TextYess leverages Nebuly to build highly engaging AI Shopping Assistants.
Tuomas Lounamaa
Head of Marketing @ Nebuly
July 9, 2024
Overview.
Nebuly enabled TextYess to pinpoint areas of improvement and continuously enhance their AI shopping assistants user experience.
Scenario.
Use case
AI shopping assistant
sector
eCommerce
llm in use
OpenAI ChatGPT-4o
Results.
90%
time saved
on analysis
on analysis
8X
insights from
conversations
conversations
63%
less negative
interactions
interactions
Challenge.
TextYess is a fast-growing, WhatsApp-focused AI shopping assistant that enables eCommerce platforms to turn conversations into orders. As TextYess and the number of interactions on their LLM-based shopping assistant grew, flagging suboptimal LLM responses became a challenge.
What TextYess realized they needed:
• Scalable visibility into LLM user interactions
• Flagging each suboptimal response
• Pinpointing and understanding successful user interactions to replicate high-converting experiences
What TextYess realized they needed:
• Scalable visibility into LLM user interactions
• Flagging each suboptimal response
• Pinpointing and understanding successful user interactions to replicate high-converting experiences
Results.
With Nebuly, TextYess is able to scale its user base and business while having a consistent overview of the quality of its shopping assistant user interactions.
Nebuly's user feedback feature automatically flags each negative and positive (not neutral) LLM interaction for TextYess. The insights allowed them to end manual output reading, pinpoint areas of improvement and continuously improve their shopping assistant's user experience.
Nebuly's user feedback feature automatically flags each negative and positive (not neutral) LLM interaction for TextYess. The insights allowed them to end manual output reading, pinpoint areas of improvement and continuously improve their shopping assistant's user experience.
90% less time spent
analyzing LLM user conversations
8x the number of identified,
actionable insights from conversations
63% decrease in negative interactions
after one month of use
“The growth in user conversations made manual review impossible for us. Nebuly's ability to automatically track the most common conversation topics and flagging poor LLM responses has been a game-changer.”
Riccardo Russo
CEO, Co-Founder TextYess
Customer Journey.
STEP #01
Free trial with
a batch data set.
a batch data set.
TextYess started using Nebuly in a self-serve manner by importing a batch set of LLM conversation transcripts using the Nebuly API.
STEP #02
Work with Nebuly
to look at result.
to look at result.
After the data import, insights were available on the Nebuly platform. In a kickoff call, we identified key metrics and patterns to focus on, while also adding a few custom reports.
STEP #03
Connect and start
analyzing live results.
analyzing live results.
After the initial analysis proved valuable, a secure data flow between TextYess's database of transcripts and Nebuly was established, enabling continuous insights and LLM improvement.
Ready to get started?
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