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How to Project Customer Retention for a Subscription Business August 6, 2012

Posted by seanrmoran in 2012, ltv, models, subscription.
Tags: , , ,
2 comments

We’ve posted before about how to estimate lifetime value (“LTV”) for an ecommerce business and for a subscription business, and have provided a sample cohort analysis for each (ecommerce and subscription).  This is one of the most important factors in understanding unit economics.

Recently, Eric Liaw sent us a very interesting May 2006 paper entitled “How to Project Customer Retention”, authored by marketing professors Peter Fader (Wharton) and Bruce Hardie (London Business School) and published in the Journal of Interactive Marketing in 2007.  In it, the professors explain how previous attempts to project retention rates using line-fitting regression models failed, even after introducing quadratic or exponential functions.  Since we had advocated essentially using an exponential line fit for subscription LTV estimation, we figured it was worth reading.  The authors show that exponential form fitting is too conservative and underestimates actual retention rates.

Professors Fader and Hardie decide to start from scratch with a simple assumption: what if each customer has a fixed probability of renewing his or her contract at the end of each period?  So if I’m a big movie fan, let’s say I’m 80% likely to renew Netflix each month, but you’re caught up on Breaking Bad and only 30% likely to renew each month going forward.  Probability varies by customer, but each customer’s rate remains constant over time.

It turns out that, based on probability theory, this simple assumption implies that the distribution of renewal rates can be characterized by a statistical model.  Over time, the difference in each individual’s probability to renew suggests that individuals with lower renewal probabilities will generally drop out before those with higher probabilities.  Incidentally, this also explains why incremental retention may appear to improve over time, when it’s actually a likely side effect of the remaining customer mix.

After some mathematical gymnastics, the authors unveil the model they’ve derived: the shifted-beta-geometric distribution.  The authors tested the model by using the first seven years of data from a given sample to project renewal rates at the end of the final five years in the sample.  The model proved to be quite accurate, within 3% of actuals, and much better than linear or exponential form fitting.

A few quick caveats: this model is appropriate only when the data reflects a discrete renewal period, such as a defined monthly or annual cycle.  Also, the model should be reserved for projecting behavior in contractual settings, such as subscription renewals and other observable customer exit points, rather than ecommerce or other businesses where the customer can remain dormant for long periods between orders.

We’ve uploaded a spreadsheet here, along with directions for how to use it yourself.

Hope this is helpful.  We look forward to hearing from you regardless, but especially if:

1)    You use the model and have any feedback on results

2)    Your company uses any other methods to capture, analyze, and project customer retention

3)    Your innovative company achieves valuable unit economics.  As previously mentioned, we like to see LTV / Customer Acquisition Cost > 2.5 and payback periods under 12 months.

If you found this post useful, follow us @lightspeedvp on Twitter.

How to estimate lifetime value for an ecommerce business; Sample cohort analysis June 15, 2012

Posted by jeremyliew in Ecommerce, ltv.
18 comments

A couple of years ago I did a post on how to estimate lifetime value for a subscription business where I uploaded a sample cohort analsyis that others can use as a template.

I’ve been asked several times how the analysis would differ for an ecommerce business, so I finally got around to uploading a sample cohort analysis for an ecommerce business. Please note that this is a SAMPLE only. Data is dummy data, so you should not use it for benchmarking purposes. I have not allowed editing to the google doc so that the spreadsheet will be useful to anyone who finds it, but you can download it and edit it offline as you see fit.

For an ecommerce business, rather than focusing on the percentage of retained subscribers per cohort, instead you focus on the net revenue (after discounts, returns and refunds) from that cohort in a given period. This revenue has to be normalized by dividing by the number of (original) buyers in each cohort so that you can make meaningful comparisons. You should focus on revenue per (original) buyer in each period for each cohort as the raw data from which you can build a lifetime value analysis. Then you should average across cohorts to understand “typical” revenue per sub in period 1, 2, 3 etc, where period 1 is the first month (quarter/ year) when you see a buyer make a purchase.

You still typically see a steep drop off in revenue per buyer after the initial period. But a well run ecommerce business that does a good job of retention marketing and line expansion should see stable revenue per buyer after the initial drop off. This is in contrast to subscription businesses which typically continue to see attrition over time. If you do see continued drop off, you should model that in a similar way that I do it for subscription screen sharing businesses, but if you see relatively stable out month revenues per buyer, it’s OK to model that in the out months.

Lifetime Value is calculated as the cumulative contribution of an average customer, so you have to multiply lifetime revenue by contribution margin. Contribution margin should include all variable costs except one time acquisition costs. This typically includes COGS, packaging, shipping and handling, reverse logistics, inventory obsolescence/write offs, customer service, credit card charges, hosting costs, fraud accruals etc. It would not include fixed costs such as photography, production, site development, merchandising or other overhead.

The two most importnat metrics that I look at to gauge the health of an ecommerce business are LTV/Customer Acquisiton Cost ratio and payback period. This is why i highlighted these two metrics in the spreadsheet.

I like to see LTV/CAC > 2.5 (which tells you that you have a robust long term business with enough margin to cover overhead) and Payback periods under 12 months.

If you found this post useful, follow Lightspeed on Twitter @lightspeedvp

2011 Consumer Internet Predictions December 3, 2010

Posted by jeremyliew in 2011, advertising, Consumer internet, Ecommerce, ltv, mobile, predictions, social games.
22 comments

Once again Lightspeed is going on the record with some prognostications for what the future holds. Before I try gazing into my crystal ball to see what 2011 will bring for the consumer internet industry, let me first see how I did on last years predictions:

1. Social games overflow out of Facebook

Grade: C+. While the amount of social gaming on other social networks, especially the Asian networks, has significantly increased over the course of the year, the vast majority of social gaming still takes place on Facebook. While Farmville.com now has 6M UU/month, this is still only 10% of the number playing Farmville on Facebook.

2. Brand advertising starts to move online, boosting premium display, video and social media

Grade: A. The recovering economy has really boosted brand ad budgets in 2010, with online ad spend back to setting records again. Automotive and CPG in particular are both seeing significantly increased online budgets. The online video networks are doing terrific business, and even Yahoo is benefiting from increased brand spend, seeing revenue growth for the first time in a while. Many brand advertisers are spending their experimental budgets widely in social media as they attempt to figure out how to promote themselves through Facebook, Twitter, Foursquare and other platforms. The key driver of this renewed confidence from brand advertisers is better measurement of brand metrics that can show the impact of online advertising beyond clickthrough.

3. Direct Response Advertising becomes ever more efficient

Grade: A. According to Adsafe, approximately half of display advertising inventory is now moving through exchanges, Demand Side Platforms (DSPs) and realtime bidding platforms, with another 23% moving through Facebook’s self service ads. These platforms are rapidly commodifying a lot of “low quality” ad inventory, enabling the use of data and targeting to find the best use of this inventory, and thereby creating a very efficient marketplace. Direct response advertisers have benefited the most from this transparency.

4. Finding money and saving money online

Grade: B-. Saving money online has been a real driver of ecommerce growth in 2010. The breakout categories of 2010 are Local Deals (Groupon, Living Social* etc), and Flash Sales (Gilt, RueLaLa, HauteLook, Ideeli etc), and both are squarely aimed at helping consumers save money. Finding money online (principally online lending) has not seen the same level of explosive growth in the US, although in Europe and India there has been real growth in microlending (including “pay day loans”) from companies ranging from Wonga to SKS Finance. I think we’ll see more from the online lending space in 2011, so I may just have been too early on that part of the prediction!

5. Real time web usage outpaces business models

Grade: B-. Twitter continues to grow in usage, overtaking Myspace to become the third largest social network in the world. Foursquare and Gowalla have grown too, but off of much lower bases, such that only 4% of internet users currently use a check-in service. Facebook also joined the Location Based Services (LBS) party this year, enabling Facebook places, which some speculate is getting 30M users already. Last year I speculated that monetization would be hard for these businesses since CPM models have traditionally been hostile to user generated content, and local ad sales is an expensive and difficult proposition. But these companies have innovated new monetization models. Twitter, through its Promoted Tweets, Promoted Trends and Promoted Accounts, is not selling media on a CPM basis, but rather selling attention, and the early returns suggest that brands are willing to pay for more attention. Similarly, the check-in services are attracting experimental budgets from national retailers as well as forward thinking small businesses who are eager to attract new customers into their stores, and reward regular customers. While the revenue numbers may not be huge in 2010, there is certainly promise to the business models that are developing on these platforms.

Overall for 2010, I figure a B average, a little worse than last year. But there is always grade inflation when you grade yourself, so let me know what you think. Now, on to my predictions for 2011:

___________________________________

1. Putting fun into ecommerce

In 1995, when Amazon was founded, e-commerce was like the proverbial talking dog. It wasn’t about how well the dog could talk, it was amazing that the dog could talk at all. The first generation of ecommerce sites were focused on functionality, getting the dog to talk better. We got everything from price comparison engines to aggregated user reviews to one-click checkout. These early innovations were focused on optimizing the “workflow” of shopping to get users into the checkout as quickly as possible.

This worked great for most internet users at that time because back then most internet users were men, and in general, men do not like to shop. They treat it like a chore, a necessary evil that would ideally be minimized and optimized to take the least amount of time possible. Then they could get back to doing something they enjoyed, perhaps playing video games, or watching football!

But a few years ago, that changed. There are now (a few) more women online than men. And in general, women tend to enjoy shopping more than men. Certainly more than playing video games, or watching football! If you enjoy shopping, you don’t want your “workflow optimized”. You don’t want to be rushed to the checkout as quickly as possible. Instead, you want to linger, to be delighted, to discover new things, to find great deals. You want shopping to be fun.

The Flash Sales sites and Local Deals sites both make shopping fun by offering deep discounts. This is the mechanism that they use to entice shoppers to buy something, even when they are not looking for anything specific. But discounts are not the only way to make shopping fun.

Sites like Modcloth make shopping fun through discovery. Modcloth highlights women’s clothes from modern, indie and retro designers. Because each item has limited supply, and selections are constantly changing, Modcloth builds an urgency that has users coming back frequently to see what’s new and to make sure that they don’t miss out.

Shoedazzle* makes shopping fun by democratizing the personal stylist experience. After users take a style quiz to assess their profile, they are shown a selection of shoes, bags and accessories that have been specifically chosen to match their taste. Each month they get a new selection of on-trend pieces that fit their profile. JustFab and JewelMint have subsequently launched with similar models.

More models keep popping up. Recently launched Birchbox focuses on sending cosmetic samples to its users to help them discover the perfect eyeliner or blush. Pennydrop is a Facebook app that lets users peek at discounted and constantly dropping prices on items and jump in to buy when the price is low enough.

All these sites play to the idea of making shopping fun. I expect to see more applications of these formats, as well as more new formats, all under this overarching theme. A little social shopping anyone?

2. Self-service ad platforms find their ceiling, and brand advertisers seek other avenues

As noted above, about half of display advertising inventory is now moving through exchanges, DSPs and realtime bidding platforms. Yet these platforms are only two to three years old. While perhaps only 10% of online ad revenue is currently flowing through these channels, the trend here is clear. Today, two thirds of online ad spending comes from direct response advertisers, and soon the bulk of these budgets will likely flow through bidded platforms such as these, including Facebook ads. Direct response advertisers move their budgets quickly to follow results, so this could happen within the next year or two.

Brand advertisers are also experimenting with bidded platforms. Each of the big ad agencies have their own trading desks. However, adoption on the brand side will likely be slower and far from complete. Many of the exchanges, DSPs and RTB platforms allow for bidding strategies that are easily optimized for click-through rates, but optimizing for brand metrics is much harder. Brands also care more about content adjacency and brand safe content, and these are harder to guarantee on an exchange type platform, where in some cases, ad impressions are traded several times before finding their final buyer.

In addition, exchanges by definition can only support standard ad units. Many brand campaigns incorporate custom elements, ranging from social media and other earned media components to custom microsites, site takeovers, roadblocks and other high impact units. These are often tied to specific publishers, and bundled into a broader media buy including standard ad units. Premium publishers depend on this sort of creative advertising to maintain the ad rates required to support the creation of high-quality content, and I think it is likely that this symbiosis between brands and premium publishers will continue to capture a large chunk of the brand ad budget. In fact, I expect to see a proliferation in custom ad units from the biggest and most premium publishers as they work to capture a greater share of brand budgets. Non-premium publishers that have reached the scale to become “must buys” are doing exactly the same thing. Twitter’s Promoted Tweets, Promoted Trends and Promoted Accounts, and Facebook’s Social Ads and Likes are all great examples of this trend.

3. Competition shifts from user acquisition to user retention

Today many e-commerce and subscription companies are growing very quickly through smart marketing. They are taking advantage of cheap media to cost effectively acquire new customers. As I’ve mentioned above, I think the exchanges will continue to make it easier for direct marketers to reach their customers. Facebook’s self service platform is still a relatively inefficient market, allowing savvy, analytical marketers to quickly and cheaply gain market share. However, in some categories (e.g. Local Deals) Facebook has quickly become efficient and there is already a “market price” for a new Local Deals subscriber. As more marketers take the plunge into Facebook’s platform, more categories will become efficient, just as Google became an efficient market over time for almost all keywords. Once this happens there will be a market clearing price for new customer acquisition across almost all categories, and smart marketing will no longer be as much of a differentiator.

On what basis then will winners pull away from the rest? Companies who are able to derive the highest lifetime value (LTV) from their users will squeeze out their competitors with a lower lifetime value. How can you improve LTV? There are three key factors:

  • average revenue per user
  • gross margin
  • average lifetime.

The e-commerce and subscription based companies that pull away from their competitors in 2011 will find a way to differentiate themselves from their competitors on one or more of these dimensions.

4. Social games chase hardcore gamers

Notwithstanding Disney buying Playdom* this year and EA buying Playfish last year, Zynga is still the market leader in social gaming. Their enormous installed user base gives them a real advantage in customer acquisition cost over their competitors; their ability to cross-sell installs to their new games at zero cost allows them to get a new game to scale with much lower marketing spend then smaller competitors.

To combat Zynga’s might, the other social game publishers have to focus on games with a very high LTV. High enough that the publisher can afford to rely on paid customer acquisition alone to build a user base, and still make money. Kabam (once know as Watercooler) pioneered this approach with Kingdoms of Camelot, a relatively hardcore social game that is reputed to be doing low to mid single digit millions in monthly revenue from  about 750k Daily Acitve Users (DAUs) – a monetization rate that is dramatically higher than the norm for social games. Other publishers have taken note, and I would expect more games aimed at the hardcore gamer market to emerge over 2011.

5. Year of the tablet

Smartphones transformed the mobile internet. Apps will drive $5bn in revenue in 2010. Mary Meeker presents some great insight into the future growth potential of mobile in her Web 2.0 Summit presentation, Ten Questions Internet Execs Should Ask and Answer.

The same thing will happen with tablets. While the iPad has the tablet market largely to itself this year, that will change dramatically in 2011 and beyond, just as Apple’s iPhone had the truly web-capable smartphone market to itself in 2008, but is now a minority as competition emerged from Android, WinMo7 and the modern Blackberry.

The key difference between these new platforms and the PC web isn’t mobility (although that is part of it), but rather that these devices are always on and always with you. However, use cases differ between the phone and the tablet.

Phones are with you all the time, in particular when you are out of the house and out of the office. The most popular genres of app fit well with this “on the go” usecase. Local information, “snacky” entertainment, music, games have all been killer apps on smartphones. Some web incumbents made the transition well, including Yelp, Flixster*and Pandora. Many new companies also gained ground on the phone through this disruption.

Tablets tend to live in the living room. They lend themselves more to leisure than PCs, and to more protracted content consumption than phones. Killer apps might include, video, music, games, and “reading”, broadly defined. Again, some web incumbents will make the transition well, but once again I expect to see new companies gain ground through this disruption.

What do you think will happen in 2011? This time next year ,I’ll look back to see how accurate I was. In the interim, stay tuned for more Lightspeed predictions in other tech sectors over the next few days.

_________________________

* A Lightspeed Portfolio company

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

Posted by jeremyliew in Ecommerce, ltv, subscription.
42 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.

UPDATE: June 2012 – I have a new post describing how to estimate lifetime value for an ecommerce business using cohort analysis.

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

Posted by jeremyliew in CAC, ltv.
24 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