Sieben Möglichkeiten, wie sich für Microsoft der Kauf von Linkedin auszahlt

Übernahme:

Von Thomas H. Davenport
15. Juni 2016
Getty Images

Für Microsoft ist der Kauf von Linkedin eine Investition in die neue Welt der Arbeit. Der amerikanische Wissenschaftler und Berater Thomas H. Davenport beschäftigt sich seit vielen Jahren mit der Frage, wie immer bessere Analytik das Management verbessern kann - egal ob in der Logistik oder im Personalbereich. Er analysiert im folgenden Text sieben Wege, wie Microsoft das riesige Netzwerk mit seinem immensen Datenschatz nutzen kann.

Microsoft just bought LinkedIn for $26 billion dollars, or $196 per LinkedIn share, a 50% premium to the Friday market closing price of $131. Microsoft is known for overpaying for its acquisitions, including Skype, Nokia's handset business, aQuantive, and the attempted deal for Yahoo. So how can it avoid repeating its past mistakes? Twenty-six billion is a steep price, but there is plenty of potential value in LinkedIn. In order to realize it, Microsoft should consider some of the steps below.

  • Target ads with greater accuracy and granularity. LinkedIn makes money in part from targeted ads, most of which come from employers. I just got one, for example, from the Public Company Accounting Oversight Board. The ad grabbed my photo and said "Picture yourself at PCAOB," suggesting I might like a job as an audit regulator or inspector. This has about as much relevance to me as the "Yoga Sling 2 Flip-Flop" - pitched to me on Amazon this morning - does to my fashion portfolio. I have provided LinkedIn with the employer and job title for every job I've ever had, yet it can't seem to realize that I am not an accountant. Potential employers would pay much more for ads that are really well targeted.
  • Sell career advice based on the world's largest employment database. LinkedIn has an incredible asset in the employment records of its 433 million "members." It has begun to market access to the database, primarily to employers and search firms. But putting some real intelligence in LinkedIn would allow it to provide valuable advice to members. It could say, for example, "If you had Python programming skills, you'd be 47% more likely to get a data scientist job." Of course, LinkedIn could charge for that kind of advice. It does provide free services, such as "People You May Know" or "Jobs You May Be Interested In," but the quality of such recommendations is low.
  • Validate credentials. LinkedIn asks for education and job credentials, but they're all supplied by the user. I could report that I had a PhD in nuclear physics from MIT if I wanted to, or that I'd been a NASA astronaut in a previous job (in case it's not obvious, neither is true). For its data to really be of value, LinkedIn needs to find a way to validate credentials, at least for members who are willing to pay for the validation. There's a lot of public data available that could be used for this purpose. In some cases it might even be worth working with employers or universities to validate credentials.
  • Help employers find just the right person. I have heard from several employers and recruiters that LinkedIn is only somewhat useful for recruiting: It's great for finding people you've heard about from some other source and communicating with them, but the search process is haphazard. In part this is because of the lack of credentials verification. But it's also a result of there being no clarity about what constitutes a "nuclear physicist" or "data scientist" or "sales ninja." It would be great if LinkedIn could establish some clarity around role terminology.
  • Create a LinkedInstitute. Aside from being a really cool name, an internal research institute that exploited LinkedIn data could push the company to use its data more creatively and productively. This would mean partnering with academics and external experts, giving them access to data, and both publicizing and capitalizing on their findings. As Russ Walker of Northwestern and I wrote a year ago, many different insights from LinkedIn's data could be turned into data products and sold to members or recruiters. LinkedIn could also sell data products to universities, such as "Courses that lead to great jobs in gerontology" or "Find out where you stand among talent suppliers to the P&C insurance industry."
  • Create more data products. Many of these new services I've described here might be called "data products" - new products and services derived from data and analytics that can be sold to customers. LinkedIn was an early creator of these, with "People You May Know," "Jobs You May Be Interested In," and "Groups You May Like." But the company could go much further and develop, for example, People in Your Profession You Could Learn From, How to Make Your Resume More Successful, The Credential You Need Most, and so forth. The company could also go beyond the "matching" approach it has used for its data products and create some scores - your Employability Score, Predicted Compensation Score, or Thought Leader Score, for example.
  • Make premium memberships more valuable to consumers. LinkedIn is a "freemium" service provider, with some free services and several types of paid premium accounts. The premium services probably are worthwhile for recruiters, and perhaps even active jobseekers, but they don't add a lot of value to people who are employed and happy in their jobs. Making the new data products I've described available only to premium members, and doing the same for some existing free offerings, would definitely increase revenues.

Taking these steps would require that LinkedIn and Microsoft bulk up on their analytics, data science, and cognitive technology capabilities. LinkedIn used to have great data scientists (DJ Patil, Jonathan Goldman, and Monica Rogati, for three examples), but many, including these three, have moved on to other organizations. The rejuvenation of LinkedIn under Microsoft will require adopting some of the strategies above to lure them back.

Much of the money that Microsoft has ponied up for acquisitions has been written off a few years later. The company wrote off $6.2 billion from its aQuantive acquisition and $7.6 billion from the Nokia phone business. Adding some value (and investing more money) in making LinkedIn really hum is probably the only way to avoid overpaying in this instance as well.

Zum Autor
Thomas H. Davenport lehrt als Distinguished Professor am Babson College in Massachusetts. Er arbeitet außerdem als Research Fellow am Bostoner MIT Center für Digital Business und als Senior Advisor bei Deloitte Analytics.

Artikel
Kommentare
0
Diskussionsregeln

Wir freuen uns über lebendige, konstruktive und inspirierende Diskussionen auf HBM Online. Um die Qualität der Debattenbeiträge sicherzustellen, werden unsere Moderatoren jeden Beitrag prüfen. Eine Nutzung der Kommentarfunktion zu kommerziellen Zwecken ist nicht erlaubt. Beiträge mit vorwiegend werblichem, strafbarem, beleidigendem oder anderweitig inakzeptablem Inhalt werden von unseren Moderatoren gelöscht.

© Harvard Business Manager 2016
Alle Rechte vorbehalten
Vervielfältigung nur mit Genehmigung der manager magazin Verlagsgesellschaft mbH
ANZEIGE
Die neuesten Blogs
Nach oben