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(At Best) A Poorly Pitching Data Scientist.

Troy Sadkowsky - Friday, July 13, 2012

To set the context straight away, this blog article is about pitching your ideas (not baseballs).  And as a professional data scientist you will continually be coming up with ideas, however it is rare to be in an environment where you can continually convince others that your ideas are actually good ideas.  Whether you are trying to convince investors to buy shares or trying to convince your management to allocate budget there are some core fundamentals that need to be address. 


I’ve been hanging out at River City Labs for the last 8 weeks which is a co-working space in Brisbane and when the opportunity to do a practice pitch was posted out by the founder Steve Baxter I knew this opportunity would be too valuable to miss out on.  

When the call-out email for pitchers came in on that Monday at 12:49 I must have given it less than 1 minute thought before putting up my hand.  I had opened it, read it, and replied by 12:53, and by 4 o’clock that day I was scheduled in for a Pitch to do in 9 days time!  

Plenty of time, right?  All I needed to do was find some guides, read them, draft it up and practice.  Pitching is something totally new to me, so I figured the most important part of that plan would be the practice.  So before I went home that day I had mapped out a schedule to practice my pitch to one person every day before it was time to do it in front of the full panel of highly successful people that the Labs had arranged for the event.

The problem was that it didn’t quite work like I planned.

It wasn't until late Wednesday before I’d even start working on it and before I knew it, it was Tuesday morning the day of the pitch and I’d only pitched it in front of two people.

To say the pitch went poorly is giving it way more credit than what it was due.

However, despite it being extremely nerve racking and me being way under prepared it still was a great learning experience.  And luckily, I was given permission to video it.  The video enabled me to analyse all the great feedback I got on what I didn’t do.  From the video footage of all the comments, I’ve done my best to condense it into the following 5 points.  

1.  State the current position and the future vision.
2.  Make a quantitative prediction of what the vision is worth at a defined point in the future.
3.  Present n supporting facts that indicate your prediction will be true.
4.  Tell a hypothetical story about this future vision in a way that those listening to it will be able to relate to it. 
5.  Be prepared to defend all aspects of the above points.


You would have noticed that on step three I throw in a variable n.  Working out the model behind n is a topic for another blog, however, my current hypothesis is that n is inversely proportional to the level of synergy within the group.  That is to say that the less you have worked with those you are pitching to the higher the number of supporting facts (n) you will need.

If you are interested in seeing what not to do when pitching to a room full of investors tweet me (@tsadkowsky) and I’ll show you the video.

One last thing... 

If you’re interested in learning more about how to pitch well for investors, check out Yaro Starak’s latest blog entry.  Yaro and his CrankyAds team pitched after me and Yaro - I know your pitch was great anyway but I am sure I lowered the bar for you with mine (I've got video proof).

Talk again soon,

Troy Sadkowsky


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(At Best) A Poorly Pitching Data Scientist.

Troy Sadkowsky - Friday, July 13, 2012

To set the context straight away, this blog article is about pitching your ideas (not baseballs).  And as a professional data scientist you will continually be coming up with ideas, however it is rare to be in an environment where you can continually convince others that your ideas are actually good ideas.  Whether you are trying to convince investors to buy shares or trying to convince your management to allocate budget there are some core fundamentals that need to be address. 


I’ve been hanging out at River City Labs for the last 8 weeks which is a co-working space in Brisbane and when the opportunity to do a practice pitch was posted out by the founder Steve Baxter I knew this opportunity would be too valuable to miss out on.  

When the call-out email for pitchers came in on that Monday at 12:49 I must have given it less than 1 minute thought before putting up my hand.  I had opened it, read it, and replied by 12:53, and by 4 o’clock that day I was scheduled in for a Pitch to do in 9 days time!  

Plenty of time, right?  All I needed to do was find some guides, read them, draft it up and practice.  Pitching is something totally new to me, so I figured the most important part of that plan would be the practice.  So before I went home that day I had mapped out a schedule to practice my pitch to one person every day before it was time to do it in front of the full panel of highly successful people that the Labs had arranged for the event.

The problem was that it didn’t quite work like I planned.

It wasn't until late Wednesday before I’d even start working on it and before I knew it, it was Tuesday morning the day of the pitch and I’d only pitched it in front of two people.

To say the pitch went poorly is giving it way more credit than what it was due.

However, despite it being extremely nerve racking and me being way under prepared it still was a great learning experience.  And luckily, I was given permission to video it.  The video enabled me to analyse all the great feedback I got on what I didn’t do.  From the video footage of all the comments, I’ve done my best to condense it into the following 5 points.  

1.  State the current position and the future vision.
2.  Make a quantitative prediction of what the vision is worth at a defined point in the future.
3.  Present n supporting facts that indicate your prediction will be true.
4.  Tell a hypothetical story about this future vision in a way that those listening to it will be able to relate to it. 
5.  Be prepared to defend all aspects of the above points.


You would have noticed that on step three I throw in a variable n.  Working out the model behind n is a topic for another blog, however, my current hypothesis is that n is inversely proportional to the level of synergy within the group.  That is to say that the less you have worked with those you are pitching to the higher the number of supporting facts (n) you will need.

If you are interested in seeing what not to do when pitching to a room full of investors tweet me (@tsadkowsky) and I’ll show you the video.

One last thing... 

If you’re interested in learning more about how to pitch well for investors, check out Yaro Starak’s latest blog entry.  Yaro and his CrankyAds team pitched after me and Yaro - I know your pitch was great anyway but I am sure I lowered the bar for you with mine (I've got video proof).

Talk again soon,

Troy Sadkowsky


Trackback Link
http://www.datascientists.com/BlogRetrieve.aspx?BlogID=4599&PostID=302930&A=Trackback
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