Advanced analytics projects offer enterprises game-changing predictive insights, but measuring their effects isn’t always easy.

WebbMason Analytics has developed a three-part framework to calculate and validate the impact of advanced analytics solutions on both cost and revenue.

Part 1: Determining the true cost

Analytics teams often work on multiple initiatives simultaneously. We build time-keeping technology into the agile development process to track overhead.

This certifies project costs, improves estimate accuracy for future projects, and facilitates resource management.

Part 2: Activating the solution

In the region of 25 percent of analytics projects are never activated because the business lacks the technical and cultural maturity to use the insights.

Collaborative Due Diligence

To guarantee activation, WebbMason Analytics has developed steps to ensure data, systems, and processes are flexible enough to incorporate insights. We call this collaborative due diligence. This is a benchmarking phase where we identify the current state so we can track and compare improvements over time. This includes metrics and KPIs, as well as assessments of the systems and business processes responsible for incorporating new insights.

Businesses tend to have an idea of where they are. For example, they can tell me their customer churn is somewhere around 30 percent. But, for large organizations, the difference between 28 percent and 32 percent can be staggering in terms of the number of customers.

Our job is to get as accurate a picture as possible to use as a benchmark.

To make sure our clients will realize the benefits of their investment with WebbMason Analytics, we conduct an upfront and comprehensive evaluation of the following:

  • Are business processes flexible enough to adapt to change?
  • Does the company have the right people to interpret and facilitate new approaches?
  • Is the organization’s culture open-minded and willing to accept insights?
  • What new technologies are needed and can we leverage existing infrastructure?
  • Is it clear which departments and stakeholders are responsible for the data?

Part 3: Realizing the returns

Developing a data science model is only part of the effort. We partner with business stakeholders to evaluate, produce, and maintain solutions that will significantly increase the likelihood of successful activation.

Over the course of developing hundreds of analytics solutions, we have identified and systemized critical evaluation points to ensure a project will accomplish its pre-defined goals.

Model Evaluation

Methodical testing of the new solution with selected stakeholders in a controlled experiment to understand the revenue lift the product is capable of generating before it is released enterprise-wide.

Roll out and maintenance

An implementation plan to bring the solution to people across the enterprise and provide ongoing user support, model maintenance, and ROI measurement.