Marketing Mix Optimization

Marketing Mix Optimization
Since the beginning of Marketing, calculating the ROI has been one of the most challenging processes. Not direct link exists among the different expenditures and the revenue got in several different locations and forms. There is an increase in the number of optional tactics across an increasing number of channels. And this while facing a more and more rapidly changing world of consumers.

Today’s companies need to be serviced fast and accurate analytics. They may react almost in real-time to how their marketing campaigns are performing. With the huge amount of data to analyze, new techniques have to be used. 

The new challenges for Marketing Mix Optimization 
  • Agent Based Models -ABM- are now too big and difficult to feed and parametrize
  • Your data sources are no longer available with the needed quality
  • Old linear regressions are of no use to create predictive models
  • OLS regressions with manual modelling techniques are really difficult. There are bigger and bigger number of variables to be fine-tuned. This turn this human driven process into an impossible task

 

The new resources

The only true solution is to make use of MACHINE LEARNING and ARTIFICIAL INTELLIGENCE:  putting the fine-tuning of big OLS models into an intelligent loop. Exabai has created new and more accurate modelling techniques that offer 50% higher accuracy in a 90% shorter timeframe.

 

Download the attached documents below to see some useful insights.

Marketing Mix Optimization by Exabai

Marketing Mix Optimization by Exabai

Sport Clubs Ticket Optimization Model

Sport Clubs Ticket Optimization Model

 

Optimización de Marketing Mix

Case Study: leading U.S. Wireless Brand
Situation: with over $800 million in marketing spend, ROI and optimization was sought
Approach: marketing mix optimization
Results: measured savings near $200 million plus CEO and CFO engagement
Beyond savings and ongoing ability to optimize an evolving media mix, the model effort also measured the non-media baseline which showed contributions of service, pricing, mobile devices, plans, long term brand equity, and early social media.

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