Enhancing Engagement through Strategic Naming: Insights from a Loyalty Programme Evaluation
Key Outcomes
Accelerated Analysis Process:
Yabble Count reduced the analysis time significantly, coding 20,000 comments in just 1 hour compared to 69 hours of manual work, offering a 1000x speed advantage.
Rapid Decision-Making:
The combination of YouGov Omnibus surveys and Yabble AI analysis delivered detailed insights in a 48-hour turnaround, enabling quicker strategic decisions regarding the clients's loyalty program.
Cost Efficiency:
By automating the coding and analysis process, Yabble Count dramatically reduced the resources and time needed for qualitative analysis, allowing for more frequent and detailed consumer feedback at a lower cost.
Client Background: Premium Media Service Provider
A leading telecommunications and media company sought to reevaluate its loyalty program’s branding to boost customer engagement and clarify consumer perceptions. As a premium service provider, the company aims to maintain its reputation while encouraging wider participation in its loyalty offerings.
Research Objective: Deep Dive into Consumer Sentiments Regarding Loyalty Program Names
The challenge was to determine if the existing name suggested additional costs or exclusivity that could deter engagement. The company needed insights into alternative names—Rewards, Extra, and More—to assess their potential to better communicate the program’s benefits and appeal to a wider audience.
AI-Powered Solution: Exploring Consumer Sentiment with Yabble Count
For qualitative analysis, YouGov utilized Yabble Count, an AI-powered thematic tool, to extract and analyze themes from open-ended responses. This AI-assisted approach enabled a deep dive into consumer sentiments regarding each proposed loyalty program name.
Strategic Impact: Significantly Streamlined Qualitative Analysis Process
Manually theming and coding 500 responses to four open-ended questions typically takes 6 to 11 working days. Researchers would spend 4–6 hours setting up an initial codebook, followed by 33–67 hours coding 2,000 individual responses. An additional 5–8 hours would be needed to refine themes and 6–10 hours for summarizing and synthesizing the data—totaling 48 to 91 hours of work.
Count analyzed thousands of answers, surfaced key themes, and generated insight summaries in minutes. This gave the client a 2-day turnaround from commission to completion.
