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100s of Modern Businesses Are Leveraging Predictive Analytics to Stay Ahead, Make Informed Decisions, and Drive Growth!
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.
ABC Ads Keywords Analysis
XYZ company wants to utilize advanced technology, such as machine learning, to enhance its advertising campaigns and improve its return on investment.
Despite utilizing various marketing strategies across different platforms, such as YouTube, Facebook, Instagram, and websites, the company has been experiencing suboptimal performance in its campaigns.
By utilizing machine learning, they aim to analyze their data, uncover hidden patterns, and increase the effectiveness of their advertising campaigns by optimizing for higher click-through rates and conversions.
Ads Clicks Optimization and Prediction
The primary objective of this demo is to decipher the drivers that significantly influence the number of clicks and subsequently, the conversions, by employing a predictive machine learning model.
By understanding the relationship between various campaign attributes (like creative dimensions, platform used, search tags, etc.) and the resultant clicks and impressions, we aim to predict the Clicks for the upcoming campaigns. This predictive insight will serve as a linchpin for XYZ company to recalibrate their marketing strategies, optimize ad placements, and creative content, thereby enhancing the performance and increasing conversions for product X.
Marketing Mix Model
Marketing Mix Modeling (MMM) is a sophisticated technique that analyzes the impact of marketing variables on sales performance. By quantifying the effectiveness of each element in the marketing mix, MMM helps businesses allocate their resources more efficiently and improve the ROI of their marketing efforts.
This approach utilizes historical data to understand how various factors, such as advertising, promotions, and price changes, drive sales, allowing for informed decision-making when it comes to budget distribution across different media channels.
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.
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 we will train AI model to understand propensity to buy.
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.
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.
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.
RFM Customer Segmentation
RFM (Recency, Frequency, Monetary) Customer Segmentation is crucial for several reasons.
RFM segmentation allows companies to tailor their marketing efforts based on individual customer behavior. By understanding the recency of a customer’s interactions (such as their last purchase or data usage), the frequency of their engagements, and the monetary value they contribute (their billing amount), companies can personalize marketing messages and offers to target specific customer segments more effectively.
Store and Product Demand Forecast
The challenge with traditional forecasting methods is their inability to handle the myriad of factors influencing product demand effectively.
Seasonality, market trends, promotional activities, and external factors like economic indicators or even weather patterns can all impact demand. These complexities often lead to inaccurate forecasts, resulting in overstock or stockouts, which can be costly for businesses.
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.
Successful businesses aren’t built on guesswork
Join industry leaders who are leveraging data-driven decisions to scale their biz
John Twigg
John Twigg, CEO Ascent Systems
Who has time to really learn this stuff in the business world today, when you can just use a tool like Graphite Note and get into AI model testing right away? We’re enjoying the ride!
Ursula O’Hara
Marketing Director at Nick's Plumbing
I am so impressed with Graphite Note’s accuracy and how easy it is to use. The models that we’ve created have given me great insights into who my customers are, who they were, and given me predictions on who they can be.
Arthur Valle
Founder of WISARDS Wintec's Applied Research
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.
Mark Smith
Co-founder at Katana
Graphite Note brings a massive competitive advantage for us in the lead generation space. We got data scientist that never sleeps – and from that we can unlock insights quickly and accurately.
Nadav Rappoport
Ben-Gurion University of the Negev
Graphite Note is an invaluable tool for educational purposes allowing me to demonstrate the power of machine learning in a practical and accessible way.
With its no-code approach I can easily train different types of models for various tasks using my own data or the provided sample data.
Martina Margitic
Service Delivery Division Lead, Notch
Graphite Note is an intuitive, user-friendly, and easy-to-use. It lets us design and tweak data science models at an impressive speed. But more than that, it helps you tell a story with your data and truly focus on what is most important: business values instead of columns and columns of data. It feels like having an extra set of expert hands on deck.
