by Melba Kurman
The “tech transfer health index” is a simple but powerful technique to quantify the impact and productivity of the entire long tail curve of technologies in a university’s IP portfolio. Here’s why we should adopt it. When I worked in a university technology transfer office, we spent a lot of time pulling together performance metrics. We had 14 different reports, each with its own subtle nuances and unique methodologies. Needless to say, despite our best efforts, our metrics didn’t reconcile well over time and unintentionally gave the impression that our tech transfer office was somewhat, uh, creative in our accounting. The problem, however, wasn’t just accuracy.
Our metrics missed the mark because they didn’t reflect the whole story: we counted mostly technology activity in the head of the long tail curve of distribution – the high-earning technologies, new startups, and issued patents. However, most staff time was spent managing “tail” technologies – filing provisional patents, marketing technologies, keeping on top of licensees who weren’t paying their bills, putting on events, and processing all types of agreement-related paperwork. Another limitation of our approach was that we counted all commercial licenses the same way, regardless of their associated impact or revenue (of course revenue is not a perfect proxy for impact, but lumping together anything with a signature on it created a meaningless and distorted depiction of our performance). Finally, we tallied metrics in our own, idiosyncratic way that was hard to explain to outsiders, so even our AUTM metrics could not be easily compared to those from a different tech transfer office.
Enter the tech transfer health index. I got the idea to create a tech transfer health index in a conversation with a faculty friend. I was describing the university commercialization RFI responses I’ve been reading. A common theme amongst responding universities is their quest for for performance measures that would 1) focus on more than just revenue from ”big hits” 2) better convey the activity of their entire set of active licenses from the high earners all the way down the tail, and 3) indicate the large amounts of invisible and unheralded staff time and labor that’s an essential part to marketing and managing an IP portfolio. In addition, though not mentioned by university respondents, based on my experience, effective metrics should be hard for tech transfer offices to interpret in unique ways, or unintentionally “game;” watertight metrics would increase stakeholder confidence in the TTO’s transparency.
Turns out that faculty have found a solution. Most universities now use a performance evaluation technique called the H-Index to measure the impact and productivity of their faculty’s scholarly work. The H-index is most commonly used in the context of counting the number of times a particular researcher’s papers have been cited by their peers. Before the H-index, tenure committees simply tallied up the total number of citations but did not consider their value and distribution. The H-index was created in response to flaws inherent in the traditional citation-counting method. Tenure committees discovered that (like a home run “greatest hit” technology), a researcher could claim a large number of citations, but not reveal they all came from a single paper, a “one hit wonder.” Also, (kind of like counting large numbers of provisional patents or low-value license paperwork) a scholar with a lot of citations could be basing her count off of several papers that were cited only once or twice, a sign that while she wrote a lot of papers, none of them had a significant impact on other researchers.
The H-index can be applied to assess the health index of university IP portfolios. Calculating the tech transfer health index is easy. I’ll bet you already have data on how much revenue each patent has earned over its lifetime. Use that data for your first health index analysis to evaluate how diverse and well balanced your licensing efforts are.
- Dig up the spreadsheet that lists the revenue earned by each patent (patents are a cleaner data point than technologies since they’re a finite IP unit).
For example, in the diagram above, this tech transfer office’s health index is three. So this office has three patents that each earned at least $3,000 over their lifetimes. Of course when you chart your own health index with real data, your numbers will likely be much larger.
So how are you doing?
If you chart your portfolio and discover a long tail curve that’s very steep, your office is relying on a few patents that are earning most of your revenue. In other words, a low health index. Or, you may have a low health index if your long tail curve starts low and stays flat. A low flat curve indicates that your tech transfer unit is licensing a large number of patents but not getting a lot of revenue back from them. It’s not necessarily bad to not earn much revenue (after all, getting technologies out the door and into use should be the ultimate goal). However, a low, flat curve indicates you may be spending a lot of time and money on paperwork. However, an upside of quantifying a low health index of this type is that you can prove that your unit is managing a large volume of essential but unappreciated long tail-related paperwork.
You have a high health index if — like a productive and impactful researcher — your long tail curve starts high and gently curves downward. This means your office has found the right balance between impact (high earning home runs) and productivity (large numbers of low-income licenses). Congratulations!
Here’s the value of using the health index:
Rewards real tech transfer activity, not just fees: Conventional ways to increase revenue such as charging high fees or striving for a home run license will not improve your health index. Instead, the health index improves only with consistent and long term licensing activity over a broad spectrum of technologies.
Promotes true economic development: Your tech transfer office will have better ammunition with which to convince university administrators that there’s value in getting and maintaining a large number of low-revenue licenses from ”tail” technologies. You can now quantify more than just high-revenue licenses.
Makes it possible to compare large and small universities: Tallies discriminate against small universities. The tech transfer health index makes it possible to directly compare universities that have very differently sized IP portfolios.
Get credit for a well-rounded licensing portfolio: Your health index will confirm that your office is doing justice to the entire long tail curve of available technologies. You can point out that the large volume of low-earning, low-visibility patents and licenses may not earn a lot of money, but your office is effective in meeting the essential purpose of the Bayh Dole Act, to get technologies out the door into use.
Versatility: The health index is versatile. Instead of patents, on the horizontal axis, one could plot other finite IP assets such as technology disclosures or startups. On the vertical axis, instead of using dollars, one could use other values such as the number of web hits for technology disclosures, or for university startups, capital raised.
Widely applicable: the health index can scale up or scale down. It can be used to assess the performance of a single licensing officer, a group of universities, or an entire geographical region (innovation cluster), or an industry segment such as biotech or nano-scale manufacturing.
Easy to use in public: If the names of the patents, technologies or whatever you’re analyzing are removed, it’s possible to publicly and safely share your unit’s health index results.
Assess internal operations: You could use the health index as an internal management tool to figure out how efficiently you’re managing various aspects of your operation-related activities.
In the unlikely event that someone were to interpret their metrics in a non-standard way, the health index would be harder to manipulate than standard straightforward tallies of new licenses, new startups, etc. However, realistically, no metric system is game-proof. For example, some researchers attempted to game the H-index by creating Citation Clubs where they set up fake “journals” with their friends and aggressively cited one another’s low quality papers.
Consider how hard it would be to set up something like a Citation Club in a university tech transfer office. A tech transfer director, desperate to create a good impression on his higher-ups, in theory, could create a “Startup Club.” He could incorporate several “fake” startups (kind of like sham journals) that are wholly owned by the university. Next, this director could “negotiate” several licensing deal with himself (kind of like having his friends cite his articles) and put himself on the startups’ board of directors (hooray, another award on the CV!). He could assign a tech transfer office employee to be CEO of the startup (despite zero revenue and no product). Voila, in one fell swoop, this hypothetical tech transfer office could enjoy an increase in revenue, more licensed technologies, plus a few additional new startups. But realistically, no one would do this. Even in the unlikely event that someone created a “Startup Club” to improve their performance metrics, the Club would be promptly dismantled by the powers-that-be.
If you have estimated your health index, I’d love to hear how it went. Is anybody willing to share their actual data with me?
TOOL: Thanks so much to the person who created the Excel tool that calculates the health index. Some people had problems with the zip file so I put the tool into an older version of Excel and now it will download as a proper file, not a zip. You can download the tool HERE. It makes you a chart and calculates your health index.
Melba Kurman writes and speaks about innovative tech transfer from university research labs to the commercial marketplace. Melba is the president of Triple Helix Innovation, a consulting firm dedicated to improving innovation partnerships between companies and universities.