How to Crack the Code for B2B Sales Efficiency

How to Crack the Code for B2B Sales Efficiency
October 3, 2019
Sales efficiency drives enterprise value, and it's important to track sales efficiency with data.

This blog post was originally published by Rekener, now a Brainshark company.

Modern SaaS businesses all aspire for better sales efficiency. In "10 Laws of Cloud Computing," Bessemer Venture Partners describes sales efficiency as "oxygen" for your business.  It's an appropriately powerful metaphor. Without it, your business dies. 

Sales efficiency gets a lot of attention from SaaS executives, and for good reason.  Sales efficiency drives enterprise value, and it's important to track sales efficiency with data.  There are several high-level sales efficiency metrics, including lifetime value (LTV), customer acquisition cost (CAC), magic number and net dollar retention (NDR).  I'll review each of these metrics later in this post.

The problem is that sales efficiency metrics don't help sales managers in their day to day operations.  Sales efficiency is an important aspirational goal for high-level executives, but sales managers are on the front lines of customer acquisition where action is more important than aspiration. Sales managers need data to be actionable. Specifically, they need it to help diagnose what's working and what isn't with sales reps so they can coach reps on the sales floor and in weekly one on ones.

There is a big operational gap between the aspirational goals of better sales efficiency and the actions that sales managers need to take every week to deliver results.  In Cracking the Sales Management Code, Jason Jordan demonstrates that most companies do a bad job of breaking down high-level business goals like sales efficiency into individual goals and activities that can be managed in weekly one on ones.  If you haven't read Cracking the Sales Management Code yet, I highly recommend it.

SaaS businesses aspire to have high sales efficiency but there is a big operational gap between the aspiration and what sales managers need to take action in weekly one on ones.

Cracking the code on sales efficiency is critically important.  As the SaaS industry explodes, the stakes keep getting bigger and bigger. In the second quarter of 2019, SaaS spend hit the $100B run rate and now represents 20% of total enterprise software spend (Synergy).  In the CRM vertical, SaaS spend in 2019 is projected to be $42B, which is 75% of total CRM spend (Gartner). Sales efficiency is an acute need for SaaS business models, but sales efficiency is not unique to SaaS. Sales efficiency is important for any business with any kind of recurring revenue.

In this post, we'll cover:

  • What is sales efficiency?
  • How to crack the code for sales efficiency? 
  • Why the CRM is not the answer
  • Build-your-own solutions with spreadsheets and BI tools
  • Cracking the code with Rekener

What is Sales Efficiency? 

There are multiple metrics for measuring sales efficiency. All of the sales efficiency metrics are used to measure how well a SaaS business acquires customers, retains customers and monetizes customers.  


In 2008, Philippe Botteri (now Accel, then Bessemer) introduced the concepts of lifetime value ("LTV") and customer acquisition cost ("CAC") in his "5 Cs of SaaS Finance."  In early 2010, David Skok of Matrix Partners published two key guidelines for SaaS startups.  These guidelines are frequently referenced as the definition of sales efficiency. 

David Skok's guidelines for sales efficiency are (1) LTV/CAC greater than 3 and (2) less than 12 months to recover CAC.

Magic Number

Magic number measures the incremental annual recurring revenue generated by the sales and marketing investment in a quarter.  A magic number greater than 1 is generally considered to be efficient, which means that it takes a year to pay back a quarterly sales and marketing investment. Tom Tunguz of Redpoint showed that most SaaS businesses operate with a magic number of around 0.8.  Maintaining an efficient magic number is a never ending battle.  Tunguz illustrated this point with the following graphic that shows how magic number changes over time for the average SaaS company.

Tom Tunguz's research shows how the magic number of the average SaaS company changes over time.

Net Dollar Retention

Net Dollar Retention ("NDR") is another metric that is used to measure sales efficiency.  NDR is the ratio of revenue from existing accounts at the beginning of a period plus expansion and minus churn divided by the revenue from existing accounts at the beginning of the period.  NDR of over 100% means that retention and expansion is more than offsetting churn. Tunguz recently published a good analysis of the correlation between NDR and Magic Number.

How to Crack the Code for Sales Efficiency

The formula for cracking the code for sales efficiency has two parts.  It's good to have one or the other but in order to succeed, you must have both.

  • Part 1 - Use conversion ratios to work backwards from sales efficiency targets to create team and individual goals.
  • Part 2 - Give your sales managers the individual goals in an operational framework that they can use for data-driven coaching in regular one on one meetings.

Part 1 - Work backwards to create team and individual goals

Here's how to work backwards from sales efficiency targets to create team and individual goals.  Start with the things you know: (1) your sales efficiency goals and (2) your level of sales and marketing investment. 

From there, calculate the amount of new bookings that you need to generate in order to achieve your sales efficiency goals.  Once you know the amount of bookings you need to achieve, you can use historical conversion ratios to work backwards and calculate the team and individual goals that your sales managers need.

Here's an example of how to break down goals for a typical B2B, go-to-market motion that includes product demonstrations and multiple opportunity stages.

  1. Opportunity Targets. To calculate the number of opportunities your team needs to generate in a certain time period, start with the bookings target and use historical numbers for opportunity to deal conversion rate, average selling price (ASP) and sales cycle.  Use the same approach to calculate targets for all of your opportunity stages.
  2. Demo Targets. Use historical demo to opportunity conversion ratios to calculate demo targets.  
  3. Lead Targets.  Leads come from different sources, so you need to look at the historical lead-to-demo conversion rates for each source.  Examples include sourced leads, leads from marketing programs and leads from free product trials.
  4. Activity Targets.  Historical conversion ratios for activities to leads and demos can be used to create activity targets.  Activity metrics for sales include calls, emails and sourced contacts.  For marketing, activity metrics are often centered on marketing campaigns, which may include ads, content, events or other programs.  

Once you know the team level targets, you can break them down further into individual goals.  When you have individual goals, then sales managers can use the goals to manage their sales reps.  My colleague Greg Keshian has written two great blog posts on how to work backwards from goals in order to determine which metrics sales managers need.  One post covers inbound BDRs, and one discusses outbound BDRs.

A similar approach can be used for different go-to-market motions.  For example, if your sales motion is more transactional, you might use historical conversion rates for free trials to paid customers.  Tom Tunguz of Redpoint Ventures recently conducted a survey of efficiency metrics for companies with a free trial.  These numbers are really helpful for figuring out what your operational metrics need to be in order to have best in class sales efficiency.

Part 2 - Put it into operation with a data-driven coaching framework

Once you have translated the sales efficiency targets into individual goals, then the second step is to enable sales managers so they can coach sales reps to achieve the individual goals.  In short, you need to put those goals into action with an operational framework that works for sales managers. 

Greg Keshian has published an operational framework for data-driven sales coaching that will get the job done.  The framework includes four elements:

  1. Benchmark performance against peers.
  2. Track achievement against goals.
  3. Trend performance over time.
  4. Record feedback, coaching, goals and actions in order to get commitment from reps and hold them accountable.

Why the CRM is Not the Answer

The CRM is a great platform for capturing data about sales prospects and customers, but it is a horrible operating system for cracking the code for sales efficiency.  In fact, it fails both Part 1 and Part 2 of our formula.

First, the CRM data structure makes it very difficult to work backwards from sales efficiency goals to create team and individual targets. As I described above, this analysis depends heavily on historical conversion ratios.  Calculating conversion ratios in the CRM is difficult because the data is usually stored in different data objects.  For example, in Salesforce, sales activity data (such as calls or emails) are typically in the Task object.  Demo data will be in the Events object.  Opportunity stages are tracked on the Opportunity object. 

Calculating conversion ratios depends on doing calculations with data from multiple data objects.  In the CRM, it is challenging to do conversion rate calculations, and almost impossible to track how those conversion rates are changing over time.

If you want to include data from other systems, you can usually import the data into the CRM.  But, when you do, the data goes into an object in your CRM (sometimes in a custom object), and you end up with the same cross-object reporting problem.  A good example is lead response analytics.  This requires cross object calculations such as calls per lead and demos per lead.

Second, the CRM doesn't give sales managers what they need to operationalize data-driven sales coaching.  It's easy to create lots of reports.  But Salesforce and other CRMs are bad at cross-object reporting.  As a result, it is difficult to see a holistic view of individual sales reps.  Without this, it is impossible to compare reps to one another.  You also can't track how performance is changing over time and versus goals and targets.

Build-Your-Own Solutions with Spreadsheets and BI Tools

Companies are spending billions of dollars on teams of business analysts and arming them with spreadsheets and BI tools.  Sales and marketing analytics are two of the top functions that are driving the adoption of BI software (see Forbes).  Gartner forecasts the BI and analytics software market to grow to almost $23 billion by 2020.

Can these home grown solutions be used to crack the code for sales efficiency?  The short answer is no.  Business analysts can use spreadsheets or BI tools to work backwards and break down goals as described in Part 1.  But these solutions are difficult to operationalize so they fail Part 2.

There are many positives of this approach.  Spreadsheets and BI software are powerful analytical tools. When you extract data from the CRM into a spreadsheet or a data warehouse, you free the data from the constraints of the data objects in the CRM.  From there, business analysts can manipulate the data, including calculations of conversion ratios.  With conversion ratios, analysts can perform the Part 1 analysis and produce team and individual goals.

The biggest limitations of build-your-own solutions is that they are expensive in time and money and they aren't easy to operationalize.   

Spreadsheets take time to build and are difficult to keep up to date.  They also don't handle large amounts of data very well.  They depend on having lots of business analysts, which is expensive. 

Compared to spreadsheets, BI solutions are more powerful, can handle more data, and they can be automated.  Data from different sources can be added to the data warehouse and joined with CRM data.  BI solutions also have excellent visualization software that analysts can use to create reports.  

The total cost of ownership of BI tools is very high.  And the cost of the software is only the tip of the iceberg.  It takes time and money to integrate the data, set up a data lake, and clean up the data so it is ready to use in a data warehouse.  From there, the amount of analytics that you can do with BI tools depends on the number of business analysts that you have, which can become very expensive as you scale.

The biggest problem, when it comes to Part 2 of our formula for cracking the sales efficiency code, is that the output of spreadsheets and BI tools is difficult to integrate into an operational framework that works for a sales manager.  There are multiple problems:

  1. Sales managers end up with so much data that they can't easily diagnose problems with their sales reps. 
  2. It's difficult for sales managers to use BI data visualizations to convince sales reps of what they need to improve.  This is essential for getting commitment to improve. 
  3. It's challenging to show trends over time for individual sales reps. Sales managers need an easy way to hold sales reps accountable by tracking the improvement over time and against goals. 
  4. It's difficult for sales managers to manipulate the data themselves. Instead, sales managers end up going back to the analysts and asking for more and more dashboards, which just compounds the problem.

The end result is that BI tools and visualizations aren't good for data-driven sales coaching in regular one on one meetings with sales reps.  

Businesses spend billions of dollars on data analysts and business intelligence tools in an effort to improve sales efficiency.

Cracking the Code with Sales Scorecards

Part 1 - Scorecards Make it Easy to Work Backwards

Scorecards integrate data from the CRM and other data sources.  Rekener (now Brainshark) processes the data in the cloud to create a comprehensive view of sales reps.  With the data in Rekener's cloud-based data warehouse, operations and analytics personas can create metrics to calculate historical conversion ratios.  These ratios are automatically updated and can be tracked over time.  With these metrics, sales efficiency goals can be translated into goals at the individual rep level.  

For companies with multiple different segments or a variety of sales motions, scorecards are flexible enough to break down sales efficiency metrics for all cases.  This solution is highly scalable because a small number of operations people can automate a massive amount of business analysis work.

Part 2 - Operationalize with Sales Rep Scorecards

Scorecards enable sales managers to improve sales performance with data-driven sales coaching.    

Sales Rep Scorecards package all the power and analytical insight found in leading BI platforms into an easy-to-use application that sales managers can use in weekly one on ones.  Scorecards are very different from dashboards when it comes to coaching sales reps.  Dashboards are great for visualizing data, but they lack the context that helps sales managers figure out what's working and what isn't.  Scorecards put data into coachable context so sales managers can

  1. Diagnose problems;
  2. Motivate sales reps;
  3. Get reps to commit to improve; and
  4. Hold reps accountable over time.

Here's an example of a sales rep comparison feature that puts the rep's performance in the context of his peers.  When the data is in context, the weaknesses and strengths become obvious with a simple red/yellow/green user interface.

Sales managers use sales rep scorecards to coach sales reps so they can achieve the individual goals that drive sales efficiency.

Sales scorecards offer a powerful combination for cracking the code for sales efficiency, because they're designed to give sales managers context into how reps are performing, by benchmarking reps on important KPIs, trending performance over time, and tracking progress against goals. Request a meeting with our team to learn more.