In the current digital age, it is more important than ever for companies to understand their consumers. By tracking conversations across digital platforms, analyzing lifestyles, and using machine learning to auto-classify customer feedback, companies can gain valuable insights into their customers. This understanding can then be used to create better marketing strategies and improve the overall customer experience.
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Research for everyone.
- Use pre-built machine learning models to analyze customer reviews or other text data—no coding required!
- Alternative to: MonkeyLearn, Amazon Comprehend, and Chattermill
- Make your own machine learning model by dragging and dropping elements into an intuitive interface.
- Best for: Entrepreneurs, Marketers, and product managers who want to speed up text data analysis with a machine learning platform
- Related categories:Support, Marketing, Data & Analytics
What is Kimola Cognitive?
In the past, companies have had to rely on surveys and customer feedback forms to understand what their customers want. However, this process is no longer effective as customers are now using digital platforms to communicate with each other. In order to understand consumers, companies need to track these conversations and analyze the data.
Machine learning can be used to automatically classify customer feedbacks and reveal insights. This will help companies understand what customers are saying about their products and services. In addition, companies can use machine learning to track consumer behavior online and offline. By understanding the consumer’s lifestyle, companies can develop better marketing strategies.
Are you looking for a way to take your business to the next level? If so, you should consider using Kimola Cognitive. This no-code machine learning tool can help you gather, analyze, and classify customer feedback so you can make the necessary changes to grow your brand.
Kimola Cognitive is easy to use and can be customized to fit your specific needs. It’s also scalable, so you can use it as your business grows. And best of all, it’s affordable.
If you’re ready to take your business to the next level, sign up for Kimola Cognitive today. You won’t be disappointed.
I think the Kimola Cognitive is great and very easy to use.
An easy-to-use tool that helps you better understand your customers and their needs is Kimola Cognitive.
Drag and drop to analyze text in more than six languages without any code at all.
The most common terms in categories like companies, persons, and locations can be accessed through the use of a dataset.
You’ll be able to create a report with strong search and complex filtering options to disclose customer insights.
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Create a report based on the data you’ve entered and learn more about your customers’ buying habits in real time.
You’ll be able to get customer feedback anonymously with the help of the Kimola Cognitive browser plugin.
With this browser add-on, you may construct “airsets,” or “consumer review datasets,” from any compatible online media.
It’s also a cinch to categorize and assess the tone of internet discussions. Get started by swiping through the comments on YouTube, Tripadvisor, AppSumo, and G2.
Kimola Cognitive
In order to gain new insights from consumer input, marketers, researchers, and data nerds can use Kimola Cognitive, a no-code machine learning platform.
Aimed for experts in the fields of marketing and market research. You don’t need to know how to program.
Kimola Analytics
This tool for social listening, known as Kimola Analytics, allows marketers to keep tabs on all of the different places where their target audience is talking about their products and services in order to develop more effective marketing campaigns.
To collect and analyze data from any website, use the Kimola Cognitive browser extension.
Models for monitoring data such as customer sentiment and hate speech analysis are available in a gallery.
Furthermore, Kimola Cognitive is regularly publishing new machine learning models and ensuring that they are up-to-date for a variety of business requirements.
There is a way to classify customer evaluations and use that information for your business no matter if you work for a corporation or on social media.
Pre-built machine learning models can be used to automatically identify big datasets, such as customer feedback, hate speech, and more.
If you have a large enough dataset, you may train a custom machine learning model to better understand your customers’ specific preferences.
The Kimola Cognitive user interface and API allow you to access all of your customized machine learning models.
In addition, you can use the platform to select the statistical model with the highest accuracy rate for your dataset.
What is a machine learning platform?
A machine learning platform is a system that allows for the construction, testing and deployment of machine learning models. The platform provides a set of tools that enable data scientists to work with data, build models and deploy them in a production environment.
The platform typically includes a set of libraries for working with data, a set of tools for building models and a set of tools for deploying models. The platform may also include a set of APIs for working with data and deploying models.
The machine learning platform enables data scientists to iterate quickly on models and deploy them in a production environment. The platform makes it easy to track the performance of models and experiment with different algorithms and model types.
The machine learning platform is a key part of the data science workflow. It enables data scientists to build and deploy models quickly and easily.
What are the types of machine learning platforms?
Machine learning platforms are systems that provide users with the ability to develop, train and deploy machine learning models. There are a number of different types of machine learning platforms, each with its own advantages and disadvantages.
The three main types of machine learning platforms are:
1. Commercial machine learning platforms:
These platforms are typically provided by tech companies and come with a subscription fee. They usually offer a wide range of features and support for multiple programming languages. Examples of commercial machine learning platforms include Google Cloud Platform and Amazon SageMaker.
2. Open-source machine learning platforms:
These platforms are free to use and are typically developed by a community of volunteers. They usually have fewer features than commercial platforms but can be a good option for those on a budget. Examples of open-source machine learning platforms include TensorFlow and Apache Spark.
3. Custom machine learning platforms:
These platforms are developed specifically for a company or organization and are not generally available to the public. They offer the advantage of being tailored to the needs of the organization but can be more expensive to develop and maintain.
We love this tool because of its:
- No code, easy-to-install Chrome Extension that connects with most major platforms
- Saves you time when finding relevant feedback within reviews
- Text Analysis features to improve stakeholder output
Best Machine Learning Platform & Pricing Plans
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In a world where consumers are constantly connected, it’s more important than ever for businesses to understand what their customers are saying online. By tracking conversations across digital platforms and analyzing customer lifestyle data, businesses can develop more effective marketing strategies and gain valuable insights into customer sentiment.
Machine learning can be used to automatically classify customer feedback, making it easier for businesses to identify patterns and trends. This can help businesses to improve their products and services and make better decisions about how to engage with their customers.
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One-time purchase of $79.00
License Tier 1 – ( $79)
- All features above included
- 10,000 queries (rows of data) per month
- 1 custom ML model(s)
License Tier 2 – ( $159)
- All features above included
- 35,000 queries (rows of data) per month
- 2 custom ML model(s)
License Tier 3– ( $299)
- All features above included
- 100,000 queries (rows of data) per month
- 10 custom ML model(s)
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Overall, by tracking consumers across digital platforms, analyzing their lifestyle and using machine learning to auto-classify customer feedback, businesses can gain a better understanding of their consumers. This understanding can then be used to create more targeted and effective marketing strategies, as well as to improve the overall customer experience.
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