My Investment Research Checklist

I was talking to family and friends about various investments made (and some missed out on) over the years. Have gotten decent at picking winners (using a Value-Investment framework inspired by this book that mixes intuition, selection, and analysis). Here’s the base-level rubric for what I look into before I investment into any company. Some analyses go further. Generally, 9 months to 1 year is taken before any action…essentially driving the associations of the entity into muscle memory and relying on intuition, alongside any other company/product/market, to make the call.


  • Read on 3-6 months of articles on stock and target company
  • Read on 3-6 months of analyst coverage on stock
  • Find Market Cap potential of target company
  • Find Market Leader
  • Identify Competitor Set
  • Identify Lead Competitor(s)
  • Find Growth Darkhorse(s)
  • Identify any market regulation


  • Determine CEO capabilities and habits
  • Determine CEO’s style of communications (past 2 years)
  • Identify Product Roadmap
  • Read last 5 product announcements
  • Linkedin Lookup on Corporate/Employee Composition (specifically product/marketing/engineering)


  • Read past 10 10-Q, 2 10-K
  • Identify, if possible, Sales & Marketing + R&D spend
  • Identify, if possible, Growth of S & M + R & D spend

Would love to hear your thoughts! Feel free to DM via LinkedIn.

Elon Musk & 2018

I’ve written about Tesla’s growing community of advocates back in 2013…well, it’s now becoming real. Though is it happening because of an explicit push or something happening naturally due to increased sales?

My hypothesis is that I think Elon is being Elon and going on a major PR push in 2018.

Been tracking since February and it keeps on confirming along the way. With the Starman launch (February), Grimes (May), and “Pedo” (sigh) (July), I think there’s something going on in terms of media coverage on various issues in the US/Worldwide, and Elon Musk is either trying to take advantage of it or doing his own thing. Feel free to add the search term “pedo” to this Google Trends chart…I won’t.

Maybe this is Elon “taking control of the narrative”?


I haven’t done a multivariate analysis with Tesla’s share price, but it might be worthwhile now. Once the shorts/media started their campaign last year, it became clear that Tesla’s share price was going to be affected in a hedge-fund kind of way. I think this company should be valued, at least, at 2-2.5x higher than the current by end of 2018, if the market is accurate and corrects itself.

SJ Data Charts – Open Sourced

Thanks to some love over at Code for San Jose, SJ Charts is now SJ Data Charts and fully open-sourced here! has been fun and the documentation, as well as the community forums, have been great to work with. Wish there was more in-grained analytics support via Google Analytics and Mixpanel, but that seems to be a work-in-progress. Here’s what it looks like now:



The charts now have time-based charts for Housing Prices, Unemployment rate, and Jobs by Sector for the City of San Jose. The idea behind this app is to show various analyses via a top-level tabbed navigation from the Code for San Jose community. Basically, to build out useful analyses for the open data available from the City of San Jose.

SJ Charts

After dabbling with Pandas and asking around for better methods of building out interactivity, lo’ and behold: arrived. As a side project, I’ve been doing some data analysis on Open Data from the City of San Jose. Here’s the output: SJ Charts.

SJ Charts

It’s a fun, interactive dashboard to view Job-by-Sector data between 2008-2015 for the City of San Jose found via the source here or in a translated form, here. The intention is to expand with other analyses on this dashboard app and apply some checkbox love, but that might change depending on feedback. Looking to give back to the city I grew up in.

To give feedback, you can reach me via all the normal social channels. Github repo coming soon!

San Jose City Data Analysis Fun

So, I took a few weeks to get on-ramped with Pandas 0.23.0 and Python and am still learning the ropes. Though, I wanted to share some output. Namely, using the San Jose Open Data Portal, there were some good insights to be had from looking at economic data pushed out by the city for 2006-2016.


What did I do?


  • Looked at unemployment rates across various housing prices (condos/townhomes & single-family homes) using ordinary-least-squares regression.
  • Looked at, over time, labor force and housing price changes.


SJ Economic Changes, 2006-2016



Condo/Townhome prices vs. SJ Metro Unemployment



Single Family Homes prices vs. SJ Unemployment


What did I find? Namely, housing prices are increasing rapidly compared to labor change (i.e. job growth) in San Jose over the course of 2006-2016. Also, there’s a strong correlative relationship between something that we all take for granted and as obvious: increased housing prices in San Jose runs with lowered unemployment rates.

If you’d like to look at the data analysis done, here’s the GitHub repo. If you have any questions/comments/suggestions on more analysis, feel free to contact me via Twitter, GitHub, or LinkedIn.

Applying ROI to Prioritization

I’ve been working through a few updates on a prioritization framework for use in a feature and sprint planning session. The very nature of the sessions are to align on what a team is going to be working on with ensuring it’s appropriated towards the highest priority items. After a few recent chats with other PM’s, there was a needed sense of balance between business needs, product fit, and cost.

Here’s the latest update that aims to provide that sense of balance:

  • ROI – Return on investment of fitting product theme fit, churn
    potential, and roadmap fit relayed to a weighted average against t-shirt
  • Cost – These are the t-shirt sizes that engineering generally comes up with to assess viability of a themed story or issue.
  • Legend – It’s an updated map or legend that provides PM’s and Engineers the ability to map back specific priorities to the ROI number.

Feel free to use the template; if you find it useful, let me know. You can reach me via Twitter @aakashhdesai or Linkedin @aakashhdesai.

Thought Experiment — Public Service Matching

I’ve been on a brief hiatus for a couple of months
and have been thinking about the next endeavor. After some personal time off (even now), there was one idea that’d come to mind. The hard part about it is getting feedback from a product perspective. How do you do that in a way that garners enough attention, brings in interested individuals, and siphons the idea into something usable?

So, I’m writing this as a thought experiment to see what feedback I could garner for an app idea (and prototype). Namely, with public service infrastructure, the hardest part about getting engaged with an institution is how do you reach them and about what? There’s some movements in the Civic Tech space with Open 311, but how do you talk about moments of epiphany about positive comments or questions?


  • 3M residents in silicon valley; 1M in San Jose
  • 50 requests incoming through mobile apps; 1,000’s coming through phone
  • Inefficiency issues in getting the right feedback
  • Inefficiency in the right departments receiving the feedback


  • Customer Service Directors — Government employees who run day-to-day operations for customer service departments within departments in a municipality (both city and county). They need to quickly triage and propagate work incoming requests in an efficient and effective manner.
  • Community Relations Directors — Government employees who directly interface with the public in regards to social media and email. They need to ensure the department and municipalities are directly interfacing with the needs of their constituents and are kept happy with the service provided. They want to provide greater engagement and quality interactions with citizens and department staff on feedback and requests.
  • Residents — Citizens and individuals within specific geographic regions that depend on public infrastructure to live and perform tasks day-to-day. They need to have infrastructure to run efficiently and effectively. They want not have to interface with their public works departments, but understand that it takes feedback in order to populate how well the infrastructure is running.


  • Community Relations Directors: Customer service responses don’t go to the right departments.
  • Customer Service Directors: Customer service requests don’t have enough information to be serviceable.
  • Residents: It’s difficult to send customer service requests and feedback to public agencies.

Product Offering

Provide a mobile app experience that adds a description, location, and photo. Using a matching algorithm, provide recommendations on departments to send the request to and sends a customer service request to individual public service departments. Each request requires a verified e-mail address and, potentially, a physical address.


App (prototyped on Ionic Framework)

Distribution Strategy

First, target individual geographic regions, such as Silicon Valley, to improve on ways to match filters to recommended departments. Stick to regions with a distribution strategy of tech-savvy, civic oriented residents and focus on garnering normative samples of early adopters. Build up regions supported based off of data-informed recommendations from feedback from the user base. Then, expand using volunteer groups and customer service departments as advocates of a free mobile app
across iOS and Android.

Competitive Analysis – Briefs and Features

Everything is fair-game between qualitative and quantitative user and competitor research. For SaaS-based businesses especially, there’s a need to stay knowledgeable of the market and its competitive landscape. It’s hard enough focusing on features that bring delight to customers, but there’s also a perspective that staying even-keel within your competitive space brings on real help to your product line and the people that aim to support it.

Let’s take an example, let’s say Sally Sue is talking to a prospective customer who’s knowledgeable about multiple options available to her. She asks about X, Y, and Z features and to see if the product supports it. For the Sales rep, let’s call him Dan, he needs to be knowledgeable that not only do we have support, but the product also does support these base-line features (or at least has a number of other features that fit well for Sally). Sally just wants to know enough to make an inspired decision.

Competitive analysis for a product manager isn’t just about feature-development, but also about ensuring a holistic view of the product. For me, I like to take a qualitative view with competitor briefs and append a quantitative analysis that is feature based. It uses a weighted-value analysis with a likert scale approach to cross-match User Interest and Features across the market. On the far right, there’s a percentage column for aggregated value fit of features provided to users (AH) and a weighted value fit (AI). You don’t worry about doing any formula work, I was able to add in a weighted value analysis formula onto the sheet.

What does this help with? A lot of things:

1. You have an understanding of the competitive landscape for your particular product area or space.

2. You can share a static document to your Sales, Customer Support, or any Field team member in a SaaS model to support prospective customers.

3. This helps your tech writers and documentation team to ensure knowledge is spread throughout the organization.

4. Product development efforts are propelled to ensure your base-level feature set is known, understood, and ideated on.

Feel free to use the templates for the brief and feature-based weighted value analysis; if you find them useful, let me know. You can reach me via Twitter @aakashhdesai or Linkedin @aakashhdesai.

Thoughts on SaaS Product Pricing

Pricing is very important to the overall value of a product offering. It’s a first-order property of a product alongside its feature set that’s required to build the formula of value for any customer. In no real order, they pay attention to design, features, and price when making a purchasing decision. For pricing, it directly relates to the value that a user/customer will derive from that product.

For any product I’ve managed, I’ve been lucky enough to have pricing responsibility and owned it outright. Typically, SaaS-based organizations like to move price modeling off to finance or other groups. Product manager owns the success of the product; that means they own the price too. Just like with design and engineering, they need to work with cross-functional teams to find the right model (sales, finance, business systems, etc.). That includes customer research, modeling, and institutionalizing pricing to succeed out in the market. It’s required to set up a product and have it evolve as quickly and easily as possible with Sales and their customers.

Selfishly, I’ve been developing a price modeling and strategy template over the years. Want to share it out and make it available as a draft to iterate on. Hopefully, other PM’s out there find it useful.


  • Pricing Scenarios – Lists out various pricing strategies for a product you’re aiming to ship to a customer base. Segments out price models based off of target adoption and purpose of the pricing strategy.
  • Customer Monthly SaaS Tiered Blend – Spreadsheet input form for various MRR, Upfront, customer adoption, and top-level SaaS metrics you’ll need to accurately model and strategically align your product with its pricing methodology. Below lists the top values you’ll need to input into this sheet to model it all.
  • Tier Blend – Annualized view of key inputs and metrics set up in the above tab that applies a table-based view of the various customer segments, run-rates, and customer base growth across a five-year span.

Key Metrics (a primer on key SaaS metrics):

  • MRR – Monthly recurring revenue you expect to charge for the product applied on a tier-based view.
  • Upfront – Tier-segmented initial price to the customer for the product.
  • Total % of Customer Base – Segmentation of the customer base, in the initial year, that adopts the product.
  • Customer Base Adoption – Segmentation of the customer base, for subsequent years, that adopts the product.
  • Customer Base – Total initial count of customers expect to adopt the product in the first year.
  • Annual Growth Rate – Expected growth rate of the customer base on an annualized basis.
  • Churn – Expected churn rate of product’s entire customer base on an annualized basis.

The modeling template has helped quite a bit just looking into various models for customer research, modeling, and communications out to business teams who are ensuring its success. There’s a deeper question just how useful it is to a product manager to do this compared to just handing it off. I’ve always been of the opinion that you a pm puts their teams in the best position possible and that means to being over-prepared and know context better than any other team member. Take a look at the modeling and strategy template, let me know what you think via Twitter @aakashhdesai or Linkedin @aakashhdesai.