Challenge: Love2Stay struggled to balance the speed and quantity of review responses, dedicating 20 hours a month to reply to all reviews. However, prioritising the needs of onsite guests often led to delayed or unanswered responses.
Solution: With BFN-AI, Love2Stay reduced their review response effort from 20 hours per month to just 1 hour, decreased response times to under 1 minute, and fully automated responses to positive reviews on Google.
About Love2Stay
Owned and operated by Salop Leisure and recognised as one of the UK's top caravan sites, Love2Stay is a Caravan and Motorhome Club Affiliated Site located on the outskirts of Shrewsbury, Shropshire.
This touring site and glamping village, set in 22 acres of landscaped grounds near the England-Wales border, features sleek, modern architecture and superb facilities.
The Challenge: Balancing Guest Feedback with Automation.
Love2Stay is managed by a small, experienced team dedicated to creating exceptional guest experiences, a core value of their brand.
Although they recognise the importance of responding to reviews, they must balance this task with other responsibilities, allocating specific times for review responses without neglecting other duties.
They were looking for a solution that would allow them to reduce response times, increase automation, and free up their small team to focus on more complex, personalised interactions.
Key Objectives:
- Automate Review Responses.
- Enhance the quality of replies.
- Include caravan-relevant nuances within responses.
- Ensure AI replies are consistent with their brand tone.
- Make AI responses sound authentically human.
- Naturally, integrate SEO keywords into their responses.
- Support their 'early booking" marketing campaign within replies.
What We Did
BFN-AI implemented a tailored solution for Love2Stay, focusing on:
- Custom Prompt Engineering: Optimised output quality and automation efficiency through custom prompt design.
- Direct integrations with Google and TripAdvisor: Streamlined the response process by connecting directly with both platforms.
- Development of caravan-specific AI instructions: Created AI guidelines that are nuanced to Love2stay's unique accommodation types - lodges and touring pitches.
This approach ensured that BFN-AI was specifically trained on caravan-related topics and tailored to Love2Stay’s unique facilities.
The AI-generated review responses were personalised, accurate, and indistinguishable from those authored by their front office team, maintaining the resort’s personalised touch in every review response.
How we did it
Park Facilities & Location:
- AI Training Goal: Ensure the AI recognises the diverse customer types—including caravan owners and campers—as well as various accommodation options like caravan pitches and holiday lodges
Writing Style:
- Training Goal: We directed the AI to generate replies that mimic conversational human writing characteristics, reducing the risk of responses sounding overly emotional or robotic—a common issue with poorly trained AI-generated content.
Dynamic Personalisation:
- Training Goal: Programmed the AI to automatically use guests' first names or "Dear Guest" when only a username is available, mention the park's brand name, reference staff appropriately in positive contexts, omit staff names in negative scenarios, and conclude with the signature of the Guest Relations Manager.
SEO Keywords:
- Training Goal: Instructed the AI to seamlessly incorporate crucial SEO phrases such as “caravan park in Shrewsbury,” ” and “Glamping Shrewsbury” into responses.
Special Instructions (Book Early):
- Training Goal: Configured the AI to occasionally suggest guests book early if they are considering rebooking next season.
No Context Reviews.
- Training Goal: Prompted the AI to respond to both negative and positive reviews that only included star ratings but no written comments.
Handling Negative Reviews:
- Training Goal: Taught the AI to acknowledge negative experiences empathetically, ensuring responses are professional but carefully worded to avoid language that could be interpreted as admitting fault or liability.
This comprehensive approach ensured that every aspect of the AI’s operation was tailored to enhance the guest experience, maintaining the park's brand standards and improving overall guest satisfaction.
The Results
Since partnering with BFN-AI in July 2024, Love2Stay has dramatically reduced administrative time from an average of 20 hours per month to just 1 hour.
Their response rates surged from 50% to 93%, all while enhancing response quality.
Additionally, the high quality and consistency of BFN-AI’s responses enabled 100% full automation of positive reviews on Google, allowing responses to be published within seconds.
These enhancements have significantly improved Love2Stay’s reputation management strategy, boosted their SEO, and resulted in enhanced guest engagement and a stronger online presence.
Responses Rates: From 50% to 93.3%
Skyrocketing Response Rates: Love2Stay boosted their response rates from just 50% to an impressive 93.3%, exceeding their goals and setting new standards in guest engagement.
Included on Google's Pack of 3 for core keywords.
Secured Top Search Placement: Love2Stay earned a spot in Google’s Pack of 3 for core keywords, boosting online visibility and attracting more direct bookings.
Improved the quality of responses
Enhanced Response Quality: Delivered nuanced, AI-generated replies that are almost indistinguishable from those crafted by their front office team, maintaining Love2Stay’s authentic voice and elevating guest satisfaction.
In addition to improving response times, BFN-AI has helped Love2Stay unlock several key advantages:
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Full Control: Pulls in all their review data from Google and Tripadvisor, ensuring semi and full automation with no need to jump between platforms.
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Respond with Accuracy: Instructions for generated responses include embedded fields that are contextually appropriate for specific entities, pulling directly from the business’s information.
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More Time for Negative Reviews: Delivered high-quality AI-generated 'first draft' responses for negative reviews, empowering staff to focus on resolution without extensive edits.
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Improved Local SEO: By consistently responding to guest reviews across major platforms, Love2Stay improved its Google ranking and discoverability.
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Book Early Strategy: Supporting the 'Early Booking' marketing campaign by embedding direct booking messages into AI-generated responses.
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AI-Powered Insights: BFN-AI scans all negative reviews and proactively generates AI-powered, easy-to-read monthly impact assessment reports for management. These reports highlight the most common issues raised in negative reviews, enabling swift analysis, prioritisation, and resolution of pressing concerns, ultimately reducing the volume of negative feedback.
"Automating the process has been truly worth it, and the response quality is first class"
Mike Harris, Managing Director, Salop Leisure Limited.