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  • Meme

    I’d be interested to see the comparison between _enterprise_ SaaS and normal $39.95/month SaaS.

    The churn on enterprise SaaS would presumably be incredibly low, because once a company invests in integration, workflows, training, etc. for a particular system, it’s going to be very hard to switch.

    • http://blog.asmartbear.com Jason Cohen

      Great point, but not necessarily. For HubSpot, for example, they have plenty of (100s, if not 1000s) enterprise-sized customers, and yet measuring CHI (which is their prediction of customer happiness, defined as the likelihood that they’ll cancel) still drives almost everything that every employee does.
      If the contract is yearly instead of monthly, you could make the argument that churn is necessarily over a longer timeframe, yes. But that doesn’t mean the churn is immaterial! It means that straight up “cancels” — i.e. customer stops paying — isn’t the right metric for you. Rather, you need something more like CHI — something you can measure monthly or even daily which tells you essentially the same thing, so you can react faster and keep those customers.
      Because the flip-side to your argument is that enterprise customer acquisition is also extremely expensive, so you HAVE to get more than a year of service out of them, so cancellation is still vital.

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  • http://twitter.com/aydin Aydin Mirzaee

    Great Post!

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  • http://about.me/mikeschinkel MikeSchinkel

    Didn’t you say that cancellation rates were not the most important thing in “The full story of “the one important thing” for startups”?


    BTW, your blog’s home page takes *forever* to load.  Can’t you get the guys at WP Engine to improve that…?  :-)

    • http://blog.asmartbear.com Jason Cohen

      Yes, I said that for us it’s not the #1 thing. But it’s still important!

      • http://about.me/mikeschinkel MikeSchinkel

        Glad to see your response.  I agree. 

        Actually when I read your other post I saw the logic of it but it left me with a pit in my stomach that made me feel bad. While numerically more Google AdWords seems to make more logical sense, working on cancellation rates makes more intuitive sense to me. Both are important but I think the latter is critical. Glad to see you didn’t toss it out with the bathwater.

        • http://blog.asmartbear.com Jason Cohen

          Here’s a better way to put it:

          While cancellation rate stays low, signups are the most important thing.
          If we see a rise in cancellations, that means the company is literally broken, which means growth is no longer important.

  • http://twitter.com/brianpiercy brian piercy

    I’m going to find a way to apply this to my semiconductor business IF IT KILLS ME.

    We measure things by the socket. (specific customer, specific pn#.) Our cancellations are usually driven by customer’s product lifecycles and/or end market success, plus our $R is usually a percentage of the total. (A commodity split btw 2-4 suppliers.) 

    And we’ve got a significant leadtime between “signup” (awareness, samples, price negotiation, ….) and 1st production shipments. Yes, I know. The measurements are more complicated.

    My point? The model works – if you do a decent job of segmenting the pretenders from the winners aka “cancellation by age”.

    Very nice post. Special props for the use of Binky.


    • http://blog.asmartbear.com Jason Cohen

      Good for you! Check out Dharmesh Shah’s Business of Software talk from 2010 about “CHI.” They have a way of trying to predict “customer happiness,” meaning the % chance that they’re going to cancel, based on correlating various factors.
      The result is that far before they actually technically “cancel” you can tell it’s not looking good, and either take an action to fix or at least plan around it, both for forecasting and the amount of time you spend with them.

  • Levi Bauer

    You need to look into a great statistical simulation method called Monte Carlo.  I won’t get into the details here as you can just search and come up with thousands of pages of info, but it’s greatest benefit (in my mind) is getting out of the precise  number mindset.

    At the end of the analysis it will provide you with a probability for each number instead of an exact number.  For example, instead of saying that LTV will be $1,000 it will say:

    10% probability of $10
    20% probability of $200 
    50% probability of $1,000
    20% probability of $1,200
    10% probability of $2,000

    …or something like that.  Much more useful in my mind.  Let me know if you’re interested in this concept and I could whip up an example for you based on your real numbers.



    • http://blog.asmartbear.com Jason Cohen

      Yup, I’m familiar. It’s a great idea — would be neat to have a web tool where you enter in a few parameters and you get the distribution instead of a number.
      Note that it’s ALSO useful to have a single number when you’re combining with other things, to be simpler.

  • http://www.facebook.com/profile.php?id=1403502623 Mark Streich

    Have you performed cohort analysis by “why the customer signed up with us?”  If you do decide you can afford give-aways, it may impact how long they stick around.  The phone companies know this, as they lock in customers to pay for the give-away (discounted new phone, etc.).

    • http://blog.asmartbear.com Jason Cohen

      Terrific idea. We do segment “coupon” from “non-coupon” but your idea is better.

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  • Fred Guth

    Thanks for the great post!

    You’ve mentioned 3 stages of churn rate, but I was wondering, if you have 15months of data, couldn’t you use 15 stages? Your churn rate will probably converge after some months anyway, and you could use the last rate as the long term churn.

    Does it sounds correct? Too complicated?

    • http://blog.asmartbear.com Jason Cohen

      You could but it didn’t change the numbers much.

      Usually there’s not that many forces dictating cancellation, and your model shouldn’t be more complex than reality. That is, people cancel after 8 months pretty much at the same rate and reason as after 9.

      Jason Cohen

  • http://www.sahilparikh.com Sahil Parikh

    How many month’s data should one take to compute an accurate picture of LTV and LT? 1, 3, 6 or more?

    • http://blog.asmartbear.com Jason Cohen

      I would plot it at least monthly, more often if you have more transactions, and watch it over time.
      The trend is often more telling than the absolute number.

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  • Guy Nirpaz

    Thank you for the very comprehensive post! I truly think it’s important to understand the cancellation rate metric and how to calculate it!

    Creating a cohort analysis based on “time to cancel” can indeed allow focusing on long-term users, however I believe cohort can also be measured based on engagement level.

    I’ve quoted your post and added my thoughts in Totango’s blog: http://blog.totango.com/2011/10/3-ways-to-do-cohort-analysis-on-saas-churn/

  • http://twitter.com/jan_lukacs Jan Lukacs

    Excellent article, thanks for sharing all this with the community! I had a tough time finding how others compute LTV.

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  • Robinyearsley

    Jason, great post, very comprehensive.

    Question: Have you trended your findings/ results over time? Are the number abetting worse of better, as you take propriate action(s)?

    Thinking from a future protectionist viewpoint, I would want to know the underlying root causes of ‘why’ my customers were cancelling, and build a top 10 list with an appropriate prevention and recovery list of tactics for each.

    Applying the 80/20 rule to these lists, I would then build in tactical ways for the customer to experience these actions before cancelling (albeit in a different tense since they wouldn’t have actually cancelled yet!)

    One final consideration. Gym Memberships. Check out search archives of Harvard Business Review. They did an awesome study on why people cancel and one key point was ongoing usage of the service, caused by psychological barriers that ‘creep’ over customers, over time. So, Gyms focussed on the enjoyment factor of the experience: free coffee, newspapers, handy health tips, humorous posters, attractive assistants etc etc.

    What can SaaS providers do?

    Splash pages with cartoons on logout? Free Apps, Special reports via email, customer ezine tackling other challenges, links to value add… The list is huge. Surprise and fun is also important. Built on tip top support and reliability.

    Any thoughts on these and your original post??


    • http://blog.asmartbear.com Jason Cohen

      On trending over time, you *must* do that. The trend is more important than the actual number. In fact different businesses will natural have different numbers — it’s whether it’s not growing (or, if you’re actively trying to affect it, if it’s shrinking) that’s most interesting.

  • http://twitter.com/dessner dessner

    Jason, I agree with this thoughtful and thorough analysis, thank you for your insights.

    I would like to contribute one additional layer of analysis for determining how much to invest in customer acquisition.  When acquiring a customer to a SaaS offering, you’re paying the full cost of acquisition upfront (the Google CPC, the trade show fee, etc.) only to receive revenues from the customer in monthly increments over their entire lifespan.  

    To determine an appropriate acquisition cost we must look at the present value of the future revenue.  Using your simple example of $50 per month over 20 months yields $1,000 nominally.  But the present value of the $1,000 may only be $900 or $950.  This present value of customer revenue is the number you should use to determine the appropriate investment in customer acquisition. 

    • http://blog.asmartbear.com Jason Cohen

      That’s true. Although nowadays interest rates are such that you probably can ignore NPV.
      I think you need CAC very much less than LTV, so a small change in LTV shouldn’t change your mind.
      Alternately, this is a good argument for getting annnual prepay, even providing a discount for it. The amount of te discount is of course your NPV calculation!

      Jason Cohen

  • http://graduatetutor.com/ Senith @ mba tutor

    A smart bear indeed! Thank you for this great post. 

    I understand cancellation rates. Can you share your thoughts on how life time value can be computed if the product is not a periodic payment? We at http://graduatetutor.com/ provide private tutoring for MBAs. So by definition it is personalized and students can use an hour a week, or a few hours a day and stay for just a day or a few weeks or months/years too. 

    • http://blog.asmartbear.com Jason Cohen

      No matter the payment system, you’re answering this question:

      What is the expected value for total revenue?

      “Expected value” is a statistical concept which could be loosely interpreted as “weighted average.”  It’s not the “most likely value” but rather the average, weighted by the probability of each value.

      So a rough way to do it would be to *convert* your complex thing into an average monthly rate.  For *each* customer you compute:

      equiv_monthly_rate == total_revenue / total_months

      Then you could average those rates to get your standard monthly rate, then use the logic from this article.

      Of course using averages like this covers up important data, like how spiky it might be, or how certain customers might take hours consistently and others don’t, and all sorts of other things.

      But it could at least get you a ballpark answer, and something you could track over time.

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  • DaveB

    Any opinion about billing contributing/causing some cancellations? What I am asking is, have you analyzed, say, the model where a credit card is on file and monthly monies are just charged as opposed to a quarterly or annual large invoice, where customers have to be notified that payment is due and they then realize now is the time to cancel if I plan to? They might just keep paying if a credit card or direct debit were automatically taking the money without any red-alerts sounding ? 

    To bring it back to cancellation rate calculations, I am curious if anyone has analyzed how billing models affect cancellation rates, and if one model works better than another for retention.  My company now requires annual commitments for its SaaS service, but you can pay monthly with a credit card on file, quarterly via credit card, wire transfer, or invoiced payment, or annually (same options, with a deep discount for this annual prepayment). My suspicion is that when we give a company 30 days warning that their annual payment is due, they start to really look at the service hard and decide at times that this is the time to get out from under.  My last company only did credit card or direct debit, unless the customer was very large and then was willing to manually billed them. 

    • http://blog.asmartbear.com Jason Cohen

      That’s a great question, and no I don’t have data of my own.

      It’s probably true that the smaller the payment and the less fuss is made over it, the less likely the customer is to cancel.

      From a financial perspective that’s important because that’s money in your pocket.

      From a “healthy business” perspective that’s NOT good because it’s masking the truth, which is that people don’t really need your stuff, which is a more fundamental problem.

      • Bert

        I agree that customers cancelling yearly prepayments/commitments brings a very valuable signal to the company. Did they not renew because price is too high in comparison with competitors? A lack of new features compared to competitors? Customer service quality has gone down? Etc.

        In other words, you’re gaining valuable information that you would otherwise not receive from customers who don’t think twice about a perceived lower cost (because of lower monthly payments). Customers will do work for you that you should be doing anyway (reviewing price and value compared with competitive options), so make sure to capture the reason for these cancellations very diligently.

    • RJ

      It depends. Annual commitments definitely reduce churn when you have an average lifetime less than 12 months. However, this can and does affect signups…some just want to use the service for a short time not a long time. That’s where it gets difficult to continue to show value. There are so many ways to manipulate these numbers. Starting from how and how often you communicate with them. This is typically a big trigger because you may be reminding them to quit. What’s a better strategy is to automatically renew and require opt out to unsubscribe.

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  • StBedeofBerwick

    Hi Jason,

    Some interesting stuff here. I’ve done some similar work on retention and the marginal advantage of keeping SaaS customers longer than average. Take a look and let me know what you think.




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