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Kimola Cognitive
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How Kimola Cognitive Works?

Text Analysis

Kimola Cognitive offers a text analysis technology to deliver the most frequent words and phrases that are contextually important in a dataset. These words and phrases are displayed as a word cloud that can group words from the same root and take terms as a block rather than single words.

Word clouds simplify the analysis of consumer feedback for researchers. Using word clouds, professionals quickly understand customer perceptions, identify pain points, and discover emerging trends, guiding them towards informed decisions and product improvements.

Entity Recognition

Also, these contextually important words and phrases are automatically classified with labels, such as Organization, Person, Work of Art, and Location, so researchers will have answers to questions like "Which celebrities are included?", "Which companies and brands are mentioned?" and "What are the most common location names in this dataset?". Besides widely known labels, Kimola has unique labels like Nutrition, Interest, Race and Ethnicity, and Disease. This technology is known as Entity Recognition, and it becomes a convenient tool for marketing and research professionals to reveal insights in a customer feedback dataset.

With Entity Recognition, it becomes effortless to distinguish between various elements and their relationships, facilitating a deeper understanding of feedback data and guiding strategic decision-making for more effective marketing campaigns and product enhancements.

Learn more about Text Analysis

Pre-Built AI Models

Kimola Cognitive comes with a gallery of ready-to-use AI models to automatically classify customer feedback for the most common use cases, like sentiment and hate speech analysis, along with consumer conversations around SaaS products, mobile apps, and games.

Effective customer feedback analysis relies heavily on text classification, which Kimola Cognitive excels in using pre-built AI models customized for various industries. Customer feedback is often an unstructured data deluge, and text classification helps businesses categorize this information into both predefined and dynamic labels. This categorization is critical to understanding specific concerns, sentiments, and trends. By using pre-built AI models, researchers are not just saving time, but they're also gaining access to a specialized toolkit that caters to the industry's unique needs. They are fine-tuned to recognize the nuances and jargon specific to your field, making the classification process faster, more accurate and actionable.

Sentiment Classification

Kimola Cognitive offers pre-built sentiment classifiers which are fine-tuned for customer feedback analysis to provide a nuanced understanding of consumer sentiment. Rather than manually sifting through countless customer reviews or comments, sentiment analysis can categorize feedback into positive, negative, or neutral sentiments. By automating this process, researchers can efficiently analyze large volumes of data and uncover trends, common issues, and emerging concerns. This way researchers can also track sentiment changes over time, providing insight into the effectiveness of customer experience improvements or marketing campaigns.

Content Classification

You can choose one or more pre-built content classifier to quickly sort customer feedback into categories, like product issues or customer service problems. You get a deeper and more precise analysis using specialized industry models with pre-defined or dynamic labels. This helps you identify what needs improvement, what makes customers happy, and what trends are emerging. Our pre-built AI models turn messy feedback into actionable insights, making your decision-making easier and more tailored to your industry's needs.

Learn more about Pre-built AI Models See all Pre-built Machine Learning Models

Deploy Custom AI Models

Every research is a different journey in terms of understanding the needs and motivations of consumers, so requirements will usually be unique for each analysis. That's why Kimola Cognitive supports deploying custom AI models trained by your own dataset. The platform automatically chooses the best-performing statistical model for your training set to ensure the best accuracy rate possible. Your custom AI models are hosted on Kimola Cognitive and can be used via the web user interface and API.

Custom AI models go beyond using pre-built models. Researchers can train AI models using their expertise without needing to write any code. This means they can customize the AI model to fit their research goals and classification needs. This creates a tool that understands customer needs and motivations at a granular level, uncovering hidden insights and trends that are often overlooked by standard solutions.

Kimola Cognitive makes AI models trained in any language available in -currently- 26 languages, enabling easy analysis of customer feedback from around the world!

Learn more about Custom Machine Learning Models

Scrape Customer Feedback

Customer feedback analysis starts with data collection, and we made it as easy as pie.

Kimola offers a free web browser extension for research professionals to collect customer feedback from various platforms. This way, researchers can focus on what they do best -understanding and analyzing the feedback- rather than struggling to access and assemble data. This automated process simplifies the creation of comprehensive datasets, liberating researchers from the tedious and time-consuming task of manually sourcing information. It supports over 20 mediums, including Amazon, Yelp, YouTube, Capterra, and Trustpilot.

The significance of customer feedback scraping goes beyond convenience; it offers a broader perspective. Customer feedback is scattered across the internet on many platforms and websites. By aggregating this feedback, researchers can paint a more complete picture of customer needs and motivations. It's not just about understanding customer feedback from one source; it's about accessing diverse opinions and experiences. This rich tapestry of data provides a holistic view of customer sentiment, helping researchers uncover common themes, issues, and trends that span different platforms.

With a web scraping tool, we aim to simplify the data collection process, enabling researchers to delve deeper into customer needs and motivations from a broader, more encompassing perspective, ultimately leading to more informed decision-making and strategic insights.

Learn more about Web Scraping
  • Amazon
  • Youtube
  • Yelp
  • Trustpilot
  • Google Play Store
  • Instagram
  • Appsumo
  • Capterra
  • G2
  • Etsy
  • Udemy
  • Trip Advisor
  • Airbnb
  • Google
  • Best Buy
  • Booking
  • Influenster
  • Reddit
Get Started Now!

Kimola offers a 7-day free trial with no commitment. If you need to talk to an expert, send us a message explaining your needs so we can organize an e-meeting.

Turn Customer Reviews
into Powerful Marketing with GPT

Kimola Cognitive offers a GPT technology as an add-on to help you create powerful marketing materials considering your customer reviews. By using this feature, you can generate SWOT analysis, social media content, brand slogans, FAQ list and even product descriptions inspired by your customer feedback dataset in any size.

Turn Customer Reviews into Powerful Marketing with GPT
See Use Cases

Customer Feedback Analysis Artificial Intelligence

Customer feedback is like the inside scoop straight from the customers themselves. It's the lowdown on what people think about a product, service, or brand, and it's super important for companies trying to stay on the ball. You've probably seen it all around, from online reviews and surveys to chit-chats on social media. This kind of feedback is a game-changer for companies, helping them make smart moves and keep up with what folks want.

The power of Artificial Intelligence (AI) has unlocked new possibilities in customer feedback analysis. By utilizing Large Language Models (LLMs) and machine learning algorithms, businesses can now extract invaluable insights from the vast amount of customer comments and reviews available. These advanced technologies are particularly effective in sentiment analysis, allowing companies to instantly identify whether a customer's sentiment is positive, negative, or neutral, and even go beyond to identify specific emotions. With their superior ability to understand the nuances and context of language, LLMs are highly skilled at deciphering complex customer comments and extracting actionable information. This automation and precision have revolutionized the customer feedback analysis process, helping companies to stay on top of customer sentiment and respond to their needs in real-time, ultimately leading to greater customer satisfaction and success.

Using Artificial Intelligence (AI) and Language Model Models (LLMs) for customer feedback analysis offers numerous benefits. Firstly, it enables businesses to process vast amounts of data quickly and efficiently, which is crucial in the age of big data. Rapid analysis empowers companies to identify emerging trends, address issues promptly, and make data-driven decisions that enhance customer satisfaction and loyalty. Additionally, AI can categorize feedback, enabling businesses to identify recurring problems or customer pain points, which can be prioritized for resolution. Furthermore, by tracking sentiment changes over time, companies can gauge the effectiveness of their customer service improvements or product enhancements. AI-driven customer feedback analysis is a game-changer, offering businesses a competitive edge and helping them fine-tune their offerings to meet the evolving needs and expectations of their customers in real-time.

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See the Sample Report

See how Kimola analyzes and classifies a raw dataset and turns into a PDF report.

Plans & Pricing

Pricing is based on query limit, custom model count and support options.

Yearly (Save up to 20%)
$ /month
billed annually
  • 10,000 queries
  • -
  • Text Analysis
  • Pre-built ML Models
  • Web Scraping
  • Files in xlsx, xls, tsv, csv formats and API
  • -
  • Ticket
$ /month
billed annually
  • 35,000 queries
  • 2 Custom ML Models
  • Text Analysis
  • Pre-built ML Models
  • Web Scraping
  • Files in xlsx, xls, tsv, csv formats and API
  • 1 hour onboarding meeting
  • Ticket, Live Chat
$ /month
billed annually
  • 100,000 queries
  • 10 Custom ML Models
  • Text Analysis
  • Pre-built ML Models
  • Web Scraping
  • Files in xlsx, xls, tsv, csv formats and API
  • 3 hours onboarding meeting
  • Ticket, Live Chat
$ /month
  • Custom Amount
  • Custom Count
  • Text Analysis
  • Pre-built ML Models
  • Web Scraping
  • Files in xlsx, xls, tsv, csv formats and API
  • Custom onboarding
  • Ticket, Live Chat
Compare All Plans
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Frequently Asked Questions
About Kimola Cognitive

  • Kimola Cognitive offers text analysis technology that identifies the dataset's most frequent words and phrases. These are displayed as a word cloud, and they can group words from the same root together. The technology also supports Entity Recognition, which automatically classifies words and phrases into labels like Organization, Person, Work of Art, Location, Nutrition, Interest, Race and Ethnicity, and Disease.

  • Kimola Cognitive includes pre-built AI models for common use cases like sentiment analysis, hate speech detection, and analyzing customer conversations around SaaS products, mobile apps, and games. These models can automatically classify customer feedback based on these everyday use cases. They are fine-tuned to recognize nuances and jargon, making classification faster and more accurate.

  • Yes, you can deploy custom AI models in Kimola Cognitive. Since each research project may have unique requirements, the platform allows you to train your own custom AI models using your dataset. Kimola Cognitive automatically selects the best-performing statistical model for your training data to ensure high accuracy. These custom models are hosted on the platform and can be accessed via the web user interface and API.

  • Kimola offers a web browser extension that research professionals can use to scrape customer feedback from various platforms. This extension simplifies collecting data from over 20 mediums, including platforms like Amazon, Yelp, YouTube, Capterra, and Trustpilot. It helps researchers save time and effort and allows them to focus on analyzing feedback rather than sourcing customer feedback manually.

  • Kimola offers pricing based on query limits, custom model count, and support options. The pricing ranges from $199 to $999 monthly, depending on the selected plan. Check the specific plan details on the Kimola website for a breakdown of features and usage limits for each pricing tier.

  • Yes, Kimola is committed to enabling all sizes of businesses, from coffee shops to global corporations, to conduct research by analyzing customer feedback.

  • To get started with Kimola Cognitive, you can visit the Kimola website and sign up for a free account. Once you've created an account and started your free trial, you can explore the features, choose the plan that suits your needs, and use the platform for customer feedback analysis.

  • Kimola Cognitive can be used in various industries for customer feedback analysis. It's particularly useful for businesses with a growth mindset obsessed with understanding what customers really need.

  • Kimola offers support options as part of its pricing plans. These support options may include customer support, training, and onboarding. The specific support options available will depend on your chosen pricing plan. You can typically access support through the Kimola website or by contacting their support team.

  • Yes, Kimola Cognitive has the capability to handle multiple languages in text analysis and data collection, making it suitable for businesses with a global customer base. Please note that for the most up-to-date information on Kimola and its features, you should visit their official website or contact their support team.

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