Live Demo: 
See Graphite Note in Action

Explore Our Live Demos
and Experience the Power of 
No-Code Predictive Analytics
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Get Inspired with Live Demos

Are you curious about the power of no-code predictive analytics, but not quite sure how it works? Our live demos are the perfect way to see this technology in action and experience the benefits for yourself. With just a few clicks, you can explore real-time examples of how our software can help your business make data-driven decisions that drive growth and success.

By watching our live demos, you'll see firsthand how easy it is to use our no-code predictive analytics software. You don't need to be an expert in data science or programming to get started. Our intuitive interface and simple drag-and-drop tools allow you to quickly build and test predictive models that can help you uncover insights about your business and customers. Plus, with our real-time data visualization capabilities, you can easily spot trends and patterns that might otherwise go unnoticed. By seeing our software in action, you'll gain a deeper understanding of how no-code predictive analytics can help you make smarter, more informed decisions and stay ahead of the competition.

So why wait? Take a few minutes to explore our live demos and discover the benefits of no-code predictive analytics for yourself. With our user-friendly interface and powerful features, you'll be amazed at how easy it is to gain valuable insights and make data-driven decisions that drive your business forward. Try our software today and see the difference it can make!
Graphite Note Live Demo - Timeseries Forecasting

Sales Forecasting

In this demo example, we are predicting the number of bike rides that a company can expect over a given period of time.
By leveraging the power of machine learning algorithms, it can be possible to accurately predict the number of bike rides a company might have in any given month, day, or hour.

By taking into account factors such as weather, holidays, and seasonality, the predictive accuracy of revenue forecasting with machine learning can be greatly increased. For instance, a bike ride company might be able to accurately predict the number of bike rides it will receive during the summer months, which would give it time to adjust its business plan accordingly.
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Graphite Note Live Demo - Predictive Lead Scoring

Predictive Lead Scoring

In this demo example, we are using the CRM dataset with historical sales leads.

Predictive lead scoring is a powerful tool for businesses of all sizes to quickly and accurately gauge the strength of leads. With a no-code tool like Graphite Note, businesses can leverage the power of predictive lead scoring without the need for complex coding or IT resources.
Graphite Note provides an intuitive and easy-to-use interface that allows users to quickly create predictive models by choosing from multiple pre-built algorithms, saving time and money. Additionally, with its robust reporting capabilities, users can easily monitor the performance of their predictive models in order to gain actionable insights about their leads.
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Graphite Note Live Demo - Timeseries Forecasting

Predicting Product Promotion Effects

In this live demo, we are predicting the impact of product promotions on sales for a retail company over a specific period. By harnessing the power of machine learning algorithms, we can accurately forecast the sales uplift a company might experience during any given promotion.

Taking into account factors such as the type of promotion, the time of the year, and seasonality, the predictive accuracy of sales forecasting with machine learning can be significantly enhanced. For instance, a retail company might be able to accurately predict the sales uplift it will experience during a '30% off' promotion in the holiday season. This foresight allows the company to adjust its inventory, staffing, and overall business plan accordingly.

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Graphite Note Live Demo - Predictive Cross Selling

Predictive Cross-Selling

In this demo example, we are predicting the likelihood of someone buying additional products from us.

A financial services company wants to offer health insurance to its existing customers who visit their website. The company will recommend health insurance policies based on customers' profiles and ask them to fill out a form if they are interested. Customers who fill out the form are considered positive leads, and the company's sales advisors will then reach out to them to try and sell the proposed health insurance. This approach is intended to make the sales process more efficient.

We are building a Machine Learning model that will predict new leads.
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Graphite Note Live Demo - Predictive Churn

Predictive Customer Churn

This demo example aims to build and evaluate a machine-learning model based on the churn dataset. The model will help predict whether a customer is likely to churn by taking into account their demographics and past service usage.

The churn dataset is a commonly used machine learning dataset containing customer retention information. It consists of variables like customer demographics and service usage, which can be helpful for predicting customer churn.

Customers benefit from actionable insights that allow them to take corrective measures to retain and increase their customer base.
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Graphite Note Live Demo - Search Console Forecast

Search Console Forecast

In today's digital age, website traffic is crucial for businesses and organizations to succeed. To improve website traffic, it is important to have an understanding of how search engines work, what users are searching for, and how to optimize content to increase visibility.
Google Search Console is a valuable tool that provides insights into how users find and interact with websites, including information on search queries, click-through rates, and impressions.

Using this data, it is possible to predict website traffic and make informed decisions about content optimization and marketing strategies.
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Graphite Note Live Demo - Predictive Maintenance

Predictive Maintenance

Predictive maintenance is a strategy that uses data analysis and machine learning techniques to predict when and where machine failures are likely to occur. By analyzing data from sensors and other sources, predictive maintenance can identify patterns that indicate when a machine is likely to fail and alert operators to take preventative measures before the failure occurs.

In this case, we have data on several variables that could be used to predict machine failure.

Specifically, we have information on air temperature, process temperature, rotational speed, torque, and tool wear. We also have a label indicating whether the machine has failed in a given data point.
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Now that you are here...

Graphite Note simplifies the use of Machine Learning in analytics by helping business users to generate no-code machine learning models - without writing a single line of code.

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Arthur Valle
Founder of WISARDS Wintec's Applied Research in DataScience Hamilton, NZ
"Graphite Note allows me to quickly import my data, create some models and get the insights I need in just a few minutes, literally. The features are very intuitive and even someone without previous experience of Machine Learning would easily get the expected outcomes."
Angel Todorov
Chief Analyst, Phyre
"Graphite Note is our preferred solution because of human interaction, we can not only count on the models but on the support of experienced professionals to get the answer we need from the data. They help us reduce the time to market and experiment w/o hiring more data professionals."
Domagoj Vidovic
Director of Digital at RTL Hrvatska
“In our line of business, we are collecting tons of valuable customers' data that helps us understand them - and their behavior. A chunk of that data was incomplete. We were looking for a simple, yet powerful solution to fill the gaps in our data by machine learning regression and classification models. Graphite Note helped our digital team to achieve that accurately, and in no time.”
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