Transform how you make business decisions.

EXPECT MORE FROM YOUR DATA

The consumption and delivery of analytics has evolved in the last decade. Use of analytics has expanded and users of analytics expect more sophisticated solutions to their business problems.

We see three key components our most demanding clients want:

  • Cutting edge techniques that combine technology, complex data, and advanced analytics
  • Decision options and not insights
  • Access to results anywhere and at any time in the decision cycle

GET A FRESH PERSPECTIVE

Throughout this major shift in how data is consumed, we developed a unique and highly differentiated approach to solving business challenges. It’s been honed across projects large and small. It’s time-tested. It works. And it’s called Decision Engineering.

DIFFERENT APPROACH, DIFFERENT RESULTS

We take a different perspective when we kick off a new project with a client. Traditional diagnostic methodology starts with streamlining the data then figures out what can be drawn from that data. Decision Engineering starts with what decisions can be made and then combines the power of analytics with latest technology to build a sustainable and scalable system for the client.

5 WAYS DECISION ENGINEERING IS DIFFERENT

Here are just 5 of the 10 key ways the Absolutdata Decision Engineering Methodology™ is different:

  • Start with the decision you want to make, not the data on-hand
  • Build a dynamic system as a solution, not a static model
  • Deliver a forecast of the future, not a report of the past
  • Build analytics into the work flow, not just apply models to data
  • Results are consumed more by top executives, not just by the data-savvy
The approach is so different some clients have struggled at first.
But no one argues with the results. Here is an example…

DECISION ENGINEERING APPLIED:
REDUCE CHURN FOR A LARGE TELECOM COMPANY

A leading mobile service provider had seen an uptick in customers leaving for the competition. They set out to launch a a major initiative to reduce churn.. They knew the wealth of data on-hand could be used to get better results from their marketing campaigns.

TRADITIONAL APPROACH

Identify and list the customers who are likely to churn.

  • Start by collecting the available data
  • Apply analytics to build a predictive churn model
  • Generate a list of customers likely to churn

The client then launches their preventive retention campaigns, focusing precious marketing dollars on the customers identified.

RESULT

All is good. The client gets:

  1. A predictive churn model
  2. Incremental lift from reduced churn

Every bit counts in the world of churn.
But to our Decision Engineers, it’s not enough.

DECISION ENGINEERING APPROACH

Build an automated intelligent decision solution.

  • Start with the decision: Decide the best campaign message, offer and timing for each customer
  • Identify and collect only what data is needed.
  • Build a system of models designed to work together:
    • Predictive churn model with segmentation
    • Customer Lifetime Value (CLV) of each mobile user
    • Predictive next-best-action model
    • Savablity model to identify “Persuabable” customers

RESULT

Much greater business impact. The client gets:

  1. A dynamic platform to continuously suggest campaigns
  2. Increased ROI by focusing on the right users with the right offers
  3. Reduced marketing spend
  4. Higher lift than the traditional approach
Decision Engineering is a unique approach that delivers more impact.
And keeps delivering as your business evolves.

TALK TO OUR DECISION ENGINEERS

Contact Us
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