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Why Automated Marketing Mix Modeling instead of multi-touch attribution or traditional MMM?

The world is one giant web of network effects, and so is the relationship between marketing, communication, audiences and sales. The problem is that our brains can't process more than 4 variables with any accuracy.

 

 

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Let's talk about network effects first

When a network effect is present, the impact or value of a product or service increases according to the number of people or organizations using it.

 

Many times, network effects are positive, enabling businesses to create more value for more and more people and organizations. But as we've all seen, network effects also can magnify negative and even toxic relationships, driving us into some very dicey places. Climate change is an excellent example of a web of multi-factor, multi-vector network effects that continue to alter the risk profile for many people around the world (watch this video with CEO Mark Stouse, comparing climate change with analyzing marketing’s true business value)

 

The more factors are involved -- people or otherwise -- the harder it is to clearly understand what's more or less important and what's absolutely not relevant at all. Each factor is potentially an accelerant on each and every other factor. This is very much the case when it comes to sales, communication and marketing with sometimes over hundreds of individual activities driving sales. And then we have the ultimate complicator of all: Time.

Time introduces non-linearity into the network

The concept of Time Lag is the notion that everything we do takes some amount of time in order to produce one or more effects. Time lag in a given relationship can be very quick, or it can be months or years. We also commonly use phrases like "ripple effect" or "knock-on effect" to describe this reality.

The concept of Time Lag is the notion that everything we do takes some amount of time in order to produce one or more effects. Time lag in a given relationship can be very quick, or it can be months or years. We also commonly use phrases like "ripple effect" or "knock-on effect" to describe this reality.

Your brain + data is not enough

We humans love our intuition because when we're right, we feel almost psychic. We like to think that we can look at many factors and "see" the truth. Indeed, researchers have found that few things dump as much adrenaline and dopamine into our systems as the feeling of "being right."

 

There are many situations where opinion and intuition deliver a lot of value. No scientist or mathematician would argue otherwise. But accurately understanding a network effect isn't something you can do by staring at data and figuring it out.

 

The problem is that our brains can't process more than 4 variables with any accuracy.  Remember when you felt completely overwhelmed by the complexity of an urgent decision? You've experienced exactly what I'm talking about. Watch this video with CRO Christopher Engman explaining why our brains in not enough.

What about multi-touch attribution?

The average B2B marketing organization measures between 50 and 250 independent variables (defined here as marketing investments that create a variety of experiences and outcomes across various audiences). B2C companies can run the gamut, with Consumer Package Goods (CPG) companies often running as many as several thousand variables. No matter your situation, you've got far more moving parts than you can possibly understand without help.

 

This is why visualization tools (like Tableau, Power BI or Qlik) and multi-touch attribution tools (like Neustar, Visual IQ, Marketo and Google) are not enough when proving the business impact of different marketing activities. These tools are a good support to map out the customer journey and for tactical changes in the communication, like to understand what headline work best on a website. But for analyzing business value and optimizing spend, they are not enough.

 

On top of this, the issue with multi-touch attribution is that we more often clear our cookie history and jump from one device to another, making it difficult to connect the “touches”. We also digest more content on social media channels like Youtube, Quora, and Whats app without a clear CTA and without visiting the corporate website. And when we’re not online we watch TV, read magazines, go to events and listen to the radio, none of what is accounted for in the multi-touch attribution model. This means that the multi-touch attribution at best is covering around 40% of the total customer journey.

 

This is why one can argue that powerful regression analytics, Marketing Mix Modeling, is not only a must, it really must be considered the inarguable core of any analytics effort.

Traditional marketing mix modeling is the golden standard but it is slow, expensive and requires PhDs

Scientists and researchers have used powerful regression analytics to better understand the many cause and effect relationships in the world around us. The good news is this math is extremely well-suited to understanding the multiplicity of factors driving business performance, including their relative relationships to one another and their relative significance.

 

Even if they haven't used it, many marketers and communications pros will recognize traditional Marketing Mix Modeling, or MMM, as the application of deep math and econometric-style analytics to better understand marketing's impact, value, return on investment, and time-related escalation and decay.

 

Today, MMM's mathematical accuracy and power are virtually unassailable. Its biggest challenges have been operational. Assembling the data, tech stack, talent, and domain knowledge necessary to run MMM has been very difficult and expensive, making it hard to scale in large organizations.

 

Its time-consuming complexity also has meant that even experienced data science teams often struggle to deliver MMM analytics in time to affect key business or marketing decisions.

Automation and Augmented Intelligence have created a "Super MMM” – giving you answers in minutes instead of weeks or months

Proof has automated the powerful regression analytics. Our platform absorbs data as it comes available -- no more batching -- and delivers instant updates. You get the answers and the proof you need in minutes, instead of weeks or months.

 

For companies using MMM today the result is a huge improvement in agility, accuracy, power and speed enabling them to use their valuable data scientists in more efficient way, diving deeper into certain areas.

 

For companies not already using MMM this opens up a new world of marketing ROI that only has been available to the ones who could attract and afford PhD. mathematicians. Proof Automated MMM enables customers to understand the network effects that are driving their business, all while transforming their understanding of marketing from a line in the expense ledger to a huge business multiplier.

 

Proof is the word leader in Automated Marketing Mix Modeling.

  • The world itself is one giant web of network effects, and so is the relationship between marketing, audiences, business performance and a great many external factors.
  • The human brain can't handle more than 4 variables with any accuracy.
  • In marketing today we have between 15 to 500 or more variables.
  • Amidst this swirling reality, regression analysis and other forms of deep math are your only reliable GPS.
  • Traditional MMM, based on regression analysis, is very slow, difficult and expensive, making it hard to scale in large organizations.
  • Automation and Augmented Intelligence have created a "Super MMM” – giving you answers in minutes instead of weeks or months

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