9 Lessons Universities Can Learn From Tesla and Wall Street: Part 3

Strategy
9 Lessons Universities Can Learn From Tesla and Wall Street: Part 3
By Arnie
January 20, 2021

In Part 1 of this three-part series, I discussed how several megatrends in organizational models and technology have impacted other industries, as well as how they will affect the higher education industry. During a time of drastic change and disruption, it is vital for colleges and universities to reevaluate their current strategies, processes, and software systems, and to implement a plan of action that will prepare them for the post-pandemic future. Higher education institutions would do well to learn lessons from Tesla and Wall Street.

In Part 2 of this series, I discussed how data has enormous value and why it’s important for universities to maximize the benefit of their alumni engagement data rather than give most of its value away to social media companies. The decreased costs of capturing and analyzing data will allow universities to obtain significantly more information about their alumni. In addition, the shift from silo-based to holistic alumni engagement will enable multiple university departments to simultaneously benefit from alumni engagement insights. 

In Part 3 of this series, I discuss how development will increasingly work together with prospect development as co-equal partners. Tighter integration between the two departments will allow for higher productivity and more dollars raised, as the data experts of prospect development know how to leverage the latest technology to mine data. Moreover, gift officers need to focus not merely on collecting more data, but also on obtaining the right kind of data, in order to successfully move a prospect through the pipeline. 

Drawing upon thirty years of experience with Silicon Valley, technology, and Wall Street, I believe that the following can be a roadmap for optimizing the fundraising value chain. 
 

Valuing Data More & The Rise of Prospect Development

In parallel to the massive rise in technology capabilities in the 2000s, there was a significant shift in trading room dynamics on Wall Street. In years past, financial engineers (quants) crunched the numbers in the back room and acted as service providers to the traders, who worked on the front line with clients. Increasingly, however, the quants were placed on the trading floor, sitting next to the traders as partners. 

Over the past three decades, the financial industry has transitioned to rely more and more on data and computations. I believe that the higher education industry will follow suit by increasingly incorporating AI, machine learning, predictive analytics and sentiment analysis as standard practice in advancement. 

Although prospect researchers understand the value of data, not all fundraisers seem to share that same view. At a past APRA conference, one prospect researcher lamented that a Development Officer had ignored their research and said, “Just give me the prospect’s phone number and I’ll call them.” 

I’m a ‘people person’ too, but I believe DOs who ‘wing it’ will fade away, just like the ever-shrinking number of floor traders on the stock exchange. They will be replaced by DOs who embrace all of what technology enables prospect development to provide. Gift officers will be more effective when they leverage data-rich quantitative and qualitative prospect profiles that can now be achieved cost-effectively and at scale.

At the 2020 APRA conference, several speakers spoke about ways to improve communications with gift officers. Tellingly, at the 2020 CASE conference in San Francisco, I did not hear any speaker mention the reverse — that is, how gift officers can improve communications with prospect development. However, I believe that current megatrends will change this dynamic.

In the future, I see the two departments working as partners in the same way that quants are partnered with traders on Wall Street. Forward-thinking frontline fundraisers will embrace this change because it will be in their self-interest. They will have more ‘tools in the toolbox’ to which they can apply their experience and creativity. 

Megatrend: Companies are increasingly adopting hybrid models where relationship builders and data analyzers work in tandem.

Lesson #7: In the future, prospect development will become tightly integrated with development as co-equal partners rather than service providers, which will allow for higher productivity, increased success, and more dollars raised.

The Right Data, Not Just More Data

Not all data is equally important. Just collecting more quantitative data, such as wealth information, is not the answer. The future of fundraising lies in uncovering qualitative data. 

Quantitative data has its uses, such as for prioritization through wealth screening. But quantitative data isn’t sufficient to move a prospect through the pipeline and convert them into a realized donor.

Even within qualitative data, distinction needs to be made between shallow data and deep data. Examples of shallow data are knowing that a prospect reads Time magazine or likes funny cat videos on Facebook. On the other hand, deep data is knowing that a specific faculty member had a significant impact on an alum’s life.

Shallow data can be used in predictive analytics models to determine a prospect’s likelihood to give. However, it is not actionable by Development Officers because it does not connect the alum to the university’s mission and priorities. As every DO understands, just because a prospect is wealthy and a computer algorithm says that the alum has a high likelihood to give does not mean that they will give. Major gift fundraising requires the hard, long work of building relationships. Gift officers need deep data that provides actionable insights for cultivation.

Qualitative data needs to be layered on top of quantitative data to provide the DO with deep insights into ‘what makes a prospect tick.’ The right qualitative data can increase the probability of a successful ask, increase gift size, and increase gift velocity. 

Quantitative data gets a prospect into the pipeline, but qualitative data moves them through the pipeline.

Megatrend: Data is the new oil.

Lesson #8: Instead of more data about what’s in a prospect’s wallet, gift officers need more data about what’s in a prospect’s heart and mind. 

 

Optimizing the Fundraising Value Chain

Tesla’s successful, data-driven approach can be applied to alumni engagement as well. Just as Tesla continuously monitors its vehicles over time, your university should keep track of alumni’s changing interests, needs, priorities, and goals. 

The high school senior applying for admission, the university student, the alum, and the donor are all the same person, just at different points in their life. Having the same approach for alumni at vastly different life stages would mean ignoring the “data updates” occurring in their lives.

To maintain a strong relationship with each individual throughout all of those life stages, the university must adopt a data-driven, hyper-personalized model. Prospect researchers understand how current technology can provide a personalized experience for each alum. Moreover, they understand that access to the full range of data that comes from personalized engagement has massive potential value for fundraisers and the university as a whole. Just look at the market valuation of social media companies like Facebook to see how valuable that data really is.

The rate-determining factor for successful fundraising is the amount of time available for a Development Officer to move a prospect through the pipeline. The ideal is for a DO to devote all their time to developing a deep relationship with each of their prospects. This is challenging at the best of times, and even more so in an environment of headcount freezes, budget constraints and social distancing.

This is why technology is so critical as a force multiplier. If technology can be leveraged to free up the DOs’ time, then gift officers can cultivate more prospects in less time. 

Too often the focus is on absolute return on effort, instead of also considering the relative return on effort. With that framing, the question becomes not “Where can I add value?” but “Where can I add the most value?”

Gift officers need to ask themselves where they can have the greatest impact along the fundraising value chain. Is it discovery? Is it uncovering actionable cultivation insights? Is it filling out impact stories in contact reports? The goal is to identify those tasks that can be accomplished using technology. Undertaking this exercise will free up more of their most precious commodity — time — and enable them to focus on where they can make the biggest impact. 

Megatrend: Thirty years ago, hardware was more important than software. In today’s technology value chain, software is more important than hardware.

Lesson #9: The post-pandemic environment requires a reexamination of the fundraising value chain. Prospect development — the data experts — know how to leverage the latest technology to mine both quantitative and qualitative data. Tighter integration between development and prospect research will enable gift officers to focus on the higher value tasks that result in more dollars being raised.

 

Conclusion

It is important to note that megatrends are not sudden, unpredictable phenomena, like the current pandemic.

Megatrends may be difficult to discern; however, the signs are always there. A common mistake is to assume that a megatrend must be ‘obvious.’ Unfortunately, by the time a megatrend is ‘obvious,’ it may be too late.

That was the fatal error for many people during the 2004 Indian Ocean tsunami. Many beachgoers ignored the low wall of water moving towards them because they assumed that a tsunami would ‘obviously’ be a giant wave. 

In the technology industry, the shift in value from hardware to software is a megatrend. A megatrend in the automobile industry is the shift away from fossil fuel combustion engines to electric vehicles. 

Tesla was founded in 2003, but the megatrend that led to its success actually started in 1970. This was the year in which the first Earth Day took place — thus signaling the birth of the modern environmental movement — and the Environmental Protection Agency was established. To many observers at the time, the megatrend they portended was no more obvious than the faraway ripple of a tsunami on the horizon. 

The megatrend gained further momentum after the OPEC oil shock of 1973. The rise of Japanese automakers in the 1970s and 80s was due to their ability to adapt to the oil crisis by producing smaller, more fuel efficient cars. 

On Wall Street, the expression “dead cat bounce” is used to describe a temporary recovery of a stock or bear market that is followed by a continuation of the downtrend. Universities should be careful not to mistake a post-pandemic recovery bounce for long-term recovery. Megatrends don’t disappear; they only build and develop over time.

Lower oil prices in the 1980s and 1990s did not change the underlying megatrend of environmental awareness and climate change. However, General Motors killed off its production of electric vehicles in 1999, citing lack of demand. This is reminiscent of other unfortunate predictions, such as 

I think there is a world market for maybe five computers” (Thomas Watson, President of IBM, 1914-1956) 

and 

There is no reason anyone would want a computer in their home” (Ken Olson, Founder of Digital Electric Corporation, 1977).

Forward-thinking universities will be well-served to examine the current environment and understand the positive and negative megatrends that they are in the midst of. There are threats, but there are also opportunities. Now is the time to embrace meaningful change rather than superficial adjustments. 

Recap

Here are all nine lessons that higher education institutions can learn from Tesla and Wall Street:

Lesson #1: Megatrends are like waves. Learn how to spot megatrends so that you can ride them. Don’t wait until the wave crashes over you.

Lesson #2: When the assumptions in your world drastically change, you have to take bold, not incremental, action. It’s important to distinguish between activity that has the appearance of ‘doing something,’ but in reality only deflects attention away from making the tough strategic changes that are really needed.

Lesson #3: A gift officer’s ability to develop a long-term relationship with a prospect is their ‘software.’ Reimagining alumni engagement so it provides them with actionable cultivation insights maximizes their value.

Lesson #4:  Make sure you get the cookie and not just the crumbs. The entire value of your alumni community’s engagement belongs to your university, not a social media company. 

Lesson #5: Technological advances have dramatically decreased the cost of acquiring and analyzing alumni data. Advancement needs to go far beyond capturing quantitative data and augment it with qualitative data and online behavioral data.  

Lesson #6: Leverage technology to automate the process of collecting alumni-generated content, analyzing it, and ensuring that development and other departments can readily access the specific content that will best serve each of their objectives.

Lesson #7: In the future, prospect development will become tightly integrated with development as co-equal partners rather than service providers, which will allow for higher productivity, increased success, and more dollars raised.

Lesson #8: Instead of more data about what’s in a prospect’s wallet, gift officers need more data about what’s in a prospect’s heart and mind. 

Lesson #9: The post-pandemic environment requires a reexamination of the fundraising value chain. Prospect development — the data experts — know how to leverage the latest technology to mine both quantitative and qualitative data. Tighter integration between development and prospect research will enable gift officers to focus on the higher value tasks that result in more dollars being raised.

Disclaimer: I own Tesla stock. I bought it several years ago when it was priced at $28 per share (post-split equivalent). At the time of writing, its stock price is $800. (Prices have been included to make it clear that this article is not based on hindsight bias.) Transformational companies often experience extreme volatility in their stock price. So, Tesla’s share price could be dramatically lower at the time of reading. Nonetheless, I stand by my thesis that, among other reasons, Tesla’s data-driven approach, which values software over hardware, will result in their long-term success.

Wall Street