Dacture drives business outcomes by making predictive modeling easy for everybody. By answering a couple of questions, we can help you better understand the relationships in your data, train models, and predict results. Our goal is to make machine learning and prediction accessible for everybody by removing technical hurdles. We aren’t launched yet, but we’re getting very close!
Dacture is for the non-experts. With Dacture, you don’t need a background in coding or statistics to generate machine learning (ML) models and powerful insights. Many SMBs and teams within the enterprise have data that could unlock valuable knowledge for decision making, but they don’t have the expertise to build the models themselves. Often data science teams are focused on larger projects or swamped with other requests. Dacture is making machine learning and prediction for those everyday scenarios.
In this, and future blogs we cover some general concepts related to predictive modeling/machine learning and Dacture.
What is predictive modeling?
Predictive modeling is a method of analyzing data to discover key insights, and then draw conclusions or make predictions. It’s a sub-field of the broader artificial intelligence (AI) space, specifically causal AI.
Why does machine learning matter?
ML matters because it allows people to quickly synthesize large amounts of information, and use the patterns discovered to make more informed decisions. Using ML as part of your decision-making process is especially useful within the enterprise because it helps you focus your efforts on outcomes driven by data.
What can Dacture do?
Dacture can help with analysis and prediction for a variety of data types and scenarios. Below, we highlight a couple of very common use-cases that we’ve had prospective customers share with us, but Dacture is not limited to this.
You want to:
- Understand the correlations in your customer success data. Here’s an example of how that might look:
Here you can see that a customer’s last support experience isn’t really related to whether they stay with or leave the platform, but stability of the platform is highly correlated to whether a customer stays with the product.
- Quickly predict customer churn based on your historical data so that you can focus on customers who are at risk of leaving your platform.
- Predict which customers are most receptive to up-sell/cross-sell opportunities, and even what the value of those opportunities could be.
- Predict how inflation changes will impact your business.
- Identify which features of your product are really driving customer retention and having the most impact.
With these scenarios and many more in mind, our goal with Dacture is to make machine learning easy for everybody. Traditionally, coding ML models requires basic statistics and computer science knowledge. Understanding the output from ML models requires an even deeper level of statistical knowledge, including model scores, terminology specific to ML and statistics, P values, F scores, common models names/approaches, etc. How we output our results removes all of that complexity.
An example of how our supervised learning flow works:
- Train a model based on existing data by answering a few questions. We’ll choose the best model, and tell you what is having the largest impact.
Using the same SaaS company’s customer success data, after training a model, we can show exactly how much of a factor up-time (platform stability) is for predicting if a customer will leave the product.
- Make clear predictions using the trained or new data. In this case, you could predict which customer would leave the product. More on this and the next point in the next post.
- Experiment with the results to see what happens if you adjust values.
If you want to learn more about what Dacture can do your for organization, discuss use cases, or see a demo, you can schedule something with us, or you can email us at email@example.com.