The world is littered with prediction products using machine learning and artificial intelligence, so where do you start? We have a collection of technologies that will make your prediction projects successful.
Email: natalie@ecosystem.ai
Phone: 
+1.650.319.7304
Phone: 
+27.21.813.9226
Companies are not extracting value from their customer generated data. It’s hard, because humans are complex behavioral beings. We have a suite of products and algorithms to enable the process of intelligent engagement. Our platform includes leading open source components to enable automated prediction.
The ecosystem.Ai Workbench, Jupyter Notebooks, and API's are used to manage the entire prediction process. Our unique re-enforcement learning runtime engine is then used to deploy your models, and let them compete with each other to find the best behavioral predictive result. You can deploy the runtime natively on Google Cloud, Microsoft Azure and AWS.
Relationships matter to all of us, but machines find it difficult to understand the human dynamic of interaction science. We have created a set of algorithms that will assist you with intelligent interventions as you engage with customers across your channels. This will help you find customers more reliably, predict when customers will leave you, and know when and what product or service to sell.
In order to understand how customers behave, you require an intelligent approach to categorization. Our Categorization Engine uses a number of unique algorithms and models to understand behavior. If you want to influence aspects of your customer's spending and earning behaviors or interaction profile, use this engine before you use any of the traditional analytics tools.
Predicting human behavior requires more than just mathematics and machine learning / artificial intelligence. We are all part of an integrated society with key norms and influences that impact most of buying behaviors, travel, and eating decisions etc. Our social constructivist and social functionalist data enrichment use behavioral algorithms to enable more accurate predictions. We use a number of data points that represent a customer's digital, human-contact, and personal activities.
Why do customers really stop buyng what you're selling? This elusive challenge cannot be solved by looking at historical data only. The churn algorithm is designed to allow for dynamically adjusted interventions throughout the customer engagement life cycle. Using happiness indicators, product distraction vectors, account balances-to-zero, activation errors, etc all assists with reducing churn.
Most segmentation models use financial measures and a limited number of behavioral characteristics to segment customer markets and preferences. Our computational social science view of the customer allows for dynamic behavior analytic and segmentation based on a number of changing dimensions.
Predicting your customer's behaviors are critical to understanding what they'll buy next. This algorithm is used to predict the propensity by which a customer is most likely to engage in a service or buy your product.
Financial health is an important aspect of modern life. Understanding how transactions affect everyday life is a crucial component of customer engagement. We have a suite of algorithms that assist with understanding the philosophy of money including life rituals people engage in when earning and spending.
We have an end-to-end approach that solves a number of behavioral prediction challenges. There are a number of stages that work in unison to deliver reliable and accurate predictions. The ecosystem.Ai Runtime Engine is where value gets realized by deploying models into your production environments.
Do you need answers to some of these critical questions:
Are your predictors
mathematically correct, but results are not useful?
Do you find it difficult to manage your H2O deployments and models?
Is your process of comparing model results cumbersome?
Are your models in production not performing well?
Do you deploy re-enforcement learning approaches to automate behavioral model accuracy?
Finding evidence of human behavior in data has been the primary purpose of our
journey. It's not just about the mathematical and statistical correctness, or
prediction-accuracy and -recall rates anymore. Humans are dynamic beings that perform many
different roles including customers, clients, employees and patients. Algorithms need to
include
key dimensions of how humans really behave and engage with each other and the environment.