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The Waterfall Model: Not Just for Startups and VCs

5 Aug

Brian Ascher, a partner at Venrock, wrote a great blog post a while back about how the waterfall model may be the “single best financial reporting tool ever”. That might actually have been an understatement.  I highly recommend reading his post, by the way, if you aren’t familiar with a waterfall model and want a good primer as well as the example spreadsheet below.


In a nutshell, a waterfall model allows you to lay out your projections over a period of time (monthly numbers over a one year period; weekly numbers over a year, or daily numbers over a month, for example) and at the end of every period, compare your actuals to your projections then revise your estimates for the periods moving forward based on what you’ve learned. The waterfall model doesn’t provide you with all the answers; however, it gives you a good idea of how you’re doing with respect to your original and revised plans and as a result, figure out what additional questions you need to ask yourself to understand why. It’s an incredibly powerful tool given its relative simplicity.

VCs and Startup CEOs/CFOs have been using waterfall models for decades to measure progress against plan and to help validate assumptions about growth, cash balance, user adoption, and a number of other important business metrics. Outside of the VC/startup/board community, however, waterfall models seem to be underutilized. Maybe it’s because startups need to move quickly. They’re constantly making assumptions, learning, understanding which assumptions were good and which ones weren’t, then revising their plan of attack quickly as they continue to move forward …and a waterfall model helps them understand that and react quickly. There are a few reasons that waterfalls can be particularly helpful in the area of Customer Success as well, given a similar need to move quickly in order to proactively manage recurring revenue:

Reason 1: You need a plan, and you need to know how you’re tracking according to the plan

A waterfall model enforces management to a plan. The interim checkpoints, by nature, hold you accountable to that plan, and if there is a variance, force you to do three things: 1) Acknowledge the variance.  If you set up your waterfall model correctly, the interim periods you define should be frequent enough to allow you to take action while there is time to impact the outcome; 2) Ask why there is a variance; and 3) Re-plan the future periods given what you now know.

Reason 2: Your assumptions aren’t always right

Planning, or more precisely, getting a plan right, is an ongoing process. People make plans based on assumptions. Managing an existing customer base can be tricky, and having frequent enough visibility into key metrics in order to take meaningful action allows you to challenge your assumptions with enough time to take meaningful action. One important point to clarify here: This isn’t an opportunity to make excuses for why you didn’t hit your numbers.  This is an opportunity for you to understand what you need to do differently to improve your performance (while there’s still time) and create more accurate plans and forecasts in the future.  If you do need to re-plan, the waterfall still allows you to measure against your original plan and your revised plan.

Reason 3: Trends are interesting, but without a comparison to your original plan, trends don’t give you the entire picture

Growth is great. Improvements in key metrics are great. In order to run a business and plan/manage it successfully, though, you also need some predictability. Waterfalls provide you with a historical snapshot of how well you did delivering to plan.  You always have historical information on your original plan, your re-plan, and your actual performance for each measurable period – in one table.  It’s a simple, yet very effective visual tool.  If you ended up growing up-sell revenue 25% quarter over quarter is that good? What if your original plan was to grow at 30% QoQ?

So, with all that justification behind us, here’s an example of where and how I’ve used a waterfall model in Customer Success:

Planning and Forecasting Retention and Churn:

I recently blogged about the many Customer Success Automation solutions coming to market to help companies manage a SaaS customer base more effectively. Whether you’re using one of these products or whether you’re just starting to get your head around managing your customer base, it’s very valuable to understand which of the data elements and assumptions you’re using to identify “healthy/reference customers” or “at risk customers” are accurate, and which ones require you to go back and think again.

A team of mine once needed to forecast churn risk from the existing customer base and had very little valid historical information from which we could create projections.  We started by looking at customers using broad-stroke definitions of various health levels.  We assigned customers a “health status” of Red, Orange, Yellow, and Green, then based on their contract renewal month, assigned a probability of renewal based on that health status. We eventually began adding criteria to more clearly define health status, including usage metrics (not just frequency of logins, but how effectively were they using the system), customer responsiveness, and other indicators of risk associated with their business and usage model. We looked at our first months data and saw where we were off, then went back to our assumptions and looked at where we might possibly have miscategorized customers. We also looked at whether our percentage ratios by health status were accurate (for example, did x% of our “orange” customers actually cancel).  We gradually increased our sophistication level as we gathered more data and continued to refine our assumptions in our waterfall model. By the end of our first full year of deploying the model, we were within 5% accuracy forecasting revenue retention and churn.

In addition to forecasting retention and churn, a waterfall model can be useful in other areas of customer success, including:

  • Planning and forecasting up-sells
  • Modeling the rate at which you plan on improving service levels and/or resultant customer feedback scores
  • Planning and forecasting adoption of certain strategic product features across your user base

Pretty much any key metric you want to track and measure against can be managed using a waterfall model. You may want to start with a couple of the ones above, then determine if tracking others will be useful. Just be ready to dig into the underlying data to ask “why” the variances are occurring… and keep asking “why?” until you see patterns emerge. Then act.

Are Your Early Warning Signs Early Enough?

19 Jun

Good Customer Success teams are analyzing data in order to understand the characteristics of their customers.

Great Customer Success teams are analyzing the right data in order to understand the characteristics of their customers.

Nowhere is analyzing the right data more critical than in understanding the early warning signs of at-risk customers.  In generating awareness for this Thursday night’s CSM Forum Event: Detecting the At-Risk Customer Relationship, Mikael Blaisdell points out in his most recent blog post the one certainty behind at risk customers: “A customer that is not getting the desired level of measurable value out of their relationship with your company is one that is surely headed for the exit door.”  I couldn’t agree more… or sooner.

In my last post, I indicated that the primary objective for a CSM organization should be to ensure that a customer is getting value from the implementation of your company’s solution.  The CSM organization, whether highly personal, highly leveraged, or highly automated, needs to know which customers are at risk due to a lack of demonstrated value.  Most organizations try to identify indicators of risk.

Generic Indicators: The Lowest Common Denominator

There’s one great thing about generic indicators of risk, such as system login/usage trends, support cases opened, and NPS values:  Everyone can relate to them.  There is almost always a correlation between one ore more of these factors and the level of churn risk associated with a customer, so everyone knows that they’re relevant.  The problem with most of these indicators, however, is that they don’t always provide you with an early enough warning to allow you to take action that will save the customer, and in some cases, there isn’t even a cause/effect relationship between these criteria and churn.  By the time most of this data is available, the customer knows they have a problem, and in some cases (like NPS) the customer has already gotten to the point of communicating it to you.  In the case of usage metrics, I recently spoke with an executive who came from a financial services company.  She told me they initially looked at usage data to determine which customers were at risk; however when they dug deeper and looked at the process that customers who defected went through, it was clear that by the time usage had declined, the customer had already run a month in parallel on a competitor’s solution and had already migrated off of theirs.  Horse gone …no need to close the barn door.

Getting Beyond the Generic and Going Upstream

Really understanding whether a customer is getting measurable value from your solution requires that you look at indicators that are specific to you and to them.  Think about your key selling points.  Establish a Value Roadmap that you plan out with your customers and help them measure progress on a regular basis.  This can be done with CSMs in a high touch environment or with system metrics and drip campaigns for low touch / high volume customer relationships.  For example, if you offer a digital marketing solution, help your customers benchmark their existing conversion rates, then set goals and objectives for improvement and measure progress against those goals.  If they aren’t achieving those objectives, then get out ahead of the problem.  Do what you can to make them successful on your solution.  Especially in the case of low-touch / high-volume deployment models (think B2C SaaS), great products will have relevant metrics reporting and prescriptive recommendations built in.  Your offering shouldn’t just be about creating a technical solution to a problem, it should be about helping your customers get better at solving that problem with your offering. Keep in mind that your full “offering” includes product, services, and additional company interactions.  Think about applying marketing automation principles for your installed base where the overt goal isn’t conversion, but successful use of your solution.

Also look at other data you have that might either indicate or result in a poor customer experience:

  • Have you had any recent system performance issues? If so, which customers were logged on at the time?
  • Have any of your key customers recently started following or connecting to your competitors on social media?
  • Are they not engaging with your drip customer marketing campaigns even though you’re sending them relevant content?
  • Are they not implementing the recommendations your team or the system has been providing on how to be more effective with your solution?

Are Generic Indicators Useless?

No.  Absolutely not.  And in many cases, they can still be useful as early warning indicators, especially in combination with other indicators.  Every company, however, is going to need to take a good, hard, honest look at their own data to really understand which elements are early indicators – in their world – and which elements are simply correlated, but might be able to provide them insight on the trajectory of the customer relationship once they’ve been identified as “at risk” and a plan of action is put in place.

What early warning signs have you identified that are specific to your business and how have you used them to: 1) address the existing at-risk customers in the short term; and 2) and put a proactive plan of action into place to keep those issues from affecting other customers in the future?

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