PS#091: 10 Ways to Build an Investment Thesis – Combining Strategies (Part 5)
Before we wrap this series, I wanted to give some guidance around combining these strategies.
First and foremost, as I mentioned in the overview, these strategies should be thought of as prompts to jump start your exploration. A fulsome thesis will require diving into all the details I highlight in my course.
With that being said, throughout this series, I’ve hinted at the fact that these strategies are often most effective when combined across categories.
Today, I’m going to walk through a few examples of this in practice. For this exercise, we’re going to layer in additional questions to one main question…
What data exists that didn’t exist before?
Without further ado, let’s get started.
Example #1: Outdated Solutions + Parallel Industries.
For this first example, let’s combine questions from the incumbent and market categories, specifically…
What are the old, outdated solutions?
What’s working in parallel industries that can be copied?
For this combination, the retail industry comes to mind.
For so long, the retail industry flew somewhat blind in understanding the needs of their business and preferences of their customers. Everything from inventory management to advertising was limited by the insights they had into customer behaviors.
Meanwhile, in the tech industry, every piece of data that could be collected and analyzed was collected and analyzed. And, as technology inserted itself into more and more aspects of our lives, there was more and more data that was available.
The retail industry had an old, outdated model for collecting data and understanding their customers. With the advancements in technology, there were entirely new data sets that could be leveraged to improve the entire experience, including inventory, customer service, sales & marketing, compliance, etc.
Example #2: Bundling and Unbundling + New Behaviors.
In this second scenario, we’ll combine the incumbent and customer & user categories, specifically…
What are their new behaviors?
What can be bundled or unbundled?
For this one, I’ll highlight a thesis I’ve pursued for many years around compliance.
The pace of our world has driven a higher volume of complexity for businesses around compliance and regulatory burdens, including privacy, sustainability, foreign investment, etc. The amount of regulatory and compliance burdens has been increasing almost exponentially. This has caused a strain on internal resources and a whole set of new behaviors.
One of those new behaviors is that companies want to be in control of their regulatory data.
Previously, most companies had been much less focused on managing this data, usually outsourcing the job to a 3rd party. Well, this changing regulatory environment (which would also fall into the idea of changing markets) has caused a clear change in customer behavior.
This change in behavior has opened up a new opportunity for companies to create software solutions that allow resource-constrained compliance teams to manage their regulatory data in house. These new companies have unbundled a whole set of 3rd party services, which historically included software and services, into intuitive software platforms that offer more visibility and control.
The new behaviors (and changing markets) created a need for an unbundled software stack, separate from the incumbent’s software + services model.
Example #3: Proposed Solutions + New Technologies.
For the final scenario, we’ll combine the customers & users category with changing markets, specifically…
What are their proposed solutions?
What can be done now that couldn’t be done before?
The recent advancements in AI have opened a whole new world of possibilities. Many of the ideas and proposed solutions were too limited, time consuming, or costly to really implement in practice. However, with the recent wave of advancements, many previously unattainable solutions are well within our grasp.
Just think about AI’s ability to identify, interpret, extract, structure, and understand complex data sets. These capabilities have created opportunities to capture and leverage data in ways that were simply impractical. Whether that data lies in software, documents, photos, videos, etc., there is an opportunity to create completely new approaches to solving problems.
We’re seeing this happen right before our eyes as a whole new generation of companies leverages AI to build upon entirely new (and previously impractical) data sets.
Series Summary.
This concludes our series on strategies for building investment theses.
I hope this helps provide a starting point and some structure for your thesis building efforts. In my career, I have found being able to change the prompt in some way has helped open my eyes to new opportunities. I hope these prompts do the same for you.
And of course, this is simply a starting point. There are many, many ways to do this. Find the ways, questions, prompts, etc. that work best for you.
Finally, just as a recap, we walked through 10 different strategies across 3 categories (i.e., incumbents, customers & users, and changing markets). For simplicity, the link to the previous posts are included below.
Good luck and happy thesis building.
