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Marketing strategy

Marketing Sensitivity Analysis

How a tool ripped out of the Private Equity world can help you with your go-to-market and/or venture capital fundraising plan.

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Private Equity Tools for Marketers

The most challenging course that I took during my MBA at Berkeley was taught by Professor Peter Goodson, a true legend in the Private Equity world. Working alongside his business partner, the famed Jack Welch, Peter transformed nearly 70 companies over the last 40 years totally nearly $100 Billion.

Think of his work as similar to what HGTV’s Property Brothers do for single-family homes in suburban Vancouver but on the scale of massive enterprise deals like Hertz, Goodrich, and Tyco. 

The most important skill that I gained from his class was how to assess businesses–not just by it’s current state but what you could do with it based upon your expertise.  The most important tool behind making a decision on the value of an organization is the sensitivity analysis. This analysis quantifies your assumptions by identifying the independent variables and assigning them each a range. What do I think this could be? What is the worst case scenario? What is the best case scenario?

This enables you to study how various sources of uncertainty in a mathematical model contribute to the overall model’s uncertainty.

What is a Marketing Sensitivity Analysis?

For marketing and growth professionals, whose job it is to design complex strategies that meet the company and product portfolio’s diverse goals and ultimately grow the bottom-line, this can be an essential tool to understanding. The sensitivity analysis can help us predict how many users, customers, or dollars of revenues we can anticipate based upon our integrated channel, monetization, and product strategies. The inputs that you use should be based upon historical and/or market research.

I like to build mine as a micro-funnel. Here is a simple example:

Worst Case for Search Ads

  • Avg. CPC: $10
  • Total Spend: $1,000
  • Landing Page Conversion: 10%
  • Finish Checkout: 5%

Worst Case Cost New Customer for $1,000 :  ($1,000/$10)*10%*5%= 0.5 customers
or $2,000/customer

Median Case for Search Ads

  • Avg. CPC: $8
  • Total Spend: $1,000
  • Landing Page Conversion: 25%
  • Finish Checkout: 8%

Median Case Cost New Customer for $1,000 :  ($1,000/$8)*25%*8%= 2.5 customers
or $400/customer

Best Case for Search Ads

  • Avg. CPC: $5
  • Total Spend: $1,000
  • Landing Page Conversion: 50%
  • Finish Checkout: 10%

Median Case Cost New Customer for $1,000 :  ($1,000/$5)*50%*10%= 10 customers
or $100/customer

 

I like to build these out for each of my performance and earned media channels projected over 36-months with optimization variables for each of my assumptions. 

Your output might look something like this with your assumptions listed in yellow below your worst, median, and best projections.

I then layer on dependent variables such as virality, upsell, churn, and search engine authority which will be impacted based upon the productivity and investment into each channel specifically. When you graph these, they should start to tell your overal performance story. What channels should you invest in and when? What is the ceiling to any specific channel or market.

Transactions by Channel (Median)

Once you compile your data, you get the final outputs for marketing specific use cases. something like this:

Once you have an understanding of baselines, if you are a founder or CEO, you can work into your full financial, operational, headcount, and valuation models.