Fight churn, retain your customers.

Entrepreneur.com: How 9 Successful Companies Keep Their Customers

I recently wrote an article featuring retention tips from nine B2B companies that know how to keep their customers, where any company, big or small, B2B or B2C will find applicable tactics, strategies, and customer-focused mindsets.

See the article on Entrepreneur.com:

CLICK HERE: How 9 Successful Companies Keep Their Customers

Losing a customer often seems personal, not just a statistic. But in terms of business success, startups now more than ever need to show not only that they can attract customers but that they can keep them. Increasingly, investors look at customer retention to determine whether an entrepreneur’s product or service will ultimately succeed in the marketplace.

There are many examples of successful companies that have innovated to ensure that their customers have a great experience, receive value and stay loyal. So I reached out to colleagues, friends and fellow entrepreneurs who have been particularly impressive at building ongoing relationships with customers. I asked them to share tips that have driven their success.

Their resulting insights about 9 companies provide a wealth of best practices for any entrepreneur looking to establish a growing and loyal customer base:

Calculating the Value of Churn Reduction, Part 1: The Correct Calculation Of Customer Lifetime Value

Much has been said about the value of retention and acquisition, and there are many different theories – unfortunately, much of this can be confusing and misleading. We are going to explain how to calculate these important metrics of a SaaS company correctly. First, we have to start with customer lifetime value (CLV) – many people often use simplified versions of CLV that can be very far off. In this article series I will explain the importance of cash discounting and its application in SaaS metrics. This is a slightly complicated subject (for those of us who never studied finance), but fortunately two professors, Sunil Gupta of Harvard and Donald R. Lehman of Columbia, have already derived a relatively simple equation that does it for you! The first three articles in this series will convince you why and teach you how. Later in the series we will go over the valuation of acquisition campaigns and the value of customer retention. But we can’t get to those until we get a really good understanding of CLV.

Main Result

The best formula for CLV was created by Sunil Gupta and Donald Lehman and first published in their seminal paper, “Customers As Assets [PDF]“, (Journal of Interactive Marketing, 2003, 17.1 pages 9-24). It is:
CLV=M * r/(1+i-r)
where:
  1. M is the margin on the customer(s), i.e.recurring revenue (RR) minus Cost of Goods Sold (COGS)
  2. r is the retention rate
  3. i is the interest rate for discounting project investments
The parameters of the formula (margin, retention and discount rate) can all be stated assuming either an annual or a monthly period (or any period you want, for that matter) as long as they are consistent. Consistency of the time period is the main “gotcha” when using the formula, and we will explain how to do it right.
If you know exactly what those three terms mean and you know how to convert them to a consistent time period, you may want to just skip to the article by Gupta & Lehman! My discussion here is for people who are either new to SaaS metrics or want to see the least math possible but still get the important points. In this post, and those that come after it, we will explain exactly what those terms mean, and explain enough to know why that is the best formula for CLV, but we will introduce the bare minimum of mathematical equations.

Expected Lifetime of a Customer

CLV means the value today of a customer, including the money you expect that they will pay you in the future. The first thing you need to know is how likely the customer is to last as a subscriber. For that you use the churn rate and this equation:
Expected Lifetime=1/churn
For the mathematically-minded, this equation is derived from an exponential decay model of customer lifetime. For the non-mathematical, the intuition is that if a customer has a 1 in 10 chance of cancelling each month, you expect they will stay about 10 months. Make sense? Sure! Note that if your churn rate is monthly, your expected lifetime is also in months, and if your churn rate is annual then the expected lifetime is in years. We’ll have more to say about this in a little bit. So if this is the expected lifetime, does that mean that the customer lifetime value is just monthly recurring revenue (MRR) times the lifetime, or CLV=MRR/churn? No! Although you see this equation used in some SaaS metrics articles (we won’t link any names) this is wrong and will overstate the value of a customer. It is wrong in two respects: it doesn’t include the cost of acquiring and maintaining the customers, and it doesn’t discount or devalue the cash that will be paid by customers in the future. Without accounting for these you will overestimate the value of a customer and make bad decisions about acquiring and retaining them. We actually won’t use the equation above directly, but it’s important to get an idea for how expected lifetime value comes into play and how you calculate it. So that equation is meant to give you some intuition more than anything else. Next we will explain discounting future cash flows in some detail. Here is a short preview of what to expect:

Calculating the Value of Churn Reduction, Part 2: Discounting Future Cash

In the last section we introduced the Customer Lifetime Value (CLV) formula of Gupta & Lehman, and we began to explain it starting with the expected lifetime of a customer. So was that it? The lifetime value of a customer is expected length of the customer’s subscription times the profit you make on them each month. That is:

CLV=M / c
So if the churn rate is 10%, the CLV is 10 times the recurring margin, if the churn rate is 5% the CLV is 20 times the margin, and if the churn is 1% then the CLV is 100 times the margin. This approximation to CLV is widely used, but it has some serious flaws and as we will see it can significantly overstate CLV…
To be continued…

Entrepreneur.com: 4 Steps to Knowing What Your Customers Want Better Than They Do

Sparked’s mission is to help companies deliver more value to their customers. And that starts with making sure you know your customer as well as possible. Sometimes, that means getting to know them even better than they know themselves. How is that possible? Well, in my latest article on Entrepreneur.com, I explore the methods that leading companies use to learn about their customers – methods that you can apply to your unique case as well.

4 Steps to Knowing What Your Customers Want Better Than They Do

TheNextWeb: How to avoid getting burned when hiring overseas

Sparked International! Sparked CEO Ben Rigby is in London this week with current and potential new clients, while Sparked Managing Director Joseph Pigato discusses how to build teams overseas. From assembling a battle-tested hiring team to mining your networks and ultimately making a rock-solid hire, this article on TheNextWeb should be your first stop if you’re looking to expand your company’s presence across the globe.

Worst Retention Call Ever

Warning, this retention call is painful. It’s so bad that it seems like it’s got to be a joke. If Comcast had a bit more insight into reasons for churn, they wouldn’t have had to comp their rep on getting through these questions… which has resulted in a good deal of reputation damage (already ~800k listens).

Deception of NPS

The Net promoter Score system, while useful in a few narrow applications, is not the general purpose tool that some companies claim it to be. Here are a few of the more obvious deceptions that an NPS score might be hiding…

Deception of NPS Sources:
http://experiencematters.wordpress.com/2014/01/27/why-net-promoter-score-may-not-align-with-business-results/
http://blogs.forrester.com/richard_evensen/11-04-18-stop_using_nps_net_promoter_score_but_please_save_the_question
http://www.measuringusability.com/blog/nps-ux.php
http://infoquestcrm.co.uk/2012/10/the-cassandra-phenomenon-and-customer-satisfaction-surveys/
http://wordofmouthindex.com/press-releases/foresee-releases-word-of-mouth-index-womi-benchmark-showcasing-customer-loyalty-scores-for-the-top-100-u-s-brands/

Customer Satisfaction and Achievement of Service Goals

Since we launched Retention Radar, a lot of people at SaaS companies have asked us what type of data they should put into a machine learning model in order to analyze their subscriber churn. The answer is very simple in theory, but there are a lot of challenges in practice. Let’s start with a simple reality check, without any statistics and data science: At the end of the day, subscribers to a service who think they are getting what they pay for are going to keep on paying (okay – only as long as they can – but voluntary churn is most churn in a typical SaaS company.)

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The Top 5 SAAS Metrics To Track

Tracking metrics when you’re running a SAAS business is mandatory – it’s the only way you can hope to gain any intelligence about how to chart a course for the future. Should you be spending to acquire more customers? Fixing problems that currently exist? If you’re not constantly tracking and measuring, you don’t know whether your business is coasting along comfortably or perched on the edge of disaster.

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The Top 5 Books Every Customer Success Manager Should Read

In order to be truly effective at your job as a Customer Success Manager, you need to be willing to learn from the examples and lessons of others. You also need to be able to adapt those lessons to your own situations. We’ve scoured the web to locate the best books on Customer Success and Customer Experience. Each of these volumes will provide a new level of insight to help you be more effective at your job!

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Probabilistic Revenue at Risk with Confidence

Tomasz Tunguz at RedPoint just posted about Revenue at Risk (RaR) which inspired us to share our approach to calculating this important statistic.

Revenue at risk (RaR) is an important number for SaaS services to watch, but the version that many people use is not as informative as it should be – maybe that’s why many companies don’t bother. When you say “risk”, it means you are measuring something bad that could happen, but might or might not – hence risk. What is missing from most RaR calculations is the notion of how much risk we are talking about. Consider the standard calculation for probabilistic revenue at risk: Read more