Everyone wants their product to go viral. Why wouldn’t you? It’s like the holy grail of marketing.

Too bad it’s BS.

If virality was a “thing” that could be reproduced on demand it would be the only marketing that would exist. Why would you do anything else?

And yet I constantly get marketing services companies pitching me on their ability to make things go ‘viral’. When I advise CMOs at other companies it is one of the top questions asked. My own team regularly asked me what we are doing to get our product to go viral.

Before I demolish the idea of virality completely, I will say there are some basic things you should do. You should make it easy for people to talk about your product. You might want to have a basic referral award system. Ideally you want to make your product visible when it is used.

These are all smart business practices. They should help on the margin, but none of them are going to make or break your business. The rest of this post will explain why virality doesn’t work. A future post will explain why doing some ‘good enough’ things might still be a good idea.

Why Virality Doesn’t Work: It’s all about the funnel

I own my understanding of this concept to Tony Wright. Tony is a friend of mine and the founder of multiple start-ups including RescueTime. He is the best person I personally know at making things go viral. The best example of his experience is CubeDuel. CubeDuel was “Hot or Not for LinkedIn”. When you signed up for CubeDuel it pulled two people from your LinkedIn network and asked you who you would rather work with in the future. If you rated 100 people you could find out your own scores. But when you got to 100 it would almost always say, “Not enough people have rated you yet. Share with friends to get yourself rated.”

It had easy tools for sharing. It was fun to play (lots of pictures buzzing in and out as you made selections. Super fast UI). It had sharing incentives as well as incentives to engage more (“Rank 500 people to see the ratings of your network.” “Rate 1000 people to see the top employees at your company.”  “Rate 2000 people to see the top people in your city.” Etc.)

Tony was responsible for most of those features (the basic product was created by Adam Dopplet, co-founder of UrbanSpoon and Dwellable).

CubeDuel was so effective in the early days that it broke LinkedIn. Rumors spread that LinkedIn blocked it because it was too good and they wanted to create the product in-house, but the reality was they just spiked the use of the LinkedIn API so fast that it couldn’t keep up. It was fixed in a day or two.

But “breaking LinkedIn” made a great story. The media picked up on it. It was talked about all over as the ultimate viral product. Its growth was off the chart.

Only Tony shared the numbers with me. It actually wasn’t viral at all.

To understand that, we need a definition of viral.

How do we measure Virality?

The idea of a viral product is one in which your existing customers do your marketing for you. If every one of your customers gets two friends to use your product and each of those gets two of their friends and you can keep up that rate indefinitely you will soon have everyone on the planet using your product. It sounds pretty good.

In fact, it doesn’t even need to be that good for it to be infinitely good. As long as each customer gets >1 friends to use the product, you will eventually get everyone on the planet using it. The number of additional customers each customer acquires for you is called the “virality index”. If your virality index is >1.0 you have a viral product. If it’s less than 1.0 (but greater than zero) you have a product with a word-of-mouth channel that augments (but never replaces) your existing marketing.

On the third episode of “How to Start a Start-up” podcast (an excellent podcast for those who listen to those things), the founder of HomeJoy talks about how important small changes are. She uses the example of how if you can make small changes to get your virality index from 0.98 to 1.02 you have just made your product go viral. While that fact is true on its face, I hope to show you that your initial product does not have anything close to a 0.98 virality index.

The way to do that is to think about funnels.

Let’s focus on sharing your product through one particular channel: Email referrals (this same method applies to every channel, but this particular one has a long history of quantifiable metrics which makes the example more powerful).

Your product looks like this:

1. You ask your users to share your product with friends
2. Your users email their friends about the product
3. Those friends read the email and click through to your ‘sales pitch’
4. They read your pitch and register for your product

Voila. Virality.

If the numbers are high enough.

Let’s go through some benchmark numbers. We will be very optimistic.

1. Let’s say you get 100% of your users to share your product with their friends (well done! Sounds like an amazing product!)
2. Let’s say each of them shares your product with 100 of their friends they think would like your product
3. Each of those friends receive an email. A very good email open rate is 20%. Let’s say because it’s coming from friend we get double that. 40% open rates.
4. These potential customers read the email and some decide to click through to your full sales pitch. A great click-through-rate (CTR) is 5%. We double that again to 10%.
5. They land on your pitch page. A great sign-up rate for a free product is about 10% (that would be ridiculously high for a paid product). Let’s say you get 20%.

That funnel looks ridiculously implausible. But let’s see what it’s virality  index is.

100% send rate x 100 contacts x 20% open rate x 10% CTR x 20% conversion rate = 0.4 Virality index

This means that every customer you sign-up will get an additional 0.4 customers for you (And those customers will get you an additional 0.4, who will get you an additional 0.4, etc.). It’s a pretty great scenario – when all is said and done for every customer you acquire with traditional channels you pick up an additional 0.66 customers. That should let you spend more on traditional marketing, but it sure isn’t going to be ‘viral’.

And remember we made up ridiculously unrealistic funnel metrics. More realistic funnel metrics would look something like this:

10% send rate x 50 contacts x 10% open rate x 5% CTR x 10% conversion rate = 0.0025 Virality index

Every thousand customers is getting you an additional 2.5 customers. Ouch.

So why does anything go viral?

I just proved it’s impossible for something to go viral. And yet Instagram, Farmville and Gangnam Style all exist. Have I just claimed they are impossible?

Kind of.

I’m claiming that trying to be the next Instagram or Farmville is a lot like trying to get hit by lightning a couple of times. Many people have been hit by lightning twice (in the same day or even seven times), but it’s pretty random. The same is true for going viral. Every metric will vary within a bell curve. Sometimes a metric will be very far to the right on a bell curve. When that happens we call it viral and start trying to rationalize why it happened.

You DO have influence on many of those metrics. You can make it easy to send out the email to someone’s entire address book; You can provide incentives to send it to both customer and potential customers; You can create compelling emails and landing pages that have high conversion rates. All of those things will make your product ‘more viral’, but in no world can it increase the expected virality function anywhere close to 1.0. For that you need luck.

Or specific types of products that can skip the funnel altogether.

Viral Products

A good way to identify a viral product is to see if it skips part of the funnel. If something about the product lets it get a 100% rate in a part of the funnel where normal (or great) products have a 20% rate, that’s a big difference. If a product can do that a few different parts of the funnel, it has a reasonable chance of actually going viral.

Let’s look at Farmville.

Farmville was the first in a series of games that took advantage of the Facebook platform to go viral. Let’s look at what their funnel might have looked like.

1. 100%of users shared Farmville with their friends (built into the game)
2. Initially they shared it with all of their facebook friends. Let’s say the average number of friends was 400 people
3. At the time the feed was a feed of everything  happening in your network (Facebook wasn’t screening for the best stuff), so we can assume, sooner or later, 100% of your friends saw your Farmville update. In fact we can go further. Most friends would see your update multiple times, giving them many chances to respond. It was kind of like you were emailing your entire address book every day over and over and almost every email was getting opened. Let’s estimate that each person in your network saw your update an average of five times (this may be too low)
4. CTR might be a little higher than average at the beginning until people understood what this was. Let’s say it was 5%.
5. Let’s say sign-up to play Farmville was a standard 10%

What is Farmville’s virality coefficient?

100% x 400 x 5 x 5% x 10% = 10

Ten!

Every user of Farmville adds ten additional users of Farmville!

Remember a great virality number is anything over 1.0. Farmville should have blown up (it did for a while), and then it should have kept blowing up until everyone who ever logged into Facebook was playing (And I know that is not true as I have never played a game of Farmville). At a 10x coefficient, assuming each ‘hop’ from one set of users to the next took a week, it would only take 9 weeks to have a billion users of Farmville. In 10 weeks therewould be more users of Farmville than there are people on the planet.

Assuming the funnel is right and Farmville is not 100% penetrated into Facebook users, what’s wrong with our math? We left out one factor: Inoculation.

Inoculating Viruses

There are always people who are immune to every virus. The same is true of viral products. No amount of awareness and exposure was going to get me to play Farmville. And as Farmville expanded the percent of people who were not interested became a higher and higher percentage of the people their “virality” was targeting. Its growth slowed down, stopped and eventually went into reverse. (But it sure was a good run while it lasted!)

Farmville had a very high inoculation rate. Even though it’s vitality ratio was off the chart, it’s inoculation rate served to slow down it’s exponential growth and hurt it’s (very) long term prospects.

It’s a function of the universe that every viral product will eventually hit an inoculated population, but some hit it much faster than others. Farmville’s inoculation index was particularly high. Not only was a large population immune, but many people who used the product dropped off. While some people became very addicted and played a lot (and shared a lot), the friends of the addicted group quickly became immune to the virus. And Farmville died.

Not every viral product looks like Farmville.

Consider Instagram.

Instagram had similar characteristics to Farmville. Every time a user took a cool photo with an Instagram filter and shared it with friends it was an advertisement for the product. The Instagram filters were shared with user’s friends over and over again. But unlike Farmville seeing different cool photos one after another from different friends was less likely to make you annoyed and more likely to make you interested in figuring out how YOU could make cool photos like that. The virality effect actually went UP over time. And unlike Farmville it was not a small number of power users who stuck with the product. Instagram had a very high customer retention rate, so even if the virality effect slowed down over time, net user growth stayed positive due to low levels of churn.

CubeDuel

Which brings us back to CubeDuel. According to Tony, CubeDuel had a very high virality index of about 0.7. That was enough that every time they signed up a user from a traditional marketing channel they picked up another 2.25 additional users through viral spreading. That is pretty awesome. But it only works as long as you continue to prime the pumps with new users. You can’t just sit back and let the virality take over.

The next piece of good news was that CubeDuel was getting lots of media coverage (remember “The Product That Broke LinkedIn”?). Coverage begets coverage.  After the story of breaking LinkedIn there were a series of stories about how viral the product was (Little did the reporters know it wasn’t actually viral at all!).

Tony and Adam saw the writing on the wall. They knew the product would take off as the 0.7 virality function did its work to build on the press coverage, but they also knew that as the press coverage died down eventually the traffic will dive down too. Their product was great and fun to use, but it wasn’t fun to use every day. After playing with the product for a while, users were ready to move on.

Tony and Adam sold CubeDuel to investors who were more optimistic they could continue to make the product go viral.

Today?

CubeDuel.com redirects to RedCross.org.

So irrational exuperence in virality is at least getting a little more traffic to a respected charity.

Unless that is the plan for your business, focus on more traditional marketing channels and don’t count on virality to make your business plan.

Two week ago I wrote about the importance of Marginal Value when thinking about marketing. Last week I expanded on the concept by looking at the impact of fixed and variable costs when you are calculating your true marginal costs of incremental marketing. Today we will flip the story from costs to revenue and try to measure incrementality.

Incrementality is fundamentally tied with attribution (in fact I cover it briefly in my book chapter on Attribution). Once you attribute a customer (or revenue) to a channel you need to realize that you may not have got it exactly right. If you got it 100% right then it is 100% incremental. It is possible in theory, but it rarely happens in practice. The next step is estimating how incremental you believe the revenue is.

I like to think of incrementality as being a percentage. If a channel is 100% incremental then you know that every customer you get from that channel is an additional customer to your business. One way of thinking about it is if you turned that marketing channel off, you would lose all of those customers.

If a channel is 50% incremental, it means half the customers you are getting from that channel you would have got anyway from a different channel. If you turned the channel off you would only lose 50% of the customers you are claiming to that channel. When a channel has <100% incrementality we say it is cannibalizing other channels (i.e., when it grows, part of its growth is incremental and part of it is stealing – cannibalizing – other channels)

If a channel is 200% incremental, it means that for every customer that channel generates, it generates an additional customer in a different channel. If you turned a 200% incremental channel off you would lose all of that channel’s customers, but you would also lose the same number again in other channels. When a channel has >100% incrementality it is synergistic (i.e., when it grows it also grows the other marketing channels around it).

Different marketing channels have fundamentally different incrementality rates.

An example of a very incremental channel is Brand TV advertising. A sophisticated business will estimate the customers generated from brand advertising. They usually look at when the TV spot airs and correlate it with website traffic  that spikes at the same time. They create some sort of decay curve on that spike so that they still give the TV spot credit for those web customers hours (or even days) later. But even with these methods, the attribution fundamentally under-values the TV advertising. Let’s make a short list of the impact of that TV spot that you are not giving it credit for:

• Web traffic that comes to the website weeks or months later (GoDaddy has a Superbowl ad. They see a traffic boost for years as their recognition goes from zero to the stratosphere)
•  Non-web customers that you can’t tie back to TV
• Improved CTR on your paid search ads (Which drives up quality score, and decreases your CPC)
• Better SERP results for the SEO (as Google sees more type-in traffic and increases your Panda score)
• Business Development opportunities – people reaching out to you
• Higher conversion rate on recruitment (and people coming to you)
• Improved B2B relationships (or leverage in negotiations)

I’m sure you could think of other effects of TV. I’ve rarely seen a company try and model all these effects. Instead they measure a few of them and then accept that there are significant synergies that they cannot fully measure. Those synergies are >100% incrementality.

Cannibals!

The reverse of television advertising is coupon sites. Have you ever got to  the end of a checkout and seen a field: “Enter a Coupon Code”. What?!? I can get a discount?!?

The first thing I do is go back to Google and search for “Company Name + coupon” or +discount or +code. Every now and then you find a coupon site. You click on the coupon site, get a code and enter into your checkout. You have just given that coupon company attribution for the sale. But it is very clear you would have purchased anyway without the coupon. The coupon site just cannibalized the original channel that brought you to the site.

Every marketing channel is cannibalistic sometimes. Even a super-synergistic channel like television will have cannibalization. Some people who see the ad and act on it (and you measure them – say with a phone number on the TV spot), would have come to you anyway, through one of your other marketing channels. In the case of TV, the synergistic effects far outweigh the cannibalization effect.

Every channel is synergistic sometimes. Someone may be shopping on the coupon site. They see your company but forget to copy the coupon. The next day they come to your site through Direct Type-In and convert. You give the credit to DTI, but the real driver was the coupon site. The coupon site was synergistic in that particular case, but overall it is still a cannibalistic channel.

When Cannibals Matter

If you only had one marketing channel, then you don’t need to worry about cannibalization. If all your marketing is free, you also don’t need to worry about cannibalization. Cannibalization matters in three cases:

1. When a marketing channel isn’t profitable, but you think it is

Coupon sites are a very good example of this. Let’s say the coupon site is only 20% incremental (i.e., 80% of those customers you would have got anyway if you didn’t have a presence on the coupon site). Let’s say you are paying the coupon site $20 for every $100 they generate. And let’s say the coupon is a 10% off coupon. (And your business is 100% fixed costs – so 100% margin on that $100).

Spending $20 to make $90 ($100 – 10%) sounds great. Until you look at the incrementality. If the coupon site is only 20% incremental, then $80 out of the $100 is revenue you would have got anyway, even if the coupon site did not exist. So you are really spending $20 to make $20. But then you are giving a $10 discount. So you are spending $20 to make $10. You can’t make that up in volume…

These situations happen all the time. (It’s a related story to the selection effect in Loyalty Programs I wrote about a month ago)

2. When a more expensive channel cannibalizes a cheaper channel

Imagine Amazon had 100% market share of all book sales online. All of the customers were coming to Amazon through Google Paid Search. Amazon was profitable, but they were giving away dollars to Google (Let’s say $0.50 of every $1 they make in profit). Then Amazon introduces an email program. The program is 0% incremental. It is completely cannibalistic. All it does is steal sales from the paid search channel and give it to the email channel.

But that’s okay.

Every $1 sale that moves from paid search to email is saving Amazon $0.50. It doesn’t matter that it’s cannibalistic, as long as the cheaper channel is cannibalizing from the more expensive channel

3. When You aren’t spending enough due to attribution

Using the last example, once Amazon launches email they may start seeing Paid Search as less effective. There are two scenarios on how email could be cannibalizing paid search.

In the simple scenario once email launches, people stop searching and come direct to Amazon. In that case #2 applies.

A more complicated story is that people still search, but then Amazon hits them with an email. When they buy Amazon attributes the sale to Email.

In both scenarios email has cannibalized paid search, but in fundamentally different ways. In the first example, email did all the work. If search did not exist it wouldn’t matter one iota. In the second scenario it’s hard to say which channel did the most work. But Amazon is just choosing to attribute the sale to email. When that happens the rational thing for the paid search manager to do is to reduce Amazon’s Paid Search spending (since Amazon had to pay for that click, but paid search got no credit for the sale)

If it turns out that the customer needed both channels to convert, you need to be careful when the cheaper channel cannibalizes credit from the more expensive channel.

Next Week: I will put attribution to the side for week or two while we dive into how customer life time value fits into all this.

Last week I shared some thoughts on marginal unit economics. It is THE fundamental thinking needed to do effective marketing. In order to understand what your marginal unit costs are you need to understand what part of your costs are fixed and what parts are variable.

It’s a tougher problem than the textbooks make out to be.

Let’s use a restaurant as the example.

Some restaurant costs are obviously fixed:

• Rent
• Owners’ time
• Manager cost
• Furniture leasing costs
• Menu costs

The most obvious variable cost is the cost of goods for the food eaten by the marginal customer. That cost could become fixed if the food was about to go bad, but let’s assume the restaurant is better managed than that.

The biggest question is the cost of the servers and cooks. The first server and cook are pretty fixed. You need someone there to make and serve the food even if no customer walks in the door (unless you have the manager or owner do it). In theory any staff beyond the first (for a specific role) is variable depending on how many customers you expect to have.

Basically you have a step-change function. The first two staff can handle x number of customers, but once you get to a specific point you need more staff. There is a fuzzy zone where you  could have one server who is scrambling or you could have two servers who are relaxing. That fuzzy zone decreases in size the bigger the restaurant but likely never really goes away.

After you have staffed your night, then the marginal staff cost for any given customer is zero.

So are staff costs fixed or variable?

It depends.

If you are talking about bringing in one additional table of customers it is almost definitely fixed. If you are talking about bringing in 1000 additional tables of customers they are definitely variable.

“In the short term all costs are fixed. In the long term all costs are variable.”

We saw a similar dynamic at A Place For Mom. We would source leads for our senior living advisors (SLAs) who would work with families to help them find senior housing. Overall we knew that SLAs could handle about xx leads per week. If they got more than that on a consistent basis then we saw our overall conversion rate start to slowly decrease. However, we also knew that the SLAs could handle almost twice the normal volume in a given week without any loss of overall performance.

So on a given week we had no marginal operations costs from sending an additional lead. But for planning purposes, the operations costs of handling leads were fully variable.

I believe staffing at restaurants is similar.

It was a complicated problem for a bunch of Harvard/Wharton/Stanford/McKinsey/Bain/BCG MBAs to solve at A Place For Mom. One can’t expect an owner-chef at a restaurant to figure out the math.

But without the math it is impossible to make rational marketing decisions.

What are your marginal costs?

Simplify it.

You know your costs that are definitely variable (COGS). You have a bunch more costs that are short-term fixed, long-term variable. Figure out their marginal cost to serve an additional customer. Add those two numbers together. You now have a range on what your marginal costs are.

For restaurants I have spoken to this works out to 30-60% of their revenue (30% is the cost of the food +30% for the cost of the staff if the staff cost were 100% variable). It means that if the restaurant rebates more than 70% they will be losing money no matter what they do (with some exceptions coming later). If the restaurant rebates 40-70% they may or may not be in trouble depending on how many customers they give that big a rebate to.

Less than 30% they will be making money on the margin – even if the total margin of the company is only 5%.

And that is the key insight.

I have spoken to many businesses who say, “My total margin at the end of the year is only 10%, so if I give a 10% discount I make nothing.”

That is true if you gave a 10% discount to every single customer. But it is not true at all if you give a 10% discount to incremental customers.

Let’s see it in a chart:

Now what happens if he gets an additional 1000 customers at a 10% discount:

See what happened there? Even though we gave a 10% discount (our ENTIRE MARGIN!) to these new customers, we ended up growing our total margin from 10% to 12.8%. It’s magic.

As long as you can acquire customers for less than the cost to service those customers your business’s margin dollars will increase, regardless of the percentages. If you acquire customers at a higher marginal margin your overall margin percent will increase – even if those marginal customers have a lower total margin than your existing customers (in the above example, you could discount 45% on those incremental customers and your 10%

It’s a little mind-warping, but there is no more important concept to understand in marketing a business. And it’s a way of thinking that even many Fortune 500 CMOs don’t seem to fully internalize (in fact CFOs tend to get it more often than CMOs in my experience)

One (very very important) caveat: This only works if those new customers are truly incremental. If they aren’t it’s called cannibalization and the whole house of cards can come tumbling down.

Up next: Cannibals!

When I taught my first MBA course I knew that the students would not remember everything I taught them. I also knew that whatever I taught first (and returned to throughout the course) would be internalized more than anything else. So I thought long and hard about what the first thing should be.

I landed on Marginal Value.

It’s a relatively simple concept: Instead of looking at averages, you look at the costs and benefits of an incremental decision. If the benefits outweigh the costs then that action is worth taking. If they don’t then you should not take that action.

As simple as it is, it is ignored by many many marketers.

Consider two very different businesses:

A retailer sells physical goods. Say they sell only one product: Product A. They sell it for $100 and it costs them $70 to buy it. Their margin is $30. If they discount Product A more than $30 (30%) they will be losing money on each sale. Now $30/sale may not be enough to keep them in business (they need to pay for their staff, the real estate rent, marketing, overhead like accounting, etc.), but if they sell enough they will be fine. But no amount of sales at $69 will keep them in business. In fact, the more they sell the more they lose. It’s the classic joke of “Sure we lose money on every sale, but we will make it up in volume.” More volume only works if your marginal benefits are higher than your marginal costs.

But a discount less than $30 could work.

If they sell 1000 units at $100, that’s the same profit as selling 2000 units at $85. If a 15% discount more than doubles their sales, then they should absolutely do that. But if they discount 29%, then they need to sell 30,000 units. That seems unlikely…

A lot of retail is like this – or worse. The marginal margin on products after their cost is very low. It doesn’t leave much room for discounting. And when you do discount, the lift you need to see if so high that it is unlikely to be the right choice. Do we really thing they will double their sales with a 15% discount? (and many products have much lower margins than 30%).

The second extreme example is an airline.

Airlines have huge fixed costs. They need to pay fees to the airports; they need to pay for their planes; they need to pay for the maintenance on their planes. In theory each flight is a variable cost: If they don’t make the flight, they don’t pay for the pilot or the stewards or the fuel (and likely a lot of maintenance as well). In practice they are committed to a certain flight schedule (and given the huge fixed costs of owning the planes it makes a ton of sense to keep them in the air as much as possible).

Now that we assume the flight is taking off, the marginal costs of seating a passenger is really really low. A little more fuel required to lift the passenger and their luggage. The free soda or peanuts provided. It takes a little longer to board which may increase the costs for the gate staff? (That last one is really reaching). I would be surprised if the marginal cost of an additional traveler is more than $20 on a $200 flight.

This means that an airline could give much larger discounts than a retailer and still make the business work. Let’s say a flight is half-full with 100 people at $200/seat. If the airline were to discount the seats by 15% to double the sales (same effect as we showed for the retailer) they would go from making $18,000 after marginal costs ($200 x 100 seats = $20,000 revenue – $2000 costs) to $30,000 ($170 x 200 seats = $34,000 – $4000 costs). That’s a lot better than the break even scenario of the retailer for the same discount.

Airlines obviously know this, which is why their pricing schemes are so much more complicated than retailers (imagine if Old Navy T-shirt prices changes as often as airline tickets?).

Airlines and retailers are an extreme examples. Most businesses sit in-between the two. But most businesses don’t automatically think about marginal economics and so make lots of mistakes.

Retailers discount far more than they should.

Restaurants don’t discount nearly as much as they should.

Next: Fixed and Variable Costs

Last Autumn I taught a course on Online Marketing & Analytics at the University of Washington MBA program. As part of the class I assigned three books. While there was some complaining  about the amount of reading (“Do we have to read the ENTIRE BOOK?” I was asked more than once), overall the books were very well received. At the end of class a few students asked if I had other book recommendations.

This post will be the first in a series on some of my favorite data-driven books. Most are not specifically marketing books, but I learned a lot from all of them.

To start with the three books I required for the course:

How Brand Grow, Byron Sharp

I reviewed this book last year and I recommend reading that earlier (more thorough) summary. The basic point of the book is that most of marketing ‘best practice’ is wrong, but by looking at the data you can find some things that actually work. It is a summary of what we actually KNOW with respect to marketing vs opinions.

Some of the things we KNOW:

1. Brands get big by growing penetration, but by increasing share of wallet
2. Niche brands exist, but brands you consider niche, aren’t
3. Within a category every brad shares the same customers (Mountain Dew and Diet Coke are drunk by the same type of people)
4. Differentiation of product is generally not a good idea.  But you need to differentiate your brand (i.e., unique name, color, tag line, jingle, etc.) so that you are memorable and recallable
5. Loyalty programs are generally a bad idea
6. Customer acquisition is far more important than customer retention

That’s just the start. As I told my class: Read the entire book.

Everything is Obvious (Once you know the Answer), Duncan Watts

By now, if you read non-fiction at all you will have read the Tipping Point. Now you need to read Duncan Watts. Duncan Watts is the science side of the Tipping Point (vs. Malcolm the storyteller). The difference is that Malcolm tells a great story (the best) and Duncan tells you the truth. Watt’s earlier books were interesting, but nowhere near as entertaining at Gladwell’s. This book though is fantastic. It’s a fast, entertaining read and it will put the record straight on the whole idea of mavens and connectors.

Watts is both an academic expert on network theory, and an actual practitioner (he worked for both Yahoo and Google). The title of the book refers to the fact that when we look back at the past we are very good at explaining everything that happened as stories. That causes two problems:

1. It gives us confidence that we can do the same into the future. And evidence suggests we can’t
2. It makes us think that when we see two things one after the other we can assume that A causes B. It often doesn’t

In the world of experiments you can prove this (I’ve long lost count of the number of times I’ve been surprised by the result of an A/B test). In the real world you can’t. So instead we use the characteristics of successful things and use those characteristics as explanations for why that thing was successful.

Some examples:

• Harry Potter was successful because it had a young protagonist who lived in a world that was similar to our own, but more exiting. He was an outsider with a destiny. He was up against impossible odds. But he had a core group of friends that any kid could relate to. It mixed high adventure with the challenges of coming of age. In other words: Harry Potter was successful because it was like Harry Potter
•  Michael Jackson was successful because he started young with a lot of support. He understood the music business from the inside with his entire family. But he was an outsider who needed to breakout on his own. He came along at a time when we were ready for a King of Pop. In other words, Michael Jackson was successful because he shared the characteristics of Michael Jackson

Watts drives home this point with two very compelling stories. The first is about the Mona Lisa which is we are told is the most famous painting because it has the characteristics of Mona Lisa – but there is a twist. It turns out the Mona Lisa was not famous for a long long time. It wasn’t until about 100 years ago that the Mona Lisa was stolen and its fame blew up. It was the theft that made it famous. Now that we have forgotten the origin of its fame, we attribute its prestige the same way we do everything else: by describing it and defining success as that description.

The second story is a music experiment.

Watts used an early-days social network and divided into separate test groups. Each group was given access to the same alternative music. In every group except one there was a real top-10 list of the music that was listened to the most. If you believe that the music at the top of the charts is there because of intrinsic characteristics, then each Top-10 list should be pretty similar.

They weren’t.

Every list had a different #1 hit. If a song was a #1 hit in one world, it tended not to be at the bottom of the chart in the other worlds, so there was some correlation, but that correlation was very small. Basically if we were to rewind the world to 1975 and run it forward again it is unlikely Michael Jackson and Madonna would be superstars a second time.

(Which is a very hard thing to get your head around).

Read the entire book.

Zero to One, Peter Thiel

This is the most recent and most popular book on the list. A lot has been written about Zero to One in the past six months. I don’t think it is a valuable use of my time to re-tread over this ground. But I will share a one of my take-aways that I haven’t seen mentioned very often.

Thiel talks about ideal price points for new products. He says there are four:

$1 products can spread virally. Your marketing plan is PR, SEO and social

$100 products can be marketed with paid online marketing. They work too.

$10,000 products are sold by professional sales people. You need to actually talk to someone at this price, and at $10,000 you can afford the commission of a good sales person.

$1M products are sold by the founder and CEO.

There are two big gaps:

$10 products will not spread virally, and you don’t have enough margin to do paid marketing. They tend to fail.

$1000 products don’t sell without a salesperson (like $10,000 products), but they don’t have enough margin to afford a good salesperson, so they tend to fail.

He talks a fair amount in the book about $1000 products and how they often involve selling to small businesses (and failing). He spends less time on the $10 product, but it is an equally awkward price point.

I wish I had read his book before starting my start-up.

We created a product that connected consumers to restaurants. We filled restaurant  seats when they would otherwise be empty at a deep discount for the customers. It was a great product with better benefits for both sides of the arrangement than any other product on the market (much better than Groupon, Restautant.com, Coupon Books,  Yelp ads, etc).

The issue?

We charged the consumer $10 to get 30-50% off their meal.

We had to hire salespeople to signed up single-location restaurants.

Effectively we went after the $10 and the $1000 price point at the same time…

More book recommendations next week (or when I’m next inspired…). If you have favorite books you can recommend please list them in the comments.

In the early days on facebook (actually not-so-early, but still many years ago) they opened up the ability to claim domain names for pages. For example, my facebook url is facebook.com/nevraumont – I got my last name because I claimed it first. A buddy and I also jumped at the chance to claim a whole bunch of other facebook urls.

If you had a “page” and that page had at least 20 “likes” you could claim any url you wanted. It was like the early days of the internet. A buddy and I got to work. We build pages, added ~5 links to them with relevant topics, bought facebook ads to get 20 likes, and then claimed urls. We have hundreds of them, about a dozen are really good.

One of my favorites is facebook.com/travel.

Over the years the two of us have tried to build side-businesses on top of the urls. It’s a lot harder than it looks. I am more than confident it’s possible if one were to work on it full time. Both of us have way too high opportunity costs to do that. If you (or someone you know) is interested in building a business on top of a premium facebook domain, let me know. We have names in the travel, weddings, automotive, education, financial and legal spaces (among others). Basically the categories that were making money though Google search at the time.

But I digress.

One of the businesses we build was StopoverTravel.com. The idea was to create a repository of tour companies, and then link to them for affiliate revenue. For fun I started writing content on the site. Once a week I would write about a cool thing to do in the world. The site still exists. If you are looking for travel inspiration, check it out. While this was happening, Google launched Google Surveys. It’s a very inexpensive way to survey people on the internet. I had some free credits so I gave it a try. My goal was to get results that could conceivably go viral (I will talk about why that is basically a terrible idea in another post).

This is that story.

What Google Doesn’t Like
The headline I wanted to run was something like: “People have a lot of sex on small group tours” I wanted to get data on how often it happened, how long the hook-ups lasted, and for even more fun: who it happened with.

My first survey design was set up like this:

Screening question: Have you ever gone on a small group tour? Anyone who said “No” would be eliminated and the next questions would only be asked to people who said “Yes” First question: “While on a small group tour, did you ever have sex with someone you met on tour?” The possible answers were things like, “No”, “Yes, with a fellow traveler”, “Yes, with a local”, “Yes, with the tour guide”

I thought it would be great.

Virality here I come!

I submitted.

Then I got this email from Google:

Thank you for using Consumer Surveys. However, your survey has not yet begun running.

We do not allow surveys to run with your submitted content per the Nudity, Obscenity, and other Adult Material.

We don’t allow surveys that contain nudity, obscenity or sexually suggestive material.  Surveys should not relate to porn, dating with a sexual or mature nature or sexual aids & devices.

Please remove all references to adult material and re-submit.

Oops. Time for a new word for “sex”. I asked my partner. Here was his list:

• “Hook-up”
• “Casual relationship”
• “Physical relationship”
• “One night stand”
• “Make-out”
• “Go all the way”
• “Score with”
• “Become intimate with”
• “Biblically know someone”

(My partner is great)

I re-submitted the survey with the word “sex” replaced by the word “Hook-up” (in quotes).

I hit submit.

A day later I received another email from Google:

 Thank you for using Consumer Surveys. However, your survey has not yet begun running.

We don’t allow surveys that contain nudity, obscenity or sexually suggestive material.  Surveys should not relate to porn, dating with a sexual or mature nature or sexual aids & devices.

Please remove all references to adult material and re-submit.

Apparently no “hook-ups”. I tried again. This time replacing the offending language with “Did you ever begin a relationship (even if very short term)?” Google wrote me back:

Thank you for using Consumer Surveys. However, your survey has not yet begun running.

We do not allow surveys to run with your submitted content per the Nudity, Obscenity, and other Adult Material.  The second question of your survey has innuendo of dating with a sexual/mature nature.

Please remove the part from your second question that reads “(even if very short term)”.

This question could work if it were about non-mature content.  For example,

“While on a small group tour have you ever begun a relationship?”

Answer Choices: Yes – I met my future Spouse Yes – I made lifelong friends No

Answers that point to specifically who in the group (tour guide, locals, etc) and the language about “even if very short term” make the current question seem like it is about mature content.

Still a no. But at least they were being helpful now. I was speaking to a real human being who was making judgement calls. I made some more changes and wrote him back:

Thanks,

Oops. I think I pushed too far. I took a different tactic:

Hi,

Radio silence. I wrote back again:

Last month I shared the applications I use to help succeed with the “Getting Things Done” system. In this post I am going share other applications I use on a regular basis to increase my efficiency. As I said last week, these are just the tools I have actually found to work – tools I have incorporated into a system that actually has improved my efficiency. There are many tools out there that I am sure work in theory (Everynote comes to mind) that I haven’t been able to make work in practice. Please feel free to share your successes in the comments below.

The Tools:

Transportation: Uber, Lyft, Flywheel, Tripit

I’m still amazed that everyone isn’t using Uber (or at least everyone who lives in an Uber-allowed city that owns a smartphone). It’s 25% cheaper than taxis and a heck of a lot more convenient. If that wasn’t enough, the cars and nicer and the drivers are more pleasant. Taxis are going the way of the dodo.

When UberX isn’t available I will use Lyft. Lyft is as good a product as Uber, but generally, in my unscientific estimations, their time to pick-up is significantly slower. One advantage of Lyft is they can pick you up at the Seattle airport. UberX can’t do that for some reason.

Rarely I will use Flywheel. Flywheel is a taxi-company’s answer to Uber. It works the same way (almost) but with slightly higher charges – including a $1 flat charge MORE than if you picked up the phone! The $1 Charge goes to the provider of the app obviously and no account was made of the ‘savings’ from avoiding the dispatch costs. Sometimes I will actually pay the $1 because the dispatch experience is so terrible (I’ve seen a 4 minute pick-up time on Flywheel. Then I called the dispatch and was told it would be 30 minutes. I assume they were giving the fare to a friend).

The other travel app I have become increasingly reliant on is Tripit. Tripit automatically picks up all travel emails for you and both puts them into the app in a nice summary format, and adds them to your calendar. With one click you can share trips with friends. And if it misses the trip for some reason (it happens sometimes) you just forward the confirmation email to plans@tripit.com and it fixes the itinerary. Fantastic product. There is a paid version but I use the free one and have never looked back.

Contact Management:

LinkedIn is getting better and better as a mini-CRM tool. It’s definitely not all the way there, but they are allowing start-ups to link into their APIs and that has created some real innovation in the space. Even with a lot of looking I haven’t found the mini-CRM I think I want (I’ve sketched it out. Maybe I will get it built myself someday). In the meantime, here are the applications I use on a daily basis:

Rapportive

This is a simple chrome extension for Gmail. It pulls in LinkedIn and Facebook information (including pictures) into your gmail account so you can see it at a glance when you are reading or writing messages. A simple, ‘nice to have’.

EasilyDo

EasilyDo is an iPhone app that bills itself as a complete CRM tool. It’s not that, but it does have some really nice intuitive features. Here is what I use it for:

• It scans your email every day and gives you lists of people that are not in your address book. You can add them with about 3-clicks
• In that same email scan it will find people who ARE in your address book and find new information to add (phone numbers, email addresses, physical addresses). It’s a really smart feature that works. Again: Click three times and the info is added to your existing contact
• It also goes through your address book on an ad hoc basis and finds duplicate accounts. With a few clicks you can merge those accounts into single accounts. A godsend.
• The above three options require a fair amount of clicking to do at scale. They also have a paid version that does all three automatically. It’s likely worth paying for, but I haven’t pulled the trigger yet
• Other smaller features include:
• You can set automatic SMS to go out under specific conditions. I have it SMS my wife if I leave the office after 5pm, letting her know I’ve left (and am ‘likely’ coming home)
• It feed in local events you can check out or delete with an easy swipe. I’m not a sports fan, but I like that it feeds me the local sports schedule so I know to avoid traffic on specific days/times
• It feeds in any events you get on facebook and allows you to add them to your calendar
• It has its own ‘newsfeed’. It shows only the stuff that it thinks you will find most interesting from facebook. It’s like a ‘best of’ to cover friends getting married, giving birth, getting new jobs, etc.
• It also has a daily facebook picture highlights. It just pulls the top pictures from your facebook graph you can look through very quickly and then swipe away
• It pulls in your tracking codes for any deliveries. It pulls in travel itineries. It pulls in OpenTable reservations.
• It gives you the weather and expected travel time from your home to work (if it’s that time of day) or work to home (at the end of the day)
• It has other features too, but I haven’t fully explored the app beyond those listed above

Newsle

This is another simple application. It connects to your LinkedIn account. Then it scourers the web looking for news stories on anyone you are connected to on LinkedIn. It is fun to read about news generated from my friends and acquaintances (and another reason I don’t accept strangers or “Twitter friends” into my LinkedIn network).

Unroll.me

I love this application. When you sign-up (it’s free) it scans your inbox for automated emails. It shows you every mailing list you are on. Then you just scan the list and choose one of three options for each:

1. Unsubscribe
2. Stay Subscribed
3. Roll-up

The first two options alone are fantastic. It’s the easiest way to mass-unsubscribe to all those things you keep meaning to. The third option is added brilliance. There are lots of mailing lists I don’t want clogging my inbox, but I’m not ready to fully unsubscribe. Roll-up is the answer. I get a roll-up email daily (you can choose the frequency). The roll-up email has screenshots of all the mailing lists I have ‘rolled-up’. Instead of getting a dozen emails a day, I get one with 12 emails inside it. If I am interested in that particular ‘inside email’, I can click through and read the entire thing.

That daily roll-up email also tells you if Roll-up has found any new mailing lists you have subscribed to (or more likely have recently sent you something that it did not pick up before). Every week or so I go on and do a mass subscribe/unsiubscribe/roll-up of anything it’s found that week.

Navigation:

Even though I have an iPhone and Apple Maps is the default application when I click on an address in my calendar, I use Google Maps almost exclusively. I find it’s significantly more accurate. The only exception is Waze (An Israeli company that has been purchased by Google). Waze crowdsources traffic patterns to give you real-time suggestions to change your route to avoid traffic. It’s great in theory, and my friends in LA love it, but it has issues in Seattle (I think due to not enough people crowdsourcing for it). It will often send you on a 1-block detour that I’m pretty sure saves no time at all. One time in really bad traffic (on a roadtrip to a comedy concert) it started sending us back and forth on the same road. I think it’s best use in a city like Seattle is coming home from a long weekend when you are trying to figure outwhich highway to take, or whether you should hop off the highway to take the back roads.

Twitter:

If you haven’t read my Twitter Follow/Followback policy I suggest you do so now (It’s the most popular thing I’ve ever written according to my analytics).

Okay. You are back. Here are applications I use to manage my twitter account.

BufferApp

There are lots of Twitter applications that help you spread your tweets out over time. Buffer is my preference mainly because it’s so easy. I pay for their premium membership which lets me ‘buffer’ up to 199 tweets. It also lets me link a half-dozen or so social media accounts, so I use it for Facebook and LinkedIn as well.

Here is how it works:

You create a posting timeline for each of your social media accounts. I post 3-times per day on Twitter, once a day on Facebook and LinkedIn. I’ve changed that frequency over time. It’s largely based on how much interesting content I find on an average day I want to share. Turns out that’s a little more than 3/day on Twitter, which has let me build up a ‘buffer’ of almost 200 tweets. (I’ve hit the 199 cap twice). On Facebook I restrict it to once a day mainly because I think that’s the most my friends really want to hear from me regarding interesting web content.

Anytime I find something interesting I link to it in a tweet on BufferApp.com (desktop) or on my iPhone app (phone). Sometimes I can do it within an application (Like Feedly – see below). If I think the tweet is not ‘topical’ (i.e., it’s not particularly relevant right now – say I found data on the lineage of Ghengis Khan) then I put it at the end of my queue. If it has something to do with recent news (or say it’s Halloween-related near the end of October) then I put it in the front of the queue. Buffer makes it easy to do both.

I tend to do my reading in clumps. BufferApp allows me to spread out that content over time in my twitter feed rather that throwing it all at my readers at the sametime.

BufferApp has a free option, but it limits you to about a dozen tweets in your feed. I pay them $10 a month or so to get a feed of 199 tweets.

ManageFlitter and TribeBoost

I’ve spoken at length about ManageFlitter and Tribeboost in my Twitter post (but you’ve already read that, right?). When I began follow/unfollowing folks in my space I started with ManageFlitter. When I got to scale (~3000 followers) I started working with Tribeboost. At about 10,000 followers, Tribeboost capped out at following 600 people per day. At that point I ramped back up Manageflitter as a supliment. I now follow 600/day with Tribeboost (completely automatic, ~$100/month) and 300/day with Manageflitter (I have to create the lists for them to follow – I do that with their “Power” Tool) (~$60/month). Unfortunately there is no way to automate the unfollowing with ManageFlitter if I continue to use Tribeboost (the systems don’t talk to each other so Manageflitter would end up unfollowing people immediately after they are followed by Triabeboost).

My solution is not ideal. I manually go in about once a week and unfollow the people I have followed with ManageFlitter that have not followed back. It means giving those folks well over a week to follow-back (I generally like to give a week), and clicking about 2000 times a week. Painful, but I’ll do it with two screens open, so I’m multitasking and clicking doesn’t take much brainpower. Given the delays it means I end up following a LOT of people who aren’t following me back – even after a week. I can get away with it because my follower count is high enough now, but this method wasn’t really feasible until I got to about 10-15K followers and had a lot of businesses following me (which I don’t automatically follow-back) which gave me ‘room’ in my account.

FollwerFrenzy

I’m not sure this tool is good to use, but I haven’t stopped yet. Basically you give them a a list of hashtags. They automatically favorite tweets for me with those hashtags. It gives me a booked-mark list of a lot of interesting content. It has the added benefit that some people will follow you after you favorite their tweet (a little less than 10%). This is why I only pro-actively follow ~900/day. Since you are capped at 1000/day, the extra 100 gives me room to follow-back any real people who follow me due to the book-marking.

Echofon

Because I follow-back I follow a lot of people (18K as of this writing). Since I tend to follow people who are in the marketing and data analytics space, my tweet stream is surprisingly good. But sometimes I want to read a subset of the stream – say people I know in real life, or mass media agencies, etc. For that I create lists. Echofon is what I use to read the streams from these different lists (it amazes me that this is not built into the base Twitter ap!). I’m sure there are better solutions for this and that Echofon could do a lot more for me if I let it, but thisis what I do right now and it seems to work.

JustUnfollow

I’m sure this app does a lot too. I use it for one thing. Every day it sends me an email with a list of everyone who has followed or unfollowed me. It gives me some basic metrics on how I’m doing. I can take the two lists and subtract one from the other to see what my net gain is. I can look at my gross gain and see if whatever recent technique I used worked (or whether that tweet I made that went mini-viral had any impact). And I can scan the ‘unfollows’ to see if there were any real people on there to get an idea if I offended anyone (It’s almost always spammers and companies that unfollow me). Handy I guess but hardly necessary.

Socialoomph

I use Socialoomph to schedule posts about my blog. Whenever I write something new (like this post), I hop onto my Socialoomph account. I schedule a tweet saying something like, “New Marketing is Easy Post: Applications I Use”. I schedule it for the day after the blog post is scheduled to go live. Then I start scheduling more. Out of my 20K+ Twitter followers on average one of my tweets is seen by about 2000 people. So I Tweet about the same post many times – once a day in fact. I schedule the tweets to go out every 25 hours (so a slightly different time each day). Each tweet is unique. I work my way through the post on one screen and SocialOomph on the other screen. When I read something in the post I think might be interesting to some people, I write a tweet about it. I repeat through the entire article. When I get to the end of the article, I’m done. Sometimes that takes 3 tweets (and 3 days) sometimes it takes 60 (and two months).

Content:

I’m often asked how I find the content I tweet about (or talk about to my friends, “How do you know that!?!?” is a common refrain…). Here is how I do it:

AlienBlue/Reddit

Reddit is awesome. Lots of people from around the world crowdsourcing the coolest stuff. I read it almost exclusively on my iPhone with the AlienBlue app. I paid for the premium version ($2 one-time charge) but I am not sure what it gets me. I’m happy to give them the $2. It’s worth it.

If you don’t use Reddit, your first experience can be terrible. You start with their default ‘subReddits’. Some are fine, but they likely aren’t the things you are most interested in. The other issue is the subReddits often get dumbed-down vs the specialized ones with more engaged readership. Sometimes a SubReddit will move from non-default to default and you can actually see the decline in quality (DataIsBeautiful is an example. I shared a LOT of content from that subReddit before it was default, and very little afterwards).

Here are some of the subReddits I subscribe to that I will sometimes pull content from for my Twitter feed:

/dataisbeautiful

/askhistorians

/AskReddit

/Bestof

/Books

/IAmA

/AMA

/News

/WorldNews

/Science

/Technology

/Tech

/HistoryPorn

/Business

For my own personal enjoyment I read:

/MarvelStudios (updates on what Disney is doing with the Marvel properties)

/TheWalkingDead (commentary after each episode)

Feedly

It took me a long time to get an RSS reader. It’s changed the way I consume content. Feedly is amazing as a desktop application, and even better as an iPhone app. I subscribe to blogs and then swipe through their headlines inside the app. When I see something interesting I click through to read it. If I think it’s worth sharing I can even send it to buffer without leaving Feedly. It is likely my most-used app on my phone (maybe more than email and twitter)

Here are some blogs I subscribe to (only listing the ones that get updated regularly. Others like Malcolm Gladwell and Michael Lewis are subscribed to as well, but they rarely have new content):

FivethirtyEight

Datablog (The Guardian)

Flowing Data

Information is Beautiful

Bryan Caplan

Dan Arierly

Steven Landsburg

Tim Hartford

Freakonomics

HBR

Slate Articles

Techcrunch

The Economist

Wired

The New Yorker

Marginal Revolution (Tyler Cowen)

Altucher Confidential

Dilber Blog (Scott Adams)

Sam Harris

Slate Star Codex (a new favorite!)

Seth Godin

Tim Ferris

Xkcd.com

Occam’s Razor

I also follow about a dozen SEO blogs. Most of the content they share is junk, but I keep it in one folder on my Feedly and scan it from time to time. If anything really important comes up in the SEO space it will be surfaced here and I’d rather not miss it.

Podcasts

I listen to a lot of podcasts. I don’t listen to music when I run, instead I take in podcast content. I run about 6-7 hours a week. That’s enough for me to stay updated on this list:

This American Life

Serial

Tim Ferris Podcast

RadioLab

The Moth

Freakonomics

Startup (New. I’m liking it. By one of the reporters from TAL and Planet Money)

Dan Carlin’s Hardcore History (Excellent. But LONG. 3h+ podcasts the come out once every quarter or so)

Self-Tracking

I go back and forth on how “into” self-tracking I am. For a while I went in deep. Then I lost a lot of data and got discouraged. I will get back into it in the new year I think. Without going into too much details, here is what I have used

Reporter App

The best way to track things during the day (or at the end of the day, or the start of the day). Simple and easy to use. And very customizable. I created tracking questions like, “How well did you eat today ranking from 1-5”, and “What dreams do you remember from last night” and “What did you eat since your last report”

Fitbit

I use the Force (from before it was recalled). It’s my watch and step counter. It’s a slight motivation to walk a littlemore than I otherwise would if I wasn’t tracking.

Everyday

iPhone app I used to take a picture of my wife every day as she got more pregnant. Then I lost all the data. I now have a better way to back it all up (i.e., I pay Apple $2/month). Will try again taking a daily picture of my baby girl when she arrives.

Automatic

It plugs into your car and tracks everywhere you go – and how good your driving is. It also helps you find your car when you can’t remember where you parked…

Swarm/Foursquare

I don’t care about the games, but it’s an easy way to keep track of the restaurants I have eaten at if I want to find them again. It also has some nice food recommendations. More actionable than Yelp when you are already at the restaurant and are trying to figure out what to order.

TomTom Watch

I use TomTom for running. It’s not great, but it tracks heart rate better than other things out there (from the wrist). And the GPS is pretty good. Biggest issue I have with it is it sometimes takes a few minutes to find a satellite. Which wouldn’t be too bad, but you can’t even start the timer part until it’s found a safelight. I don’t have the patience to wait, so I start running and then have to estimate how long it took to find a satellite if I want to run a specific length of time (as I often do)

Other applications

I’m not sure where these fit in, so I am including them all at the end

Postmates

Courier delivery on demand. Love these guys. I pay about $3 for them to deliver Chipotle to my office for lunch. I’ve also used them to buy me a pair of jeans from Banana Republic, and pick up a 2L bottle of Coke from the convenience store for my wife at 2am. It’s like a TaskRabbit that actually works. (Bonus App: Chipotle app lets me pre-order my lunch for pick-up)

Dashlane

Took me a while to get on the password protection train, but it was well worth it. I have double authentication on my Gmail and financial accounts. For everything else I use Dashlane. I need to remember my (very complicated) Dashlane password. Then Dashlane manages everything else for me. When I create a new account I click a button and Dashlane creates a ridiculous password (Something like: Fgj33^53ndq@#4T). It is built into my phone and browser, so when I go back to a site, Dashlane automatically populates the password for me. So simple. I pay about $20 a year I think. Bought three years in advance.

Kayak and Zillow

These are my go-tos for finding travel options and real estate respectively. Both have done a very good job and (at least for now) stand head and shoulders over the other options in the space.

That’s it for now. As I add new Apps I will come back to this post to update the list. Are there apps you like a lot that aren’t here? Please comment below – and describe HOW you use them. The how is as valuable as the what I’ve found with these products.

I recently wrote an article for MediaPost’s Boomer section. The ask was for an article on how Boomers are using the internet. I shifted the focus a little (as I tend to do) to talk about how many companies focused on seniors are just now realizing how important the internet is (shocking I know…). Unfortunately this is happening just as Boomers are shifting from the internet to mobile.

Seniors (and Boomers) have tended to be late adopters. That’s helpful for marketers as we can learn from other industries and we don’t need to be overall innovative (just innovative within the senior space). The issue is that it seems those marketing to seniors also seem to be late adopters.

There are companies trying to do cutting edge stuff in the senior space. The issue is those folks are TOO ahead of where their market is. It’s a fine line.

I call it “fighting the last war”

Here is the full article:

http://www.mediapost.com/publications/article/241352/are-you-fighting-the-last-war-in-your-boomer-mar.html

My first real specialty in marketing was customer lifecycle management (CLM). That’s a fancy term for caring about the total profit you generate from a specific customer. That can mean everything from channel management (figuring out which sources of customers are more valuable for you than others) to cross-selling and upselling; From customer acquisition to customer retention. It was a pretty broad way to look at marketing – and a very analytic way. It was a nice base for the rest of my career, and I highly recommend it to students looking for an initial foray into marketing.

One sub-specialty within CLM that I spent a lot of time with was Loyalty Programs. Loyalty Programs have become very popular over the years. A big reason is that they are so visible. When a company has an excellent CRM system or distribution system or save desk it is basically invisible to the public and their competitors. But a Loyalty Program is obvious to everyone. When a CEO sees a competitor (or even a company in a different industry) with a Loyalty Program they will often ask their CMO: “Why don’t we have a Loyalty Program? I’ve heard that Loyalty is more important than customer acquisition, so shouldn’t we have one of those?”

When a CEO asks for something that involves spending more money on marketing, most CMOs say yes.

The result is more companies with Loyalty Programs – which causes more CEOs to ask, “Why don’t we have one of those?” It’s a flywheel.

A trend like that can get something started, but most businesses are pretty good at shutting things down if they aren’t working. Somehow Loyalty Programs have stuck around, and if anything become more prevalent (especially in travel and retail). And yet I argue that most of the time they are a bad idea (in retail – travel Loyalty Programs are generally done right). The reason is Selection Effect.

First: What do I mean by a Loyalty Program?

“Programs” that drive loyalty could mean many many things. Amazon Prime drives loyalty, but in general it is not considered a Loyalty Program (but maybe it should be). In general people mean one of two things when they call something a Loyalty Program:

1. A program that gives you points when you spend at a company. Those points sit in your account and, after they accumulate, you can redeem them for product or merchandise  (usually the same company’s products, but often other companies as well)
2. A tier-based program. Tier programs give customers defined benefits after they have performed specific activities in a set time period (usually something like a specific number of stays, number of miles traveled or dollars spent)

I will use a separate post to talk about the second type of Loyalty Program. For now let’s stick with the classic “earn and burn” points-based program.

There are definitely different varieties of points-based Loyalty Programs, some of which at first glance don’t look like point programs at all. The most common of these is the “Frequent Visit” stamp card. You’ve definitely seen these: Buy ten coffees, and your next one is on us. Another example (if you are as old as I am) were those Subway stamp “passports”. These examples are very different from the more a standard Marriott Rewards Points, but the principle is the same: Spend now to earn points (Marriot Points/”Coffee Points”), then later spend those points for stuff (Hotel stays, free coffee). The only difference is how many points you earn when you spend and how much those points are worth when you spend them.

What’s the value of a Loyalty Program?

It’s definitely not to drive Loyalty. Point based Loyalty Programs are effectively complicated discount programs. In exchange for being tracked (sometimes companies don’t even ask for that), a customer gets points today, that can be used for discounts tomorrow. The difference between paying $0.90 for a coffee and paying $1 for a coffee, with a 10-stamp loyalty card is almost the same – or for that matter a $1 coffee that earns you 10 points, with each point being worth 1-cent on future coffee purchases. Here are the differences:

1. Breakage: Some percentage of people will not use the points you give them. Those un-used points are called “Breakage”. Depending on the design of the program breakage rates can vary from 0% to 80%. In the above example, if your program has 33% breakage (pretty common), you could give you 30-points on that $1 coffee purchase and it would be the cost to you as offering the coffee for$0.90.
2. Psychology: Generally people value things that aren’t cash at a lower rate than cash (Duh). People don’t buy gift cards for more than their face value – especially for their own use. But sometimes if a program is designed right and you pull the right strings you can get people to act on points when you would not be able to on dollar discounts. Usually this isn’t done with the base program, but rather with promotions. “2x points” sounds a lot better than “An extra 1% off”, and you would not be surprised to know it gives a much bigger sales lift.
3. Selection Effect: Only some people will join your Loyalty Program. Most people will just not care enough. Who joins? You should not be surprised to know it is to some extent price sensitive people, but mostly it is people who are your highest use customers.

Apart from those subtle differences (that are all worth exploring as I wrote more about Loyalty Programs on this blog), basically Loyalty Programs are price discounts. And price discounts do not generally drive loyalty.

So if the value of the Loyalty Program isn’t loyalty, what is it? There are a number of uses, but the most common one is to collect data on your customers. Without something like a loyalty program, Marriott would only know people per visit. They wouldn’t know who their best customers were in order to treat them differently.

But that is NOT the reason most companies build Loyalty Programs. Most build them because they think they will drive Loyalty (and because everyone else is doing it).

Why do companies think Loyalty Programs are Driving Loyalty when they are not?

Most senior executives aren’t stupid. Why would they think that their Loyalty Program is driving Loyalty when it really isn’t? The answer is the Selection Effect.

If you take all your customers and break them into two groups: Those on your Loyalty Program and those not on your Loyalty Program, it will be very very clear that the customers on your Loyalty Program have (1) Longer Tenure with your company, (2) Spend more, (3) Churn at a lower rate.

Wow.

That sure sounds like the Loyalty Program drives Loyalty.

But it’s a classic case of correlation not being causation. In this case the impact is the reverse:

Customers who are planning to spend more with your company in the future (and usually have spent more in the past) are the most likely to join your program. Why? Because they have the most to gain.

There is a cost to joining a program. The cost usually isn’t very high, but it does usually mean signing-up with some forms and keeping a card in your wallet. But that small barrier to entry is enough that many customers will not join your program – specifically customers who aren’t planning on spending very much with you in the future. Meanwhile the heavy users would be crazy not to sign up and get 1% or more off all of their purchases.

So when you compare Loyalty members to non-members, the loyalty members always look more loyal. But the kicker is it’s not the program that is making them loyal, it’s their loyalty which is getting them in the program.

Most companies stop right here by the way. The head of the Loyalty Program shows the bar chart showing members spend more than non-members and the CEO is happy and they keep spending money on the program.

Some companies try to get more sophisticated. The first thing analysis will do is compare Loyalty Members before and after they join the program. They eliminate all the members that were not purchasing for at least 1 year prior to joining, and then look at their spend before and after joining. The really good analysts will try and create a control group of similar customers who did not join the program. It’s commendable to try, but this has a similar, if slightly more complicated problem.

Imagine two customers that are identical. They have both been shopping at your store regularly for a year. Then one day they both come into the store. Customer A is planning to move to Phoenix in the next few weeks. Customer B actually lives across town and was only visiting when he visited his mom who lives nearby. As luck would have it, Customer B just bought s new home near his mom, so he is planning to visit the store more often in the future.

Guess which customer signs-up for your Loyalty Program that day?

And guess how much impact the program itself has on his increased spending?

It’s another example of selection effect. You can’t get around it.

Getting Around Selection Effect

There actually is one way to get around selection effect. It’s called A/B testing. Take a group of customers and randomly assign them to two groups. Then do something to Group A but not to Group B. Watch and see what the long term impact is across the two groups.

Because they were randomly assigned there was no Selection Effect.

The problem is that this only works if you do things “Below the Line”. As soon as something is public and everyone can see it, you can’t keep Groups A and B separate anymore.

This makes A/B testing great for website landing pages or email campaigns or direct mail campaigns or even television campaigns (if done regionally). But if you want to create a company-wide Loyalty Program, it’s really hard to only offer it to 50% of your customers.

But it can be done.

The best time to do it is when you are first planning to launch the program. Instead of launching it fully-formed to the public, you can try launching it in ‘beta’ as a below-the-line test to a select group of your customers. And instead of offering it to your best customers, offer it randomly. Take your entire customer database and divide it in two. Email half of them with an offer to join the program and don’t send anything to the other half.

Then, instead of measuring the lift of people who joined the program vs those who did not, just measure the lift of the group that was OFFERED the program vs the group that wasn’t. If you did your job right in randomized group selection any difference you see will be driven by the existence of the program. You may even see a lift in the people who you offered the program to, but they didn’t join (this is the best case – increased sales without any increased cost).

Any time you create any program without doing a proper A/B test you are at risk of having your results invalidated by the Selection Effect. Loyalty Programs are the biggest example, but I’ve seen it with everything from add-on services (Customers who buy the security package have lower churn, so let’s give away the security package for free), to customer service (customers who visit the branch more often are less likely to churn, so let’s drive more customers into the branch).

Beware the Selection Effect.