Data Science & Predictive Modeling
The ability to accurately predict the future can make all of the difference in terms of making intelligent decisions for your organization. No matter the use case, we are able to use data science and machine learning to turn your raw data into useful predictive information! Some popular and useful examples for predictive modeling that we see include:
-Custom lead scoring models that predict value and conversion probability of sales leads
-High-accuracy forecasting models for sales, website traffic, profit, or other important metrics
-Customer & employee retention models to proactively predict and reduce churn
Customer & Audience Personas
Understanding the different types of customers you have can make all of the difference in terms of targeting the right leads or audiences. Building customer ""personas"" can optimize marketing strategy, messaging, and service offerings by identifying characteristics and behaviors that are unique to your most valuable or highest-spending clients. You can use this information to more precisely target the type of customers or audience members that will provide you with more value in the long run.
Recommendation systems can predict whether a product will be preferable by a user or not. We build recommendation algorithms that represent user preferences for the purpose of suggesting items to purchase or package together. While the most common use case for recommendation algorithms is for e-commerce sales, we can build a recommendation algorithm for any use case as well as integrate that algorithm within your internal systems to automate the process.
Anomaly detection tools can notify stakeholders of erratic, concerning, or unanticipated trends. The use cases for this type of predictive modeling can vary from being notified of suspicious website visitor indicitive of hacking to alerts for a major drop or spike in sales. For any data that you want to monitor for outliers, anomaly detection can help you stay proactive.