Question on marketing / growth strategy for my sports app

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Question on marketing / growth strategy for my sports app (http://www.850sportsapp.com). I’m about to take on investment so let’s say I had £30k to spend on growth but have a target of 30k installs, what would you do? Would you optimise CPC ads to try and get a £1 CPA (installs) or would you go for something less measureable, more risky but with the potential to hit that number with that money? If so which channels would you try?

When it’s 10 to nine and you just want to know what happened in the sports before you get into the office, 8:50 will give you a summary of everything you need written by sports journalists within a simple, beautiful app.
850SPORTSAPP.COM

Three key marketing takeaways to ensure positive revenue

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Three Key Takeaways

  1. To have efficient marketing, you need to know where your customers come from and which channels bring the most valuable customers. KISSmetrics has an automatically tracked property called Channel. It categorizes people into seven different channels based on their referrer.
  2. In the KISSmetrics funnel and revenue reports, you can segment people by any property, not just channel. Using KISSmetrics’s channel segmentation, you can get an understanding of where your customers come from. Since KISSmetrics connects every touchpoint to your customer, you can get the very first touchpoint and the very first channel that brought someone to you. You’ll be able to see how the channels at the very top of your funnel perform.
  3. When you know who is sending you visitors and customers, you’ll know where to target your time and money. You’ll also see which channels don’t work. Simply put, channel segmentation allows you to make better marketing decisions.

With thanks to KISSmetrics blog post on ‘Using Channels in KISSmetrics to Learn Where Your Most Valuable Customers Come From’.

Simple metrics count

 

The power of storytelling

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With the film ‘American Sniper’ about to premiere in the UK about a US military sniper here’s my short story on the subject, to illustrate the power of storytelling.

A short story. I met a couple outside a pub in London a few years ago, and by chance we got talking, and they told me about a good friend of their’s who had recently been working as a U.S. sniper.

Now their friend now no longer in the military, but they were worried about him, in particular he had been using a new expensive laptop in full view of people outside this very pub the day before, in a place where it was very easy for any passer-by to just grab it, and run off with the laptop. His friends said to me they didn’t understand how he could be so ‘careless’ and were at a loss what to do.

It seemed like they wanted me to say something; remembering something I replied that in my opinion their friend’s training and experience on the battle field as a sniper meant he had learned to shut himself off completely from any fear, and this mental state had clearly persisted into civilian life. This in my opinion explained why their friend had no worries using his laptop in such a cavalier way in public. They seemed to like my answer. 

The key difference between traditional software and software as a service: Growth hurts (but only at first)

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In the traditional software world, companies like Oracle and SAP do most of their business by selling a “perpetual” license to their software and then later selling upgrades. In this model, customers pay for the software license up front and then typically pay a recurring annual maintenance fee (about 15-20% of the original license fee). Those of us who came from this world would call this transaction a “cashectomy”: The customer asks how much the software costs and the salesperson then asks the customer how much budget they have; miraculously, the cost equals the budget and, voilà, the cashectomy operation is complete.

This is great for old-line software companies and it’s great for traditional income statement accounting. Why? Because the timing of revenue and expenses are perfectly aligned. All of the license fee costs go directly to the revenue line and all of the associated costs get reflected as well, so a $1M license fee sold in the quarter shows up as $1M in revenue in the quarter. That’s how traditional software companies can get to profitability on the income statement early on in their lifecycles.

Now compare that to what happens with SaaS. Instead of purchasing a perpetual license to the software, the customer is signing up to use the software on an ongoing basis, via a service-based model — hence the term “software as a service”. Even though a customer typically signs a contract for 12-24 months, the company does not get to recognize those 12-24 months of fees as revenue up front. Rather, the accounting rules require that the company recognize revenue as the software service is delivered (so for a 12-month contract, revenue is recognized each month at 1/12 of the total contract value).

Yet the company incurred almost all its costs to be able to acquire that customer in the first place — sales and marketing, developing and maintaining the software, hosting infrastructure — up front. Many of these up-front expenses don’t get recognized over time in the income statement and therein lies the rub: The timing of revenue and expenses are misaligned.

The income statement alone therefore can no longer tell us everything we need to know about valuing a SaaS business.

Even more significantly (since cash is the lifeblood of any business), the cash flow timing is also misaligned: The customer often only pays for the service one month or year at a time — but the software business has to pay its full expenses immediately.

Thus, as with many innovative new businesses, cash flow is a lagging not a leadingindicator of the business’s financial health.

Take a look at the cumulative cash flow for a single customer under a SaaS model — the company doesn’t even break even on that customer until after a year:

cumulativecashflow_a16zsaasprimer

And as the company starts to acquire more customers, the cash flow becomes even more negative. However, the faster the company acquires customers, the larger it grows its installed base and the better the curve looks when it becomes cash flow positive:

Cash flow becomes even more negative before getting significantly better.

For the full post please read: Understanding SaaS: Why the Pundits Have It Wrong | Andreessen Horowitz.

George Clooney petition – sign here!

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If you want to support George Clooney’s petition to support Sony against the hackers who stopped The Interview movie release then sign here on my blog, simply by leaving your comment. Thanks.

On November 24 of this year, Sony Pictures was notified that it was the victim of a cyber attack, the effects of which is the most chilling and devastating of any cyber attack in the history of our country. Personal information including Social Security numbers, email addresses, home addresses, phone numbers and the full texts of emails of tens of thousands of Sony employees was leaked online in an effort to scare and terrorize these workers.

The hackers have made both demands and threats. The demand that Sony halt the release of its upcoming comedy The Interview, a satirical film about North Korean dictator Kim Jong Un. Their threats vary from personal—you better behave wisely—to threatening physical harm—not only you but your family is in danger. North Korea has not claimed credit for the attack but has praised the act, calling it a righteous deed and promising merciless measures if the film is released.

Meanwhile the hackers insist in their statement that what they’ve done so far is only a small part of our further plan. This is not just an attack on Sony. It involves every studio, every network, every business and every individual in this country. That is why we fully support Sony’s decision not to submit to these hackers’ demands.

We know that to give in to these criminals now will open the door for any group that would threaten freedom of expression, privacy and personal liberty.

We hope these hackers are brought to justice but until they are, we will not stand in fear. We will stand together.

Ladies from Taguatinga show their support for Kaka

The girls from Taguatinga have got your back George!

Natural variation sounds a lot less boring than it really is

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Sorry about the title. What I wanted to say was this. A boiler engineer from British Gas came round yesterday after we’d lost power for the second time in two weeks. so help fix it. He got to work, got out his laptop to do a diagnostic, and did a good job. The engineer, Kirk, at the end of his work said he could also fix the cause of why the boiler heating wasn’t working, blocked hydraulic pipes which other BG engineers said were unfixable, which thus made me smile. He explained that he had a lot of experience of the boiler model we had from his work in Islington and Bethnal Green where many were the same Valliant model as ours. But that it was funny all you had to do was go to the next borough and there were a different set of boilers. Funny, I thought later in the pub talking to Shirley, because he’d just reminded me of a powerful teaching from the ‘University of Life’, that not all boilers are the same, and not all boiler engineers are the same.

What’s great is that this principle applies to so many different things in life. Just when you get thinking all cab drivers are unhappy dudes, you’ll meet a cheery one with a great sense of humour. Just when you have given up testing a growth hacking hypothesis to death and are about to give up, you’ll get a result which suggests another positive direction. Natural variation is out there, take advantage of it!

How to Start a Startup: all the Y Combinator backed course videos and notes

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Kilimanjaro

Shopping.com was one of the dot com boom startup successes

Date Speaker Topic
9/23/14 Sam Altman, President, Y Combinator
Dustin Moskovitz, Cofounder, Facebook, Cofounder, Asana, Cofounder, Good Ventures
Welcome, and Ideas, Products, Teams and Execution Part IWhy to Start a Startup
9/25/14 Sam Altman, President, Y Combinator Ideas, Products, Teams and Execution Part II
9/30/14 Paul Graham, Founder, Y Combinator Before the Startup
10/2/14 Adora Cheung, Founder, Homejoy Building Product, Talking to Users, and Growing
10/7/14 Peter Thiel, Founder, Paypal, Founder, Palantir, and Founder, Founders Fund Competition is For Losers
10/9/14 Alex Schultz, VP Growth, Facebook Growth
10/14/14 Kevin Hale, Founder, Wufoo and Partner, Y Combinator How to Build Products Users Love
10/16/14 Walker Williams, Founder, Teespring
Justin Kan, Founder, Twitch and Partner, Y Combinator
Stanley Tang, Founder, DoorDash
Doing Things That Don’t ScalePR

How to Get Started

10/21/14 Marc Andreessen, Founder, Andreessen Horowitz and Founder, Netscape
Ron Conway, Founder, SV Angel
Parker Conrad, Founder, Zenefits
How to Raise Money
10/23/14 Alfred Lin, Former COO, Zappos and Partner, Sequoia Capital
Brian Chesky, Founder, Airbnb
Culture
10/28/14 Patrick Collison, Co-Founder, Stripe
John Collison, Co-Founder, Stripe
Ben Silbermann, Founder & CEO, Pinterest
Hiring and Culture, Part II
10/30/14 Aaron Levie, Founder, Box Building for the Enterprise
11/4/14 Reid Hoffman, Partner, Greylock Ventures and Founder, LinkedIn How To Be A Great Founder
11/6/14 Keith Rabois, Partner, Khosla Ventures How to Operate
11/11/14 Ben Horowitz, Founder, Andreessen Horowitz, and Founder, and Opsware How to Manage
11/13/14 Emmett Shear, Founder and CEO, Twitch How to Run a User Interview
11/18/14 Hosain Rahman, Founder, Jawbone How to Design Hardware Products
11/20/14 Kirsty Nathoo, Carolynn Levy,Partners, Y Combinator Legal and Accounting Basics for Startups
12/2/14 Tyler Bosmeny, Founder and CEO, Clever
Michael Seibel, Partner, Y Combinator
Qasar Younis, Dalton Caldwell,Partners, Y Combinator
Sales and MarketingHow to Talk to Investors

Investor Meeting Roleplaying

12/4/14 Sam Altman, President, Y Combinator Later-Stage Advice

Fake tweet campaigns come under fire from Indiana scientists

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Check out the conclusions of a recent analysis from scientists at the School of Informatics and Computing at the University of Indiana, on fake vs real tweet memes that could have serious implications for corporate social marketing campaigns in the future. Scroll down to the interesting point highlighted in bold. (PDF: Fake-tweets-identifier)

In this work we proposed a framework to deal with the problem of clustering memes in social media streams, Twitter in particular. Our system is based on a pre-clustering procedure, called protomeme detection, aimed at identifying atomic tokens of information contained in each tweet. This strategy only requires text processing, therefore is particularly efficient and well suited for a streaming scenario. Memes are thereafter obtained by aggregating protomemes on the basis of the similarity among them, computed by ad-hoc measures defined according to various dimensions including content, the social network, and information diffusion patterns. Such measures only adopt information that can be extracted in a streaming fashion from observed data, and they allow to build clusters of topically related tweets. The meme clustering is carried out by using a vari ant of Online K-means, which integrate s a memory mechanism to keep track of the least recently up dated memes. We used a dataset comprised of trending hashtags on Twitter to systematically evaluate the performance of our algorithm and we showed that our method outperforms a baseline that only uses tweet text, as well as one that assumes full knowledge of the underlying social network.

One crucial feature of our system is that it can b e extended to work with any clustering algorithm based on similarity (or distances). In this paper, for example, we chose to present Onlin e K-means b ecause of its simplicity; however, during our design we also tested other metho ds including density-based and hierarchical data stream clustering algorithms (e.g., DenStream [10] and LiarTree. Although a complete benchmark and tuning of these alternative methods was out of the scope of our analysis, we collected evidence of the ease of extension of our framework to different algorithms.

In the future one could extend the set of features incorporated by our clustering framework, considering for instance entities such as images. Furthermore, our preliminary analysis suggests that the introduction of time series as features may yield significant performance improvements. Our long-term plan is to integrate the meme clustering framework with a meme classifier to distinguish engineered types of social media communication from spontaneous ones. This platform will adopt supervised learning techniques to classify memes and determine their legitimacy, with the aim to detect misinformation and deception campaigns in their early stages. The platform will be optimized to work with the realtime, high-volume streams of messages typical of Twitter and other online social media.

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