What's your process for figuring out what metrics to hold sales accountable for?

My process for figuring out what metrics to hold sales accountable for is related their percentage to quota each month//quarter & their W2. You can never take your eyes off the target. We are responsible for first and foremost revenue and making the company money. My favorite engineer use to call me "Paycheck" because he knew any deal I sold helped him get paid every 2 weeks. You'll want to make sure that AEs and sales managers are hitting the goals they set at the beginning of the year to have their best W2 ever! It's also critical to get buy in from your sales team on what's getting measured that makes them a better AE long-term. 

Your process should be dependent on your targets:

• Are you focused on new logo acquisition vs. expansion?
• Is your goal to drive more revenue through self-serve as you go up market?
• How are you holding the team accountable? What metrics is most important to them (how they get paid)?
• Are you based on monthly, quarterly, semi-annual or annual quotas?
• What are the key KPIs that your business is struggling with? How can we incentivize (spiffs) the team to focus their efforts here?

If there is a metric that you need to focus on (i.e. generating new pipeline this quarter), I recommend putting a spiff in place for the team to overachieve that target. Sellers are incentivized by money. Make it fun and competitive.

These are just a few things to consider as you look to build your process around your key sales metrics.

To effectively define the metrics for which you should hold sales accountable, I look at a few things:

1. Understand the "Sales Math" of the business across some core universally applicable SaaS Sales metrics

2. Compare the performance of the top 1/3 AE's against the bottom 1/3 AE's and look for which metrics contribute the most to high performance.

3. Go deep in those categories and correlate the activities top performers do differently to achieve these results. Quantify these activities to define supporting metrics which will lead to success.

To break this down, let's understand the foundational "Sales Math." This is the equation to hit quota. The equation is fairly simple, but everyone's vernacular is different. It is actually extremely important to have very well defined steps in the equation to get consistency across your entire team. For example, we use opportunity stages with clear exit criteria for the buyer & seller to provide consistent insight into our Sales Math. So I would actually use a Stage 1 Opp Created - instead of Discovery Call, and Stage 3 Opp instead of Demo. For the purposes of this article, I'll use general sales terms that each business should be able to use as a starting point and customize from there. Here are the metrics that go into the Sales Math equation:

• Activities to create a Discovery Call

• # Discovery Calls per quarter

• # Demos per quarter

• Discovery Call to Demo conversion ratio

• # Closed Won Deals per quarter

• Demo to Closed Won conversion ratio

• Average Deal Size

• Average Deal Cycle

These metrics will allow you to create the math to hit quota. If the current team's metrics do not consistently lead to the results you're looking for, then the Sales Math may be aspirational. If your team is executing against plan, then this may be your actual current metrics. Regardless, this is what you should feel confident telling AE's is the realistic, attainable and surpassable way to hit quota.

For example, it could look like:

$250k Quarterly Quota

Average Deal Size of $84k

3 Deals to hit quota

Close ratio of 33%

9 Demos needed per quarter

60% conversion ratio of Disco to Demo

15 Discovery Calls needed per quarter

50 Activities to create a Disco

750 Activities needed per quarter*

*one note on activity. It's a metric I'll always track to understand a baseline level of effort, but I will often leave this out of the Sales Math when dealing with higher complexity sales and more senior AE's. Up to you if this should be in your Sales Math equation.

Now take your Sales Math, and map your high performers against your low performers to look for which metrics have a high correlation with success. This exercise can be extremely surprising, so be open to what the data shows you, and hold your strong opinions loosely.

Let's extrapolate this exercise across two different scenarios:

Scenario 1 - Enterprise

Here's how the exercise played out when running it against a more enterprise business (numbers are directional):

1. Activity, Discovery Calls and Demos were almost identical across high & low performers. This told me that pushing "more activity" was only going to have so much impact on performance.

2. The Closed Won conversion of top performers was 46% vs. 25% for the low performers. This was a huge gap, and had major implications on the Sales Math.

3. The Average Deal Size of top performers was $160k vs. $70k for low performers. This is also a huge gap compounded the success or struggles of each group when combined with the stat above.

So the key metrics to optimize were Average Deal Size and Demo to Close Ratio. We wanted to maintain our activity levels, but really lean into increasing ADS and strategies to help with Deal Execution.

Based on this knowledge of what would have the biggest impact in high performance vs. low performance, we added in some metrics & activities that would contribute to these results:

• Updated our account prioritization to ensure a focus on the top deals & tracked activity against Priority 1 accounts

• We blocked off time each week to prospect into our top accounts & scheduled strategy sessions to help get more meetings with these accounts

• We tracked # of Discos with P1 accounts

• # of Demo's with $100k+ Opportunities

For Deal Execution

• We tracked multi-threading in each account

• Have we made an executive connection?

• We created a cross-functional meeting to lean into competitive differentiation strategy

• We set a threshold for accounts that needed a key deal review & updated our process to improve efficiency and make room for more accounts reviewed each week.

Scenario 2 - Transactional

Here's how the exercise played out when running it against a more transactional business (numbers are directional):

1. There were two camps of high performers. Those with extremely high activity, and those with higher disco to demo efficiency. Our most consistent top performer was a combination of both. Low performers fell into a similar pattern of either low activity or low conversion of discos to demos.

2. Deal size and win rate didn't have dramatic differences outside of 1 AE who closed the largest deal in segment history. This wasn't repeatable so we eliminated that result instead of putting too much time in hunting whales.

3. Average Deal Cycle for top performers was 39 days vs. 52 days for lower performers. Top AE's were closing deals faster, which allowed for more time to close more deals.

From this data we defined additional metrics and activities to drive better results:

• Upped the baseline activity volume expectations - there is a diminishing point of returns, but higher volume was almost always a component of success. We raised the bar, but also coached our highest volume AE's to lean more into their efficiency metrics instead of pushing to just do more.

• Managers went deep on quality of discovery calls coming into the funnel

  • Title & Seniority level of Prospects - lower conversion was correlated with lower titles.

  • Was the company in our Ideal Customer Profile? Quality of company greatly impacted conversion

  • Why now? Did we offer someone a gift card or just bug them until their defense was worn down? Or was this call predicated on funding, a new hire, an inflection point in the business, intent or some other business catalyst?

• Managers inspected quality of prospecting messages

• Managers inspected quality of discovery calls

• We rallied around creative promos to help the team close deals faster

• We replicated decks top AE's were using to build value and establish trust faster

In both Scenario 1 and 2 - we started with the baseline Sales Math, and through comparison of top performers vs. low performers we were able to lean into the 2 key metrics that had an outsized impact on performance. We then defined key activities and additional metrics which we could hold the team accountable to, that we knew would correlate towards greater success across the team. How easy was that? :)

Usually my process for figuring out what metrics to hold accountable for looks something like...

Step 1: Work with a cross-functional group to align on the ultimate goals & objectives that are aligned with the strategy of your sales team or revenue organization.

Step 2: Then take those goals & objectives and break them into the core tasks, behaviors, and milestones that if achieved will be a leading indicator to the success of your team.

Note: these tasks/behaviors/milestones should be focused on the inputs that you & your team can control rather than outputs.

These are examples of inputs:

• # of personalized outreaches to net new prospects

• # of calls to net new prospects

• # of first meetings

• # of new sales experiments started/completed

These are examples of outputs:

• Revenue

• Pipeline Coverage (usually 3x)

• Weekly Pipeline Generation

The outputs are important to measure & keep track of, but when it comes to managing your team and holding them accountable, focus on what they can control...the inputs!

Step 3: Once you have a draft of those leading indicators, socialize them to other cross-functional leaders to confirm & validate their agreement that those are the right tasks, behaviors, and milestones to measure. After you have aligned on what to measure, it’s all about determining how you are going to measure it. If you can’t measure it, then you can’t manage to it.

Step 4: Then it’s time to go on a roadshow across the organization meeting with executives, cross-functional partners, and the sales reps themselves sharing:

1) the “why” behind the KPIs you chose & how they connect to the overall goals/strategies of the team

2) how those will be measured, reported, and socialized

3) the expectations for the team of what they need to do to be successful. 

I’d also recommend giving yourself some flexibility by communicating these will be the KPIs to “start” and over time as we learn more we can iterate as needed.

Step 5: Finally, it’s about creating a cadence of regular review and communication on those KPIs. Personally, I recommend a weekly cadence if possible and socializing those KPIs as publicly as you can to drive accountability, with the caveats that new motions take time to adopt & KPIs may be iterated on over time.