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Congrats to Playdom and Disney! July 27, 2010

Posted by jeremyliew in games 2.0, playdom, social games, social gaming.
1 comment so far

As reported today, Lightspeed Portfolio company Playdom is being bought by Disney for up to $763M.

Congrats to both parties, as well as to John, Dan, Ling, Chris, Rick and the rest of the Playdom team!

How to estimate Lifetime Value; Sample cohort analysis July 19, 2010

Posted by jeremyliew in Ecommerce, ltv, subscription.
8 comments

In many businesses, repeat purchase behavior is a key driver of value. Many companies track % of repeat purchases as a key business metric. This is useful in steady state, but can sometimes be quite misleading if the company is showing substantial growth. By definition, growth implies many first time customers, and the mix of these new customers can distort the view into how much repeat purchase behavior is actually occuring.

I prefer to try to analyze repeat pruchase behavior, and hence, estimate lifetime value, by doing cohort analysis. This is approximate by definition, but it can give you some sense of lifetime value well before you actually see a full customer lifetime, which can help in accelerating decisions about marketing and customer acquisition.  I recently posted about how you can improve LTV and CAC for your subscription or repeat purchase business.  But how do you estimate Lifetime value?

I’ve uploaded a spreadsheet with a  sample cohort analysis, using representative but dummy data to illustrate how to do this.

In this particular example, I look at a hypothetical subscription business. Assume that the business has been in operation for one year. First, divide the users into cohorts depending on when they initially subscribed to the service.  I calculate retention at the end of month N by dividing the number of subscribers still subscribing after month N by the total number of subscribers that started in each cohort.  These are the numbers in blue. Obviously, for the subscribers that started in month 1, we have 12 months of retention data, for the subscribers that started in month 2 we have 11 months of retention data, and so on.

By averaging across the cohorts, you can get an average retention rate at the end of one month, two months and so on. As the cohorts age, there are fewer datapoints to average over, and hence the potential for error is greater. However, it is still a useful exercise to get an early indication of how the business looks.

A typical pattern found in subscription businesses is that after a steep drop off after an initial period, month-on-month attrition rates tend to level off. You can see a similar pattern in this example, where after the first month, month-on-month attrition rates are around -6% (ie month N subs ~ 94% of month [N-1] subs).

If you see a pattern like this, you can extrapolate forward using the same month-on-month attrition across several years. As you can see in the model, we extrapolate an average lifetime of 9.77 months by extrapolating forward over 5 years of data.

So if you were a subscription business charging $20/month with 90% gross margins (after accounting for customer service costs for example), then you would attribute a lifetime value for a new customer of 9.77 x $20 x 90% = $176. This sets an upper bound of what you would be willing to pay to acquire a customer (although in practice, you would prefer to see a ratio of CAC/LTV in the 25-35% range).

This example is for a subscription business where the key value driver is the number of active subscribers. However, you can conduct similar analysis on any type of repeat behavior business. In a social business the metric might be activity (e.g. how many users posted a photo this month), and in a social game the metric might be dollars spent in virtual goods that period. The measurement periods may vary according to the tempo of the business. Many social games do their cohort analysis on a daily or weekly basis,  whereas some ecommerce companies whose purchases are less frequent may do their cohort analysis on a quarterly basis.  This will dictate how long you have to collect data before you have enough data to project forward.

Different billing mechanisms can complicate this (e.g. an annual billing system will by nature skew average lifetime upwards) and while these can be important levers, it is usually helpful to hold billing constant and compare cohorts on a same-billing basis, at least initially. However, this cohort analysis is also useful tool to see what the impact of changes in billing, registration flow, product features etc can have on retention as you can often see an increase in early month retention from later cohorts.

The spreadsheet for the sample cohort analysis is read only but you can download it to play with it yourself.

I’d love to hear from others how they estimate lifetime value.

Why online brand spending will create new winners in online ad networks July 14, 2010

Posted by jeremyliew in advertising, branding.
9 comments

One of Lightspeed’s consumer internet predictions for 2010 is that brand advertising dollars are going to start to flow online at scale. Two thirds of all ad spending in the US is for brand advertising, yet three quarters of online ad spending is direct response.

The recession of the last couple of years has provided a catalyst to drive more brand marketers online in an effort to seek greater efficiency in their media buys, and as they have tasted some success, they will continue to spend online as their marketing budgets recover.

Late last year the IAB put out a very interesting study about building brands online. I recommend that you read the whole thing if you are involved in the online advertising industry.

For those of you who won’t, here are some highlight charts:

Marketers believe that the internet can be a branding mechanism:

But the bulk of online advertising volume today is not considered effective for brand building:

This is because most online ad inventory has been optimized for direct response advertisers, whereas brand marketers want to see their traditional metrics (click image to see full detail):

Furthermore, brand advertisers want relationships with the media companies that they work with, not simply self service efficiency (again, click image to see full detail)

Most brand advertisers have primarily stuck with portals and big publishers who offer brand safety, reach/frequency control, reporting on the metrics that they care about and strong relationship, but often tied to higher priced media. As brand advertisers seek better efficiency from their online media budgets, they will turn increasingly to ad networks. Although there are over 300 ad networks today,  the vast majority of them have grown over the last 10-15 years by optimizing their offering for the direct response advertisers who have constituted the vast majority of online advertisers to date. I think we’ll see a new generation of ad networks emerge who are tuned to cater to the specific needs of brand advertisers, and I’m actively looking to invest in companies with this mindset.

Congrats to Living Social for launching 25 new cities today! July 13, 2010

Posted by jeremyliew in living social, local.
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The local deals market continues to grow rapidly. I’ve posted before about why we invested in Living Social.  Today Living Social launched 25 cities, going from 27 cities to 52. Here is a snapshot of our coverage today:

Groupon continues to be the market leader, in 94 US cities and (through acquisition) 21 countries in Europe and Latin America.Today we put a bit of a dent in their lead, but we watch them every day.

We also continue to watch the new entrants, including Buy With Me, Tippr, DealOn and Homerun. While none of them are live  in more than 10 cities at the moment, some have announced plans to be in as many as 20 cities by year end.

Congratulations to Mandy, Randy and the whole sales team for their amazing efforts in getting ready for today’s launch.

How can you improve LTV and CAC? June 15, 2010

Posted by jeremyliew in CAC, ltv.
9 comments

A lot of the startups that have quickly reached millions in monthly revenue rely on the arbitrage of being able to acquire customers through paid marketing for less than the lifetime value of that customer.

CAC < LTV

As a reminder, lifetime value, or lifetime contribution as it should perhaps more accurately be named, is given by the formula:

LTV = Expected Life x ARPU x Gross Margin

where Arpu = average revenue per user in each time period. This equation can hold whether you are in a subscription business or an ecommerce business with repeat purchase behavior. Although you need to be a bit more nuanced with non subscription businesses, the same cohort analysis techniques still allow you to approximate LTV in this manner. I’ll post more about this later.

Both LTV and CAC are key metrics that the management teams should be focused on improving.

Usually CAC is a blended average of several customer acquisition costs from several different channels. Each of these channels typically can be optimized by better targeting, better copy and creative on the advertising, better landing pages,optimization ofthe flow through to checkout, more and better payment options, and increased viral pass along.

Some of the tools that can be used to improve LTV include retention programs to extend life, cross sell and upsell campaigns to increase ARPU, and improving gross margin.

Both metrics are very important. However, if resource constraints force a choice between focusing on one or the other (it can be hard enough to do one thing at a time at a startup!), I would choose to focus on lifetime value. There are two reasons for this. Firstly, most of the improvements that can be made to LTV will improve LTV for all users, both current and future, and regardless of channel of acquisition. In contrast, many of the improvements that can be made to CAC are channel specific (e.g. copy of a particular ad) and none of them improve the economics of existing customers, only new customers. You get more leverage out of your efforts on LTV.

Secondly, because LTV is typically already higher than CAC, an x% increase in LTV has more impact to the company than an x% reduction in CAC.

I’d love to hear what others think about this choice, and about other ways to improve both CAC and LTV

Lessons from the leaders – Engagement in social games May 11, 2010

Posted by jeremyliew in game design, game mechanics, games, games 2.0, social games, social gaming.
4 comments

On Friday I moderated a panel at the social gaming summit featuring speakers from Zynga, Playdom, Playfish and Crowdstar on the topic of engagement best practices in social games. Socialtimes has a brief writeup of the session:

Social gaming giants tend to focus on hits. An under performing game can be a cause for concern and even shutdown in some cases. Mark Skaggs jolted the crowd by stating that Zynga aims for a 60% same-day repeat engagement of a newly released game but their core focus is on long term retention of around 30%. Zynga manages to attain 1.5 million DAUs on the first day, upwards of 3 million DAUs at times. Zynga’s game Mafia Wars saw signs of stagnation in players repeatedly doing jobs and diversified the experience by adding an array of places players could visit, instilling adventurous emotions in the adventurers.

Sebastien emphasized engagement as a key point of focus for their games along with mass appeal. Sebastien also discussed Playfish’s shutdown of one of their previous games Quiztastic, stating, “one of the ways to create engagement in Quiztastic is through highly relevant content that’s only relevant to a narrow set of friends. However, it turned out to be massive engaging for the active contributors but not others.” Another game Playfish shutdown was Minigolf Party because too much was being demanded of the players. The panelists agreed, concluding that a balance is necessary to engage a mass-audience.

Christa brought in her unique perspective as CFO of Playfish, commending the rapid success of their game Social City. She attributed growth to additions of surprise mechanics – specifically random animations that rewarded users with an aesthetic and delightful experience, encouraging them to return frequently for more.

Since I was moderating I wan’t able to take good notes, but here are some of the other points that were made on the panel:

Appointment Mechanic (also known as farming mechanic) suits the casual gameplay style of social games and brings people back. Good to offer different timeframes of “harvest” to match way people play the game, typically in multiples of two hours. Four hour timeframe good for players logging in at beginning of day, lunch and end of day. Two day timeframe good for players who play primarily at work who need to deal with the weekend

Whether you apply a “hard” penalty to the apppointment mechanic (e.g. crops wither, no reward) or a “soft” penalty (e.g. collection bucket full, no incremental reward above cap) depends on the style of gameplay.

Plot can also help drive engagement and retention. This can be both plot secondary to gameplay (Easter eggs in the game, animations that change over time) or primary to gameplay (e.g. Mafia Wars/Mobsters narrative arc).  Players come back to find out what happens next.

Special Events can drive engagement, which sometimes translates into increased retention. These special events can be both in the game (e.g. the Weekend of “superberries” on Farmville, which added 3 million DAUs for a week and an incremental 1.5m DAUs permanently) and outside the gmae (e.g. the Taylor Swift dress in Sorority Life the day after the AMAs).

Real Life relationships, love, flirting and friendship, can drive special actions which support long term engagement. e.g. Pet Society and Restaurant City drive tens of millions of virtual roses, and many real roses, to be exchanged on Valentines Day.  Friends For Sale (launched by Lightspeed portfolio company Serious Business, now part of Zynga) is the prototypical example.

Low latency is important. When users have to wait a long time for pages to load, they leave.  This can be improved both by optimizing web ops, as well as by modulating the complexity of graphics etc based on browser and OS type.

DAU and DAU/MAU (Stickiness) are highly correlated. The causality arrow flows both ways.  It isn’t enough to just build a good game, nor is it enough to get frequent posts to the feed. Both need to be balanced, and feed posts need to be “reasonable” from the point of view of a user. You need to understand and accept the motivation of the post.

Viral channels are now more about engagement/reactivation than about growth.

Key metrics are 1 day, 3 day, 7 day retention, then long term retention. 1 day retention target is 30-60%, less than 30% and you may not have a fixable game.

If you were there and had other important points that I missed, please add them in comments.

We estimate Zynga revenues around $270M in 2009 and $240M in 2010 YTD May 3, 2010

Posted by jeremyliew in games 2.0, social games.
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14 comments

This also appears as a guest post on Techcrunch

________________________

There has been a lot of speculation about Zynga’s revenue. Last week Business insider said:

Zynga, the social gamesmaker behind Farmville, has a revenue run-rate around $600 million, a source close to the company tells us. Another source confirms that Zynga is doing well over $1 million in revenue a day.

Businessweek says:

More than 120 million people play Zynga’s online games. Employee headcount has almost quadrupled in the past year, to 775. Revenue for the three-year-old company should surpass $450 million in 2010, according to two people who have been briefed on its financials.

We thought that we would estimate Zynga’s revenue ourselves by looking at publicly available info. Here is what Linus Chung and I did:

  • Focused on only top games on Facebook
  • For each game, pulled DAU numbers on first of every month since 1/1/09 from Developer Analytics.
  • To get the average DAU for each month, took the average of the first of the month and the first of the following month. So for March, the average DAU for the month is the average of DAU on 3/1 and DAU on 4/1.
  • Inside Virtual Goods published a monthly ARPU range (low and high end) for each game genre. In general, we used the average of low and high, with some exceptions:
    • For virtual gifts, we used the high end: $0.50. This only affects Friends for Sale.
    • For simulation games (e.g., CafeWorld, PetVille), we used the low end: $1.00. These games have been wildy popular in terms of users. We assumed that the recent increase in users results in lower monetizing users being added.
    • For poker, we used the low end: $2.00
    • For FarmVille, we estimated ARPU at $0.50 due to its scale
  • Mapped each game to its game genre, and multiplied average DAU each month with the ARPU.

This estimate is likely to be inaccurate for many reasons, notably (i) the coarse estimates of revenue/DAU (rounding to the nearest 50c), (ii) the low end of range estimates for many of Zynga’s most popular games, and (ii) the fact that we ignore revenue from MySpace, Zynga’s websites, and mobile. None the less, it shows some interesting results:

Again, note that these are all estimates. However, our estimates show that revenue ramped fast over calendar 2009. The H1 ramp was driven by Poker and Mafia Wars, and the H2 ramp driven by Farmville, Cafeworld and Fishville. Our estimates show that revenues have been flatish since the beginning of 2010, with a decline in older games compensated for by the launch of Treasure Isle.

Feel free to see the details and play with the assumptions yourself – the spreadsheet is here. It is a read only Google Doc so that your changes won’t affect others who are later to check it out, but you can download the spreadsheet to change assumptions. Note that there are four tabs to the spreadsheet (at the very bottom of the page). To download, click File–> Download as –> Excel.

Play with the assumptions, and let us know what you think.

Why Lightspeed invested in Living Social April 29, 2010

Posted by jeremyliew in Ecommerce, growth, local.
Tags: , ,
9 comments

Well, I’ve been busy since the beginning of the year! Earlier this week Lightspeed’s investment in ShoeDazzle was announced, and today our investment in Living Social was announced.

Both companies reflect our belief in entertainment commerce, or push commerce.  The traditional model of ecommerce treated buying things like a chore, crossing things off of a list. Some of the newer commerce models, the ones that are quickly growing to millions in monthly revenue, help users discover great deals and great items that they were not explicitly looking for. By manufacturing serendipity, they help create demand, rather than just fulfilling demand. Living Social falls into this category.

Living Social sends a local deal each day via email (see some examples here) to residents in 18 cities across the US. The deals are usually 50-80% off on a local restaurant, spa, bakery or similar merchant. If the deal appeals to you, you buy it directly from Living Social. Living Social makes money by keeping a cut of the revenue, and sends the rest on to the merchant.

The leader in this category by far is Groupon. Groupon has seen tremendous growth, from $100k in revenue in January 2009 to $10M in revenue in January 2010. Living Social got started 6-9 months after Groupon, but is seeing similar growth. According to Hitwise, traffic in the group buying category is up 72x since last year, with Groupon and Living Social neck and neck for traffic at around 49% each (click through to see the full graph):

Sadly they are not neck and neck for revenue, at least not yet!

There are many entrants into the local deal space, with Techcrunch reporting earlier this month:

When Yipit launched a little over a month and a half ago (!), the startup could already identify 30 daily-deal Web services. Today, the company tracks deals from no less than 66 Groupon-like websites across the United States, more than double the number it counted less than two months ago.

As you can see from the chart above (click through to see the full graph), the 66 companies are delivering 176 daily deals. Since 50+ of these deals are from Groupon, the other companies are averaging 2 cities each. This is an easy category to enter, but I believe that it will be a difficult category in which to scale. Success will depend on building the email subscriber base, which is challenging on a single city basis. The more targeted you want to be, the harder it is, whether you are relying on viral growth or buying advertising. National growth is easier, but requires a national footprint of deals to take advantage of it. That is hard to do, and expensive to do. It is an execution game which will require great management and significant capital.

I suspect some of the other 64 companies will be able to reach viable scale to join Groupon and Living Social. Both companies are well funded, and with very impressive management teams. Anyone hoping to make it to scale will need to bring the same assets to the table.

We’re excited to help Living Social continue in its growth.

Why Lightspeed invested in ShoeDazzle April 28, 2010

Posted by jeremyliew in Ecommerce, growth, subscription.
Tags: , ,
6 comments

Lightspeed led a $13m investment in Shoedazzle, announced yesterday. We are very excited to help Shoedazzle grow.

Shoedazzle is one of the companies that I was thinking of when I wrote about startups that can quickly get to millions in monthly revenue:

… are all taking advantage of one of Lightspeeds consumer internet predictions for 2010,  that direct direct response advertising is getting more efficient. A bad time to sell ads is a good time to buy ads. All these companies are taking advantage of relatively low customer acquisition costs.

If you understand your customer lifetime value, and you can acquire customers for 20-30% of the lifetime value, you are going to make money. Understanding lifetime value is hard for media companies, but it’s easier for gaming companies, ecommerce companies and subscription businesses. They have predictable customer behavior cohorts that can be extrapolated from a few months of data from a representative sample.  Running an aggressive positive arbitrage while online media is cheap has allowed all of these companies to grow revenue very fast once they get the micro-economics right.

The company is based outside of Silicon Valley (LA) and is definitely built on the back of business model innovation, as are many of the current crop of fast growth companies.

Shoedazzle has a terrific user value proposition. A member first takes a style quiz to assess her taste. Then, on the first of each month, she receives an email with five pairs of shoes that have been specially selected for her. If she likes one of the pairs, she buys it. If none of them grab her, she can either skip that month, or request a re-selection and give specific guidance as to what she is looking for (e.g. boots, or higher heels, bolder colors). Women get personal stylist advice and recommendations brought directly to them, helping them to keep abreast of the latest fashion trends.

Thematically, I am very excited about the move towards entertainment shopping, and Shoedazzle falls squarely into this category:

One of the most exciting trends in e-commerce over the last couple of years has been the trend towards “shopping as entertainment”. Traditionally e-commerce has been a chore type activity. Customers know what they are looking for (a digital camera, a new laptop) and are looking for the best product and best price with a very “research” based mindset.

This is quite unlike the real world, where a customer might walk around a mall without any particular purchases in mind, and perhaps opportunistically buy something that caught their eye in their wanderings. There is no real “intent to buy” in a trip to the mall.  It is more like entertainment time which may, or may not, lead to a purchase.

SheoDazzle captures the wonderful serendipity of finding something great as you wander the mall, and brings it into your inbox.

Kim Kardashian is one of  the co-founders of Shoedazzle, and has been instrumental to the success of the company, both through her promotion of the site, and through her fashion input into the shoe selection. But this company is about much more than Kim alone. The company prides itself on delivering terrific experiences to its members, and this has resulted in an incredibly strong and positive community, as reflected by the vibrant wall on its facebook page, the constant tweeting on twitter, and even the unboxing videos on youtube.

Notwithstanding Kim and the community, Shoedazzle is about the shoes.  And that is what has let the company grow through word of mouth. This isn’t the manufactured virality that works so well for facebook apps and early social networks, riding the transports of notifications, invites, wall posts or email importation. This is the real thing, with one happy member telling another about where they got their great shoes.

On the flip side, online commerce is an operationally intensive business. With physical goods, you get lower gross margins then you see in online media. In shoes, return rates can be high (Zappos’s average return rate is 35%). If you care as much about member satisfaction as Shoedazzle does, client care needs a lot of resources. And breaking through the noise and clutter on the consumer web is always difficult. Building a business like shoedazzle is not as easy as simply hacking all night for a few days and standing up a website. It requires deep knowledge of merchandising, logistics, customer care, marketing and promotion.

Shoedazzle has a terrific team of experienced, passionate people (with great shoes!) who are tackling this challenge, and at the end of the day, that is why we invested in ShoeDazzle.

Business model innovation is making Silicon Valley less important as a startup center April 19, 2010

Posted by jeremyliew in growth.
12 comments

Last week I noted some companies that have quickly grown revenues to over $1M/mth, including Zynga, Playdom, Playfish, Gilt, Hautelook, RueLaLa, Groupon, Living Social, Lifelock and Zoosk. Later I and others added Crowdstar, Cash4Gold, Shoedazzle, Second Life and TheLadders to this list.

It’s interesting to break this list down geographically, especially if you seperate the gaming/virtual world companies from the rest.

  • Gaming Companies in the Bay Area: Zynga, Playdom, Crowdstar, Second Life
  • Gaming Companies outside the Bay Area: Playfish (London)
  • Other Companies in the Bay Area: Zoosk
  • Other Companies outside the Bay Area: Gilt (NY), HauteLook (LA), RueLaLa (Boston), Groupon (Chicago), Living Social (DC), Lifelock (AZ), Cash4Gold (FL), Shoedazzle (LA), TheLadders (NY)

Given that the the Bay Area attracts the most VC funding (a proxy for startup activity), the fact that most of the gaming/virtual world companies are based here isn’t too surprising. But what is pretty surprising is that the vast majority of other fast growth companies are from outside the bay area.

One notable thing about many of these companies is that they innovated more on business model than on technology or product. While there is some core technology to each of these companies,  most of them have more people in functions like marketing,  sales, customer care, merchandizing etc than in technology. This is in marked contrast to the gaming and virtual world companies where the bulk of the headcount is in technology since the product is the game.

Many other “hot” companies in the bay area also show a bias towards product and technology in their employee mix; youtube, facebook, digg, etc.

When product and technology are core to the success of a company, Silicon Valley still dominates the startup scene, but when the innovation is in other functions, and technology is more an enabler than core to the product, other regions can be as competitive, if not more so.

What do readers think?