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SaaS Metrics – A Guide to Measuring and Improving What Matters

This blog post looks at the high level goals of a SaaS business and drills down layer by layer to expose the key metrics that will help drive success. Metrics for metric’s sake are not very useful. Instead the goal is to provide a detailed look at what management must focus on to drive a successful SaaS business. For each metric, we will also look at what is actionable.

Before going any further, I would like to thank the management team at HubSpot, and Gail Goodman of Constant Contact, who sits on the HubSpot board. A huge part of the material that I write about below comes my experiences working with them. In particular HubSpot’s management team is comprised of a group of very bright individuals that are all very metrics driven, and they have been clear thought leaders in developing the appropriate tools to drive their business. I’d also like to thank John Clancy, who until recently was President of Iron Mountain Digital, a $230m SaaS business, and Alastair Mitchell, CEO and founder of Huddle.

Let’s start by looking at the high level goals, and then drill down from there:

 

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Key SaaS Goals

  • Profitability: needs no further explanation.
    • MRR Monthly Recurring Revenue: In a SaaS business, one of the most important numbers to watch is MRR. It is likely a key contributor to Profitability.
  • Cash: very critical to watch in a SaaS business, as there can be a high upfront cash outlay to acquire a customer, while the cash payments from the customer come in small increments over a long period of time. This problem can be somewhat alleviated by using longer term contracts with advance payments.
    • Months to recover CAC: one of the best ways to look at the capital efficiency of your SaaS business is to look at how many months of revenue from a customer are required to recover your cost of acquiring that customer(CAC). In businesses such as banking and wireless carriers, where capital is cheap and abundant, they can afford a long payback period before they recover their investment to acquire a customer (typically greater than one year). In the startup world where capital is scarce and expensive, you will need to do better. My own rule says that startups need to recover their cost of customer acquisition in less than 12 months.
      (Note: there are other web sites and blogs that talk about the CAC ratio, with a complex formula to calculate it. This is effectively a more complicated way of saying the same thing. However I have found that most people cannot relate well to the notion of a CAC ratio, but they can easily relate to the idea of how many months of revenue it will take to recover their investment to acquire a customer. Hence my preference for the term Months to Recover CAC.)
  • Growth: usually a critical success factor to gaining market leadership. There is clear evidence that once one company starts to emerge as a market leader, there is a cycle of positive reinforcement, as customers prefer to buy from the market leader, and the market leader gets the most discussion in the press, blogosphere, and social media.

Two Key Guidelines for SaaS startups

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The above guidelines are not hard and fast rules. They are what I have observed to be needed by looking at a wide variety of SaaS startups. As a business moves past the startup stage, these guidelines may be relaxed.

In the next sections, we will drill down on the high level SaaS Goals to get to the components that drive each of these.

Three ways to look at Profitability

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  1. Micro-Economics (per customer profitability): Micro-economics is the term used to describe looking at the economics of your business on a single customer level. Most business models (with a few exceptions such as marketplaces) are based around a simple principle: acquire customers and then monetize them. Micro-economics is about measuring the numbers behind these two essential ingredients of a customer interaction. The goal is to make sure the fundamental underpinnings of your business are sound: how much it cost to acquire your customers, and how much you can monetize them. i.e. CAC and LTV (cost of acquiring a customer, and lifetime value of the customer). In a SaaS business, you have a great business if LTV is significantly greater than CAC. My rule of thumb is that LTV must be at least 3x greater than CAC. (As mentioned elsewhere in this blog, your startup will die if your long term number for CAC is higher than your LTV. See Startup Killer: The cost of acquiring customers.)
  2. Overall profitability (standard accounting method): This looks a the standard accounting way of deriving profitability: revenue – COGS – Expenses.  The diagram also notes that Revenue is made up of MRR + Services Revenue. Since MRR is such a critical element, there will be a deeper drill down to understand the key component drivers.
  3. Profitability per Employee: it can be useful to look at the factors contributing to profitability on a per employee basis, and benchmark your company against the rest of the industry. Expenses per Employee is usually around $180-200k annually for businesses with all their employees in the US. (To calculate the number take the total of all expenses, not just salaraies, and divide by the number of employees.) Clearly to be profitable in the long term, you will want to see revenue per employee climb to be higher than expenses, taking into account your gross margin %.

Drill down on MRR

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MRR is computed by multiplying the total number of paying customers by the average amount that they pay you each month (ARPU).

  • Total Customers:  a key metric for any SaaS company. This increases with new additions coming out the bottom of the sales funnel, and decreases by the number of customers that churn. Both of these are key metrics, and we will drill down into them later.
  • ARPU – average monthly revenue per customer: (The term ARPU comes from the wireless carriers where U stands for user.)  This is another extremely imporant variable that can be tweaked in the SaaS model. If you read my blog post on the JBoss story, you will see that one of the key ways that we grew that business was to take the average annual deal size from $10k, to $50k.  Given that the other parts of the pipeline worked with the same numbers and conversion rates, this grew the business by 5x.  We will drill down into how you can do the same thing a little further on.

Drill down on Micro-Economics (Per Customer Profitability)

Our goal is to see a graph that looks like the following:

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To achieve this, lets look at the component parts of each line, to see what variables we can use to drive the curves:

 

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As mentioned earlier, customer profitability = LTV – CAC.

Drill down on LTV

Drilling down into the factors affecting LTV, we see the following:

LTV = ARPU x Average Lifetime of a Customer – the Cost to Serve them (COGS)

It turns out that the Average Lifetime of a Customer is computed by 1/Churn Rate. As an example, if a you have a 50% churn rate, your average customer lifetime will be 1 divided by 50%, or 2 months. In most companies that I work with, they ignore tracking the average lifetime, but instead track the monthly churn rate religiously.

The importance of a low churn rate cannot be overstated. If your churn rate is high, then it is a clear indication of a problem with customer satisfaction. We will drill down later into how you can measure the factors contributing to Churn Rate, and talk about how you can improve them.

Drill down on CAC

The formula to compute CAC is:

CAC = Total cost of Sales & Marketing  /  No of Deals closed

It turns out that we are actually interested in two CAC numbers. One that looks purely at marketing program costs, and one that also takes into consideration the people and other expenses associated with running the sales and marketing organization. The first of these gives us an idea of how well we could do if we have a low touch, or touchless sales model, where the human costs won’t rise dramatically over time as we grow the lead flow.  The second number is more important for sales models that require more human touch to close the deal. In those situations the human costs will contribute greatly to CAC, and need to be taken into consideration to understand the true micro-economics.

I am often asked when it is possible to start measuring this and get a realistic number. Clearly there is no point in measuring this in the very early days of a startup, when you are still trying to refine product/market fit. However as you get to the point of having a repeatable sales model, this number becomes important, as that is the time when you will usually want to hit the accelerator pedal. It would be wrong to hit the accelerator pedal on a business that has unprofitable micro-economics. (When you are computing the costs for a very young company, it would be fair to remove the costs for people like the VP of Sales and VP of Marketing, as you will not hire more of these as you scale the company.)

When we look at how to lower CAC, there are a number of important variables that can be tweaked:

  • Sales Funnel Conversion rates: a funnel that takes the same number of leads and converts them at twice the rate, will not only result in 2x more closed customers, but will also lower CAC by half.  This is a very important place to focus energy, and a large part of this web site is dedicated to talking about how to do that. We will drill down into the Sales Funnel conversion rates next.
  • Marketing Program Costs: driving leads into the top of your sales funnel will usually involve a number of marketing programs. These could vary from pay per click advertising, to email campaigns, radio ads, tradeshows, etc. We will drill down into how to measure and control these costs later.
  • Level of Touch Required: a key factor that affects CAC is the amount of human sales touch required to convert a lead into a sale. Businesses that have a touchless conversion have spectacular economics: you can scale the number of leads being poured into the top of the funnel, and not worry about growing a sales organization, and the associated costs. Sadly most SaaS companies that I work with don’t have a touchless conversion. However it is a valuable goal to consider. What can you do to simplify both your product and your sales process to lower the amount of touch involved? This topic is covered at the bottom of a prior blog post:  Startup Killer: the cost of acquiring customers.
  • Personnel costs: this is directly related to the level of touch required. To see if you are improving both of these, you may find it useful to measure your Personnel costs as a % of CAC over time.

Drill down on Sales Funnel Conversion Rates

The metrics that matter for each sales funnel, vary from one company to the next depending on the steps involved in the funnel. However there is a common way to measure each step, and the overall funnel, regardless of your sales process. That involves measuring two things for each step:  the number of leads that went into the top of that step, and the conversion rate to the next step in the funnel (see below).

 

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You will also want to measure the overall funnel effectiveness by measuring the number of leads that go into the top of the funnel, and the conversion rate for the entire funnel process to signed customers.

The funnel diagram above shows a very simple process for a SaaS company with a touchless conversion. If you have a conversion process involving a sales organization, you will want to add those steps to the funnel process to get insights into the performance of your sales organization. For example, your inside sales process might look like the following:

 

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Here if we look at the closed deals and overall conversion rates by sales rep, we will have a good idea of who our best reps are. For lower performing reps, it is useful to look at the intermediate conversion rates, as someone that is doing a poor job of, say, converting demos to closed deals could be an indication that they need demo training from people that have high conversion rates for demos. (Or, as Mark Roberge, VP of Sales at HubSpot, pointed out, it could also mean that they did a poor job of qualifying people that they put into the Demo stage.)

These metrics give you the insight you need into your sales and marketing machine, and those insights give you a roadmap for what actions you need to take to improve conversion rates.

Using Funnel Metrics in forward planning

Another key value of having these conversion rates is the ability to understand the implications of future forecasts. For example, lets say your company wants to do $4m in the next quarter. You can work backwards to figure out how many demos/trials that means, and given the sales productivity numbers – how many salespeople are required, and going back a stage earlier, how many leads are going to be required. These are crucial planning numbers that can change staffing levels, marketing program spend levels, etc.

Drill down by Customer Type

If you have different customer types, you will want to look at all the CAC and LTV metrics for each different customer type, to understand the profitability by customer type. Often times this can lead you to a decision to focus more energy on the most profitable customer type.

Drill down into ROI per Marketing Program

My experiences with SaaS startups indicate that they usually start with a couple of lead generation programs such as Pay Per Click Google Ad-words, radio ads, etc. What I have found is that each of these lead sources tends to saturate over time, and produce less leads for more dollars invested. As a result, SaaS companies will need to be constantly evaluating new lead sources that they can layer in on top of the old to keep growing.

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Since the conversion rates and costs per lead vary quite considerably, it is important to also measure the overall ROI by lead source:

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Growing leads fast enough to feed the front end of the funnel is one of the perennial challenges for any SaaS company, and is likely to be one of the greatest limiting factors to growth. If you are facing that situation, the most powerful advice I can give you is to start investing in Inbound Marketing techniques (see Get Found using Inbound Marketing). This will take time to ramp up, but if you can do it well, will lead to far lower lead costs, and greater scaling than other paid techniques. Additionally the typical SaaS buyer is clearly web-savvy, and therefore very likely to embrace inbound marketing content and touchless selling techniques.

From Alistair Mitchell, CEO of Huddle: “Just calculating CAC can be extremely complicated, given the numerous ways in which people find out about your service.  To stop getting too bogged down in the detail, its best to start with a blended rate that just takes your total spend on marketing (people, pr, acquisition etc) and split this across all your customers, regardless of type or source. Then, once you’ve got comfortable with that, you can start to break CAC down by the different customer types and elements of your inbound funnel, and start measuring specific campaigns for their contribution to each customer type.”

Drill down into Churn Rate

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As described in the section on LTV, Churn Rate has a direct effect on LTV. If you can halve your churn rate, it will double your LTV. It is an enormously important variable in a SaaS business. Churn can usually be attributed to low customer satisfaction. We can measure customer satisfaction using customer surveys, and in particular, the Net Promoter Score.

If you are using longer term contracts, another key metric to focus on is renewals. From John Clancy, ex-President of Iron Mountain Digital: “

Non-renewals add to churn, but they can have different drivers. We spent a lot of time examining our renewal rates and found that a single digit improvement made a huge difference. Often times the driver on a non-renewal is economic – the internal IT department has mounted a campaign to bring the solution back in house. SaaS businesses need to identify renewal dates and treat the renewal as a sales cycle (it’s much easier and less expensive than a new sale, but it deserves the same level of attention) Many SaaS businesses make the mistake of taking renewals for granted.”

A good predictor of when a customer is about to churn is their product usage pattern. Low levels of usage indicate a lack of commitment to the product. It can be a good idea to instrument the product to measure this, looking for particular features our usage patterns that are correlated with stickiness, or a likelihood to churn.

Another measurement tool that can be very useful in understanding churn is to look at a Cohort Analysis. The term cohort refers to a group of customers that started in the same month. The reason for doing this is that churn varies over time, and using a single churn number for all customers will mask this. Cohort analysis shows:

  • How churn varies over time (the green call out below).
  • How churn rates are changing with newer cohorts, (the red call out below)  For example in the early days of your SaaS company, you may have serious product problems and lose a lot of customers in the first month. Over time your product gets better, and the first month churn rate will drop.

Cohort analysis will show this, instead of mixing all the churn rates into single number.

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Here’s a comment on Cohort Analysis from Alastair Mitchell, CEO of Huddle: “I actually think this is more important than churn, for the simple fact that churn varies over the lifetime of a customer cohort, and just looking at monthly churn can be very misleading.  Also, given the importance of payback in a year – you really want to look at churn over the course of a 12 months cohort. For instance, in the first 3 months of a monthly paying customer you will see high churn (3 is a recurring ‘magic’ number in all of retail), then reduced churn (sometimes even positive churn) over the next 3 months less and then probably more stable spend over the next 6 months. The number you really care about is the % of customers spending after 12 months (not necessarily on a monthly basis) as that’s what matters for your CAC payback calculations.”

Two variables that really matter

As we saw above, there are two variables that have a huge effect on a SaaS business: funnel conversion rate, and churn, and it is not a bad idea to graph them as shown below.

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Drill down into ARPU (Average Revenue per Customer)

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ARPU is often different for different customer categories, and should be measured separately for each category. It can usually be driven up by focusing on:

  • Product Mix: adding products to the range, and using bundles, and cross-sell and up-sell
  • Scalable Pricing:  there are always some customers that are willing to pay more for your product than others. The trick is developing a multi-dimensional pricing matrix that allows you to scale pricing for larger customers that derive more value from the product. This could be pricing by the seat used (Salesforce.com), or by some other metric such as number of individuals mailed in email campaigns (Eloqua).
    If you are using scalable pricing, it will be valuable to measure what the distribution is of customers along the various axes. You could imagine taking an action to do after more seats inside of existing customers as a way to drive more revenue. etc.

Drill down into Cash

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We already discussed Months to recover CAC as a key variable. There is another way to affect Cash: which is using longer term contracts and incenting your customers to pay for 6, 12, 24, or even 36 months up front in advance. This can mean the difference between needing to raise tons of venture capital and giving away ownership, or being able to grow the business in a self-funded manner. Given the cost of capital, you can often calculate what discount makes sense. (If capital is cheap and freely available, it doesn’t make sense to give much discount.)

If you do use longer term contracts, it will be important to measure “Discretionary Churn”. Since some of your customers are locked in and cannot churn, they could artificially lower your overall churn numbers. The way to understand what is really going on is to look at the discretionary churn, which is the churn rate for all customers that are at the point where they have the option to churn, removing those whose contracts would have prevented them from churning.

Cash Management and forecasting

Cash is one of the most important items to get right in any startup. Run out of cash, and your business will come grinding to a halt regardless of how good any of your other metrics may be. One of the most important ways to run a SaaS company is to look at CashFlow profitability (not recognized revenue profitability). What is the difference: If your business only gets paid month by month, there will be no difference, but if you get longer term contracts, and get paid in advance, you will receive more cash upfront than you can recognize as revenue, so your cash flow profitability will look better than your revenue profitability, and is a more realistic view of whether you can survive day to day on the money coming in the door.

Here is another comment from Alastair Mitchell of Huddle on this topic: “SaaS companies tuning their model should think not just in terms of the months to recover CAC, but also the topline amount of cash required to get to cashflow profitability (or the next funding round). This is probably the single biggest mistake I see in early stage companies. They don’t look ahead, using these metrics, to figure out that if the time to repay CAC is 12 months, then in aggregate they are going to need 12 months of CAC spend PLUS the number of months required of further growth to cover their operating costs (mostly engineering) BEFORE they are even cashflow positive (let alone revenue profitability). Most businesses I see fundamentally miss this and end up short; frequently through under-estimating the time to recover CAC, and churn. The readers of this blog should be focused on cashflow profitability, not revenue profitability. (Hence why your point about annual/upfront contracts is so important)”

Drill down into Growth

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Focusing on Growth as a separate parameter can be highly valuable. It is the nature of a SaaS business to grow MRR month on month, even if you only added the same number of customers every month. However your goal should be to grow the number of new customers that you sign up every month. You can do this by focusing on:

  • Improvement in the overall funnel conversion rate
  • Lead Generation Growth
  • Growth in Funnel Capacity

The first two have been covered already. The last bullet: Growth in Funnel Capacity is an often overlooked metric that can bite you unexpectedly if you don’t pay attention to it. In my second startup, I had a situation where sales growth stalled after growing extremely rapidly for a couple of years. The problem, as it turned out, was that we had stopped hiring new sales people after reaching 20 people, a number that felt very large to me, and had maxed out on sales capacity. We started sales hiring again, and a couple of years later the business hit a $100m run rate. I witnessed a similar phenomenon at Solidworks, when after 2-3 years of phenomenal growth, their growth slowed. It turned out that their channel sales capacity had stopped growing. Solidworks started measuring and managing something that would later turn out to be a critical metric: channel capacity in terms of the number of FTE (Full Time Equivalent) sales people in their channel, and the average productivity per FTE. This has helped propel them to over $400m in annual revenues.

Another great way to grow your business is by adding new products that can be up-sold, or product features that can lead to a higher price point. Since you already have a billable contract, it is extremely easy to increase the amount being charged, and this can often be done with a touchless sale.

Other Metrics

There are a series of less important metrics that can still be useful to be aware of. I have listed some of these in the diagrams below:

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After posting the above, I received a note from Gail Goodman of Constant Contact, noting that they include the cost of on-boarding a customer in CAC, not LTV as I have shown. Given that they are a public company with significant accounting scrutiny, this is likely the right way to do things.

Conclusions

If you have kept reading this long, it likely means that you are likely an executive in a SaaS company, and truly have a reason to care about this depth of analysis. I would very much like to hear from you in the comments section below to see if I have missed out on metrics that you think are important.

The main conclusion to draw from this article, is that a SaaS business can be optimized in many ways. This article aims to help you understand what the levers are, and how they can affect the key goals of Profitability, Cash, Growth, and market share. To pull those levers requires that you first measure the variables, and watch them as they change over time.

It also requires that you implement a very metrics driven culture, which can only be done from the top. The CEO needs to use these metrics in her staff meetings, and those execs need to use them with their staff, etc. Human nature is such that if you show someone a metric, they will automatically work to try to improve it. That kind of a culture will lead to true operational excellence, and hopefully great success.

Posted in Building for Success, Startup Help.

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Designing startup metrics to drive successful behavior

Great companies are almost always run by great management teams. And great management teams know that the only way to improve a process is to start by measuring it. Good metrics should also be actionable, and drive successful behavior. In this post I hope to help show how to figure out which metrics matter the most, and how to design them in such a way as to drive behavior that will lead to the results that you want.

This post is applicable to any kind of business. In a follow up post, I will use this technique to walk through the design of a set of metrics for a SaaS company. Since SaaS businesses (or any other subscription-based business) are different from standard software businesses, there are some interesting elements that we will uncover.

Think of your company as a machine

One way to look at how companies work is to imagine them as a machine that has Outputs, and Levers that you, the management team, can pull to affect it’s behavior.

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Weak management teams have only a limited understanding of how their machines work, and what levers are available to affect performance. The better the management team, the better they will understand how that machine works, and how they can optimize its performance (what levers they can pull).

When we look to design metrics, we are looking to deepen our understanding of the machinery, and how it works. Well designed metrics will automatically drive behavior to optimize output from the machine.

Example of a bad Board Meeting

Here is an example of a bad board meeting, which happens far more frequently than you might imagine. The company has just missed its quarterly revenue forecast. Good board members want to know two things:

  • Why that happened?
  • What can be done to avoid the problem going forward?

As they ask management what happened, a common answer will be that the market was really tough, and deals just didn’t close the way that they hoped. They also don’t have a great plan for what they are going to do differently next quarter, other than hope that the market improves, and that more deals will close. There is a great saying for situations like this: Hope is not a strategy.

Example of a good Board Meeting

The better management teams answer those questions differently. They will gradually peel back the covers of the machine, like peeling the layers of an onion, and expose the true nature of the problem, which of course will also highlight what levers need to be pulled to fix the problem. Lets take an example, and look at how they might do this:

    1. They will be able to tell you that revenue is composed of deals. To compute revenue, you multiply average deal size by number of deals. They may tell you that they were targeting to grow their average deal size to $x, and were successful in hitting this target. But the number of deals that they closed was below target.
    2. They will then peel back the onion one more layer, and tell you that the reason that the number of deals was below target was because 1/3rd of the salesforce missed their targets.
    3. They will then peel back another layer, and tell you that the reason those salespeople missed their targets was because they were not handed the required number of trials from marketing. However, for the trials that they did receive they were successful at converting them to closed deals at the expected conversion rate. So we know from this that the problem is not the quality of those sales people.
    4. Peeling back another layer, they will tell you that the number of trials is equal to the visitors to the site x the conversion rate of those visitors to trials. They may tell you that the number of visitors was on target, but the conversion rate fell below the previous levels.
    5. Peeling back one more level, they may tell you that they ran three major campaigns to drive visitors to the site, as well as relying on the normal levels of word of mouth traffic.  They may then reveal the true source of the problem: the ads that they had started running on Facebook were delivering a far lower conversion rate to trials than in prior months.

The contrast between the two approaches is stark. In the second case, it is clear that management will know how to fix the problem (by adding new traffic generation programs). They also know precisely how much additional traffic will need to be generated to reach the growth targets, and how many sales people are needed at a given productivity level, etc. etc.

What is surprising is just how few management teams really have their act in order in this area. For Web and SaaS businesses with smaller transactions at higher volumes, this kind of modeling and tracking is much easier, as web-based lead generation and marketing have easy to implement measurements, and the greater the volume of transactions, the more clearly patterns emerge. This is a little harder to do for channel sales, but still extremely valuable. And a little harder than that for direct sales situations with large deal sizes.

The Secret to Success

The secret to successful design of metrics is to start with the end goal and work backwards. In most companies, the end goals that matter the most are:

  • Profit/(Loss)
  • Growth
  • Good cash flow

(You may wonder why we don’t have Revenue in this list, but read further, and and it will soon become clear.)

Let’s take the first of these, Profitability, and work backwards. Working backwards means looking at the components that make up Profitability:

Profits (EBITDA) = Revenue – Cost of Goods Sold – Expenses

So to focus the management team on driving profitability, we should also track and measure Revenue, CoGS, and Expenses. Obvious, isn’t it? Well the good news is that this same principle can be applied over and over again focusing on the components of Revenue, CoGS, and Expenses where needed.

So the next step is to take Revenue, CoGS, and Expenses, and break them down to the key components. Bookings is the pre-cursor to Revenue. So let’s look at Bookings as an example:

Bookings =No of deals closed * Average Deal Size

For Reseller Channels, we might be looking at something different like this:

Revenue = No of productive resellers * average productivity per reseller

(Note: in many businesses there are several categories of deals. e.g. there could be large deals, and smaller deals. Or their could be deals from two or more different categories of customers. So the formula may have more elements to it than shown above.)

Peeling back another level, we might find the following:

No of deals closed = No of productive sales people * Average Productivity per Sales person

There will also likely be another formula to compute this, which will look like the following:

No of deals closed = No of Trials * Average Conversion Rate

These two formulae clearly indicate some of the levers that we have available to increase Bookings. We can grow the number of trials, or grow the number productive sales people, or we could try to increase the average productivity of our sales people. However we need to make sure that we grow them both together, otherwise we could end up out of balance, and have too many sales people and not enough trials to feed them, or too many trials and not enough productive sales people to close them.

The next step would be to peel back the onion a few more layers:

No of trials = No of visitors to the web site * Average Conversion Rate to Trials

No of Visitors to the web site = Normal traffic + for each traffic generation campaign: target audience of each campaign * Conversion Rate to visitors

Each time we peel back a layer to expose the components, we gain a better understanding of our machine and the levers that we can pull to make it work better. For example in the above two formulae, we can see that a big driver of the model is visitors to the web site. But this can be expensive to increase. So the other variable that we can try to increase is the conversion rate for each campaign, and the conversion rate to trials. We can try to do this by altering campaign messaging and landing pages and using A/B testing to find the optimum creative content.

We might also decide to focus our efforts on increasing the average deal size. We could do this in several ways:

  • Cross sell to add additional products
  • Up sell to add seats, or premium features
  • Develop a scalable pricing matrix that does a better job of charging higher end customers that are willing to pay more. This might involve several new axes that increase pricing, such as charging per seat, or charging per 1,000 data elements tracked, or charging for 24×7 support, etc.

As with many good ideas in business, all of the ideas above are obvious, and follow common sense.  However, you would be shocked to discover how rare it is to actually see businesses that have fully peeled back the onion to expose all the major variables and levers, and then implemented appropriate metrics to track these over time.

Trend based analysis

For every major variable that matters in our model, we will want to track how this varies over time. This will show us if we are succeeding in our efforts to improve things, and also give us early warning signs of any negative trends.

For most stages in a sales and marketing pipeline, we will want to track two metrics: how many prospects we put through that stage, and how effective were we at converting them to the next stage. For example:

Stage in Sales Funnel No of Prospects Conversion Rate
Campaigns to drive traffic Eyeballs seeing the campaign Conversion % to Visitors
Visitors Site Visitors Conversion % to Trials
Trials No of Trials Conversion % to Closed Deals
Overall Sales Process
(start to finish)
No of Visitors Conversion % to Closed Deals

 

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Peeling back the Onion on Inside Sales performance

Another area where metrics can be extremely useful is in managing an inside sales (telesales) organization. Starting with the overall sales number achieved by the whole group, let’s peel this back layer by layer, to see what we can learn:

  • Overall group performance = Sum (individual contributor performance).

Not surprisingly we need to look at how each individual has done relative to the average levels to understand the strong performers, and the weak performers.

  • Individual performance = No of deals closed * Average Deal Size

For the weak performers, it is likely that the number of deals closed will be lower than we want. The question is why? So what are the components that make up the number of deals that an individual closes? Assuming a sales process where each inside sales person is handed a queue of marketing qualified leads, and then calls these to try to schedule a demo, and the post the demo tries to close a sale, the components will be:

  1. Calls made per sales person (if this is low, they will quickly react to peer pressure when they see other sales people’s call rates)
  2. Conversion rate to returned calls. (If this is low, it means the sales person is not leaving compelling voicemails, and should be given training by someone that has a high conversion rate.)
  3. Conversion rate from phone calls to Demos. (If this is low, it means the sales person’s ability to convey the value proposition is weak, and they should be given training by someone with high conversion rates.)
  4. Conversion rate from Demos to Closed Deals. (If this is low, it means the sales person needs better demo training.)
  5. Average Deal Size. (If this is low, it could mean the sales person needs better training on cross selling, or up selling.)

The above may not mirror your inside sales process, but hopefully the method of working backwards from the end goal, and peeling back the layers to expose the components will enable you to map out the metrics that matter to you.

Sales and marketing funnel – summary metrics

We will also want to look at some metrics that cover the entire sales and marketing funnel from top to bottom. Here are some example metrics that are important at this overall level:

Lead source effectiveness:

  • CAC by lead source
  • ROI by lead source (takes into consideration cost, conversion rates to closed deals, and lifetime value of customers that came through that particular lead source)

What not to track

Some categories like Expenses are made up of many line items, and we very likely don’t want to bother with metrics for every line item, we need to answer the question: How deep should we go with our analysis? The answer to this is pretty much common sense:

  • Prioritize the components that have the biggest effect
  • Don’t put much effort into tracking things that you can’t affect
  • Don’t bother tracking items that are small, or that don’t vary much. Leave these to accounting.

Conclusions

There is nothing in this article that should be surprising or earth shattering. It is all obvious. However, as is often the case in business, it is really easy to have the vision of what to do, but far harder to execute on that vision. In my experience the mark of a really well run business is that they actually have the systems in place to automatically produce these metrics. And they use those metrics as part of the management process to run the business.

The Benefits: Good Metrics drive Actions and Behavior

One of the greatest things about putting in place the right metrics is that showing them to people will automatically change their behavior to try to improve the metrics.  Furthermore, the metrics make it clear what levers they can use to change performance.

Well designed metrics make it clear what actions are needed to hit plan

Working backwards from a specific Revenue target, management will be able to understand all the other elements that have to be put in place to reach that target. For example, if you want to hit $xm in bookings for the quarter, you can work out:

  • How many sales people are required
  • How many leads are required to feed those sales people
  • What marketing campaign spend is needed to generate those leads

If you are in a channel model, you can work out how many productive resellers are required, and given a known conversion rate from newly signed resellers going through an on-boarding process, you will be able to work out how many new resellers are required, and how many on-boarding sales training sessions need to be run. Etc.

Acknowledgements

I have had the very good fortune to work with some excellent management teams that have helped teach me these lessons. In particular, I would like to thank the teams at HubSpot and JBoss who were very advanced in their use of metrics.

Follow up blog post

Watch out for my follow up blog post on SaaS Metrics and Levers to see what happens when I drill down on the key metrics for a SaaS business. (This is applicable to other subscription businesses such as Open Source.)

Posted in Building for Success, Sales & Marketing Machine, Startup Help.

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SolidWorks: The best VAR management program in the world?

How SolidWorks grew to $400m a year in revenues by helping their VARs become world class business leaders.

SolidWorks was started back in 1993 with the vision of bringing solid modeling for mechanical design to the masses. Before SolidWorks entered the market, solid modeling was only available from PTC at $20,000 per seat, on expensive Unix workstations. Jon Hirschtick, the founder, set out to change all of that by offering a fully featured product at a fraction of the price. He also decided to use a reseller model to get the widest distribution at the lowest cost. This combination of features, price point, Windows OS and reseller channel, turned out to be wickedly successful, and within a few years SolidWorks had created a whole new mid-market and clearly established itself as the market leader. Today the company is one of Boston’s most successful startups, with revenues in excess of $400m, and an operating profit margin that would be the envy of the software industry if it were publicized.

A key part of what made SolidWorks so successful was how well they managed their VAR channel. I observed three distinct phases. The first phase was initiated by Vic Leventhal, who joined the company as COO in the early days. Prior to joining SolidWorks, Vic had been CEO of a CAD reseller, and had first hand experience of what it was like to be mistreated by vendors. His understanding of how VARs think, and what it would take to earn their trust and loyalty, were key to shaping the early VAR program.

The second phase occurred under the great leadership of Jon Hirschtick, CEO, and John McEleney, COO (who later to rose to CEO). SolidWorks had clearly recognized that revenues were directly linked to the number of effective reseller sales people selling SolidWorks, and their productivity. John McEleney focused on systematically growing those two key numbers.  Systematically meant a data-driven analysis of every piece of geography on the planet — where did they need more feet on the street, and how many? That phase took the company to over $100m in revenues.

The third phase occurred when Jeff Ray joined. Together with John McEleney, the CEO at that time, Jeff took on the challenge of slowing growth due to a lack of growth in VAR productivity, and the difficulty of adding new VARs into a territory without angering the current VARs.

Traditional approaches to solving this problem have been:

  • Add more VARs into the territory, creating too much competition and removing the incentive from the better VARs to invest
  • Start selling directly to larger customers – really angering VARs
  • Constantly inventing new sales incentive programs
  • etc.

Rather than taking this approach, Jeff chose to address the problem in a far more innovative way by looking at their existing VAR channel as an un-optimized resource, and figuring out what steps would need to be taken to further develop the business skills of that channel.

The result was something extraordinary: a program where SolidWorks provided their VARs with a full education on every aspect of running a business, equivalent to a mini-MBA program. Jeff and his team became business mentors to the VARs, educating them on all aspects of how to run a great company. This included not only sales and marketing, but also finance, HR, recruiting, business planning, etc. Their efforts paid back in spades, as SolidWorks quadrupled sales and grew their profit margins to double the industry norms.

In my experience of seeing many channel programs, including those from Microsoft, Lotus, IBM, etc. what SolidWorks put in place was revolutionary, and significantly more advanced that anything that came before it. Very likely the best VAR management program in the world. While this may not be directly applicable to smaller startups, understanding what the end game should look like can be very valuable as you start creating a reseller program of your own.

In this three part series, Jeff Ray, the current CEO of SolidWorks, humbly describes the program.

Click here for Part OnePart Two and Part Three.

I hope you enjoy reading them.
-David Skok                                                                                                                         FZVCT2HRB44U

SolidWorks was started back in 1993 with the vision of bringing solid modeling for mechanical design to the masses. Before SolidWorks entered the market, solid modeling was only available from PTC at $20,000 per seat, on expensive Unix workstations. Jon Hirschtick, the founder, set out to change all of that by offering a fully featured product at a fraction of the price. He also decided to use a reseller model to get the widest distribution at the lowest cost. This combination of features, price point, Windows OS and reseller channel, turned out to be wickedly successful, and within a few years SolidWorks had created a whole new mid-market and clearly established itself as the market leader. Today the company is one of Boston’s most successful startups, with revenues in excess of $400m, and an operating profit margin that would be the envy of the software industry if it were publicized.

A key part of what made SolidWorks so successful was how well they managed their VAR channel. I observed three distinct phases. The first phase was initiated by Vic Leventhal, who joined the company as COO in the early days. Prior to joining SolidWorks, Vic had been CEO of a CAD reseller, and had first hand experience of what it was like to be mistreated by vendors. His understanding of how VARs think, and what it would take to earn their trust and loyalty, were key to shaping the early VAR program.

The second phase occurred under the great leadership of Jon Hirschtick, CEO, and John McEleney, COO (who later to rose to CEO). SolidWorks had clearly recognized that revenues were directly linked to the number of effective reseller sales people selling SolidWorks, and their productivity. John McEleney focused on systematically growing those two key numbers.  Systematically meant a data-driven analysis of every piece of geography on the planet — where did they need more feet on the street, and how many? That phase took the company to over $100m in revenues.

Posted in Startup Help.


Startup Killer: the Cost of Customer Acquisition

In the many thousands of articles advising entrepreneurs on what they have to focus on to build successful startups, much has been written about three key factors: team, product and market, with particular focus on the importance of product/market fit. Failure to get product/market fit right is very likely the number 1 cause of startup failure. However in all these articles, I have not seen any discussion about what I believe is the second biggest cause of startup failure: the cost of acquiring customers turns out to be higher than expected, and exceeds the ability to monetize those customers.

In case you are not familiar with the importance of Product/Market fit, Marc Andreessen has a great blog post on this topic:  The Pmarca Guide to Startups, part 4: The only thing that matters.

In this blog, Marc argues that out of the three core elements of a startup, team, product, and market, the only thing that matters is product/market fit. I agree with Marc’s view that product/market fit is extremely important. However after closely watching several hundred startups that have failed, I observed that a very large number of these had solved the product/market fit problem, but still failed because they had not found a way to acquire customers at a low enough cost.

Business Model

I would like to propose that in addition to team, product, and market, there is actually a fourth, equally important, core element of startups, which is the need for a viable business model. Business model viability, in the majority of startups, will come down to balancing two variables:

  • Cost to Acquire Customers (CAC)
  • The ability to monetize those customers, or LTV (which stands for Lifetime Value of a Customer)

Successful web businesses have long understood these metrics as they have such an easy way to measure them. However there is a lot of value in looking at these same metrics for all other businesses.

To compute the cost to acquire a customer, CAC, you would take your entire cost of sales and marketing over a given period, including salaries and other headcount related expenses, and divide it by the number of customers that you acquired in that period.  (In pure web businesses where the headcount doesn’t need to grow as customer acquisition scales, it is also very useful to look customer acquisition costs without the headcount costs.)

To compute the Lifetime Value of a Customer, LTV, you would look at the Gross Margin that you would expect to make from that customer over the lifetime of your relationship. Gross Margin should take into consideration any support, installation, and servicing costs.

image

 

It doesn’t take a genius to understand that business model failure comes when CAC (the cost to acquire customers) exceeds LTV (the ability to monetize those customers.

A well balanced business model requires that CAC is significantly less than LTV:

image

Since the above two diagrams are so obvious, you may wonder why I have included them. The goal is give the reader a sense of the balancing act required to create a profitable business. Hopefully the value will become more obvious with the third version of the diagram that shows the different factors that affect the balance.

Another reason for stressing the point using diagrams is that many entrepreneurs have realized that since the web provides some amazing new ways to acquire customers at low cost, several new businesses have become possible. The only thing that you have to consider is can you monetize your customers at a higher level than the cost to acquire them.

The Entrepreneur’s Achilles Heel: Optimism

To be an entrepreneur requires great optimism, and a very strong belief in how much customers will love your product. Unfortunately this same attribute can also lead entrepreneurs to believe that customers will beat a path to their door to purchase the product. This frequently causes them to grossly underestimate the cost it will take to acquire customers.

A common scenario is an entrepreneur that has dreamt up a cool new service that they can offer via the web. As a VC, I have sat through many presentations like this, and in most cases the service is actually interesting and compelling. However in the majority of these presentations there is little or no focus on how much it will cost to acquire customers.  As I ask questions to understand the thinking, what usually comes out is something vague along the lines of web marketing, and/or viral growth with no numbers attached.

A quick look around all the B2C startups shows that, although viral growth is often hoped for, in reality it is extremely rare. When it does happen, the associated businesses are usually extremely attractive, provided they have a way to monetize their customers. (For more on the topic of Viral Growth, refer to my blog post on that topic here.)

Far more common is a need to acquire customers through a series of steps like SEO, SEM, PR, Social Marketing, direct sales, channel sales, etc. that will cost the company significant amounts of money. What shocks and surprises many first time entrepreneurs is just how high the numbers are for CAC using these kinds of techniques.

Some examples of CAC calculations

For example, if you are using Google Ad Words to drive traffic to your site, take a look at the following interactive spreadsheet. This example shows a cost per click of 50 cents, and the resulting website visitors converting to a trial at the rate of 5%. Those trials are then shown converting to paid customers at the rate of 10%. What the sheet shows is that each customer is costing you $100 in just lead generation expense. For many consumer facing web sites, it can be hard to get the consumer to pay more than $100 for the service. And this cost does not factor in the marketing staff, web site costs, etc. 

One of the more interesting things that this model shows is how rapidly cost of customer acquisition climbs If your leads require human touch to convert them, (compare cell B23 with cell B22.) This human touch can be as light as email follow ups, or as much as inside sales people doing multiple sales calls and demos. I have seen this cost vary from around $400 to $5,000 per customer acquired, depending on the level of touch needed.

 

Another shocking computation is to look at the cost of a direct field sales force:

This shows is that it is not unusual for the cost of acquiring a customer to be as high as $100,000. This number is heavily dependant on the productivity of your sales teams. In the model above, this was set to 10 deals per year per team. Given the need to cover R&D and G&A costs, the average gross margin on a deal needs to be at least $150k.

Lessons Learned – Business Planning Stage

My advice to entrepreneurs working on a new business plan is to build a model similar to those above to estimate the cost of customer acquisition. This is going to show you the dependency on several critical variables:

  • Cost per lead
  • Conversion rates at each stage of your sales process
  • Level of touch required

Then compare this to your expected monetization. As a very rough rule of thumb here are two guidelines that you might find helpful:

  • LTV > CAC. (It appears that LTV should be about 3 x CAC for a viable SaaS or other form of recurring revenue model. Most of the public companies like Salesforce.com, ConstantContact, etc., have multiples that are more like 5 x CAC.)
  • Aim to recover your CAC in < 12 months, otherwise your business will require too much capital to grow. (Banks and wireless phone companies ignore this rule, but they have access to tons of capital.)

In the early days of the business, you will not be able to accurately predict your conversion rates, and the viability of your entire business may depend on this. So I recommend building an execution plan that focuses on finding out what these numbers will be as soon as possible in the lifecycle of the business. Good numbers will enable you to raise funding easily, and bad numbers may indicate that this is not a viable business.

The good news is that if you can monetize your customers at a higher rate than the cost to acquire them, you probably have a great business on your hands.

Next Generation Business Models

Because a number of smart entrepreneurs realized the importance of lowering CAC, they created new business models such as Open Source, SaaS, Freemium, etc. that directly tackled the problem of acquiring customers. Some of the early B2B pioneers in this space were companies like JBoss (story here), SolarWinds, ConstantContact, HubSpot, etc. Once others started to see the success these companies were having, they started copying the techniques.

These new business models focused heavily on how buying behavior has changed because of the power of the web. Think about your own behavior: if you are like me, you hate having to deal with sales people, and greatly prefer to do your own research starting with search engines, and leveraging free trials, on-line videos, blogs, reviews, and your social network. To adapt to this, the new business models make use of a variety of techniques described below:

  • Extensive use of the web to drive lead flow. In particular, the best practices include using Inbound Marketing to build traffic, instead of paying for traffic with search ads. (Read Get Found using Inbound Marketing to find out more.)
  • Use of a free product or service to attract web visitors, and aim for a viral spread as they tell their friends. Examples of free products include Open Source software, services like HubSpot’s Website Grader, free versions of a SaaS service that have limited, but still valuable, feature sets, etc. For more info on this topic refer to The power of Free.
  • Use of a free trial, where the customer can easily download, or use a SaaS version of the full product to see if it works for them.
  • Leveraging the power of your customers’social networks to get viral growth where possible.
  • Use of the touchless conversion to convert trials to paying customers.
  • Using low cost inside sales when the touchless conversion is not possible.
  • Extensive use of software to automate all processes such as SEO, SEM, social networking, lead scoring, lead nurturing, CRM, etc.
  • Metrics on all aspects of the customer acquisition process to find out what can be improved. 

These techniques are frequently referred to as the Low Cost Sales model, or as Sales 2.0.

Balancing Monetization with CAC

The way in which these techniques can work together with other techniques to drive up monetization (e.g. recurring revenue) are illustrated in the diagram below:

image

 

Lessons Learned – Ways to reduce customer acquisition costs

Conversion rates play an extremely important role in your customer acquisition cost. Anything you can do to improve conversion rates is obviously a good thing. For more on this topic, please refer to the Building a Sales and Marketing Machine part of this web site.

  • Consider using A/B testing to improve conversion rates. Web traffic can be easily split so that parts are fed to different landing pages with different offers, and the resulting conversion rates measured.

Look at the level of touch required to complete a sale. Some products are easily understood, while others may require a careful walk-through by a sales person. Sometimes, the customer will want a trial with their own data. With certain complex products, this will need an on-site installation by a sales engineer, which sends costs through the roof. Consider every possible way to minimize this. For example:

  • Create demo videos that answer every likely sales question.
  • List the common sales objections that come up in the sales cycle, and provide answers to these on the web site.
  • Try using customer references to avoid the need for a trial
  • If your customers are going to compare you to the competition as part of their process, consider doing this for them, with a section of your site that has a comparison matrix with appropriate check marks.
  • If you have a light touch sales model, consider setting yourself the goal of a “Touchless Conversion”, i.e. getting rid of, or minimizing the touch required to close the sale. As shown in the model, this has a huge impact on cost of customer acquisition.

Options for products requiring high touch

The toughest business models are those that employ expensive field sales organizations. The high salaries and commissions for sales people, sales engineers, travel costs, and office costs add up to an extraordinarily high figure. And this is before you factor in the failure rate (the percentage of sales people hired that don’t become productive). It is not too surprising that VCs are not aggressively pursuing these kinds of businesses. There are some ways you can look to address the problem:

  • If you are currently using a field sales organization that sells direct, look at whether it is possible to sign up OEM deals with strategic partners to leverage their customer base and distribution power. What generally works best here is allowing the OEM to sell only a base layer of your product with co-branding. Then you can go back into their customers and upsell them. Owning the customer base is an important way to control your own destiny, and will also earn your company a higher valuation. In addition to distribution power, these kinds of relationships solve the “safe choice” concern of many buyers, and can transform your business.
  • Consider converting to a channel sales model at some stage in the lifecycle of the business. Many times this requires that you “prime-the-pump”, as most resellers won’t sell a product until they see clear customer demand. Channel sales models usually only work when the company commits to them fully, and passes all orders through the channel, so be prepared for the loss of margin this will represent to your current order flow.
  • Another option is to evaluate whether you can move from field sales to inside sales people. Insides sales people are not only less expensive in direct salary costs, but also in travel costs. Other advantages of inside sales people is that they are far more efficient due to remaining in one location, and can contact more people in a typical workday. At a minimum, look at combining inside sales with field sales to improve the efficiency of field sales people.

Conclusions

If you are entrepreneur planning your next business, you can’t afford to ignore the cost of customer acquisition. The earlier you work on this the better, as many of the best techniques require you to build your product differently.

It is also important to ask yourself the question: can my business realistically expect to acquire customers for considerably less than the amount that I can monetize them?

Once you have completed the product, you will want to familiarize yourself with all the latest techniques involved in the low cost sales model, or Sales 2.0.

From a funding standpoint, it is useful to know that your ability to raise capital will dramatically improve as soon as you have proven that you have a viable business model. Think of that as two equations:

  • CAC < LTV   (3x appears to be a rough minimum for SaaS businesses)
  • CAC should be recovered in < 12 months (for subscription businesses)

Once you have proven out the business model, hit the accelerator pedal, and invest as much as you can afford. You’ll want to grow the business as fast as possible before a competitor realizes what you have done, and tries to steal your market!

Acknowledgments

I would like to thank my partner Nick Beim and the management teams at JBoss and HubSpot, Gail Goodman of Constant Contact, Sheila Marcelo of Care.com, for contributing greatly to the ideas in this post.

- David Skok

Posted in Building for Success, Business Model, Idea Conception, Startup Help.

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Lessons Learned – Viral Marketing

A short study of this web site reveals that a hugely important factor for success in startup companies is finding ways to acquire customers at a low cost. In the Business Models section, we looked at the perfect business model: Viral customer acquisition with good monetization. However viral growth turns out to be an elusive goal, and only a very small number of companies actually achieve true viral growth.

In 2005, I invested in a company called Tabblo (acquired by HP in 2007), and had the good fortune to work with an outstanding entrepreneur, Antonio Rodriguez. Tabblo did manage to achieve good viral growth, but around the same time YouTube was launched and managed to achieve explosive viral growth. In the process of looking at these two companies, we learnt several important things about virality. This post digs deeper into what it takes to achieve viral growth, and examines the key variables that drive viral growth.

To give you a preview of this post, what you will learn is that there are two key parameters that drive how viral growth happens, the Viral Coefficient, and the Viral Cycle Time. To fully illustrate the arguments, I have included two spreadsheet models (embedded) that you can play with interactively to see how viral growth works. There is a risk with this level of depth, that some readers will find this too technical, and if you find yourself reacting that way, may I recommend that you jump straight to the conclusion, which is under the heading Lessons Learned towards the bottom of the article.

What we want to understand in these two models, is how the population of Customers changes over time. The first model that we will build looks in a very simple way at how viral growth works in the marketing world.

The Viral Coefficient (K)

Imagine you are starting a new company that plans to acquire customers through viral growth. You have several friends that you use to become your first customers, and they in turn start inviting friends to join, and those friends start inviting friends, etc.

The model at this stage has the following inputs:

Variable Name Description Example Value
Custs(0) Initial set of Customers 10
i No of invites sent out be each new customer 10
conv% The percentage of invites that convert into customers 20%

The first thing that we need to calculate is the number of new customers that each existing customer is able to successfully convert. This turns out to be an extremely important variable, and is known as the Viral Coefficient. The formula to calculate the viral coefficient is pretty simple: multiply the number of invitations by the conversion rate.

K Viral Coefficient K = i * conv%

Now lets take a look at how K affects customer growth as we go through the first cycle of viral “infection”. Our initial 10 customers will each send out 10 invitations, and successfully convert 20% of those (i.e. 2 new customers each). So the total customers after the first cycle will be equal to the starting 10, plus the new 20, which equals 30.

To fully understand the model, it’s useful to look at the second, and subsequent, cycles of growth. In the model above, only the new customers that were added in the prior cycle send out invitations. This is because it is highly unlikely that the entire population will continue to send out invitations every cycle. Every time I have looked at other blog articles or formula for Viral Growth, they appear to have gotten this part of the calculation wrong.

Understanding the impact of the Viral Coefficient

Now that we have the model built, we can play with the variables to see what effect they have. In the spreadsheet above, go to cell B6, and change the Conversion rate for invites (conv%) to 5%. This will make the Viral Coefficient less than 1. Now look at what that did to your population growth. Instead of continuing to grow, it grows to 20 people, and then stops.

What this tell us is very interesting:

The Viral Coefficient must be greater than 1 to have viral growth.

Further playing with the spreadsheet will show that increasing the viral coefficient by increasing the number of invites sent out, or the conversion rate, has a nice impact on how the population grows. Try this out by changing cells B5 and B6 in the model above. Later on we will talk about how to design your application to maximize these values.

The Second Important Variable: Viral Cycle Time

Antonio Rodriguez built Tabblo around the same time that YouTube was built. Both sites were viral, but while Tabblo was reasonably successful, YouTube exploded and amassed users at a rate that had not been seen before on the Internet. What was going on here?

To answer this question, we have to look at the Viral Cycle Time,(which we will refer to in formulas as “ct”).

The full viral cycle involves several steps that work in a loop:

The Viral Cycle Loop

The Viral Cycle Time is the time that it takes for this cycle to complete.

In YouTube’s case the Viral Cycle Time was extremely short: a user would come to the site, see a funny video, and immediately send the link on to their friends. Tabblo, on the other hand, had a much longer cycle time. A customer would post some photos on the site and invite their friends. The friends might see the photos on Tabblo, and like the experience and decide that they would use the site the next time they took photos they wanted to share. However, that is where the problem came in: it could take months before they next took photos, and decided to share them.

Later on this post, we will talk about how to optimize Viral Cycle Time – (see Lessons Learnt).

How Viral Cycle Time affects growth

To model Viral Cycle Time’s effect on growth, I searched the web, high and low, looking for a pre-defined formula. To my great surprise, there was no formula that I could find that correctly calculated customer growth, and showed the impact of Viral Cycle Time. What was also surprising, was that I did find several blogs showing formulae for viral growth, but in every case, they appeared to make the same mistake, which was assuming that the entire customer base would continue sending out invitations for every cycle. So I collaborated with my partner, Stan Reiss, who turns out to be a whole lot smarter than I am, and he helped me develop the fomulae that are used in the more sophisticated model for viral growth below:

image

A quick look at the table that shows the effect of varying the Viral Cycle Time shows that customer growth is dramatically affected by a shorter cycle time. For example, after 20 days with a cycle time of two days, you will have 20,470 users, but if you halved that cycle time to one day, you would have over 20 million users! It is logical that it would be better to have more cycles occur, but it is less obvious just how much better. A quick look at the formula tells the whole story. The Viral Coefficient K is raised to the power of t/ct, so reducing ct has a far more powerful effect than increasing K.

This explains why YouTube exploded at a faster rate than ever seen before.

Lessons Learned

There are a large number of interesting lessons to learn from the above models:

  1. Unless you have a Viral Coefficient that is greater than 1, you will not have true viral growth.
  2. The most important factor to increasing growth is not the Viral Coefficient, but the Viral Cycle Time (ct) which should be made as short as possible. This will have a dramatic effect on growth.
  3. The second most important area to focus is the Viral Coefficient (K). Anything that you can do to increase the number of invitations sent out, and the conversion rate, will have a significant effect on growth.

In addition to the above lessons that come from the model, there are some other important observations:

  1. Virality is not a marketing strategy that can be executed by the marketing department. It has to be built into your product right from the beginning. This is a function that needs to be thought through by the product designers and developed by the engineers.
  2. The most viral products are those that only work if they are shared. For example, Skype only worked in the early days if you got your friends on to Skype, otherwise you had no way to call them. If you have an application today, think about how you can make it social, where it would work better by sharing data with friends/co-workers. That provides a great incentive for customers to invite their friends/colleagues to use the application.
  3. To make the Viral Cycle Time as short as possible, we can apply the same thought process that we use in Building a Sales and Marketing Machine, where we look at what are the customers motivations and negative reactions as they flow through the viral cycle.  For example, when I reach the stage where I have to enter my friends addresses, I will not bother to do very many if I have to look them up in another program, and copy and paste them one-by-one into the browser. You can solve this problem by providing me with Facebook Connect integration to invite my Facebook friends, and an adapter to import my email contacts. (Check out the “Share This” button at the bottom of this post as an example of how this can be done.) Getting at email contacts is easy with web mail clients like GMail, etc. – but harder with Outlook. However viral products like LinkedIn have created Outlook adapters that you can download. It is also feasible to get at that information via Outlook Web Access (OWA) provided you can deal with the security concerns.

    You should also be looking for ways to encourage customers to invite people at various junctures in their use of the application. And of course, you should be asking yourself the question: is the value proposition of your product really that compelling that your customers will want to share it with others?

    Another great way to increase virality is to incent customers with a reward for every customer they successfully convert. Since this can result in an individual feeling guilty that they are making money off their friends, the best way to do this is to also provide the friend that is receiving the invitation with an equal incentive. Now your customer will feel like they are doing their friends a favor.

  4. Consider leveraging viral platforms such as Facebook, which have built in social features to let friends know what apps you are using. The wall, and status updates provide a great way for their friends see your app.
  5. Use A/B testing to figure out which approaches and creative presentations are getting you the highest conversion rates.
  6. If you are successful in creating a viral model with very short cycle times, watch out for what can happen. Several companies that have been lucky enough to achieve this have been shocked by the enormous need to scale server capacity. Fortunately with cloud computing offerings such as Amazon EC2 and S3, it is easier than in the past to scale on demand.

Hybrid Viral Models

Many entrepreneurs reading this post will realize that they may not have the means to achieve true viral growth (where they have a Viral Coefficient of greater than 1). Rather than giving up, it is worth considering a hybrid viral model. In the hybrid viral model, you make up for the shortfall in customers by acquiring those through some other means such as paid search, or SEO.

Model Limitations

The model above is pretty simplistic and does not take into consideration several real world phenomena:

  1. What happens when you grow so fast that you start to saturate the population. This has happened to several Facebook app developers. They experience very rapid growth, and then suddenly the growth dies. Andrew Chen has written a great blog post about this:  Facebook viral marketing: When and why do apps “jump the shark?”. (Side note: I don’t believe that the equation that Andrew puts forward for simple viral growth is correct, as it assumes that the entire population will continue sending out invitations at each viral cycle. However his work on saturation of the population is very relevant for highly successful viral apps.) In case you are interested in where the term “jump the shark” came from check this out: Wikipedia: Jumping the shark.
  2. What happens if you have attrition in your customer base over time. An easy way to extend the model to take this into consideration would be to add a variable to model Attrition Rate as a percentage of the entire installed base at each cycle, and simply subract this from the total population at each cycle. This topic is nicely covered in this blog post by Andrew Chen: Is your website a leaky bucket? 4 scenarios for user retention.
  3. The customers that you have may send out more than one set of invitations beyond the initial set.
  4. etc.

Further Resources

Check out Andrew Chen’s blog, as he has written extensively on the subject of Viral Growth. For example, here is one great example: What’s your viral loop? Understanding the engine of adoption.

Acknowledgements and Thanks

My thanks to Antonio Rodriguez, the founder of Tabblo, who got me started on thinking about this topic several years ago. Also to Andrew Chen, whose writings on this topic are excellent. And to my partner Stan Reiss, who took my simple logic and turned it into an elegant mathematical formula.

Posted in Building for Success, Business Model.

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