Marketing Optimization Decision Engine

“We’re not that much smarter than we used to be, even though we have much more information – and that means the real skill now is learning how to pick out the useful information from all this noise.”

Nate Silver

Each day, the complexity of marketing accelerates. It’s no wonder a recent study names CMO’s with the lowest satisfaction rating and the shortest tenure of all C-Level executives.

They’re given brand metrics that don’t link back to revenue and sales, marketing data that hardly relates to profit & loss, and can’t say how additional marketing investments will generate incremental revenue.

In this data and digital driven world, CMO’s should be able to connect all marketing inputs and outputs to future business outcomes. This is where MODE comes in to play.

MODE is the evolution of Marketing Mix Modeling.  It is better, faster, and cheaper.  It is a platform that allows Marketers to make the best possible decision on how to deploy their marketing investments and maximize sales. Still interested? Let’s go a level deeper.

Marketing Mix Modeling

MMM, a time series based regression, rely’s on what happened in the past to predict the future.

MMM can’t analyze simulations with multiple approaches.

MMM is expensive and time consuming to develop.

Marketing Optimization Decision Engine

MODE uses Bayesian Statistics.

MODE uses MCMC and Machine Learning and can predict any number of marketing approaches.

MODE will have a working model in 3 months or less that will learn and get smarter over time.

How can MODE help me make better decisions?

M stands for marketing not media.  MODE can evaluate and optimize traditional and digital Media, along with Promotions and Pricing decisions, the major pillars of marketing.  It can also assist in making regional versus national investment decisions, and assess brand metrics implications.


O is for optimization with the ability to understand multiple scenarios.  MODE looks at scenario development understanding that investment is nonlinear.  A $1mm spend on digital marketing may be efficient with a $0.5 spend towards Social Media, but change one and the other is impacted.  The MODE model understands how each change interacts with each other.


D is for making decisions aligned with C-Level goals.  MODE will optimize based on sales, revenue, bookings and variables.  But it also ties investment to probability of success.  If the street needs a 10% increase in sales, MODE provides the data necessary to show which investment will allow the highest probability to meet the goal.  Hint – it is not always the highest spend threshold, but how the dollars are deployed.


E  MODE is an engine.  A platform that is designed to be utilized every quarter or even every month.  The data is automatically and continually updated.  Many mix marketing models are developed and simply left to languish.  MODE gets smarter and more useful with every update.


Geoff Pickering

Geoff Pickering

A proven marketing executive with over twenty-five years of successful marketing and operational leadership.

‘My passion is providing world class brand management and consultation to executives dealing with today’s complex marketing landscape.’

Chris Dickey

Chris Dickey

Chris’s expertise runs the gamut of capabilities from Bayesian based decision support modeling, social CRM analytics, database analytics, brand health analytics, qualitative and quantitative research techniques, ethnography, CX and UX, web analytics, media analytics as well as advanced segmentation and modeling techniques as well as multi channel customer engagement strategies.

Neeraj Kulkarni

Neeraj Kulkarni

Data Sciences Consultant with 18+ years experience in marketing mix modeling and developing holistic analytic solutions for fortune 100 clients.

Want a predictive, anticipatory targeting model?

Send an email.

Schedule a meeting.