“Each day I move toward that which I do not understand. The result is a continuous accidental learning which constantly shapes my life.” — Yo-Yo Ma
Seasoned growth-oriented CEO/President in the cybersecurity, mobile, and cloud software technology arenas. Strong focus on marketing products to the enterprise and governments. Significant international business experience. Creates growth thru organic and inorganic means and has created superior shareholder returns and several exits.
One of your many jobs as CEO is to make the company a safe place to debate and to moderate (and participate) in those heated discussions. You will also likely have to teach others on the team how to debate given our current dependence upon technology which has resulted in a lack of in-person communication with a high desire for harmony. I find that many startups today are filled with people who are extremely conflict-averse and require encouragement and mentorship to add value to the many debates.
Think about the never-ending set of debates you had in the beginning when your tech startup was simple… What problem did we solve? Who else was having the problem? How big of a problem was it at the time? How did others solve the problem? What was our value proposition? How did we reach those customers? What were the must-have features versus the nice-to-haves? Did a certain feature prove product-market fit?
John Wooden–“Men, this is how you put your shoes and socks on.”
Like John Wooden, you are going to have to teach your team the basic elements of debate… Possibly use the example of the premise your company was founded on… For example, your company was probably built on an idea (a claim), with supporting evidence that provided an inference that your claim was true—these 3 elements were the foundation of your company’s first debate. If you deconstruct Airbnb as an example (see pitch deck) you will see that the idea (claim) was that the world needed a web marketplace platform where users could rent out spare rooms (to make money) to travelers (that would save money and share in the culture of the city) so they didn’t have to stay at high priced hotels. The supporting evidence offered to the investor was that at the time there were 630K users on the temporary housing site couchsurfing.com, 50K temporary housing listings in the USA per week on Craigslist, 2B trips booked world-wide each year, 560M online trips (Serviceable Available Market) all inferring that with events, partnerships and Craigslist dual posting, an easy to use web marketplace with a host incentive would allow them to acquire 10.6M trips & 200M in revenue. You can imagine how the debate might go with the typical ventral capital group…
Help the team see the variables within
The claim is the statement that the person on the team is making and wants their audience to accept.
Supporting evidence is a set of ideas the audience accepts as true. This evidence provides the foundation for acceptance of the claim.
The person making the argument wants to move the audience from what they believe (the supporting evidence) to what they don’t yet believe (the claim). The magic happens when the audience discovers the connection between claim and the supporting evidence–The discovery of this connection is known as the inference.
Then help them understand where the complexity of the debate begins… Supporting evidence is usually a claim to be proved. For example, how do we know ‘Price is an important concern for customers booking travel online’–This seems obvious but how do we know this is true?
Sometimes it’s easier for the team to see the framing of the
With this context, you can teach your team how to frame arguments and also poke holes in a weak hypothesis. In framing, they need to know how to take the argument as deep as it needs to go by being prepared for counterpoints.
They also need to be prepared for the generic “bomb” on all the other reasons this argument does not make sense. For example if a generic counterpoint is made that is not in reference to a supporting point–the team needs to be prepared.
When you look at an argument visibly and go several layers deep on each and every claim and counterpoint you might start realizing how many critical decisions are getting made in your company with assumptions versus with the right level debate or all the necessary data.
As the team gets better at internal debates they will learn the difference between descriptive (definition of things), relational (the relationship between things), and evaluative (value of things) arguments and how to break down or support each using an efficient strategy.
You can use tools like 5 whys and the 6 Thinking Hats as great ways to open your team’s eyes up to both the depth of an argument, as well as, the efficiencies within the argument. Another great tool is the use of Precision Questioning where you can help your team know what’s expected in regards to supporting evidence to back up claims. The basics of Precision Questioning starts with how to use different types of questions as follows:
No-Go: Why is now the right time for this idea (claim)?
Clarification: What do you mean?
Assumption: What assumptions you are making?
Data: What’s the quality of the supporting evidence?
Causes: What’s causing the opportunity (a problem in the market) that your company will address with its solution?
Consequences: What happens if you are successful? What happens if you are not successful? What are the possible side effects? What are the opportunity costs?
Actions: What should really be done to solve this problem? What specific time-bound steps will everyone take? How does this align with other initiatives?
…and most important of all, ensure your team knows that what works as a debate tactic in politics (fear, lying, bluster, vulgarity, innuendo, and refusal to admit your wrong) does not work in a startup and you as the leader/moderator of most early debates can’t let it into the discussion.
I’ve seen a few B2B revenue-producing tech companies recently that still refer to themselves as “startups”, yet they have been in existence for more than 5 years and are barely break even. The co-founders are tired and when asked, ‘why they still, consider themselves startups’, they all say ‘it’s because we’ve not found friction-free growth’.
According to Paul Graham, “a startup is a company designed to grow fast.” In this context, these companies are not ‘startups’—they are struggling companies that probably won’t make it once the co-founders are too tired to do the jobs of 5 others.
These companies all tell the same story “we are considering raising (more) money”–Yet their current investors (if there are any) won’t write the check, and new investors just see a struggling team with a struggling product in a difficult market.
All the companies I am referencing fall into quadrant ‘D’ in the following graphic–They require a complex sales cycle yet have a market price-point that is below a scalable/sustainable threshold. They were shooting for ‘friction-free’ sales when they started the company but took the revenue as it came in to pay the bills and then the weight of the customer base made them tactical—and now they are stuck with little ability to ‘pivot‘ into a momentum stream.
So, what is the solution?
The company either needs to find a way to ‘pivot’ into another quadrant (very difficult at this stage when there is not much money to invest) or build a professional services team around the domain. Most tech company co-founders didn’t set out to build a professional services organization simply because the valuation of a product/SaaS company is so much higher. However, to realize the gain of many years spent becoming an expert, the company likely has the unfair advantage of both ‘a keen understanding of the customers’ unmet needs that only a professional can supply’ and ‘knowing the people in the market to hire that are qualified to fulfill those needs’.
This will likely become an even bigger issue over
the years for 2 reasons:
The big platform providers (Microsoft, Google, Facebook, Apple, Amazon) dominate so much of the technology value chain. Today’s startups are being pushed higher and higher into the top of the chain (into the vertical) and professional services are a necessity to enable ROI at this level.
The future driving forces of technology/productivity are no longer mobile, social, apps and SaaS… They are open-source, decentralization/crypto, IoT, AI/ML, 5G, identity, quantum, no-code, serverless, microfluidics, AR/VR, robotics, voice, genomics, 3D printing, eSports, battery/power and drones—ALL of which require major investments to get to a quality Minimum Viable Product (MVP) versus the small investments of the past.
Recommendation to new founders—read Rework prior to registering the
company. It will save you many wasted years
If you knew these last 3 recessions were going to happen, how would you have prepared? Will you be prepared for the next recession? Does it matter?
The reality is that if you believe in capitalism and stick to a diversified market portfolio OVER TIME you will be alright—afterall, a quick look at the history of the Dow Jones Industrial Average ($INDU) should put your nerves at rest…
…BUT… what about all the crashes and bear markets of the past… Could you be better prepared? …AND… what about all the news (from very smart people like Ray Dalio) that the world has changed! The founder of Bridgewater Associates, thinks we’re back to the late 1930s. In an Aug. 28 LinkedIn post, as well as recent television interviews, he described three similarities between today and the decade that brought us the Great Depression. According to Dalio, if the economy begins to slow, these similarities may “produce serious problems.”
…AND… what about news from people that have predicted doom and gloom in the past successfully like Michael Burry (of “The Big Short” fame) who recently said: “The dirty secret of passive index funds — whether open-end, closed-end, or ETF — is the distribution of daily dollar value traded among the securities within the indexes they mimic.” “In the Russell 2000 Index, for instance, the vast majority of stocks are lower volume, lower value-traded stocks. Today I counted 1,049 stocks that traded less than $5 million in value during the day. That is over half, and almost half of those — 456 stocks — traded less than $1 million during the day. Yet through indexation and passive investing, hundreds of billions are linked to stocks like this. The S&P 500 is no different — the index contains the world’s largest stocks, but still, 266 stocks — over half — traded under $150 million today. That sounds like a lot, but trillions of dollars in assets globally are indexed to these stocks.” “This is very much like the bubble in synthetic asset-backed CDOs before the Great Financial Crisis in that price-setting in that market was not done by fundamental security-level analysis, but by massive capital flows based on Nobel-approved models of risk that proved to be untrue.”
You get worried… You ask, what if the world has changed? Just look at all this nationalism… that’s new right?
…BUT… there is all this news about the Yield Curve Inversion and you think… maybe you can prepare your portfolio for this… after all if you look at the last 3 recessions of 1990, 2001 and 2007 and overlay the difference between the 10-year and 3-month Treasury rates on top of these 3 recessions and see how it compares to where we are today (September 2019)—yikes, we are sitting at 37.93% and it seems like many past recessions were predicted when the probability was approximately 30%– The $INDU index is currently ~27,200. (note: The NY Fed has some history on using the difference between the 10-year and 3-month Treasury rates to calculate the probability of a recession in the United States twelve months ahead. There are some practical issues with this model outlined here. )
So, you call up some experts and ask them and they say: “The yield curve inversion does not always lead to a recession. In fact, economist Ed Yardeni has noted that an inverted yield curve can occur prematurely. For example, it turned negative a couple of times during 1995 and 1998, but a recession did not officially begin until March 2001. Therefore, we don’t think a recession is certain and more data is needed to make an intelligent decision.”
You say to yourself—JUST CALM DOWN and remember what John Templeton said, “I never ask if the market is going to go up or down because I don’t know. It doesn’t matter. I search nation after nation for stocks, asking: ‘where is the one that is lowest priced in relation to what I believe it’s worth?'”. You get it, Microeconomics is the study of individuals and business decisions (supply/demand, labor costs, production costs) and that is what is important for investing…
…BUT… more Macroeconomic news about Monetary Policy (Fed cutting rates & printing money), Trade Issues with China, Wealth Inequality (In 2016 the top 1% shared ~40% of the wealth versus ~25% in the 1980s), US GDP and Dept being way out of whack.
But you get comfort in remembering that Warren Buffet never listens to economists.
Then you calm down again and realize you are not a trader… you are an investor and you are going to have trust in capitalism and your long-term investment strategy.
I’ve been asked a few times what I meant by the phrase “Street Smarts” that I mentioned in Action #3 in the note titled “Your next 5 months as CEO” so I am going to go into more detail below given its importance…
I’ll use a version of the DIKW Pyramid that I modified to illustrate. I find that looking at the pyramid in this way bridges the gap between Information and Wisdom given the wide use of CRM and Machine Learning solutions available today (Note that I added 3 new categories to the Pyramid: ‘Background Knowledge’, ‘Prediction’ and ‘Intuition’).
Regarding the weather example in the graphic below: Machine learning can be used to ‘Predict’ the weather but this is not really ‘Wisdom’ (knowing the right thing to do) and we are also likely a few years away from a computer having ‘Intuition’ (something that one considers likely from instinctive feelings rather than conscious reasoning). The key differentiator, and what I have referred to in the previous article as ‘Street Smarts’, is the ‘Background Knowledge’ that encompasses a general understanding of how the physical world works, how human motives and behaviors work and knowledge of common facts that an average professional working in this field possesses. It’s the experience and knowledge of success, and potential difficulties and dangers of the vertical. This usually requires a lot of experience and it’s why your best employees almost always have great situational awareness.
Now, let’s relate the Pyramid to the case study outlined in the previous article.
Getting ‘Data’ on the housing market such as After Repair
Values (ARV), tax and mortgage records is quite easy. Overlaying where your company has been
successful on to that data turns it into ‘Information’ and provides you with
the ability to build Algorithms that you can easily program into your CRM and
Marketing Automation systems as ‘Foreground Knowledge’.
As the new CEO you must grow the business and that requires you to choose new markets to push into. Can you rely on ‘Foreground Knowlege’ analytics to predict those markets? Maybe, but you won’t know how to be successful in those new markets without a thorough understanding of the ‘Background Knowledge’. As a side note… This always reminds me of the following quote:
Everyone has a plan until they get punched in the mouth
– Mike Tyson
As a new CEO it is very important for you to separate the ‘Foreground Knowlege’ from the ‘Background Knowlege’ used to make decisions at every stage of the real sales process.
In our case study, the ‘Foreground Knowlege’ was straight forward… If there was a low probability of recession in next 6 months, and a property’s ARV was between 150k and 500k, and the property had been owned for more than 10 years, and the owner was behind on their mortgage or tax payments then the property may be good to purchase, fix & flip. This would be very easy to code into a simple formula to program into the CRM. The data was also very easy to come by given all the history available on past transactions. As an example:
However, what criteria for decisions were the employess really making? When asked, they were considering items such as — If the property was in an area that they had worked in before, would the job require permits, would there be a lot of capital at risk, was the location within 50 miles of a disposition manager, would it be fixed and listed in early summer or spring (best times to sell), was the owner temperament pleasant, and was the property vacant — only then would they consider it a good purchase, fix & flip.
These items are a bit more difficult to assess from available market data, but required if you are to have any ‘Wisdom’ about the items required to grow the market or to succeed in a new market.
This example outlines only one of the many decision points that needed to be understood to determine if a new market was appropriate and how to setup that new market for success.
Warning: If you leave it entirely up to the data people in your company to guide decisions you will likely fail. Only you can get the company to truely understand the ‘Background Knowlege’ (Street Smarts) required for success by asking a lot of questions. Consider using the ‘Five Whys’ technique.
As you deconstruct each of the stage gates in the company’s real sales process you will hopefully gain the ‘Intuition’ required to know when the market is changing and how to react to those changes.
Good luck! —a bit of luck never hurts when you are a new CEO.
You have now been the CEO for 30 days and you followed the
process outlined in the article “Your First
30 Days As CEO” as much was possible. You understand your inherited
business and you are not impressed. But
you realize you would probably say this about every business you go into repair—because
someone else created the mess. Guess
what? It’s now your mess because you are the CEO. You pause, then realize that every business
you will repair will have both a different set of complex challenges yet the
same 2 causes: Poor
leadership that led to poor management (never forget… Leaders deal with management
Most complex challenges however start with revenue – it’s
been flat or going down… Why? Your first month of digging into the data
should give you what you need to understand the causes. It may be due to brand issues, lack of focus,
lack of good market analytics, lack of rich customer prospect data for
targeting or generally just not keeping technically up with the market… It may
be due to being out done by competition or poor sales management, lack of
product market fit, quality, customer satisfaction etc. It may also be due to lack of investment,
making the wrong strategic bets or lack of strategic planning. Who knows—there are way too many issues to
outline but I am confident in saying that the skill of (or lack of…) leadership
had a big part to play…
There are 5 Actions below to help you stay strategic at this stage—none or all may be applicable to your situation, but my hope is that they help you take your strategy to the next level. I’ll use a recent experience (repairing a real estate wholesale business) to provide some color on how I used each of these actions.
Case Study Background
My team was brought into the referenced company by the co-founders. The company had a well-known regional brand because it ran localize television commercials for 15 years and transacted in over 3 thousand properties. However, the company was struggling to grow, and other competitors were coming into the market using the same model this company pioneered and taking away market share. The company built their business with both wholesale and Fix & Flip transactions.
Wholesaling real estate is when a company puts a distressed home under contract with the intent to “assign” that contract to another buyer. The wholesaler doesn’t plan on fixing up or selling the property and never takes ownership of the property (hence no debt is leveraged for property acquisitions). Instead, they market the home to potential buyers for a higher price than they have the property under contract for.
In contrast, Fixing and Flipping real estate consists of buying a property that needs repair and fixing it up before reselling it for a profit. The “fix & flip” scenario is profitable to investors because the average homebuyer lacks the time and funds for repairs and renovations, so they look for a property that is ready to move into. Also, most traditional mortgage lenders require the home to be habitable with no significant repairs.
Every city has a set of real estate investors—just Google “buy home for cash” and you will find several in your area. The industry refers to most of these companies as “bandits” because of the “bandit signs” that they use for marketing. However, the company I am referring to here was past the ‘bandit’ stage regarding their maturity.
The real estate wholesale sub-vertical is fascinating
because it is simple (real estate transactions) yet so complex given it’s a 2-sided
marketplace with multiple stage gates on both sides. The most interesting thing is that success-at-scale
is nuanced and difficult to see. The vertical
is also unique in many ways:
Ways to make money: Beyond the obvious strategies of wholesaling, Fix & Flip and Fix to Rent there are people making money in many unique ways given the financials involved and the number of transactions. There are people making money teaching ‘how to invest in real estate’ seminars, running master-mind groups such as https://thecollectivegenius.com/, selling data to investors such as https://www.attomdata.com/, selling technology tools like vertical CRM solutions to bandits, making loans, offering title services—this list is long.
Debt: A pure wholesaler never takes ownership of a property hence they take on little to no debt; a company doing Fix & Flip transactions usually has to take on a small amount of debt to take possession of the property, afford the construction and pay the utilities/taxes while they own the property. However, a company doing Fix to Rent transactions usually has to take on a great deal of debt.
Breadth of Communication: To be good your organization must be able to communicate effectively at all ends of the spectrum… On the Disposition side (selling properties) the team must be able to communicate across a spectrum—from construction employees with 9th grade educations to MBA types at hedge funds. On the Acquisition side (finding properties to put under contract to wholesale or purchase) the team is dealing with all types of hard personal issues (people going through recent divorces, dealing with uncurable diseases or the recent death of a family member) and mental states (many sellers are hoarders and are embarrassed by their state of affairs and the last thing they want is for many people to visit their homes).
Results of our first 30 days
So, we built the scorecard referenced in the “First 30 Days” article … If you were to look at the company as a ‘lifestyle business’ that would throw off enough cash to make the founders comfortable then more items in the scorecard would be yellow (At Market) but we were brought in to turn the organization into a GROWTH company and from this perspective it was A LOT redder than most… There were times when I personally wondered if a growth-oriented turnaround was even possible, but the one thing the company had going for it was that it was bringing in revenue and covering expenses. …and in the business of repairing organizations–revenue heals many wounds. There were also some bright spots in one of the most important categories—people.
One of the most useful deep dives from the “First 30 Days” article was the “Sales: Do a deep dive on sales stages” exercise. When evaluating how the organization worked (and eventually comparing it to how it should work) we did a deep dive on the organization’s sales stages (none of which were in the outdated CRM—the current CRM was used to capture the opportunity notes, some call logs, contact info and if the opportunity was alive or dead). Here is how the organization really worked: marketing programs were used to find sellers of properties that fit a profile (~70% of the ARV minus repairs), those sellers are nurtured through a sales process and based on the sellers circumstances (need to sell in the next ~30 days) a decision is made to purchase or assign the property. Then a purchase contract (with an expiration date) is created with the seller. This is how inventory is created and is referred to as the ‘Acquisition’ process. Then the property either gets marketed to buyers list (wholesale) or fixed up and listed on the MLS (fix & flip)—this is referred to as the ‘Disposition’ process. If you have ever purchased or sold real estate, you know how complex the process is and all the unexpected fees that take a bite out of the transaction (every state has different laws and fees). When we evaluated the stages we were amazed by how many hands touched each stage of an opportunity and the business logic used at each stage (most of which was in people heads versus algorithms in a system). Here is a list of the real stages and what we eventually built into the new CRM:
Over the course of the first 30 days I personally filled at least 3 notebooks, but I only have time here to outline my summary notes. Here is what I wrote down on day 30:
In today’s world where tech is inexpensive, and DATA IS EVERYTHING.
When you see an organization that is not deep into harvesting and leveraging
data (about its industry, its suppliers, its customers, it’s competition, and
most important itself) you should worry. This is basic 101 for growth companies—if
this is not part of the company’s DNA then it’s toast. … and this company:
Was not using data to drive their marketing (targeting)
Could not track marketing spend to results or
doing any type of A/B testing
Was not using computer algorithms to make
Could not track how and why people perform or didn’t
Could not track how long key process were taking
within the company to find bottlenecks and issues
Did not have consistent approaches to on-boarding,
off-boarding, compensating, or managing employees
Was not capitalizing on symbiotic revenue
Could not tell at any moment in time where the
company was on its P&L or budget
…and most importantly did not really understand the
variables of success. They didn’t
understand the variable that made the big companies in this space successful. …and
at a micro level they didn’t understand the variables that made them successful
with their suppliers and customers (if profit was the only variable of success)
so those variables could be scaled and replicated. I’ll refer to this as street
smarts. Most execs kind of know
what these variables are but only the successful execs can crisply articulate
them and more importantly make them programmatic KPIs that drive systems and
They were also committing the #1 sin of good old management 101—consistent
listening and communicating…
Revenue was flat because the co-founders basically mismanaged the opportunity. Over the course of the last 15 years (time this company was in business) other entrepreneurs-built companies in the same vertical worth well over 1B. So, step 1 here was stabilization and getting the company to Par (basic 101… rebuilding the company DNA), step 2 would be growth…
Actions: The Repair Begins
You will find that there is always going to be a great deal
more to do than you can afford to do in order to turn the organization the
right direction and light its fire. You
will need to pick your battles and prioritize the items with the maximum return
but the least cost. One challenge may be
that one item requires another item, so you must choose wisely. Remember, Leaders
deal with management shortfalls…
Action #1 – Establish a
consistent protocol for transparent/honest communication with employees
Your employees have the best ideas… they know how things
should be run… or at least they know how things should not be run… there
needs to be a consistent forum where people can state their mind without
fear of retribution. You as the new CEO
need to own and moderate this forum.
Repeat back what you heard at the last discussion, talk about how the
feedback has shaped your thinking or actions or why the company is not going to
proceed down a certain path. In the company
we are using as a case study here we were small enough where we could do mandatory
Friday lunches and bring in other offices via zoom meetings. We went around the group (much like a SCRUM)
and asked people to speak about good/bad issues from the current week, plans
for next week and blockers that may inhibit effectiveness. Your job as the CEO is to make sure that the
team is being honest and open and talking about hard issues. You must participate in this forum as well
and speak to what was good/bad for you this week and what your plans are for
next week and what the blockers may be that might inhibit your effectiveness. Obviously
with a bigger team you must be creative in your approach however the need is
the same—be consistent, listen, use the data or be honest why you are taking a different
path, communicate plans and positive/negative company performance. BE HONEST…
People want to work for a stable company where they are
treated fairly, where they have a future, where they are heard and where their teammates
and work are intellectually stimulating.
They also want to know that they are fairly compensated for their hard
work and the more they perform the more they make. It’s your job to create this environment if
you want the company to scale.
Action #2 — Track, measure
and automate success
If a growth company is to scale and leverage technology and
automation at all levels it must invest wisely in the platforms that run the
company such as the financial system, the CRM, the Call Telephony Integration
(CTI) system, the marketing automation, document management, analytics etc. and
the systems need to be tightly coupled yet flexible enough to quickly change
with ever changing business processes.
With our reference company the current CTI, CRM and document management/electronic signing systems had to go, and much more sophisticated platforms needed to be brought in. We chose Salesforce.com, RingCentral & HelloSign and built out the object models and business logic to map the company’s real stage gates. This was not a cheap or easy undertaking, but it was a necessary step to get the fundamentals of the company to work at scale. Widgets and reports were created to show everyone the performance of the company at the macro level (how’s the month looking, the quarter, the year—based on the pipeline and probabilities) and the micro level (how an individual was performing & how they compare to their peers (# deals, margins, type of deals etc.)). Notifications were created for handoffs between Acquisition, Disposition and finance teammates and partners. Reports were automated for opportunities with issues or business processes taking too long to complete. Automation was created if checkboxes were checked (example: automatic customer or supplier emails at certain stages with pertinent status updates).
Your company’s technology platforms must work for the team versus being a tool for management and they must be mobile. API’s need to be programmed in to prepopulate data (in real estate as an example, API data from companies like ATTOM), the systems should algorithmically help with the decisions and no communication, action, decision, event, spending, result should go untracked/measured from the creation of inventory to the sales of inventory to the support of customers and performance of partners. There is a reason that Salesforce.com purchased and tightly coupled Pardot (marketing automation), Datorama (marketing intelligence), Tableau (Analytics) and made an app exchange that allows third parties like RingCentral and HelloSign to integrate seamlessly into the platform—it’s because customers, partners and suppliers expect this from all companies.
With tightly coupled systems you will be able to determine:
What is the performance of all levels of
marketing spend (cost per lead)
What percent of leads turn into discussions
How many times the sales team needs to reach out
to a lead
What times and mediums (SMS, call, email etc.) work
or don’t work to contact a prospect
How fast does a lead needs to be contacted
What percent of discussions turn into appointments,
what causes them to and not-to tun into appointments
What percent of appointments turn into contracts,
what causes them to and not-to turn into contracts
What percent of contracts turn into revenue,
what causes them to and not-to turn into revenue
Of the leads that turned into revenue, what was
the lead origination? How much was spent on marketing and overhead to acquire the
This list can go on forever, but I think
you get the point…
Action #3 – You should
now clearly understand what makes the company successful and why (street
smarts). It’s now time to make it programmatic and then do more of it.
When we asked the employees of the referenced company “when we were successful, why were we successful?”, we heard -“we are on TV”, “we have the best SEO and are the first to show up on a Google search”, “people know our jingle”, “we have the best BBB ratings and Google/Facebook ratings”… but they really didn’t know because they didn’t ask the people they purchase houses from or the people they sold houses to. They didn’t measure the attribution of a lead—did the customer see the TV ad… then do a Google search… then read a direct mail flier… or was there an article they were reading that made them fill out a form on the website? The reality was that they were successful on the Acquisition side because of the companies honest and ethical track record; and they were successful on the Disposition side because Assignment buyers thought there was enough margin left in the property for a construction company to make a profit if they performed the Fix & Flip.
When we asked the employees of the company “what motivated sellers to sell to us?”, we heard –Divorce, Disease, Death and Financial Distress…. This always came up in the initial call as the seller usually discloses it on their own—now with a good CRM you can record this and measure if its accurate and how much of it is driving sales—or is something else we don’t understand driving sales this month? However, the team was correct–these sellers are motivated to sell in the next 30 days because of a life changing event and they don’t want to deal with a realtor because they don’t want people visiting the house and they don’t want this to take time and they don’t want to spend money fixing the issues with the property. We also found out how much empathy for the sellers situation was important–afterall this was likly one of the sellers biggest financial transactions and it was coinsiding with a life changing event… and that would be overwealming for anyone.
Once we understood these very basic success variables:
We started talking to data providers like Experian, ATTOM, Quantarium etc. for target marketing, we tuned sales scripts, we reflected how we solve the pain in content for SEO/PPC (adwords) and began to tune messaging.
We built algorithms into the CRM to help the Inside Sales Rep (ISR) determine a go – no-go decision for an appointment. This all started with the After-Repair Value (ARV) minus a high-level repair estimate minus some fixed costs to see if an Assignment is plausible; Then it looks at the mortgage to see if the seller would get anything out of a transaction. Then it factored in what the seller was expecting out of the transaction. This is all done in just a few minutes and automated using simple algorithms programmed into the CRM.
We built out other algorithms to specify the deal type and range the Home Visit Specialist (HVS) could offer the seller and enabled the Seller to electronically sign the sales contract on a tablet that the HVS brings with them to the meeting. Those docs then get automatically stored in the CRM along with the pictures of the property.
Then we went
a bit deeper and asked the employees, what makes a great property to purchase
versus assign, they said the following:
Probability of recession in next 6 months under 30% = 0 (good); else = 1 (bad) (use this link)
Correct ARV (predictability of pricing) – commodity area we know w/more than 3 examples = 0 (good); non-commodity area = 1 (bad)
Accuracy of Rehab — commodity house w/non-permit rehab = 0 (good); permit rehab = 1 (bad)
Amount of all up-capital risk (Cost of managing bills, loans, paperwork, insurance, maintenance over contract length) is <30k = 0 (good); else = 1 (bad)
Location within 50 miles of a dispositions manager = 0 (good); else = 1 (bad)
Time or year for list/exit early summer spring = 0; late summer & winter = 1 (bad)
Owner temperament pleasant = 0 (good); else = 1 (bad)
Property vacant yes = 0 (good); else = 1 (bad)
It’s easy to
turn such a list into an algorithm in a CRM with a weighted risk score that
tells an HVS to move forward with a purchase or not.
Once the deal
type was locked (Assignment or Fix & Flip) and the sales contract was
signed then Dispositions would be notified by the CRM and either marketing or
construction began—either way different processes in the CRM needed kicked off
and different costs needed to be tracked in the financial systems.
A good CRM with tightly coupled tools and well thought out business processes is worth its weight in gold—but it is also key to survival in todays business world and is the price of entry (at Par). However, for true growth, algorithms need to be built that work for your company based on the street smarts that made you successful up to this point.
“If you can’t explain it to a six year old, you don’t understand it yourself.” –Einstein
Action #4 – You should
also now clearly understand the characteristics of a company in your vertical at
the next stage of success. It’s time you
begin acting like one of those companies!
Who are the companies at the next stage of success? It’s hard to even call them competitors
because it’s likely you don’t see them because you don’t act like them. However, the exercise of the “First 30 Days”
should have made you aware of who the next stage companies are and what characteristics
they possess. For this reference
company the biggest characteristic that identified these companies was the
number of transactions per month (properties purchase/sold).
However, these stage III companies approached the market
differently and these nuanced characteristics would be the most important items
to understand if we were to move the company from point A to point B.
If you talk to the companies at stage II you will find that they all believe that the most important thing to get right is to buy the property at the right price and if this happens there will always be a buyer (If we get the Supply right, then the Demand will follow). This may be the truth; however, it is a very unsophisticated way of looking at business and blinds you to the next stage of success. Companies at stage III understand the Demand and then work to find the Supply. It’s a nuance but a very important nuance that would drive big change in how a successful company in this vertical operates. This required the company to track where buyers transact, what kind of properties they were interested in, what range of pricing, how much construction they were willing to do, how much money they were willing to bring to the closing table, how often they purchased properties etc.…. It also required the company to hire a sales team to constantly speak to those buyers. It also required the company to market to find new buyers that may be interested in future transactions. All programmatically done with marketing automation, a tightly coupled high end CRM and a new buyers marketplace portal built on Heroku (yes, another Salesforce.com platform).
How hard will it be for you to get your new company to
operate like one of the sophisticated players in your vertical? What KPI’s do they care about and how do
those differ from the KPI’s your team currently monitors? Figuring this out, acting on it and having it
pivot the company’s business practices may be one of the keys to your success.
Action #5 – You should also
now understand your customers inhibitors of purchasing—It’s now time to remove
those barriers and if possible, create symbiotic revenue streams and gain competitive
intelligence as part of your business model
With the reference company we found that the 2 biggest inhibiters to a wholesale buyer purchasing a property from the assignment inventory was:
How much room was in the deal for the buyer to
make money—hence we were transparent with our math (sales price + repairs =
How much money the buyer had to bring to the closing
table—hence we started a lending business (symbiotic revenue stream).
This new lending business would also provide a great of intel into how the market is working in general given the buyers were also getting loans to purchase other competitor’s properties.
What are your company’s biggest inhibitors to purchasing
your products/services? Could you be in the business of solving for those
bottlenecks? If you were in that business, how much competitive intel would that
provide you on your market?
Action #6 – Remove the bad apples, stop doing things you shouldn’t be doing,
double down on the things that are beginning to work
You may be able to do this earlier, and it may be a
necessary action to contain costs in order to reinvest in change. Just be consistent in your communication with
the organization and be transparent with what you are changing and why. By removing bad apples, I mean people. Once you start performing the bad apples likely
weed themselves out versus need to be told to go… Especially if you are leveraging SCRUM
to manage your new business.
In the referenced company—many of the low performers left on their own and new people with a growth-oriented outlook joined the company and those new high performers naturally made everyone else want to perform at their very best. I don’t know if the referenced company will ever be a growth company, but we did provide the co-founders a foundation for them to build a growth-oriented company. My fear for them is that poor leadership led them to this place and if they don’t get this piece right, they won’t exist in a couple years.
Every company is different and has its own challenges. You would not be there if it wasn’t for
leadership and management challenges that probably caused many other issues
limiting stability and scalability. Your mission is to quickly fix the leadership
void and then start managing the change required to gain stability and scale.
Never forget, leaders
deal with management shortfalls. Good luck!
I see a lot of people still speculating on Bitcoin using “technical indicators” and talking about “support levels” and throwing around charts based on things like the “Elliott Wave analysis”. This just doesn’t make sense to me… I don’t believe you can analyze Bitcoin like equity or a fiat currency (yet). We are way too early to use such an analysis. Bitcoin’s maturity is like gold during Emperor Augustus’s reign (30 B.C.) where he set the price of gold at 45 coins to the pound.
Bitcoin today is essentially using 1 token for both
rewarding the supply side and the demand side (to consume the service). For a
crypto economy (the network) to work it may ultimately require 2 tokens (an
“asset token” for the supply side and a “payment token” for the demand side)
–much like there is in today’s fiat economy.
Bitcoin’s 1 token approach, in theory, is great because for the supply side to provide its service it has to take payment in a combined token, so it has to be in the user’s hands for them to consume the service, so this creates the pressure to sell the combined token as the network grows. Everyone in a crypto network gets to participate from the value created instead of just one segment. The net result is that a user can’t be a passive accumulator of capital, they must be active for the economy to work. But unfortunately making users investors is asking them to understand the infrastructure. Theoretically, it would be great if users had skin in the game because they would have an emotional connection to the infrastructure–But that’s not realistic or scalable. Power users can participate, but it’s a choice.
In the real world, the users of a system don’t want to take
any risk. For example, when you get into
a taxi in NYC you don’t have to worry if Uber is going to have any impact on
the value of your taxi medallion. Humans
don’t want to think about the risk of ordering pizza with tokens that may
eventually be worth millions of dollars.
It’s just simpler for human nature. The 2 tokens must be separate
because the transaction velocity of capital is very low, and the transaction
velocity of a currency is very high so as an economy grows exchanging gold or
land (a barter system) is just not going to work. So, we need currency
(separate from capital) to accelerate economic growth. Reminder: Capital
appreciates with economic growth (it’s scarcer) and currency depreciates with
economic growth when we print more of it to keep up with inflation to keep
Unfortunately, without good governance, this leads to an economy where the people who acquired the asset tokens early become increasingly concentrated over time as the economy grows. Governance is how do we manage, control and manipulate the data to find a single source of truth.
So, it’s early. Real
early. To determine value this early you
need to keep an eye on the layers and how capital/currency, supply/demand are
interacting and most importantly how governance is evolving between layers (one
example, how to fund the development of the base chain) across each base layer
currency. …And in the end, the base
layer (the store of value) will become a commodity and a lot of the value moves
up the crypto stack (so why waste any time doing old school chart analysis on a
layer that will eventually become a commodity—that is if it is successful at
all—and given the power of Central Banks that may also be questionable…).
“Governance comprises all of the processes of governing – whether undertaken by the government of a state, by a market or by a network – over a social system (family, tribe, formal or informal organization, a territory or across territories) and whether through the laws, norms, power or language of an organized society. ….” – WikiPedia.com
Technology changes everything… and you can’t stop it.
Technology is mostly good yet sometimes bad but you can’t stop progress. Technology enables better health outcomes, it allows people to be more informed and better educated, it allows for easier and more affordable communication, it allows for automation and productivity increases… (the list is long) but just as important technology can produce negative unexpected outcomes.
Technology’s unexpected negative outcomes in the past had less potential to harm society than what is upon us today and in the near future. In the past technology disintermediated companies… In the future, technology has the opportunity to disintermediate society-hence, GOVERNANCE IS THE FUTURE <<–hint, hint, entrepreneurs there is a business opportunity here.
Governance – The Past
Up to today, governance frameworks that have had an impact on how technology is used have mostly been left up to industry groups with some government oversight and mostly due to data security concerns (PCI-DSS, HIPAA, NERC, FISMA etc..) but now countries are mandating good governance as well (GDPR being one example) primarily due to privacy. Farther down in the technology stack there are many governance organization – you can find that lineage here for the Internet as an example. However, there is little from these past frameworks that can prepare us for the future and unfortunately, our governing body (US Congress) is ill-prepared for the job but I do think we will be OK because of the governance vacuum will create an opportunity for entrepreneurs to fill.
Something Fundamentally Changed – Every Company Is Now A Technology Company
Up to now, technology has been thought of as an enabler of productivity… a driver of capitalism… but unfortunately, many companies still believe their IT team is responsible for technology. Many still believe their executives need to understand technology but it’s their IT teams job to enable it and Securities job to lock it down… they still believe their exec’s jobs are revenue & profit (selling more widgets, increasing the margin of the widgets they sell and finding new widgets to sell) … but something fundamentally changed–Every company is now a tech company (here) (here) (here) – Disintermediation due to technology hasn’t (and will not) stop. The power of data required good CEO’s to turn their companies into technology companies–but ready for this: now you may become irrelevant because of what you do… or how you are structured to perform what you do…
Learning/Robotics is here now and will drive a wrecking ball through some major
job categories. Blockchain-Cryptocurrency economics’ will change how companies
are built and how value moves between entities. CRISPR-CAS9 will produce
incredible healthcare outcomes and quantum computing presents unimagined
breakthroughs. These technologies are
more powerful than anything society has ever witnessed.
How Artificial Intelligence will change the world (here).
How Blockchain-Cryptocurrency will change the world (here). Corp structure (here).
Governance – The Future
“The real problem of humanity: we have paleolithic emotions; medieval institutions; and god-like technology… and it is terrifically dangerous, and it is now approaching a point of crisis overall.” – E. O. Wilson
The governance of Artificial Intelligence has been more challenging as there is not a great framework to call upon. (here) (here). AI as it relates to justice, data quality and autonomy involve identifying answers to questions surrounding the safety of AI, what legal and institutional structures need to be involved, control and access to personal data, and what role moral and ethical intuitions play when interacting with AI. When a citizen’s life can be shaped by algorithms who is in control of monitoring those that created the algorithms and the outcomes of such algorithms? In the past, we’ve seen machine learning racially profile bias, unfairly deny individuals for loans, and incorrectly identify basic information about users. The development of AI governance will help determine how best to handle scenarios where AI-based decisions are unjust or contradict human rights.
‘The problem is that currency and capital respond differently to economic growth. Capital appreciates with economic growth and currency depreciates with economic growth as an economy grows. Inflation is commonly thought of as the printing of new money but really it increases in prices over time as a result of economic growth. Capital as an asset type appreciates as the economy grows and is more scarce than currency. We print more currency to keep up with inflation to ensure stable prices hence it depreciates with economic growth over time. What happens over time is that capital becomes more concentrated and we have a lot of people living their lives in currency and only a few living their lives in capital. In crypto if we combine these into a single asset (Bitcoin) we don’t get the same income inequality or wealth inequality however by separating the access token (work token) from the currency token we risk the people who acquired the asset tokens early on may become concentrated over time as the economy grows.‘ – great A16z podcast on the subject
I read this @USAtoday story and had to laugh “The House Democratic Policy and Communications Committee is hosting a session Thursday morning with Ocasio-Cortez of New York (@AOC – 2.42 million followers) and Rep. Jim Himes of Connecticut (@jahimes – 76,500 followers) ‘on the most effective ways to engage constituents on Twitter and the importance of digital storytelling.’”
I’m center-right politically, and I don’t agree with some of @AOC’s views on issues, however, I do have a great deal of respect for her leadership abilities.
As a side note, I don’t disagree with what Himes is quoted as saying, “The older generation of members and senators is pretty clueless on the social media platforms”. Just review the senate’s embarrassing questions at the Zuckerberg hearing and you will see plenty of “clueless” senators … However, Congress is totally missing the point in regard to @AOC’s momentum and it will bite each of them during their next election cycle if they don’t wake up—The point is @AOC is showing leadership—watch and learn!
Congress–your constituents are people… not objects!
@AOC is listening and talking to people—Twitter is just one of her preferred communication tools. Here is the point–Many of today’s politicians tend to treat constituents as ‘objects‘ versus ‘people’ with hopes, dreams, and pains. Remember back when Bill Clinton engaged with a person directly at a 1992 town hall meeting—he talked with people. @AOC resonates because she is having a conversation with ‘people’.
Members of Congress–stop labeling people (i.e. ‘deplorables’, ‘Trump’s base’, ‘the Democrats’, ‘the Republicans’, ‘Men’, ‘Women’, ‘Black’, ‘White’, ‘Hispanic’, ‘LGBTQ’ etc..), fight for what you believe is right for your ‘people’.
Congress—we don’t want ‘managers’ we
‘Management’ is about systems, processes, policies, and resources (what all those federally appointed officials manage daily…) but ‘leadership’ is about vision, inspiration, values, and people. Leaders deal with management short-falls– Basically, leadership is required when the systems and process do not work…. Leadership is required when the policies are not applicable or do not exist… Leadership is required when there are not enough resources to accomplish the task…
In 2019, being an effective member of Congress requires you to have an open dialog with people, take a stand on issues you believe in, simplify complicated subjects and educate others, build consensus regardless of party and admit when you are wrong (and don’t take credit when you are right).
Regardless if you agree with @AOC or not… learn from her because she is showing you what we expect from members of Congress in 2019.
There is going to be a lot of debate over the next 2 years about healthcare and moving to a single-payer system. Democrats are talking about “Socialized Medicine”… Republicans are talking about “Free Market Healthcare”–as an example, here is a good article that articulates a good one-sided argument without many deep recommendations. …But what are the facts?
I’m building the following notes to start capturing data to help me formulate my opinion on the subject. I’ll use a “5 Whys” strategy to think through the issues.
5 Whys is an iterative interrogative technique used to explore the cause-and-effect relationships underlying a particular problem. The primary goal of the technique is to determine the root cause of a problem by repeating the question “Why?”. Each answer forms the basis of the next question. The “5” in the name derives from an anecdotal observation on the number of iterations needed to resolve the problem.
Let’s review the
The United States has the poorest population health outcomes. For example, we had the lowest life expectancy (78.8 years compared with a mean of 81.7 years).
If you compare the U.S. to the top 11 other countries in the world we are out of balance:
We know a “free market” healthcare system won’t work for a simple economic reason: Healthcare demand will always outstrip supply and this imbalance will always create a wide (and ever-widening) economic gulf. However, we don’t have a “free market” system today—we have a mix… We have a Medicare, Medicaid, VA, Fed / DoD, and Indian Health Services System… we have the so-called “free market” system primarily sponsored by employers and then we have the 10s of millions of un (or under) insured citizens…
So, what are our goals:
Wait! What about being the leading innovator in healthcare? What about driving more of the conversation away from “sick care” and really toward “health care” – that’s going to require entrepreneurism and a free market to innovate and capitalism to support… The reality is that this isn’t an easy fix… it’s quite complicated and anyone involved in the argument needs to know the details.
So what are the “5 Whys” of the healthcare debate?
The first two “Whys” are easy…
We know that the United States spend 17.8% of GDP ($9,403 per person) on healthcare when Canada, Germany, Australia, the U.K, Japan, Sweden, France, the Netherlands, Switzerland, and Denmark spend 11.5% and all have better overall outcomes (i.e. life expectancy as an example). Why?
We know from the JAMA study mentioned above the high U.S. spend is because Physicians earn more in the U.S., Administrative costs are higher in the U.S., and general prices for pharmaceuticals, procedures, and tests (example: MRI) are higher in the U.S.. Why?
Here is where it gets complicated… We need to dig into each of these:
The number of slots supported by Medicare (who pays for most residency slots) has been frozen for two decades after Congress lowered it in 1997 at the request of the American Medical Association and other doctors’ organizations.
What could policymakers do?
Fund more residency slots.
Allow Medicare to limit the slots for certain areas of specialization to control supply and demand.
End the requirement mandating that foreign doctors complete a U.S. residency program and allow them to complete an equivalent residency program in another country or allow foreign-trained doctors to practice under the supervision of a U.S.-trained doctor.
Allow nurse practitioners to perform more procedures that they are qualified to complete.
The reliance on multiple payers (Medicare, Medicaid, and many private insurers, all who each have their own set of procedures and forms for billing and collecting payment) drives up the costs. The American health system offers a lot of choice among health plans. This all causes physicians to spend on average 3 hours per week addressing billing-related matters, medical support workers spent an additional 19 hours per week on billing-related matters, and administrators spent a total of 36 hours per week on billing and collection matters. Why?
We are only at the beginning of creating interoperability and data standards for healthcare. There is a great deal that has been done and a lot on the table. It’s a very complicated issue but well understood. More here, here, here, here and here.
What could policymakers do?
Legislate strict electronic data standards (provider example) for interoperability and transparency.
Legislate standard electronic billing and collection policies.
General prices for pharmaceuticals, procedures & tests (example: MRI) are higher in the United States? Why?
Other countries negotiate with the providers and set rates that are much lower. In Canada and Britain, prices are set by the government and in Germany and Japan providers and insurers come to an agreement or the government steps in. However, in the United States health-care providers have considerable power to set prices, and so they set them high. Why?
In the U.S., health care delivery and payment are fragmented, with numerous, separate negotiations between drug manufacturers and payers and complex arrangements for various federal and state health programs (more). And, in general, the U.S. allows wider latitude for monopoly pricing of brand-name drugs than other countries are willing to accept. Why?
Two of the most profitable (and powerful) industries in the United States are the pharmaceuticals and medical device industries. (It is, however, true that Medicare and Medicaid negotiate prices on behalf of their members and purchase care at a substantial markdown from the commercial average prices.). These powerful industries have pushed back on government policymakers why try to legislate setting overall spending levels for payments to providers & drug makers because it would impair their revenue and profit growth.
Other countries may also have policies that result in new drugs and medical technologies being adopted more gradually. (more)
Other countries have more friendly legal environments to challenge the validity of patents. Why is the US different?
This article shows how numerous patent filings (“patent thickets”) are used to drive up the costs of Humira by AbbVie Inc. in the US as an example.
Create legislation to counteract patent thicket practices (note: Sen. Susan Collins of Maine has called for ways to counter such practices but there has been no policy put forward).
“How many businesses do you know that want to cut their revenue in half? That’s why the healthcare system won’t change the healthcare system.” Rick Scott – Senator from Florida
Let the federal government negotiate lower drug prices for Medicare beneficiaries. This would shift the U.S. policy toward a more centralized pricing system like that used in other high-income countries. Currently, the Veterans Health Administration and the Department of Defense are the only federal entities allowed to effectively negotiate directly with drug manufacturers; they pay prices that are roughly half of those paid at retail pharmacies. (more, more) RISK: Too much legislation may make our pharmaceutical sector less attractive to investments resulting in less innovative and effective drugs in the future.
This is a work in progress so I will add more as I research and learn.
Health care is a misnomer for our medical system–It should be called sick care. Doctors mostly make their money when we are sick. What if doctors really could prevent disease? —well they can, but you need to be prepared to do the work because disease prevention is about:
Lifestyle (what you eat, your weight and how much you exercise—covered here)
Keeping great medical records (don’t get me started on a. doctors keeping paper files, b. doctors making it difficult to get your medical records (push them) and c. electronic medical record systems having different formats for the data (more))
Documenting and understanding your genome (DNA)
This set of notes will dig into “4” – Your genome! My hope is to explain this subject in a way where you can understand how to get your genome data, view it at a high level, view the details and begin to understand the interworking’s of your genetic makeup so you understand the value of leaving your ‘sick care’ doctor behind and finding a true personalized ‘health care’ MD).
Step 1: Have your genome mapped
There are many low-cost direct-to-consumer DNA mapping sites and this linked article will explain a few options for you to consider (here is another). I personally like 23andMe ($199 USD) because it does a great job of explaining DNA to a novice and a professional, they seek FDA approval, and the site allows you to download your data.
Let’s first cover a few standard definitions to make sure we
are all on the same page:
DNA (deoxyribonucleic acid) – A
molecule composed of two chains that coil around each other to form a double
helix carrying the genetic instructions used in the growth, development,
functioning, and reproduction of all known living organisms and many viruses.
Chromosome – a DNA molecule with part
or all the genetic material (genome) of an organism. Human cells have 23 pairs
of chromosomes (22 pairs of autosomes and one pair of sex chromosomes), giving
a total of 46.
Genes – From 23andMe, “Genes are
segments of DNA that tell your body how to function and what traits to express.
People have about 22,000 genes in their genome. Most of these come in duplicate
– one copy from your mother and one from your father. Everyone has the same set
of genes, but each one can vary by a few letters (bases) between people. These
“variants” can lead to differences in the way you look, how you
respond to stimuli, and whether or not you are predisposed to certain diseases.”
Once you get your data back from one of these direct-to-consumer
genome mapping sites you will have access to their portal. I’m going to use 23andMe as the example, but
many are similar. When you get your report, you can easily go to the ‘Health’
section and see what it is reporting. It will look something like the
2: Download your raw genome data to a safe password protected and encrypted
If you are using 23andMe you can download your raw data
instructions. If you know what you
are looking for you can also dig into your raw data here (more on this later).
But what can you do with your raw genome data?
WARNING: This is where things get a bit tricky. There
are five very important things to know:
Some sites (like Promethease) list all the SNP markers (From 23andMe, “A marker is a specific location in the genome where a genetic sequence has been shown to vary between people. Markers are denoted by a unique identifier, most often an “rs number”) associated with different traits and diseases, as curated from SNPedia. Drawing any conclusion from this reporting is often frowned upon by geneticists. There is such a thing as an SNP that is strongly associated with a disease (These are typically the ones 23andme has FDA approval to report—example BRCA1/2 The individual gene mutations BRCA1 increases the risk of breast cancer. Angelina Jolie is just one of the thousands of women who chose bilateral prophylactic mastectomy to mitigate the increased risk of the BRCA1 mutation.) but most common diseases are not really affected by any given SNP.
The best analysis uses the compound effect of many SNPs with an understanding that each only contributes a small effect. This concept is called polygenic risk scoring (PRS). This allows scientists to take anyone’s genome and calculate your aggregate risk for certain diseases even if you don’t have one of the known major mutations. Polygenic Risk Scoring is the total score of all the minor gene variations that increase disease risk. This is a powerful upgrade to your doctor’s ability to predict disease in any given patient. This means doctors are no longer in the dark with only the family history to guide them. (here, here, here and here are 4 great articles on PRS)
Be careful of companies target marketing supplements or programs at gene variants –always check with a licensed medical doctor (MD) before taking any actions.
Step 3: Mapping your raw data to the SNPedia database (but heed warning #2 above)
I am going to
use the Promethease site. A report is $12, and it can directly connect
your 23andMe DNA data with the SNPedia human
genetics wiki. It also provides information on the effects of genetic variants
on Phenotypes (the
composite of the organism’s observable characteristics or traits, including its
physical form and structure; its developmental processes; its biochemical and
physiological properties; its behavior, and the products of behavior, for
example, a bird’s nest. An organism’s phenotype results from two basic factors:
the expression of an organism’s genetic code, and the influence of
environmental factors.) and the information is sourced from peer-reviewed
scientific publications. Keep in mind that the match against the SNPedia
database may be wrong, as the raw data is not held to the same quality level as
that which is part of an FDA approved report from 23andMe.
The report only takes 5 to 10 minutes to generate and you will get it via email as a zip file and via their website. It will look like the figure below where you have a search panel on the right and the data on the left. In the example below, you can see the SNP (Single Nucleotide Polymorphism) marker is rs1333049 (From 23andMe, “A marker (SNP) is a specific location in the genome where a genetic sequence has been shown to vary between people. Markers are denoted by a unique identifier, most often an “rs number”, or “rsid”.”). You will also see the Position (From 23andMe, “If you stretched out all of the DNA in a chromosome from end to end, you could count the position of each letter (A,C,T,G) relative to the first one in the sequence. This count is referred to as a genome coordinate or position. 23andMe uses the same coordinates as the National Center for Biotechnology Information (NCBI), build 37.”). You will also see the Magnitude (From SNPedia.com, “Magnitude is a subjective measure of interest varying from 0 to 10. Over time it should be adjusted up or down by the community.” The range is from 0 (you have the common genotype) to 10 (significant information).) You probably only want to review magnitude 3 and above.
If you click on the SNP marker hyperlink rs1333049 you will be
taken to the details page in the WIKI.
From the page above on the far right, you have links to many great sites including Ensembl and 23andMe’s detail pages.
Once on the 23andMe page you can also see the Variant (From 23andMe, “At any position in the genome that varies, there is more than one possible version (or variant) of the DNA sequence. For example, some people might have an A at a certain position, whereas other people might have a T.” Genetic variations, or variants, are the differences that make each person’s genome unique. DNA sequencing identifies an individual’s variants by comparing the DNA sequence of an individual to the DNA sequence of a reference genome maintained by the Genome Reference Consortium (GRC).) and Your genotype at a marker (From 23andMe, “Your genotype at a marker is the combination of variants that you have at that position on both chromosomes’ copies. For example, if you have the A on one chromosome copy and a T on the other one, your genotype is AT. Some chromosomes don’t come in pairs (i.e. the mitochondrial chromosome and, for the most part, the X and Y chromosomes in men), so your genotype can sometimes be a single letter.”)
There are several other tools out there to get information on each one of the SNP markers. One of the best is found here at NIH.gov. With this, you can search for many research articles per SNP marker.
Now that you have all that data, please reread Warning #2 above!
Step 4: Map your data to known polygenic algorithms
These sites are reported to be working with polygenic risk scores:
Keep in mind that this is a relatively new science that has been enabled by the mapping of the human genome. The research is coming out fast. As an example, Sekar Kathiresan and his colleagues at Harvard University and the Broad Institute have been focused on variations linked to coronary artery disease, atrial fibrillation (an irregular heart rate), type 2 diabetes, inflammatory bowel disease, and breast cancer. They developed an algorithm that could use all this information on a disease’s genetic variants to produce a polygenic risk score, a single number that would indicate a person’s risk of developing each disease based on their genomic data. Their algorithm identified 20 times more people at high risk of a heart attack than did the traditional method of just looking for the variant that indicates inherited high cholesterol. If more people know they’re at risk, they can go on medication or start making lifestyle changes to prevent the onset of the disease. You can get a copy of the report here or here.
As an example, here is data from Impute.me a non-profit (please donate) genetics analysis site run by independent academics since August 2015. Their design goal is to provide analysis at the cutting edge of what is currently known and possible in genetics research. A central part of their site is the creation of a guidebook for personal genome analysis. This book provides more in-depth explanations for many of the concepts involved and it’s highly recommended as a guide to accompany your analysis. (New: Updates to the site will be announced at twitter).
Let’s go into a couple interesting things
you can do with their site. Note that I am using the text below
directly from the Input.me website.
A polygenic risk score is a value that gives a summary of a large number of different SNPs – each of which contributes a little to disease risk. The higher the value, the higher the risk of developing the disease. Of course, the interpretation of this risk depends a lot on other factors as well: How heritable the disease is. How much of this heritability we can explain with known SNPs. And not least, what would the risk of disease be for you otherwise, i.e. without taking the genetic component into account. Because the polygenic risk score is only a risk-modifier, knowledge of these three other values are all required if you want to know your overall risk is, i.e. what’s the chance in percent. This calculator cannot provide that. But it can provide a view of the known genetic component of your disease risk, based on all the SNPs that we know are associated with the disease. This, we believe, makes it a better choice for complex diseases than the typical one-SNP-at-the time analysis typically seen in consumer genetics.
If you upload your 23andMe data after a couple of days you will have access to this site and a unique ID that will be good for 2 weeks.
This is a module that
can visualize the entire compendium of human disease – at each point showing
relevant genetic findings. The goal is to illustrate how to present genetic
data depending on a medical status.
Diseases, where one mutation has a strong medical effect on you are luckily rare. For the majority of people, learning from our genes is instead matter risk modifications and weak predictions. For a healthy adult, these are typically of little practical use. However, the assumption changes drastically if you are not healthy; If you are anyway being evaluated for a given disease, it may very well be useful to know if a different but medically related diagnosis has a particularly high or low risk.
For example, if a person is suffering from mental problems, but have not yet been properly evaluated for any specific diagnosis, then genetic risk information for all diseases related to mental problems may become useful knowledge. Because the information can then serve as a guiding point in that difficult challenge of first diagnosis. Similar examples can be made for virtually all areas of early medical evaluation.
It is the purpose of the module to help with this: By forcing browsing into pre-defined sets of disease-areas, the algorithm provides you only with genetic information that is relevant to.your current medical status. Nothing more, nothing less. Risk scores relevant to the medical area you are interested in will be shown. Fluke signals from irrelevant disorders will not. The details behind all information given here can be explored in the remaining modules of the site, as indicated when you click on each of colored bubbles above. As such this module can serve as an entry-way into the entire site, depending on your context and interest.
In the root of the tree, we find ‘feeling fine’, which is always a neutral color: People who feel fine don’t need to worry about their genetic risk scores. However, when selecting ‘heading to hospital’, climbing up the tree, the genetic risk scores are revealed as they become relevant. More of the thinking behind this module is explained in this short animation-video from 2017.
The overview of rare disease variants found in this module is not the most extensive single-SNP effects available online. They are shown here because they are all well-supported strong genetic effects, for a selection of rare inherited diseases where microarray analysis made sense. This was the reason these SNPs were included in the 2016-version of the 23andme health.
Especially the last part – that microarray analysis made sense – is very important when analyzing the genetics of rare disease; the microarray technology used in consumer genetics is not optimal because the really strong mutations typically are not measured on a microarray. DNA-sequencing is required to detect them. Therefore microarray analysis of rare disease effects has many problems with false negative results. There’s a lot of further details to this discussion, chapter 3.5 in this book is a good place to seek more information.
Nonetheless, the 2016-selection of microarray-measurable SNPs made by 23andme still is reasonably relevant to report, particularly for the carrier-information. For non-23andme users, this module has the additional benefit of translating the data for proprietary 23andme SNPs, with the caveat that because the SNPs are very rare they are often hard to impute.
This is a test of a systematic approach to drug-response SNPs. Most of the known drug-response-associated genetics concern liver enzymes (e.g. CYP2C19) and their break-down of drug metabolites. These are well characterized elsewhere already. The focus of this module is to integrate systematic multi-SNP profiles beyond liver enzymes and provide estimates of drug-response.
To illustrate how this works, the module shows the calculations that take place for a number of drug response predictions, both on a per-drug level and on a per-SNP level, corresponding to the first and the second table. The first table summarizes per-drug calculation whenever possible. If possible, a Z-score is calculated in the same way as also described in the complex disease module. If not, it is indicated as ‘not calculated’. In that case, it is necessary to look at the second table for comments on the individual SNPs from the input studies. The Z-score approach takes information from many SNPs, and can, therefore, be considered as more thorough, of course depending on the underlying scientific study.
Most SNPs in the genome are not actually found within a gene: They are ‘intergenic’. When talking about a gene-mutation however, as is done in popular media, most often the meaning is a SNP that alters the sequence of a gene. Because of selection pressure throughout our evolution, these are rare. Also, they are often the focus of scientific studies using DNA-sequencing technology to discover the causes of rare diseases. However, interestingly many of us actually have these ‘gene-breaking’ SNPs while nonetheless being perfectly healthy. The imputation technology used with this site gives the opportunity to identify a number of these based on just on genotyping microarray results. If you give your ID-code to this module a table of all measured missense and nonsense mutations will be presented.
Interpretation of the table can be done in many ways and unlike other modules, this does not give ‘one true answer’. One method is to search for SNPs where you have one or two copies of the non-common allele and then investigate the consequence using other resources such as dbSnp or ExAC. Note however that the definition of ‘common’ is very dependent on ethnicity: in this browser common just means the allele most often found in impute.me-users. However, it is recommended to check the ethical distribution in e.g. the 1000 genomes browser. Another help provided is the polyphen and SIFT-scores, which can give an indication of the consequence. Ultimately the goal of this is to satisfy one’s curiosity about the state of your functional genes. If you happen to find out that you carry two copies of completely deleterious mutations (nonsense mutation) but otherwise feel healthy, feel free to contact us. By being healthy, in spite of a specific broken gene, you’d be contributing to complete our view of genes and how they work.
Thousands of mutations in the BRCA1 and BRCA2 genes have been documented. 23andMe reports data for three mutations that account much of inherited breast cancer, but other possible mutations in these two genes are not included in the 23andme report. Many can only be detected by sequencing, such as from myriad genetics. However, dozens of extra possible mutations of interest can be reached with imputation analysis. The following lists your genotype for the directly measured three 23andme-SNPs as well as all other SNPs in the two genes that are either missense or nonsense. For interpretation, we recommend reading more about polyphen, sift-scores, and clinvar.
If clinvar is indicated as pathogenic and the SNP is measured in your genome and your genotype is not of the genotype indicates as normal, then this indicates a potential problem. The list is sorted according to the clinvar variable by default.
UK Bio-bank Calculator
A study of ~½ million UK residents, known as the UK biobank, has recently been published. This module allows the calculation of a genetic risk score for any of the published traits.
Now that you have all that data please reread Warning #4 above.
Step 5. Make a plan.
If you are high risk for coronary artery disease see a cardiologist. If you are at high risk for breast cancer, mental illness, eye problems etc. see a medical (MD) specialist.
…but be wary of Warning #5 above–don’t go see a “quack” and don’t self medicate!
..but also do your homework and understand if the specialist is up to date on the latest and greatest –for example, if you see a psychiatrist for ADHD make sure they are trained in epigenetics.