The Factors Influencing Customer Satisfaction with No-Code WhatsApp Automation: A Study of Yeti

Quality of Support is the strongest single driver of customer satisfaction with WhatsApp business automation platforms (β = 0.470, p = 0.008), ahead of ease of use, ease of setup and product direction, even though all four attributes are strongly correlated with satisfaction (r = 0.872 to 0.917, p < 0.001). Most research on chatbots and WhatsApp marketing measures the satisfaction of end consumers who chat with a brand. The business customers who buy and run the automation platform, and the platforms themselves as competing products, are almost absent as a unit of analysis. This paper addresses that gap with a cross-sectional analysis of verified G2 user-review scores for 26 WhatsApp and conversational automation platforms, representing 4,512 reviews, collected on 1 October 2026. The study grew out of a summer internship at Yeti (IncrediBee Private Limited), an Ahmedabad-based no-code platform. It tests seven hypotheses on (1–4) the link between each platform attribute and satisfaction, (5) the joint influence of the four attributes, (6) usability differences between no-code and API-based platforms and (7) satisfaction differences between Indian and global platforms. Three findings stand out. First, the four attributes together explain 89.1% of the variation in satisfaction (F = 43.12, p < 0.001), but once they are entered together only support remains individually significant; a reduced model with ease of use and support keeps almost all of that explanatory power (R² = 0.880). Second, no-code platforms are rated easier to use than API/CPaaS providers (Mann–Whitney U = 93.0, p = 0.047). Third, Indian and global platforms do not differ significantly in satisfaction (p = 0.478). The results suggest that, in a market where features and prices have converged, support is the tiebreaker: platforms win on how well they help customers and how easy they are to use, not on what the software can do. The paper discusses implications for Yeti and sets out an agenda for primary, customer-level studies.

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Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2026-11-06
DOI
https://doi.org/10.36948/ijfmr.2026.v08i06.91491
Primary Topic
AI in Service Interactions
Type
article
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article

The Factors Influencing Customer Satisfaction with No-Code WhatsApp Automation: A Study of Yeti

Mahir Khan
International Journal For Multidisciplinary Research
AI in Service Interactions
article

The Factors Influencing Customer Satisfaction with No-Code WhatsApp Automation: A Study of Yeti

Mahir Khan
article en

Abstract

Quality of Support is the strongest single driver of customer satisfaction with WhatsApp business automation platforms (β = 0.470, p = 0.008), ahead of ease of use, ease of setup and product direction, even though all four attributes are strongly correlated with satisfaction (r = 0.872 to 0.917, p < 0.001). Most research on chatbots and WhatsApp marketing measures the satisfaction of end consumers who chat with a brand. The business customers who buy and run the automation platform, and the platforms themselves as competing products, are almost absent as a unit of analysis. This paper addresses that gap with a cross-sectional analysis of verified G2 user-review scores for 26 WhatsApp and conversational automation platforms, representing 4,512 reviews, collected on 1 October 2026. The study grew out of a summer internship at Yeti (IncrediBee Private Limited), an Ahmedabad-based no-code platform. It tests seven hypotheses on (1–4) the link between each platform attribute and satisfaction, (5) the joint influence of the four attributes, (6) usability differences between no-code and API-based platforms and (7) satisfaction differences between Indian and global platforms. Three findings stand out. First, the four attributes together explain 89.1% of the variation in satisfaction (F = 43.12, p < 0.001), but once they are entered together only support remains individually significant; a reduced model with ease of use and support keeps almost all of that explanatory power (R² = 0.880). Second, no-code platforms are rated easier to use than API/CPaaS providers (Mann–Whitney U = 93.0, p = 0.047). Third, Indian and global platforms do not differ significantly in satisfaction (p = 0.478). The results suggest that, in a market where features and prices have converged, support is the tiebreaker: platforms win on how well they help customers and how easy they are to use, not on what the software can do. The paper discusses implications for Yeti and sets out an agenda for primary, customer-level studies.

International Journal For Multidisciplinary ResearchVol. 8(6)
LJ University (IN)
Openalex Percentile: Top 11%
AI in Service Interactions
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