I previously wrote about how Bayes’ Rule is the foundation of good decision making and last month posted about how it could be applied to your green light process, today I will address another application of Bayes’ Rule: Applying it to corporate development (mergers and acquisitions). As discussed in my first two posts about Bayes’ Theorem, it shows how to use past data to optimize your decision making process. There are three areas of corporate development in which Bayes’ Rule can help optimize your strategy: building a company for exit, selling to the right partner and acquiring companies that improve your value.
Creating a company for exit
Many founders start and build their business for eventual exit (e.g., sale or IPO) but if they fail to take into account Bayes’ Rule they are not optimizing their chances for a successful one. If your goal in launching a business (or pivoting your business) is to sell it, then you need to look at past data. The best indicator of whether you will be able to sell—and for how much—is other M&A (mergers and acquisitions) activity. You may have a great idea for a business, and it may be unique, but if it is in a space where there is no M&A activity you are not likely to sell eventually the company (however, that is not to say you should not start it, if your goal is something other than a sale).
To show how Bayes’ Rule applies, let’s consider two opportunities. One is a building a game company in a space where 60 percent of the companies are selling to larger companies. You have a decent idea and good team but it is not great; looking objectively you have a 50 percent chance of success. Conversely, you have a fantastic idea for a different type of game company. You are convinced that in that space you have a 90 percent chance of success. Buyers, however, are not showing much activity there and only one percent of companies in that space have an exit. Bayes’ Rule shows that if your goal is an exit, then you should launch the company where you have a 50 percent chance of success (you will have a 30 percent chance of selling your company versus less than 1 percent for starting the company that is much more likely to succeed).
My personal experience reaffirms this math. At Merscom, we were a very successful casual game publisher (downloadable games targeting women sold primarily on Big Fish and Real) . There, however, was not much M&A activity in the space. So we decided to abandon a profitable, highly successful business in 2009 to enter the social gaming space because there was an acquisition almost every week. A few months after our pivot, we were acquired by Playdom, and a few months after we were acquired, Playdom was acquired by Disney.
Choosing the right buyer
An often overlooked decision in the M&A process is to whom you should sell. This decision also can be helped with Bayes’ Rule. Sellers will often gravitate to the deal with the highest price and biggest earn-out. It is hard to say no to more money or a bigger upside, and even harder to convince your investors to say no. By applying Bayes’ Rule, however, you can choose the deal with the highest true expected value.
You should use Bayes’ Rule to value accurately all of your options. First, one deal may have a very exciting earn-out (you have the potential to make billions of dollars). You are quite optimistic about the potential to hit the earn-out; after all if things were not going well why would you have suitors to begin with? Stepping back and applying Bayes’ Theorem, you note that 85 percent of M&A deals with earn-outs end up never paying any earn-out (please remember that this is a fictitious example; I have not investigated real data). Even if you have a great company and great team, you need to apply Bayes’ Rule and discount the earn-out accordingly (again, use real numbers, not what I made up).
Secondly, you are assessing deals with earn-outs from multiple partners. You love one company and they love you but they have made 50 acquisitions and none of the acquired companies have hit their earn-out. Conversely, there is a potential acquirer who views you primarily as an addition to their portfolio and you fear they are not as dedicated to your unit’s success post acquisition. Your intuition may be to go with company number one because of its commitment to your company. Applying Bayes’ Rule, though, a deal with the second company is much more likely to lead to realizing your earn-out.
Acquiring companies that actually make you better
The cost of failed acquisitions is in the billions of dollars, yet companies continue to overestimate the value of making a deal. Ask Time Warner how prudent it was to acquire AOL, or Newscorp how the MySpace deal worked out and you see quickly how an acquisition can damage the acquiring company. The mistake that many acquiring companies make is they only look at the deal in question; they see how the companies can mesh, how it opens new markets, provides additional competencies, leads to world peace, etc. Most of the spreadsheets (and there are many) that estimate the value of the acquisition are limited to looking at the target and acquiring company and how it would impact their metrics. They are focused on different scenarios (there usually is a high, medium and low) but most of them are contingent just on growth and other factors tied to the two companies.
Applying Bayes’ Rule would add a layer to valuing effectively the deal by looking at how previous acquisitions (preferably as close to this one as possible) have impacted the acquiring company. To use the Newscorp examples, if their head of corporate development looked historically at the success rate of media companies acquiring tech companies and found only 10 percent were successful, they would have discounted the projected incremental EBITDA from the deal. Rather than relying on a spreadsheet that showed MySpace being worth over $580 million, he would have taken that spreadsheet and adjusted the expected value to take into account a 90 percent failure rate (again, I am making up these numbers just for the example). My guess is this one application of Bayes’ Rule would have saved Newscorp over half a billion dollars.
3 key takeaways
Bayes’ Theorem is a powerful tool in maximizing your return from M&A activity. There are three key areas to corporate development where Bayes’ Rule can help:
- Look at past data before launching your company so you are in a space with a high likelihood that there will be a buyer;
- When valuing an offer, or competing offers, use Bayes’ Rule to determine the value of all elements of the deal, both on a global scale (e.g., the probability of hitting an earn-out) and on a local level (e.g., the partner’s past history on acquired companies achieving earn-outs);
- When acquiring a company, use data from similar acquisitions to judge the true value it has to your business.
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