Designing Metrics for Success




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.


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


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.


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.


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.)

About the Author

David Skok

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  • The way you describe peeling back one layer at a time reminds me of the “5 Whys” process that Toyota invented and Eric Ries ( always talks about. We practice it religiously in the engineering team at HubSpot and the insights are always there, highly valuable.

    What's your take on isolating variables with regards to these metrics? Is it worth the effort or should startups typically pull all relevant levers if a particular metric is trailing plan? (By isolating variables I mean trying one lever at a time.). Most startups probably don't have enough runway to try one thing at a time…

  • David, we were discussing about these metrics yesterday since we are planning to present a funding request proposal to a group of angels in the next couple of days. I thought your earlier post on CAC / LTV was very handy to depict our positives. Now this! As you rightly pointed out, though most of us as entrepreneurs are aware of these concepts, we don't dig deep, drill down and present the numbers / issues / solutions to the board and / or investors. Looking forward to your next post on secret sauce of SaaS metrics.

  • petercohen

    David, Excellent and valuable advice.

    I'll offer one comment on evaluating the effectiveness of particular campaigns. Companies should definitely measure how many leads, opportunities, trials and wins were derived from each marketing campaign. Online campaigns and CRM systems make these kinds of metrics relatively easy to obtain.

    But companies should be cautious about assigning credit to any one particular campaign. Before a customer makes a purchase, especially a large purchase, they are likely to have interacted with the company via several campaigns. For example, they might have attended a webinar, downloaded a white paper, taken advantage of a free trial, and responded to an email promotion. In this case, it's not entirely clear which of these particular campaigns drove the purchase and should get credit.

    However, this shouldn't relieve marketing from the task of measuring effectiveness. One approach is to look at all wins and see which precise constellation of campaigns were involved in securing those customers.

    There's still some “art” involved in making decisions on marketing campaigns, though it should be based largely on quantitative measurements and can't be entirely a “seat of the pants” decision.

    Peter Cohen

  • Peter, this is a great point, and one which has led to many long discussions with my portfolio companies. I agree with you: it may not be perfect, but you should use whatever quantitative data is available to track marketing campaign effectiveness.

  • bobsteinkrauss

    excellent blog, David, good sensible business management practices

  • David —

    Related to conversion rates for each step of the inside sales funnel, do you have any benchmark or 'best practice' metrics that you have seen in your travels? Please let me know your thoughts.

  • John, these vary widely from company to company, and are really determined by factors such as the kind of buyer, type of product, price point, how easily the product can be evaluated in a trial, how well the product works, etc.

  • andydonovan

    Great post (and kudos to @aprildunford on Twitter for highlighting it). I agree that good management teams are the key to any company's success. Much of what you have provided here is a great blueprint for any start up and positive reinforcement for those who are doing what they do well…for the rest it's never too late to turn things around. Cheers,


  • There is some serious gold in here David. Well done. I know you asked for feedback so I dug deep into the lens here at HubSpot. I was only able to come up with two comments:

    1. I like your keys to success: Profit/Growth/Cashflow. However, we have a constant debate over maximizing the profitability of each customer today or maximizing our market penetration. Customers need to be profitable from the start. However, it seems that many of the highly successful SaaS plays did a land grab first, focused on creating outrageous value for their customers, and upgraded those customers to higher price points later. Profit versus market penetration may be a bit outside the scope of your article but it is something we debate on a weekly basis.

    2. On your inside sales section, I am not sure that maximizing point #3 (dials to demos) is always optimal. I think point #3 and point #4 (demo to customer) need to be analyzed together. For example, a rep may have a high dial to demo ratio but their close rate may be among the worst. In this case, the rep may not be properly qualifying their prospects and is wasting significant time on demos to non-buyers (I see this OFTEN). On the other hand, you may have a rep with a low dial to demo ratio but the best close rate on the team. This rep is likely doing a good job of finding the quality opportunities to invest their time in.

    Of course, if you find a rep with a high dial to demo ratio and high close rate, study them, spread the best practices across the team, and hire more people with the same profile.

    Very much enjoyed the read David!

  • Mark, excellent feedback. Both your points highlight important issues. To all other readers of this post, Mark Roberge is the VP of Sales at HubSpot, and has implemented the best sales management process for Inside Sales that I have seen anywhere. His understanding of how to design metrics for this process far exceeds my own.

  • Noel W. Clarke


    I like the direction you're exploring here AND I'm looking forward to your follow-up post about relevant SaaS metrics. Like you I believe, that businesses who deliver information rich (highly configurable OR customizable) products & services via online channels REQUIRE the understanding of their KEY LEVERs if they are going to be successful.

    I too have been thinking of mechanisms to create – “Real-Time” Financial Statements in dashboards that update from LIVE business data and are designed to be consumed by the Business Users.

    Great Post!

  • Great post. It's amazing how good, carefully evaluated metrics can make all the difference in the successful growth of a business. The best board books I've seen track nearly 20-30 key metrics (some financial, some operating) from meeting to meeting and those metrics are usually covered in the first 20-30 minutes of the meeting, setting the stage for more strategic discussion.

  • just.a.guy

    David — this is a great article. I've been driving the implementation of systems to track these types of metrics at our company (fast growing, recently public), and also have spent some time on the phone with one of your early stage founders (Ellen) discussing the metrics we track and how we think about them at our company.

    A few things I would add are:

    1) Be careful about interpreting causality too quickly — changes in the metrics should be the start of a discussion and not the final word. As a company grows larger, there can be management-led initiatives that affect the numbers in non-obvious ways. It's good for anyone interpreting the data to be humble about their understanding of what's happening and be prepared to work with the other functional executives to tease out the nuances before assigning causes to observed changes.

    2) What gets measured gets managed — Metrics that have predictive value today (e.g. aggregate pipeline created in a given week) could be altered reflexively by their measurement — a sort of Heisenberg Uncertainty Principle for high-velocity sales. So while there may be many important metrics from A–>Z, you don't have to and shouldn't share all of them with line managers. Firstly, it can be confusing (like the control panel of a 747) and secondly, there are some metrics that are side effects… outputs to be observed periodically and not inputs to be tweaked daily (in our business, average selling prices or cross-sell attach rates fit here) lest people engage in cluster-ball, chasing one objective after another from guardrail to guardrail.

    3) The Scientific Method is king — The metrics should drive actions, but those actions should be controlled experiments. If they're website changes, A/B testing is key. If they're lead routing or scoring changes, try them with a control group of reps first. Know what results want to see (and what side effects you don't) and religiously track the progress of each experiment. Prioritize so that you are only running a few at a time up and down the funnel as well.

    I believe that this type of sales and marketing approach is the future for enterprise-class software (irrespective of SaaS or Licensed delivery model), especially in more mature markets.

  • Great feedback and suggestions. Thanks for adding to the debate.

  • cynthiamignogna


    This is a great post! At OpenView, we try to instill this discipline and capability in each of our portfolio companies from Day 1. As I'm sure you know, it is often easier said than done. One thing is certain, management teams that can pick this stuff up quickly and run with it (even if they haven't been exposed to thinking about their business in this way before) are the teams that consistently outperform their peers.

    Great stuff!

    Cynthia Mignogna
    Finance Principal, OpenView Venture Partners

  • gbro

    To a large extent sales is (and always will be) a numbers game. The more opportunities in the pipeline the more deals you close. Of course, this basic formula has more that its share of flaws.

    When I first began in technology sales I hunted down the #1 performer in the company. I asked him these questions: Why are you so successful? What are you doing that is so effective? His name was Steve. Steve was not your prototype sales guy. He was not 6'2″ athletic, good looking and tan. Steve was 5' 7″ balding and his physique was, well lets just say, non-athletic. But he said this and I'll never forget it. “I guess I just sell to people I know are going to buy from me.” In short its called “qualification”. Sales 101 yes, but its the most important and least measurable aspect of the sales process. Its important whether you are managing a pipeline of 3 or 300 opportunities.

    But there are ways to quantifiably measure this. Here are 3 methods:
    % of deals closed, average length of sales cycle, and my favorite: did they buy something from someone. Its OK to lose a deal. It happens. Its not OK to spend month after month chasing deals that never close. It means your sales guy can't qualify and it comes with a huge opportunity cost.

    Its good to track metrics like # of leads, # of demos, # of calls, but these can always be forced or fudged by people who know they are being watched. % of deals closed and length of sales cycle are tangible ways to measure how well your sales team qualifies its opportunities.

    Greg Brown

  • Great input Greg. I agree with your comments. Thanks for adding.

  • boblight

    A bit late to the party here, but an excellent summary on the importance of metrics with great followup comments. To keep with your anology, I think many organizations crash because when they actually do “peel back the layers” they can't get past the eye pain, and thus stop doing it.

    I support the comments that also point out that metrics are not static. As a startup, you almost have to measure everything (luckily, the volume of transactions typically isn't that high). Over time, once you have built up some decent data points and further refined your goals, you can wean out and/or tweak what you are measuring to get down to the handful that truly impact performance. Then wash, rinse, repeat every Q, because people, the economy and competition never stand still……

    Thaks for sharing!

  • Great write up. I think a number of startup managers should read this!!

  • Great post, especially the board part. I know Rob Bearden down here in ATL and he's mentioned you guys. You backed Digium didnt you? I co-founded Vocalocity (saas PBX), so your comments about lead gen ring true as well. I'm now running a education social network called Openstudy, so it's starting all over again 🙂

  • Excellent writing and insight David. I would like to know if you would mind if I use some of your postings, COMPLETE postings, with full attribution on my own site?

    Much of what you write is complementary and fits nicely with the business focus to IT alignment that I write about. Many of the same principles apply because I am trying to press within my own sphere of influence toward a more business-centric technology model rather than the technology-centric business model.

    I've authored 2 pieces related to your metrics article, they are more about developing KPI's, but your article would complement them well if you don't mind me using it. You can see the ones I wrote here:

    Why Indexed KPIs are Critical for Business Performance and Success

    Using Key Performance Indicators for Building a Strategy Focused Organization

    I would really appreciate it if you would consider allowing me to post / re-post your material. If you are interested you are welcome to re-post any of mine with full attribution as well.

    Bill Wood – President
    R3Now Consulting

  • Bill, thanks for the kind comments. No problem using the posts on your own site. I would just ask that you link back to the original article. Thanks!

  • David;
    Another great post – you do keep them coming. I was going to ask the same question regarding use of your material on our site, but see that you are okay with this provided we link back and we will. Thanks in advance – will write something soon on the materials here as I think more people should know about this great source of information.
    I liked your analogy of thinking of the company like a machine. I like to equate everything to Process and a machine is a great example of the concept. Some readers may find a tool we have on our site useful, as it touches on the subject of calculating some of the numbers and using some of the metrics you have touched on in this post. We wrote a series of posts about how to calculate ROI – there are 3:
    1) How to calculate the ROI of your website as a whole
    2) The 10 best free ROI calculators on the web
    3) How to build your own ROI calculator.
    Here's the link:
    Here's the link:

  • Thanks Eric. I will take a look at your calculators. They sound very useful.
    Best, David

  • Thanks Eric. I will take a look at your calculators. They sound very useful.
    Best, David

  • Think of your company as a machine. Great read!

  • David, great stuff here. Have implemented this to a basic level but in a few months will need to take the next step and automate all of it (still manual entry/updating for now). Can you recommend any resources around implementation, specifically around automatically tracking the inside sales #s?

    Also, I think a key point for startups is that this type of implementation project falls in the transition stage from discovering product-market fit / repeatable model and the actual scaling/execution of that model. Wouldn’t it be a misallocation of resources to build out full-blown, automated metrics system before the company really knew what it was selling, to whom, and why that buyer wanted it?

  • Andrew,

  • David, not sure what happened but think your response got lost somewhere in DISQUS. Loving the blog though.

  • Andrew, sorry – looks like a problem with Disqus. Here was my intended answer:

    One great resource for tracking web conversions is KissMetrics ( They won’t help you with tracking the inside sales steps you care about. The firms that I know that are doing that are using, but I think my have had to pay a consultant to make it easier to use for this than it would be out of the box. (Easier to use means reducing to a single keystroke for a salesperson, what would have been several.)

    Once you are past product/market fit and into trying to figure out the repeatable scalable sales model, metrics can be one of your most helpful friends. However at this scale, these don’t need to be as well automated, and a simple manual data collection and spreadsheets can be good enough.

    Best, David

  • Excellent indeed! Very lucid explanation. In order to derive the benefit of metrics based systems or tools, it is highly essential that each and every team member has comprehensive understanding of the tools even with periodical workshops to the team taking the last achievement or failure as a case study. Thus, the field team or office team can precisely know the performance requirements and work accordingly towards the given goals.

  • Justask4alex2010

    please can someone help me on my project topic title” metric system in a medium scale organization”

  • Thanks fo sharing your views with us great article indeed

  • Hindi

    periodical workshops are surely the way to go
     Indian Escorts

  • Nabo

    Hi, I am trying to find a benchmark for a personal pipeline in a startup consulting company, i.e. how many opportunities can we reasonably expect at the end of the year from an employee, including possible stages

  • This is unfortunately very dependent on the particular sales cycle, deal size, etc., so data from others will not be very useful.

  • andy

    an article that is very good for a small unit of our business is solusi mengobati keputihan gatal berbau . thank you very much for the advice.

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