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Talklab - AI powered chat analytics for customer insight | Product Hunt

 1 year ago
source link: https://www.producthunt.com/posts/talklab
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Support is great. Feedback is even better.

"Thanks for checking out my project — Despite using plural pronouns in the presentation, this is a one-man project. So, I'll appreciate any feedback, especially on pricing."

The makers of Talklab
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Hello Community! 🖖

I've been working with software development for almost 20 years now, having founded a software house (named SofaCoding) that operated from 2014 to 2018. In recent years, I've been working a lot with customer support and messaging markets, which alerted me to the deficit of information that companies have about what their customers are demanding in support channels.

With the recent popularization of large language models, I decided to start a product that could use the companies’ chats and tickets to answer some key questions:

1. What are the main topics that are leading customers to support channels and how the appearance of these topics is evolving over time? 2. Who are the customers that have demonstrated negative sentiments lately, such as frustration or anger?

Thanks to the PaLM 2 and GPT3.5 language models, the first public version of Talklab can answer these questions and many more.

Currently, our software only integrates with the Zendesk platform, enabling Zendesk users to import chats and tickets. Then, Talklab will automatically generate data about the content of each chat. Using the generated tags, it's possible to create reports based on groups of tags and for a specific time period.

We're launching a preview version today that enables the Product Hunt community to explore a demo workspace and create prompt-generated filters, which we are calling 'smart tags.' To access the demo, please head to https://talklab.tech

Additionally, we're opening a free-beta whitelist registration that you can join here: https://forms.gle/z4LR2dMop66CAZBq6

-- Willian Valle 🇧🇷 [email protected]

Now this is cool! What are some of the most common actionable insights your customer finds with this tool?

@sentry_co Hi André :) Thanks for the feedback! (are you a brazilian, btw? 😆)

Generally speaking, I believe these are the greatest value deliveries that the tool can bring: 1. What are the main topics that are leading customers to support channels and how the appearance of these topics is evolving over time? 2. Who are the customers that have demonstrated negative sentiments lately, such as frustration or anger?

The insights brought by the first question can help service teams identify and respond faster to customer demands, while the second question can help reduce churns.

Hi Willian! Congrats on the launch. In my first business project over 10 years we control customer support every day be reading all messages and listening all voice records. It works, we really keep the qulity of our customer support service. But it's a human-factor. The QA who checks it, with time can be not so strong with the mistakes. Also they start friend and the quality of testing becomes weaker in time. Also we have the exam system which analyze the work of every agent and gives rating. But it's not AI driven and takes a lot of resources. Your tool can help us and a lot of B2B companies who cares about the customer support. I think I can start test it the nearest time. What about your monetization scheme?

@anna_kasumova Hi Anna. Thanks a lot for your feedback!
I believe that, whether using humans or ai, it’s important for the customer support market develop an objective set of parameters that define the quality of a service. So that we can avoid the bias and randomness that both ai and humans have. During Talklab development, I tried a lot of prompts to achieve a fair score of quality, but currently talklab just presents a score that identifies the mood of the customer, which is not suitable to identify the service quality. But, I haven't given up yet 😂 and I’m constantly looking for works or discussions in this field.

About monetization: It's the decision that is keeping me awake at night. 😅
It seems clear that I'll pursue a SaaS, subscription-based model, but I haven't determined the pricing yet. I have significant variable costs, primarily based on the number of characters sent to language models. Therefore, the plans should feature different thresholds for character counts per billing period. Do you think it would be acceptable to charge based on the number of characters processed per month?

@vallewillian I think that easier - to make monthly subscription based pricing, without symbol limits. Why? Because pricing MUST be clear for customers. And even I with a lot of experience, I really can't understand the quantity of symbols. It's hard to calculate and understand. Better if you will calculate yourself, and based on your understanding, you make pricing which cover all expenses.
Hey there! Congrats on the awesome launch of Talklab! The AI-powered chat analytics for customer insight sounds super intriguing. One suggestion to consider for future improvements would be to explore integrating real-time sentiment analysis. It could offer even more valuable insights into customer experiences. Have you considered any unique use cases for Talklab in specific industries?

@naveed_rehman Hi Naveed. Thanks for your Feedback! About the real time analysis: currently, the platform is able to process zendesk conversations within a few minutes after the ticket or chat is finished. However, initially I think about working with a limit of up to 12 hours for conversations to enter the platform. This while operating in beta. In the future, I hope to implement an email alert feature, in case a service appears that has mentioned a specific subject or sentiment.

About specific industries: There are several markets that I imagine could benefit from this type of service (such as BI areas, or companies with specific compliance needs). Today I have only implemented the basic functions, but I hope that the beta phase will help me to better understand these market demands and which use cases I should focus on first.


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