
- Airport transfer attachment: 11% → 34%
- Annual upsell revenue: +£2.1M
These gains were not driven by increased volume, but by better targeting and timing.
Why It Worked
Three factors drove the outcome:
- Unified data foundation — eliminated fragmentation and enabled context
- Behaviour-driven recommendations — replaced generic offers with relevant ones
- Timing optimisation — aligned communication with customer intent
The system did not introduce new products.It made existing products more visible, more relevant, and easier to buy.
Final Thought
Personalisation is often treated as a marketing feature. In practice, it is a data and systems problem. When customer data is unified, and decisioning is embedded into the platform, personalisation becomes measurable and scalable.
Building Data-Driven Personalisation Systems?
Intagleo Systems helps travel businesses unify customer data, build recommendation engines, and increase revenue through intelligent, real-time personalisation.