Trip Drinks partnered with Lmo7 on a 60-day AI search optimisation sprint to improve how the brand appears in large language model recommendations. The programme used Share of Model to track performance across leading models and keep the work tied to measurable AI discovery outcomes.
The priority metric for this case study was average position. In AI recommendation lists, results are heavily top weighted. If a brand is not consistently placed in the top three, visibility drops fast and selection rates tend to follow.
**The challenge**
Trip Drinks wanted AI systems to understand the brand more clearly across category and product prompts. Like many challenger brands, Trip already had strong products and brand awareness, but was under represented in AI generated recommendation sets for functional drinks. The goal was not simply more mentions. It was better ranking so Trip appeared higher in the recommendation set and captured more attention and clicks.
**How we measured progress**
Share of Model was used throughout to monitor performance across a defined prompt set and multiple models using three core metrics:
* Share of voice
* Average position
* Mention rate
This made it easy to see change over time and concentrate optimisation on what was most likely to drive commercial impact.
**What Lmo7 delivered**
Lmo7 ran a focused optimisation programme designed to strengthen how Trip is interpreted and surfaced by AI systems. Work included:
* Improving key site signals
* Upgrading priority content that AI systems commonly reference
* Strengthening citations and supporting references to increase trust and retrievability
**Results after 60 days**
Average position improved from 7th to 3rd in the broad category of functional drinks.
Trip improved its average position across tracked models from 7th to 3rd. Moving into the top three is meaningful because it materially increases visibility inside AI recommendation lists.
AI referral traffic increased by 33%
Over the same period, Trip recorded a 33% uplift in direct AI referred traffic, indicating the visibility gains translated into user action.
**Why the impact is likely larger than direct clicks**
Direct AI referral traffic is only part of the effect. Many users discover a brand via AI then return later via other channels. Based on research we have reviewed, direct referral can represent a minority of total influenced journeys, so the overall commercial impact is likely higher than what is directly attributable.
**Why this matters**
This case shows a practical model for challenger brands trying to win AI led discovery:
* Measure performance with Share of Model
* Prioritise average position not just mentions
* Push visibility into the top three
* Convert better placement into traffic growth
Note: Optimisation delivered by Lmo7, a Brandtech agency partner, led by Stephen Honight in collaboration with Trip Drinks and eCommerce Lead Barnaby Whicher.