Is the @Mattermark score the best NI Startup Score metric? #mattermark #data #startups #clojure

How To Measure a NI Startup?

It’s been a question on my mind for a good five years, to the point that in 2015 I bought a domain called nitechrank to collate all the data from various sources and see if it could be resold. No sooner as I started I saw that Mattermark was gaining momentum and I did the sensible thing and stopped.

The question though still remains in my mind, is there a good metric to tell me how a Northern Ireland startup is doing. One way is to ask the CEO of the company and hands down, 100% they will talk utter rubbish and say, “yeah it’s great, we’re just about to…..“. Let me make this simple, the phrase “just about to” means, “we haven’t”.

As there aren’t many publicly listed companies from Northern Ireland. Off the top of my head I can only think of two: Kainos and First Derivatives. Andor now belong to Oxford Instruments and UTV are now nested within ITV with the reaction from the action being that Julian has been removed from our screens. With listed companies there is a metric of performance and shareholder value. With a small startup there’s non of that just basic here say, hype and questionable cash flow projects.

So I’ve been thinking more and more about one metric that can tell me how a early stage Northern Ireland startup is doing. And I think it all points back to Mattermark.

All Hail The Growth Score

The Mattermark Growth score is the baseline on how a startup is looking to the outside world. It’s calculated on a number of sources but the main ones to think about are social sources like Linkedin, Facebook, Twitter and Instagram as well as the more entrepreneurial sources like Crunchbase and Angel.co.

With this information fed in, ran through a nice model and distilled to a number of key scores (Growth, Mindshare and Momentum) we have a good idea how a company is doing from an outsider’s perspective. Interaction with the brand will increase the mindshare in your Mattermark score for example.

There’s Nothing Like Exposure To Public Ridicule To Galvanise The Attention*….

If you are Northern Ireland CEO/Founder and you’re not looking at your Mattermark score then I suggest you get your skates on and watch it religiously.

While it’s all very nice with the “Our wee country” schizz, no one outside of Northern Ireland really gives much of a hoot about what you are saying but they will be keeping an eye on Mattermark, Crunchbase and other sources to see how things are performing.

In all fairness while it’s good to be on the Propel Programme and accelerators like StartPlanet NI, that the first foot up from the ground to the first rung on the ladder. That’s about getting you ready to be let out in the world and those two glorious words “investor ready”. Historically I’ve not see much gain on the hype cycle once a company has completed these types of things, just a few quid in the bank to last another quarter. And you know what, fair enough.

Back to the point, what I am saying is that your Mattermark Growth Score is your stock exchange price that others will look at, judge you on and figure if you’re worth looking at.

If we look at GetInvited’s Mattermark score it’s doing okay. A growth score of 48, okay there was a dip during the year but it’s on the up again.

Now, I and others know that GI raised money. And it’s something to shout about so get it on Crunchbase! Would that raise the growth score, possibly. Would it show other potential investors that there was enough faith in the team to put money in it, definitely. Showing incremental growth on these platforms sends out a strong message that things are happening within the company.

Basically every NI company needs to figure out a way of getting positive PR, up to date information on Crunchbase and some good hype on the likes of Angel.co to start getting those scores up. Once that happens then perhaps startups from “our wee country” will get in front of the noses that matter.

Non of this is secret, it’s just common sense.

Coming Soon….

In the next part I’ll start exploring the growth score numbers and seeing what we can learn and apply from them.

 

 

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Are mobile apps giving the bookies the edge? Probably. #secondscreen #tv #xfactor

I’ve had a hunch for a long time and I think on Sunday night it was, with a fair probability, finally confirmed. This is more a brain dump of thoughts than anything with a cohesive conclusion…. take it for what you will.

Apps Produce Data, Got That, Good.

My interest in data goes back a long time, way before apps were a thing to be touted as a startup idea. I survived the J2ME years when applications were delivered by 4cm x 4cm screen. There was a time where this….

…was radical, I was there, it wasn’t pretty.  How times have moved on. Now we have things like smartphones, bigger screens, better experience. Yes time forwarded on and the advances in mobile technology did too. Connectivity got better and storage got cheaper.

Data creation, collection and processing got scalable, cheaper and much much faster. And that’s just one set of data, start mushing it with other data sources we have a rich mashup of stuff.

So what better use of these technological advances than the XFactor. Here me out, the data gets rather interesting I think.

Voting is data and data is money

This is data collection of the highest order it’s:

• Rich data is direct from the viewer for free
• In real time, there’s a two minute window per song even per second suddenly have 120 data points in a time series across n viewers.
• GeoIP tracking, more than likely (though I am guessing) so there’s a good chance of knowing where abouts the viewer is. I see a heat map in the making.
• Plus the five free REAL votes about who gets saved in the bottom three each Sunday.

And that’s me just scratching the surface. I’m sure there’s more metrics pouring out of this thing that I care to imaging. Tellybug seem to have got the second screen instant feedback down to a fine, slick, art.

Who has the edge?

“They who have the data, have the edge” – Jason Bell, October 2016

I’ll park that there, it might come in useful one day. If you have data that no one else has then you have an edge on everyone else, simple. That might be in business or in this case, XFactor data on who’s going home.

A good example of having the edge in TV prediction markets was on the Great British Bake Off where some members of the production company, allegedly, were putting large volumes of cash against the winner from episode one. The betting was suspended as it become clear someone had the edge. To be fair as Bake Off is recorded in advance then plenty of people had the edge.

So last Sunday the bottom three were left in suspense, then some act was told they were saved, there was much surprise, shock and tears…. the rapping lady was not one of them. On Twitter though this happened four minutes after the reveal.

How did Betfred know this? The number of votes cast on the app…. a direct line to the data perhaps?

My conclusion, wrongly or rightly is this. The betting companies are buying in the data from either the production company or Tellybug and therefore gaining competitive advantage on the customers. To be honest it’s no different than any other outcome prediction. I could never figure out why the rapping lady’s odds on next elimination markets was so out there, Betfred (and others) already knew.

With the ratings system on the app a betting company can get the edge, it knows what the audience is thinking and as that audience in question is more than likely going to be voting then the odds can be generated with a fair form certainty.

Competitive advantage? Yes. Surprising? No. A good use of data? Absolutely.

 

 

 

 

Refining the Coefficient. Iterative Improvements In Learning. #data #machinelearning #linearregression

Refinement is an iterative process, sometimes quick and sometimes slow. If you’ve followed the last few blog posts on score prediction (if not you can catch up here) I’ve run the data once and rolled with the prediction, basically, “that’s good enough for this”.

The kettle is on, tea = thinking time

This morning I was left wondering, as Strictly is on tonight, is there any way to improve reliability of the linear regression from the spreadsheet? The neural network was fine but for good machine learning you need an awful lot of data to get a good prediction fit. The neural net was level pegging with the small linear model, about 72%.

I’ve got two choices, create more data to tighten up the neural net or have a closer look at the original data and find a way of changing my thinking.

Change your thinking for better insights?

Let’s remind ourselves of the raw data again.

2,5,5,5
5,6,4,5
3,5,4,4
4,6,6,7
6,6,7,6
7,7,7,7

Four numbers, the scores from Craig, Len, Bruno and Darcey in that order. The original linear regression only looked at Craig’s score to see the impact on Darcey’s score.

That gave us the predition:

y = 0.6769x + 3.031

And a R squared value of 0.792, not bad going. The neural network took into account all three scores from Craig, Len and Bruno to classify Darcey’s score, it was okay but the lack of raw data actually let it down.

Refining the linear regression with new learning

If I go back to the spreadsheet, let’s tinker with it. What happens if I combine the three scores using the SUM() function to add them together.

Very interesting, the slope is steeper for a start. The regression now gives us:

y = 0.2855x - 1.2991

And the R squared has gone up from 0.792 to 0.8742, an improvement. And as it stands this algorithm is now more accurate than the neural network I created.

Concluding

It’s a simple change, quite an obvious on and we’ve taken the original hypothesis forward since the original post. How accurate is the linear regression? While I’ll find that out tonight I’m sure.

 

 

Hey! Let’s talk about your AI startup! #AI #Startups #ArtificialIntelligence

“Jase, I’ve been told all the money is in AI now so I’m off to go and create a company…..”

Not the first time….

…I’ve heard this over the last couple of months. And said with all the same eager puppy excitement when chatbots were going to take over the world, literally the week before.

So my natural reaction, with humility and grace has been the same….

I’m not going to sit here and say, “don’t do it”, it’s not my place to. I do have my opinions though. Firstly….

The Big Boys Have Been There For Years

Yes, I’m sorry to say that the big companies have been working on this for a long long time already but because it wasn’t in the tech press no one really took much notice. Well a few of us did and even then didn’t act on it because the big boys were doing it.

So Amazon, Google, Facebook et al have been beavering away in the quiet and not really saying much. In stealth if you will…. remember that phrase, a lot of us used to use it in times gone by.

Investment Levels in Northern Ireland….

Too low, just simply too low. The plain fact of the matter, go to the US. As of December 2015 90% of the deals done in AI investment were into US companies. If you insist on staying here then close a $5m round at least. Staffing is going to be hard, real hard, and find folk who know how the numbers work. Using a API or third party potentially can hit you hard later on, remeber KISS? Keep It Separate Stupid!

Let’s talk data.

You probably don’t have it. So you need to generate it but you’re already too late unless you’re generating tons of the stuff and storing it. And words ringing in my when asked on a panel, “If I gave you £100 to invest what would you put it in”, I said put it into data mining. If you had been saving the data over the last six years then you’d have something to predict….. but you probably haven’t and for good AI training you’ll need a s**t load of it.

I’ve said my bit on neural networks a long while ago. It still stands, it’s early days and many don’t know the work needed, the big guys did and in that time they’ve got the data, the skills and the money.

What’s Your Edge?

Do you have one? The simple rule of thumb is the same as John Kelley, the Kelly formula is really simple.

edge/odds

What’s the likelihood of you getting the payday, divided by the odds against you. With over 900 AI startups invested on the planet and you having a slither of an edge.

0.01/900 = 0.000011% of you getting the payday.

I know it won’t stop you…… good luck.