Why LinkedIn and Microsoft Isn’t Crazy

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As far as $26 billion acquisitions paying nearly a 50% premium go, Microsoft’s intent to buy LinkedIn is proving to be one of the most amusing. While most transactions of this magnitude have analysts and reporters racing to assess fit and infer intent, this transaction is most notable for its comedic game. Instead of being besieged on Twitter with financial metrics, the announcement has instead unleashed a torrent of pointed commentary on “Joining my professional network,” Clippy or both. Our local Portland Slack even managed to find a Coldplay/U2 connection.

All of which is fair enough, some of which are legitimately funny, but none of which tells us anything about the transaction and whether it is likely to succeed.

For my part, that is a less interesting question than why the transaction occurred. Forecasting how this acquisition will play out is, to some degree, boring. It is true, for example, that the probability of success for major acquisitions in general is low because acquisitions are hard, and as many observers have pointed out, Microsoft’s recent track record here is not particularly encouraging. It’s also true that many of its highest profile failures, such as Nokia, were transacted before Nadella’s tenure and, it’s said, against his wishes.

The short answer to whether or not Microsoft will eventually be writing down LinkedIn a few years from now the way it has Nokia then is: no one knows.

What we do know, however, is that this acquisition is interesting both in terms of what is being acquired and the valuation Microsoft attached to it, and that the market didn’t seem to appreciate either.

Let’s start with what Microsoft acquired. For all of the focus on LinkedIn the product, it is unfortunately being lost that Microsoft stands to acquire a capable set of technologists. Whatever one might think of the LinkedIn UI – and Paul Ford is not the only one who believes it’s a tire fire – LinkedIn’s infrastructure engineering and data science teams are both smart and capable. Many of the engineers currently lampooning LinkedIn that are happily relying on Kafka, as but one example, typically don’t realize that the latter was built and released as open source by the former. According to its engineering page, LinkedIn is responsible for 75 open source projects across a wide range of categories. Ten years ago at Microsoft, this would not have been an asset. Today, with Microsoft emerging as a surprising champion for open source projects in the cloud and releasing its own software as open source, LinkedIn’s engineering team and their experience with open source are useful.

Not $26 billion useful, of course. The bulk of the valuation depends on LinkedIn the product. Which is another way of saying that Microsoft spent took on debt equivalent to a quarter of its available cash and short term investments on data. Which only makes sense if you value data, obviously, but more importantly have a clear idea of how to use it in meaningful ways to generate revenue.

Queue the Clippy jokes.

But the truth is that Microsoft has acquired a unique asset in LinkedIn, the company’s financials notwithstanding. There’s no other comparable dataset that contains a reasonably up-to-date employment history and professional network for a large portion of the working world. That information has value to any third party hungry for personal information, from Amazon to Facebook to Google to IBM, but it’s potentially more leverageable for Microsoft, which has spent the better part of the last three decades building out an ever more comprehensive and ubiquitous suite of tools for office workers via the creatively named “Microsoft Office.”

As an exercise, what would you do if you first were in Nadella’s shoes and second believed the Software Paradox to be a real phenomenon. One answer would be to begin acquiring unique, exclusive datasets.

First, because they can serve as the foundation for a variety of AI and machine learning products and services. Second, because data – unlike software – is ground that cannot be made up easily. LinkedIn’s software could be replicated, and perhaps improved upon, within a reasonable timeframe by at least half a dozen different large scale internet vendors. The corpus of data it accumulated from its users could not be replicated in twice as much time and at a large multiple of the cost. Hence, presumably, Microsoft’s valuation. Lastly, Microsoft presumably has attached value to denying the LinkedIn dataset to one or more competitors. There’s no guarantee that Google, as one example, would have been able to leverage the LinkedIn dataset into more robust and information rich business services – but now Microsoft doesn’t have to wonder.

The opportunity costs to an acquisition of this size and type are considerable, of course. Acquisitions also being inherently problematic, there is also a reasonable risk of failure. But those focusing on existing LinkedIn or Microsoft services and attempting to evaluate this transaction through that lens are missing the wider market context.

If this were a decade ago, or half that even, the Redmond giant’s core answer to strategic questions – any of them – would be software. Nadella appears to have internalized the reality, however, which is that software is not the commercial engine it once was. Microsoft’s past and present with Office and Windows was and is selling standalone assets, typical of traditional shrinkwrapped software. Increasingly, it’s harder to sell software for the returns that once were common – even for Microsoft.

But if standalone software isn’t where commercial value is to be found, what’s the alternative?

Consider the current state of the market. Broadly speaking, there is widespread agreement that the focus for both buyer and seller moving forward is in two major areas: cloud and mobile. Enterprises are moving to the cloud and cloud based services at incredible rates, and the story of how mobile annihilated the traditional PC market is well understood at this point. The good news for Microsoft is that it’s performing reasonably well in the cloud market at present. The bad news is that it was too little and too late in mobile, so the company has largely punted on the market for the time being.

The better news is that Microsoft has an opportunity to transcend both markets by combining hardware and software. For the enterprise buyer, could Microsoft use LinkedIn’s dataset to add predictive analytics around retention to its CRM offering, for example, or make this available as a third party service? This seems plausible. Could the company use LinkedIn data about your employer and teammates to improve mobile sharing, say, for individual users? Certainly. Is Microsoft one of the companies with the software and hardware resources required to build and perhaps even sell a bot that would answer, on demand, questions about a given employee’s work history and professional skills?

“Cortana: Where has Jane Smith worked?”
Jane Smith has spent ten years as a Java programmer for companies in the insurance and healthcare industries.”
“Cortana: What are Jane’s professional certifications or memberships?”
Jane is an Oracle Certified Master, Java Enterprise Architect, a certification that less than 5% of the applications for this position claim.”
“Cortana: What languages does Jane Smith speak?”
Jane is fluent in English, French and Spanish.”
“Thank you, Cortana. Can you schedule an interview with Jane next week?”
Of course.”

As long as they keep Tay away from LinkedIn related offerings, this does not seem like too much of a stretch.

Which makes the most important questions around this acquisition ones of execution, it would seem, more than valuation. Given the price tag and visibility of this acquisition, LinkedIn will be an interesting test of the new, Nadella-led Microsoft. But whatever the outcome, the process that led to the acquisition is nowhere near as crazy as the Clippy asking you to join his professional network-GIFs would suggest.

Disclosure: Amazon, IBM and Microsoft are RedMonk customers. Facebook, Google and LinkedIn are not currently RedMonk customers.

One comment

  1. We should probably create the “Data 100”. List the top 100 public companies by the relative value of their datasets. I suspect TWTR would be in a nice position.

    Is return on data more important than return on total assets now?

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