Leveraging Predictive Analytics and Intelligence
Topics Covered in Course
- Keys to creating a solid intelligence program foundation
- Identifying predictive intelligence areas
- Setting up a program to obtain results
- Creating Key Performance Indicators (KPI's) that are strategic
- Assigning KPI ownership
- Obtaining information that is measureable
- Obtaining information that is actionable
- Obtaining information that is unbiased
- Sample scores and values
- Creating predictive analytic types
- Associating KPI's to predictive aspects
- Associating predictive aspects to revenues
- Determining predictive analytic calculations
- Valuation of predictive intelligence analytics
- Grouping predictive intelligence analytics
- Real-world predictive intelligence examples
- Course Table of Contents
- Key Indice Definition Spreadsheet
- KPI Ownership Assignment Grid
- Assigning KPI's to Predictive Analytics Grid
- Real-world Predictive Analytics and Revenue Spreadsheet
Detailed Course Description
(design and creation being planned) Predictive intelligence analytics are used by organizations to learn from its cumulative intelligence experiences - and to use those intelligence insights to improve positioning or provide a competitive predictability advantage with lost prospects and existing customers.
What if your organization knew how to predict the likelihood of an existing customer stopping the use of their product or service? What if your organization could identify which existing customers should be upsold to and by how much? Better yet, what if your organization could very accurately predict not only which existing customers were in-jeopardy of stopping the use of your product or service, but what if you could also pinpoint the four key indices that needed to be improved upon to ensure that they remained a customer?
To put this in a more focused perspective - what if your organization leveraged predictive intelligence analytics and knew that at any point in time 23% of existing customers would classify as being in-jeopardy? And what if those existing customers, if your organization failed to address their specific concerns, had a 97% probability of leaving your organization in search of an organization that could fulfill their needs within the next 8 months?
What are those types of predictions worth to your organization?
This course will expose organizations to how they can define and measure certain key indices and establish predictability metrics based on a sampling of their existing customers and lost prospects. Organizations will then be able to establish a baseline of intelligence and begin predicting various indices in order to proactively address customer churn, re-engage lost prospects, and have the ability to calculate which key indices impact various predictability aspects. The course will also use real-world examples from organizations and showcase their results in order to understand the impacts predictive intelligence analytics has on revenues.