MicroConf 2013

Jason Cohen led off the conference with a talk about building a “money machine” that brings in $10,000/mo. Jason crushed it. Rob closed the first day and spoke about taking HitTail from $1500/mo to well beyond the “money machine” mark in 20 months. Rob burned it down. Multiple tweets showed people changing pricing strategies, making sweeping copy changes, and saying the conference had already paid for itself within minutes or hours. Welcome to my retro-diary for MicroConf 2013. As usual, I’m going to cover material that most closely resonated with me and where I am.

Immediate Actionable Takeaway

  1. Use CPC = MRR/25 to work backwards from the CPC I can achieve in available channels and arrive at a pricing scheme for Keepify.
  2. Use money back guarantees and incentivized annual pre-pay from day one.
  3. Meet weekly with the family to plan work and leisure time. We have informal understandings, but planning would be an improvement.
  4. Plan out a marketing component that doesn’t scale and execute.

The Money Machine

Jason’s talk was a great look at the math and constraints behind building a business. He explained how you could piece together a cash machine from first principles similar to his recent post on CPC for bootstrapped business. He left it as, “Predictable acquisition of recurring revenue with annual pre-pay in a good market creates a cash machine.”

Giving incentives to customers to purchase annual pre-pay plans allows WPEngine to advertise with a much higher CAC and CPC. You can spend $300 dollars to acquire a customer that is prepaying for 10 months @ $49  per month right now.

Free trials can be eliminated in favor of a 60 money back guarantee. Use multiple plans and raise prices.

You need 150 customers to pay $66 / month on average. You can get 50 by scratching and clawing (see things that don’t scale) and 25 more with guest posts and social media. The final 75 can come from basic marketing all over a period of months.

Learning

Rob’s talk emphasized that it took him a period of 5 months building and 6 months learning before he began to scale the business. All that time learning was improving conversion rates, retention, copy, adding features, and increasing customer understanding. He is planning for similar learning period in the future and it is instructive to hear. I’ve experienced similar (but smaller scale) things recently with PPC ads. You have to be willing to spend a little money and stick it out through many revisions to be successful.

Teach

Nathan Berry, Brennan Dunn, and Hiten Shah all reinforced the necessity of using educational marketing as part of the customer acquisition process. Somewhere, Chet Holmes is proud.

Copywriting

Joanna Weibe of Copyhackers gave a great talk about copywriting. She said to minimize the visibility of free offering, use email, long-form sales pages, and start testing.

One point she made seemed particularly relevant to the audience. “Stop treating marketing as an experiment” which I understood to mean that you need to view things in the long term. Don’t give up when an initial ‘experiment’ in marketing doesn’t work.

Plugging Holes

Rob touched on his Operation Retention where he improved conversion and retention throughout the funnel for HitTail. He only resumed his marketing activities when those numbers became healthy again. He gave healthy numbers as 8% or less churn and 40-60% Trial to paid conversion.

Things that don’t scale

Rob, Josh Ledgard, Erica Douglass, Hiten Shah, Jason Cohen, and Patrick all stressed the importance of talking to customers. Especially early or after cancellation.

Erica included some case studies of offering one-on-one consulting for early customers and emailing 1000 people in a few months as examples of doing things that don’t scale to learn and market. Those 1000 emails were from Leo  Widrich of Buffer and converted into hundreds of guest blog posts and Buffer buttons on blogs.

Creating Channels

Hiten talked about creating your own channels to reach customers because established channels get crowded quickly. He gave examples of Nathan Berry, Brennan Dunn, Ruben Gamez, and KISSInsights which used a ‘Powered By’ link on the surveys to connect with customers.

Workaholics

I was part of an interesting hallway conversation about how to go from a job and working on your side pursuit into working normally. Many of the attendees spent some many years working 60 hour weeks that they didn’t know how to stop and enjoy freedom they had earned. It’s a an interesting subject that needs more direct treatment.

Sherry Walling gave a good talk that detailed how she and Rob made it through rough times and built relational systems and communication that evens out the ups and downs of entrepreneurship in a relationship. Another subject that became a topic of conversation in the hallways. Kids are common among attendees and everyone is looking to build a better future for their family.

Golden Handcuffs

Reflection on some of the conversation at MicroConf and a talk with my friend Evan forced me to consider the how a growing income and a growing family have changed the equation for what my minimum “money machine” looks like. Sherry’s talk included a quote about how cleaning services were cheaper than therapy and therapy was cheaper than divorce. I agree. I spoke with others about paying others for cleaning services or lawn care, but where do those things trade-off with growing expenses and lifestyle that tighten those golden handcuffs? It’s something for me to ponder.

Thanks to Rob and Mike for another great conference. I barely scratched the surface of the value available from attending MicroConf. There is simply too much for a blog post. You’ll have to join us for the 2.5 days next time.

You can wait for MicroConf 2014 or check out MicroConf Europe in October.

Pre-Launch Marketing

I’m thinking a lot about pre-launch for Keepify these days and I thought I’d organize it all here. There are a lot of different ways to try to collect traffic, attention, etc. Some of it is more effective before launching that others. Much of it depends on your product and market. Peldi from Balsalmiq has a famous and excellent post on launch marketing, but pitching bloggers on Keepify is a bit different than a mockup tool.

To demo and feel out the product requires data, time, and perhaps even effort. It’s hard to create a great experience without critical mass to show. The point is that what I decide to do may make no sense for you to do. Jason Cohen has a great post where he records honest thoughts on getting your first few customers. He points out that there is no formula. Every route has a champion and a detractor. You have to try a bunch of things and learn what works for you.

Brainstorm

I sat down recently and wrote a list of things I could do as marketing activities. I wanted to start by brainstorming before I cut things from the list.

Content

  • Guest post
  • Long tail SEO
  • Pillar Post Big SEO
  • Video
  • Infographics
  • HN/Reddit/similar placement
  • Testimonial or writeup
  • Squidoo/Hubspot
  • Webinars
  • Press Releases
  • Article sites
  • Craigslist Ads or Requests
  • Quora, Forums
  • Case Study Articles
  • Podcasts
  • WordPress Plugin

Permission Marketing

  • Mailing List
  • Lifecycle Email

Social Media

  • Facebook
  • Twitter
  • LinkedIn
  • Discount for sharing

Ads

  • LaunchBit
  • InfluAds
  • AdRoll
  • BingAds
  • Facebook Ads
  • LinkedIn Ads
  • BuySellAds

I reexamined 8 Ways to Build Pre-Launch Mailing List Episode 72 of Startups for the Rest of Us and I built the notes below.

  1. Use your audience (blog, podcast, etc.)
  2. SEO
  3. Infographic/Viral Content
  4. HN or equivalent
  5. Facebook Ads (affordable and working)
  6. Social Media Network
  7. Niche Ad Networks
  8. AdWords (last because it ain’t cheap)

I also checked in on 7 Catastrophically Common Launch Mistakes Episode 121 for the reverse perspective.

  1. No landing page before coding.
  2. Not tracking key metrics from the start (traffic sources, conversion rates)
  3. Relying on Word of Mouth (it isn’t really there)
  4. Open betas (be direct with early leads)
  5. One single launch email (do a sequence)
  6. Free plan or low price tier
  7. Slow growth (loss of interest)

My last stop for the podcast was Episode 122 4 B2B Strategies.

The strategies are Inbound, Outbound, Paid, and Partnership. Inbound is SEO, guest posts, infographics, and podcasts. Outbound is phone, email, direct mail, etc. Paid is various forms of advertising. Partnerships are joint venture deals. If you don’t have a big network or mailing list to trade, offer a revenue share.

Rob Walling also has a post on why you should start marketing on Day One which ties into the 7 Mistakes podcast above.

Pre-Launch Effectiveness

You don’t have anything to sell yet, but you want to get attention. You need beta users. You would like to have a list of people pre-purchase or at least sign-up for a launch list. You need to collect emails for people that come by and learn about you. It’s the most essential activity you can do for marketing right now.

You need a landing page with an email signup form. You probably also want a list to connect to on MailChimp or similar. You should install Google Analytics and at least use click tracking for conversions. This will really help when you have multiple traffic sources and you want to know how well you converted and from where. It also lets you compare ad network numbers to a baseline (though they may legitimately disagree).

Your landing page should probably be using some form of A/B testing. I found Optimizely to be affordable and easy to get started with. They do a very nice job of on-boarding and engineering the first-run experience. It’s almost worth signing up just to experience.

In the past I have resisted A/B testing for a simple landing page collecting email addresses with small amounts of traffic, but in reflection it was a catastrophic, arrogant, silly mistake. Don’t be me. Be the guy A/B testing. You can absolutely learn from A/B testing small traffic loads and you’ll be surprised how quickly executing these techniques can change your traffic outlook. So start now.

Most of the things listed above drive traffic to your landing page. It’s important that you do well with those activities, but the landing page is really critical. If you don’t know how to write copy it’s probably worth reading some good resources on copy. I’ve also read Ogilvy, The Copywriter’s Handbook, CopyBlogger, a headline book, and more. I’m starting to get a good feel for what should be in my copy, but I still frequently write tremendously bad copy. It’s a process. Every time. I’ve changed my conversion rate from < 10% to 33% with copy changes and A/B testing (It’s still not as good as it could be). Some variants are more than 50% better than an alternative. Would you like 50% more signups? Yeah. Do that.

You might be wondering what is working best for me. Some niche ads are doing well, but content has performed admirably and I’ve yet to really focus there. The truth is that I’m going to keep trying things up until it’s Launch Marketing. Many things only work after a launch (like joint venture deals) and I’ll have a better idea of what to keep doing, what to try, and how to structure my copy or content. I’ll write more on traffic strategies as I pursue new ones. Happy hunting.

Model Selection for SaaS Churn Prediction Using Machine Learning

This is a post in a series about churn and customer satisfaction. If you want churn prediction and management without more work, checkout Keepify. If you want more details, email away.

Recently I have been developing machine-learning systems that will predict SaaS churn. Churn prediction has many desirable business benefits and applications, but here I will focus on the technical details of selecting a durable model for predicting churn and some of the lessons I’ve learned along the way.

Beginnings

Most learning problems should be attacked initially with a linear model. I tried two versions of a linear approach in the early days. The first was an attempt to predict the number of months a user would stay using linear regression. This was a terrible failure. It was essentially 90% wrong. The root mean square error was absurdly high. I think this was the wrong approach with the wrong data, but it was a fun initial experiment to get some momentum. The last was an attempt to classify users as churners or non-churners using logistic regression. I’ll address that one more in the next few sections.

Literature Review

After my initial failure, I decided to fire up Google Scholar like my old days in graduate school and try to find some meaningful research on a similar subject. It turns out that a lot of subscription-based services like cable, Internet, and periodical publications fund both academic and industry research in churn prediction. There isn’t any apparent research on SaaS specifically, but the foundations of predicting churn for a newspaper subscription should be similar. In fact, I thought that SaaS should have far superior data to use in prediction.

The research says that the most successful models are Logistic Regression and Random Forests. Many people have shown the efficacy of Support Vector Machines to fall in between these popular options. Neural networks are another popular option with varied, but solid results [1]. My later experiments tried to use some of this insight and focus on models that had the most promise.

Experiments

I decided to use Weka to try a lot of different experiments quickly on the same data set. I was careful about separating my data into strict train and test segments, but I was happy use various datasets to experiment with different learning hypotheses. Weka performed beautifully for me and came with an additional benefit, the JVM. I was processing some of the data transformation in Ruby and I wanted to integrate this system into a Rails application. JRuby made working with Weka and Rails incredibly easy.

It was easy to transform my existing data to ARFF file formats for Weka and I managed to test out nearly all of the relevant classifiers that Weka supports. I have not used SVM or Neural Networks for reasons I explain in the next section. Bayesian Nets and AdaBoost show promise as classifiers for churn prediction in my experiments, but they don’t show up much in the literature.

Classifier Comparisons and Selection

Random Forests dominate the research landscape as the model of choice and my experiments bear that out. Random Forests win. A lot. The intuition to explain why is two-fold. Random Forests are extremely robust without performing feature selection. They do their own version of feature selection that works well for this problem. Random Forests are based on decision trees that classify data pretty well across a small number of known classes. They’re especially effective when certain feature values correlate highly with certain classes. Decision trees (and Logistic Regression) share a final benefit. They show how the classifier works internally in an understandable way. If your customers churn when they use feature X only once per month then you can see that in how the decision tree is structured. This is powerful insight.

Logistic Regression works really well, if not quite as well as Random Forests. It not only presents a model that explains how it works, but it does so with more emphasis on how sure it is whether a customer falls into one class or another.

I didn’t use Neural Networks in any experiments in large part because it isn’t something I could do out of the box with my data in Weka and it famously does not lend any insight into how the classification works. Neural Nets are a black box. Ideally, my classification engine for Keepify will be able to provide more insight for customers than classification alone.

Support Vector Machines are a very cool combination of linear classifiers that optimize a hyperplane. They are a sexy choice, but the performance is not quite so nice as Random Forests, they don’t show their work like Neural Nets, and they are really slow. I can generate predictions for thousands of customers with hundreds of features using a Random Forest in less than a few seconds. SVM might take minutes or worse.

In the end, I decided to use Random Forests and Logistic Regression. I do plan to experiment further with AdaBoost, however, as it is effective at eliminating bias from data sets that have classes with low prevalence.

[1] http://cjou.im.tku.edu.tw/bi2009/DM-usage.pdf

How To Predict Customer Churn Using Machine Learning

This is the first post in a series about churn and customer satisfaction. If you want churn prediction and management without more work, checkout Keepify. If you want more details, email away.

Last year, Rob Walling gave a great talk at LessConf that helped me really click with the idea of Customer Lifetime Value. It also connected a few parts of my past with an interesting idea I could tinker with. He spoke at length about Hubspot’s Customer Happiness Index and reducing churn to improve CLV. It turns out that CLV varies inversely with churn. Churn prediction could dramatically move the needle for a lot of online businesses. It sounded like a cool thing to explore, but how do you predict when someone will leave? What factors are in play? What methods work?

The first step is data collection. You need to start collecting digital information about customer purchases, actions, support, and visits. More data probably won’t hurt you, but I’ve got experiments that show sparse data can work well. You likely need to collect 30-90 days of data before performing your first attempts at classification. This depends quite a bit on customer activity and the number of customers you see in that time period. The data can be in many formats, but you need at least a customer id, event id, and timestamp.

There are many choices for basic classification like this. You could use Excel, R, NumPy, Weka, Mahout, or any of a number of options. Most will be well served using a familiar tool. Failing that, try out Weka for its GUI, community, and documentation.

Once you have tools in mind, you’ll need to transform your data for the tool to consume. For Weka, this means ARFF files, which are well documented, but can also be frustrating. The ARFF parser is less than descriptive about parsing problems. For R or Python you will probably use CSV or something akin to JSON formats. The details of the transformation depend on your tool of choice as well as source and destination formats. Transformation also implies some kind of feature selection (which you should experiment with, but is beyond the scope of this post). This will likely require additional computation from the source events.

Good tools and well-organized data make running experiments really easy these days. You should be able to try out all kinds of linear classifiers without any additional effort, but be aware that the most popular models for this task are Logistic Regression and Random Forests.

Finally, you need to take the output of each classification result and use it for predicting customer churn. A test data set or cross-fold validation experiment will give you a clear idea of the efficacy for your model, but since we are interested in predicting future churn you will want to target your marketing efforts on the apparent false positives. Those users are the ones your model thinks will churn based on the data available.

How I Doubled Organic Search Traffic in Two Weeks

You want more traffic. You want it to be organic. You want it to be targeted. And you want to use a strategy that will scale to bring you more. Me too. This post details my recent success at that exact goal.

I read Patrick McKenzie’s blog post on Scalable Content Generation [SCG] an embarrassing number of times without ever understanding how to make it work for me. A couple months ago, what he was saying really hit home with me and I developed an SCG experiment.

There were two key insights from that post that propelled me forward. Only top ranking pages for a keyword get traffic and only long tail terms are easy to rank near the top for without a lot of work and calendar time waiting periods.

I had previously outsourced some keyword research (key component here is difficulty of ranking) and I identified a few potential keyword phrases that I could pair with other keywords to build a scalable content strategy around. The problem with most of them was that I didn’t have a library of content to push out 100s of pages. I also didn’t want to spend the cash to get 100s of custom articles written. It’s not really scalable content generation if I need to pay $50+ per page I can create.

Patrick uses bingo cards for this purpose and the reason struck me: he already has them, and more represent an asset. Yes, this really never occurred to me before. This meant I needed to look for keyword terms that match assets I already have and would like to grow. Now only a couple keyword terms fit the bill and I grabbed the most promising one, “cold calling scripts.”

I created a template with an image, call to action, 300+ words per page, and sharing buttons. I wrote a script to generate a list of unique industry names from some sample lists I found with some searches. I planned to create pages for every combination of “cold calling scripts <industry>” I could possibly target.

My template included a slot for some text that was unique to the industry I was targeting. I outsourced a paragraph of text on that subject to TextBroker at about $1 per paragraph. This was an affordable way to create the pages. Each template also had a space for scripting tips or a breakdown of a good or bad call script. I created a Jekyll plugin that would randomly assign one of a small library of the scripts I formatted for this task to each page. I plan to add to this library as I go. More on this later.

Using this methodology I generated an initial run of 83 pages each detailing a different industry. Jekyll generated the whole thing including categories/tags and index pages. Jekyll is quite suited to this task.

After I published the pages and linked to them from the footer of my site, I sat back and waited for Google to re-index. I think it took about 10 days before I started seeing organic search results that sent people to these pages. The keyword research paid off. The vast majority of my new pages rank of the first SERP and most rank in the top 3. After another 5 days this experiment had paid for itself in book sales.

Orange is December and Blue is January.

GA traffic compare

Screen Shot 2013-01-30 at 10.41.03 PM

Screen Shot 2013-01-30 at 10.41.16 PM

My organic traffic actually increased by 169.84%. My time on page, bounce rate, and pages per visit are essentially static, but I have seen an increase in return visitors. This is all the result of some keyword research, content generation, and content repurposing stitched together with various scripts and Jekyll.

My conversion rates on those pages are worse than the rest of the site. The site average is 14% and those pages run closer to 11%. I have more exits from those landing pages than others, and some of them have a dismal bounce rate. This isn’t really unexpected. Those pages generally have more visitors that have never heard of my book or me and the traffic is less targeted than referrals I might get from my blog or guest posts.

That said, the TextBroker content is lower quality than I would like it to be. Some of that is my fault for failing to give clear instructions, being impatient with results, and being cheap. I also have a fairly small set of script breakdowns and tips that are randomly placed on the pages. There are some significant page quality issues I need to tackle.

The nice thing about tackling those issues now is that I can measure which pages get traffic and how they convert. I can prioritize which pages I improve and even A/B test improvements to the overall template. None of this would be possible without validating the idea or without the traffic the experiment brought. If I had started with a really small set of pages (instead of 83) I might have picked ones that were harder to rank for or didn’t see substantial traffic in a month. It wouldn’t be a very good experiment.

I’m currently improving the template, working with better writers, editing some existing content, and adding more script breakdowns to improve the pages. I think these types of improvements are a worthy investment in light of Panda and Penguin. Patrick’s Bingo Card pages have no more unique content, but continue to rank well. Those updates are focused on down-ranking pages people don’t find useful and those pages that are intentionally duplicative. I want these pages to be valuable to people. I’m not interested in a demand media business.

I added 179 more pages on 26 January, but there is nothing to report yet. While I work to improve quality, I plan to experiment with some new ideas and keywords.

10 Reasons You Should (or Should Not) Attend MicroConf

Last year at MicroConf I…

  1. Met half a hundred really interesting people.
  2. Cemented relationships with half a dozen people that inspire me and remain part of my life.
  3. Left with a notebook full of ideas I tried to implement and improve my business.
  4. Spoke 1:1 with 2/3 of the speakers.
  5. Spent 18 hours a day with bootstrappers talking business for the duration of the event.
  6. Got inspired to write Cold Calling Early Customers in 4 weeks.
  7. Vowed to go back in 2013.

You should attend MicroConf this year because…

  1. Each year the conference is better run and located. Yay Tropicana!
  2. You are a bootstrapper or are interested in bootstrapping a business.
  3. There is no greater concentration of bootstrapping internet entrepreneurs interested in propelling each other forward.
  4. The speakers are top notch and they all want to deliver something valuable for the attendees.
  5. You’ll finally get to talk to and hang out with the speakers at a conference.
  6. You want some inspiration or motivation to push your business up a notch this year.
  7. You can meet new friends that will change your life (I did).
  8. The attendees will teach you more than the speakers.
  9. A/B testing, SEO, software, analytics, outsourcing, and business interest you.
  10. The speakers or past speakers live and work in a way that you’d like to.
  11. Loved Start Small, Stay Small or Sell More Software.
  12. You could teach me something and I want to learn.

You should skip out on MicroConf this year if…

  1. You are looking to go the venture route. (We’ll see you next year. ;))
  2. You have not taken any steps toward entrepreneurship including consulting.
  3. You are not willing to speak to customers.
  4. You think sales is dirty.
  5. You don’t ever want to meet patio11.
  6. Marketing is a dirty word for you.
  7. You are looking for investment opportunities.
  8. You just want a co-founder.
  9. You think any one thing is the big break you need for success (including MicroConf).
  10. Hated Start Small, Stay Small or Sell More Software.
  11. Unexpectedly tall people terrify you.

You need to sign up for the emails if you haven’t already. Tickets will be gone with the quickness. I’ve been to each MicroConf and I’ll be attending this year. I’d like to see you there. Drop me a line if you want to hang out.

Don’t expect to build something people want

Customer Development is great. It can really reduce your risk and save you years of misery. One misconception about the methodology that I myself fall prey to is a promise that building something people want is enough. It’s not enough.

You hear a lot of talk about pain killers versus vitamins and the logic is sound, but how many of the things you buy or your business buys would you really call a pain killer?  If I look around, I don’t honestly see that many. Sure, some of them save me time or money. I like having them around. Tedium is reduced, but pain killer? It’s not a compulsion. Few of these things are absolutely necessary and most of them have reasonable alternatives.

How many things that you buy happened simply because someone built something to solve a problem you had? Wait, none? Zero things flashed into your mind and wallet because they were constructed to solve your problems?

How many of the products you buy are ideal solutions for your problems? Does it feel like someone understands you and your specific context? I doubt it. I am often delighted by the smallest improvements in design. If the benefit of using the product is there, I’ll put up with a lot of hassle.

Do you always buy the best or most feature-ful option? Most expensive? Cheapest? Wait, it might be too complex or error-prone with a lot of features? And you think it would be crazy to pay $X for that, but also that there has to be something wrong with the cheapest one?

How do you make your purchasing decisions then? I’ll tell you. You get marketed to because someone else has studied you. you belong to their targeted segment. They are trying to understand you. To speak to you. To talk like you. They want to help you succeed because then you’ll both succeed. It won’t be perfect, but it will be better. They want you to buy.

Don’t expect to build something people want and have a sudden success on your hands. You have to understand your market. Speak like them. Talk to them. Find where they hang out. You have to market something people want.

Cold Contact (Calls and Email) — Lean Startup Austin Talk

This outlines my system for contacting people cold whether by phone or otherwise. It parallels my experiences and my book (coldcallingbook.net).

I gave this talk to the Lean Startup group in Austin, TX on 20 Nov 2012.

Magical Dwarves and Marketing a Product

I had a friend long ago that joked about how the laws of physics were the happy accidental result of the actions of many, many,  tiny, magical dwarves you can’t see. The theory has some merit. Quantum phenomenon are a sign of their sense of humor. It’s a cosmic wink and a nod. This was essentially my understanding of how sales were related to marketing as of 2008.

In a recent podcast, Rob Walling mentions that he doesn’t believe people with successful businesses actually thrive on “word of mouth” and that some other activity is actually responsible for their success. This is a connection that wasn’t cemented for me until I actually created a product I could sell online and went forward with the mindset that each sale would be a direct result of some marketing action by me. People won’t appear because your product or book is a better mousetrap. Your customers will only help promote it if there is some compelling reason to do so.

Traffic is a result of my SEO, content, guest posts, blog comments, and podcast appearances. Aggregate traffic is what enters the “funnel”. If you have high quality traffic, the people already know a bit about you or your product and are primed to buy or sign up. My first month of traffic into the funnel for my book converted to sale at 26.7%. About another 22% signed up for the email list by requesting a free sample. As I understand it, that is pretty spectacular and bound to decline.

I can see the decline coming. My analytics reports that I have a lot of organic traffic spinning up from a content campaign on my book site, but it isn’t converting very well. My copy isn’t that great and some of the keywords are going to pull traffic from outside my niche audience.

The conversion numbers aren’t the point. The point is seeing the world in a new way. I knew how it worked before, but I didn’t grok it. I started seeing all content as someone working a sales angle a while back. It doesn’t bother me (but a younger me is angry about it). People create great content because it gets attention and attention drives sales. Without the sales, there is no reason to create the great content. Churchill’s quote about democracy comes to mind.

Many forms of Government have been tried, and will be tried in this world of sin and woe. No one pretends that democracy is perfect or all-wise. Indeed, it has been said that democracy is the worst form of government except all those other forms that have been tried from time to time.
Sir Winston Churchill, Hansard, November 11, 1947.

Physics and calculus changed the way I viewed physical phenomenon around me. Selling a product online has changed the way I view marketing, commerce, and sales.

Teton Crest Trail

Death CanyonThis post is a complete departure from the primary substance of this blog, but it’s my blog and primary substance is in the works. This is about a backpacking trip I took in the late summer. There wasn’t a lot of specific information when I was searching to plan my trip so I decided to throw in the pieces I thought were important. Let Google sort it out.

From 13 September 2012 to 17 September 2012 my good friend Brian and I backpacked around 40 miles of the Teton Crest Trail. Any good pictures are purely accidental.

I did a lot of research for the trip before settling on a route and the dates. Since I’m a good ol’ Southern boy I chose September because there isn’t any need for an ice axe in the mountain passes. In hindsight, I think I should’ve done a mountaineering school and made the trip when the flowers were in bloom and snow was still around. Mountaineering school is now on the agenda. I’d be happy to entertain recommendations.

If you’re planning a trip to the Tetons I would highly recommend the path we took. We started at Death Canyon Trailhead and camped at the end of the Death Canyon Zone (map). We then went over Death Canyon Shelf and camped in Alaska Basin near the Mirror Lakes. The next morning we went over Hurricane Pass and through South Fork Cascade to camp in the second site in North Fork Cascade. The last day we eschewed camping the final night at Holly Lake and made the trek over Paintbrush Divide and out to String Lake in one go. From what I can tell, many regular hikers of the area recommend this route and I do as well. The photos I linked to include many of the signs we encountered and try to give you a sense of the scenery and hiking required by the route.Grand Teton from Hurricane Pass

If I were going to pick one place to absolutely see in Grand Teton National Park, it would have to be the walk from Hurricane Pass through South Fork Cascade Canyon. It would be an epic out and back day hike, but I’ve been to many a scenic mountain vista and this was completely spectacular.Death Canyon Shelf

We saw elk, deer, marmot, and approximately 137,048 chipmunks. We were spooked by a black bear near the confluence of the trails between North Fork, South Fork, and Cascade Canyon. He was standing right on the trail. Right between us and our campsite about 1 mile down the same trail. Good times. Sleep came so easily that night.

You should buy bear spray. We didn’t. We hiked during a “bear activity advisory” where you shouldn’t travel in groups smaller than three or be without bear spray. We didn’t do that. It’s probably better to listen, but don’t let spray (or even a gun — which isn’t allowed in the backcountry) give you a false sense of security. That bear was no more than 30 feet down the trail when we could see him. Being effective in that situation with spray (were the bear more alert and angry) would require practice and a very easily accessible storage location.

Water was never a problem. We both used filters and valves that allowed us to pump directly into our CamelBak reservoirs and water bottles. Even in the driest month of the year, water was plentiful. I don’t see any reason to carry more than a gallon at any time.

One final note for anyone without a lot of snow/ice hiking experience is that despite the claims of the pass status page there was still a scramble across 20-30 feet of ice at a steep angle on the descent from Paintbrush Divide. Brian and I are both pretty fit, but we’re also big and we had 60 lbs + in our packs for an extended backcountry stay. Neither of us use trek poles, but I can see the value from that particular experience. Most of the locals and day hikers seemed unbothered by the scramble, but I feel like anyone planning a trip through Paintbrush should be aware.

Grand Teton
From 2nd campsite on big table rock in North Fork Cascade Canyon 15 Sept 2012 near sunset.