How Automated Marketing Mix Modeling (AMMM) Can Help Your Business Grow Faster and Be More Profitable

fastest way to increase revenue, cash-flow and or profitability

By Christopher Engman, CRO/CMO Proofanalytics

Optimizing your go-to-market mix


If you work at a medium-sized or large corporation (B2B or B2C), optimizing your go-to-market mix could be the fastest way to increase revenue, cash-flow and/or profitability. Even outsourcing a function or building a new factory is unlikely to have as much immediate and significant financial impact as having a powerful tool like Proof.



One of our early users, a global $2B enterprise software company, saw an 8% increase in deal velocity and 24% higher profitability. An international hotel chain immediately realized how Proof could help them shrink their marketing spend to fewer categories, reduce their sales force and yet increase revenue. And advertising agencies have realized that by proving the impact of their creative work, they can charge more.

Cause and effect, including delays

Proof Analytics reveals the cause and effect relationship between your marketing activities and your sales results in order to help you make informed decisions. For example, Proof can analyze as many time-series variables as you can define to provide you with an automated marketing mix modeling (or even an automated go-to-market mix modeling).

With Proof, you’ll be able to answer four important questions:

1. How do you spend your marketing and sales budget to improve revenue, cash flow and profit?

Proof can help you see where you are overspending or under-spending on your sales and marketing.

2. How do you balance your spending among various marketing investments?

When CMOs don’t know what is working, they often spread their spending across many different categories in order to increase their chance of hitting the mark, but that is often not a successful approach and certainly not an informed one. In our experience, it’s the companies who optimize their go-to-market mix that see the most dramatic impact.

Proof helps companies succeed by showing them what’s working and what’s not across a given period of time. This enables you to reduce the amount of money you’re spending on marketing categories and activities that have weak correlations with your most important financial goals and instead spend that money on high-performing ones.

You can start by using the tool to determine spend levels for and the balance between paid, earned, shared and owned (PESO). Then you can identify your highest-performing assets – be it PR, events, Instagram, Facebook, LinkedIn, Snapchat and other SO-ME, SEO, SEM, TV, radio, print, outdoor, retargeting, customer advocacy, other influencers, contextual targeting, content groups, review ads, ABA, ABM, email, sponsorships, etc.



Proof’s analytics might give you insights that lead you to reduce your sales force by 25%, split half of that cost reduction into two marketing categories, or eliminate three marketing categories entirely. Despite an overall cost reduction of their go-to-market spend, companies who have used Proof to make such decisions have grown their revenue by as much as 17% and their profit by as much as 31%. Spending in PR often goes up and companies move into categories and increasing their spending in areas that have a high correlation factor with driving the prioritized financial KPI (revenue, for example).

The impact is relatively easy to predict since companies usually have two to three marketing categories that show no or only a weak correlation with revenue, cash-flow and profit. When they stop spending on these categories, they see an immediate cost-savings and margin increase. They will probably also see an impact when they increase their total marketing spend, and stronger proof makes that decision much easier.

These first two questions can be answered based on easily accessible data. We recommend 24 months of data or more – on spend per marketing category, and on revenue, cash flow and profit. The latter can usually be found in a company’s financial system. Less data is required for directional observations. However, the more data history you have, the more accurate and actionable information you will get. Because the analytics will enable you to tilt, the higher your spend on go-to-marketing, the more you will benefit from tilting.

Once you can see the relationship between your sales and marketing spend, and the impact each category has on your company’s financials, you can add more data and use the tool to answer even more complicated questions such as:

3. How well do the various marketing categories support each other?

For example, if I double my spending on print advertising, how will it affect search volumes? And over what timeframe? If we cut half of our trade shows, how will it affect our numbers in the short and long term? By adding more granular data from Google, Facebook, etc. for example, you can get deeper insights into other cause and effect relationships.

You will also be able to see how one cause impacts another. You can measure advertising and PR against awareness, for example. You can see how each campaign performs against awareness along with the relationships, good or bad, that occur between the two in isolation. You can also easily invert them and learn to what extent one magnifies the effects of the other.

These are the types of questions a sophisticated user can ask in order to fine-tune the marketing machine. They become more relevant when you either have very little money to spend or if you’re wasting too much.

4. How should I adjust my spend levels and mix, according to whether I want to maximize revenue, cash flow or profit over a given time period?

Imagine you’re at the end of Q3. Revenue and profit look good, but cash flow doesn’t. How will you change the go-to-market mix in order to maximize cash flow in Q4? Or, let’s say you find yourself in an economic recession, and your CEO is pressuring you to lower marketing costs: Which categories will you decrease and what will be the short, mid and long-term effects?


Multi-touch attribution tools are excellent for mapping out how to optimize conversions, but not for optimizing spend.

If you try to find the answers using a multi-touch attribution tool (MTA), you will undoubtedly place too much value on conversion short term marketing methods like SEM, affiliate networks and the likes. Multi-touch attribution tools are excellent for mapping out how to optimize conversions, but not for optimizing spend (resource allocation). They cannot discern how much value came from each marketing category and activity. In addition, digital assets that don’t have a direct digital trail, and do not lead to an action and/or have a delayed effect, are not trackable with multi-touch attribution tools – even if they eventually have business impact. They appear as low-value, even if they are seen as high-value in a mathematical tool like Proof. Finally, these tools cannot track anything that’s not online – like outdoor and other advertising, events, or direct mail. Your analysis will therefore inevitably be skewed, and you will end up underspending on marketing that has a medium to long-term payback.

…underspending on long term marketing



Visualization tools (and marketing dashboards) work well for very small numbers of variables and where there is no interdependency and no time-lag / delay

Another way you might attempt to get answers is from visualization tools. While these are great tools for processing a handful of variables, they cannot handle the significant amount of data required to show you the insights listed above. And, most importantly, visualization does not represent a relationship between variables. For this you need an algorithm that incorporates mathematical capabilities, time lag and market forces – and self-selects the right model for your data set. And while there are a handful of other cause-and-effect tools on the market, they are built for mathematicians. Proof is built for business users. Mathematicians also use it since it allows them to study bigger amounts of relationships than when making models manually.



Data science team, lead time and cost

Yet another way you might try to get direction is by hiring a data science team. They should have the complete picture, right? Sure, but not only will you will spend a significant amount of money (if you can even find the right people), it will take weeks to months before you see the results of their analysis. You also run the risk of having communication problems between your data science team and your marketing and sales leaders. Proof can deliver even better results than a data science team at a fraction of the cost and within minutes of receiving the data. That’s the benefit of automated marketing mix modeling.

Automated Marketing Mix Modeling is costing a fraction of a mathematician team and delivers the models and the recommendations in seconds instead of weeks or months.

If you want to understand true business impact, you need to get rid of the vanity metrics, attribution models and visual correlations. You need to see the whole picture. This is where Proof excels. Knowing how to adjust your marketing and sales mix will have tremendous impact – more than changes to any other area of your business.

Please reach out if you want to know more about how we can help marketing, sales and business professionals like you to:

  • Better understand your marketing and sales investments to improve revenue, cash flow and profit
  • Balance your spend among various marketing activities
  • Understand how various marketing categories support each other
  • Adjust your spend levels and mix to improve revenue, cash flow or profit over a given time period


If you’re not quite ready for automated marketing mix modeling, I hope I’ve provided you with a few insights that you can start using today – regardless of how advanced your marketing mix might be, and no matter how big your marketing and sales budget.

Follow my colleagues and myself at Proof, and we’ll keep sharing!


Also watch this great video where Mark Stouse (founder and CEO of Proof Analytics) talks about how he as a global CMO often felt and also how Climate change is an analogy for time-lag / delayed effects in marketing.