Sales Operations is a role tasked with measuring and understanding the performance of a company’s sales team. Last week, Emmanuelle Skala (head of sales at Influitive) explained in an episode of the Bowery Capital Startup Sales Podcast that Sales Ops should have 2 core aims: (1) Effectiveness (how can salespeople be as impactful as possible on a per-time basis when selling); and (2) Efficiency (how can you ensure your salespeople have the most selling time possible). In data-driven sales organizations, she or he very well might be the VP of Sales’ “right-hand person” and go-to regarding the health of the company’s sales efforts. And we’re not just talking about someone who knows the numbers in the Board deck by heart. A good Sales Operations leader can achieve at least a few quarters of reliable visibility into a sales funnel, and should be measuring leading sales indicators 18 months or more in advance of actual bookings. Sales Ops stakeholders, therefore, look beyond traditional SaaS metrics like MRR, which are lagging indicators more than anything.
Today we’ll walk through a 4-part Sales Operations framework: CFPL.
Every Sales Operations team should have a Capacity Model (CM). Almost certainly an Excel document, the CM is a Sales Ops leader’s “bible” and he or she will likely reference it daily. In terms of format, it should lay out each sales rep (both current and future) on a monthly basis, with the aim of answering the question: when will our reps start predictably hitting quota? Given that most rep ramps are around 6 months, the CM should have visibility at least 18 months out (of course the rep ramp time varies based on the business and will update as you learn more, but 6 months is a good place to start). Using the model, you’ll be able to see what your Expected Capacity is, or how many “ramped reps” you have in that month. You can compare this to the Required Capacity to hit quota (below, I’m including an example of a few summary lines your CM might build up to, courtesy of Emmanuelle).
When you’re building up to Expected Capacity, you also want to factor in a few considerations: turnover (i.e. both due to lack of fit and reps simply leaving), issues in achieving ramp (e.g. your business simply requires more ramp time), and other unexpected obstacles. Emmanuelle suggests building in a 20-30% buffer (i.e. your Expected Capacity is discounted by this amount) in order to ensure you are conservative enough to cover yourself.
If you adhere to the standard “language” of SaaS sales these days, you probably use similar terminology as does Emmanuelle at Influitive to track a deal through the stages of the sales process: Inquiry >> MQL >> SAL >> SQO >> Win. An important responsibility of the Sales Operations role is to track conversion rates between each of these stages across your organization, and also between various salespeople. Having a real-time view of these rates is important from a sales team management perspective. At Influitive, Emmanuelle has each of her sales reps report their own funnel at their Quarterly Business Review (QBR). The key here is that each rep should know how many Inquiries they require months out in order to hit his / her numbers down the road (this will also tie into your CM; see #1 above) and drive Marketing activities to ensure sufficient Inquiries. This way, Sales Ops can help identify weaknesses in certain reps’ pipelines (SQO+) very early on. This practice also drives sales coaching as it draws out which reps are best at which stages. There are a number of tools Sales Operations can use to measure funnels (in particular we would suggest checking out InsightSquared); a simple way to visualize a funnel follows.
Another interesting element of your funnel to pay attention to is the difference between the SAL (Sales Accepted Lead) and SQO (Sales Qualified Opportunity). A SAL is a deal that an SDR has accepted and passed along to an Account Executive (AE). A deal is not an “opportunity,” however, until it’s an SQO, which means an AE has spoken with the contact / executed a demo and is officially entering it into pipeline (they’ve personally qualified it and are accountable for it). Poor conversion from SAL to SQO can indicate that SDRs aren’t properly qualifying deals or that AEs are having poor “first meetings” resulting in them rejecting too many SALs. Of course, every single conversion rate should be tracked overall, by rep and also by cohort (to measure performance over time); these will vary by business, and there are no perfect benchmarks.
At Influitive, Emmanuelle asks each of her reps to report the percentage makeup of their pipeline on a few different measures. Pipeline Coverage is probably the most well known and generally aims to ensure aggregate Opportunities by Bookings in each rep’s pipeline is sufficiently larger than quota (3-4x+ is generally considered healthy in SaaS). But coverage alone isn’t enough; quality is equally as important. Here are few examples of measures Sales Operations can use to gauge pipeline quality: (1) What percentage of our pipeline comes from our Target Accounts list vs. Inbound? Target Accounts (sometimes also called a Total Universe of Accounts) are those that fit your Ideal Customer Profile, as measured on a number of factors that speak to likelihood to close. Insights you may draw out from tracking this: your rep is a poor prospector, has a poor understanding of the Ideal Customer Profile, needs more help from the SDRs, etc. Again this may impact coaching as, especially if your divide your salespeople on a territory model (vs. Round-Robin), there should be an element of proactive lead gen. (3) What percentage of pipeline supports Product Line A vs. Product Line B? Especially if your startup has invested heavily in new features, this is a critical step in measuring ROI and revenue contribution. (4) What is the average age of each rep’s pipeline? As Emmanuelle draws out, paying attention to opportunity age will highlight the key difference between your selling cycle and buying cycle. While the latter begins as soon as the target knows about your product, the former only really begins at SQO or the equivalent. Understanding what leads should be nurtured vs. actively sold is critical to proper resource allocation, and a responsibility of a good Sales Ops leader.
4) Lagging Indicators
This category comprises those sales data points that are most well-known, at least in the SaaS startup world: deals won, bookings, ACV, ARR / MRR, revenue, growth (of all the preceding metrics, MoM / QoQ / YoY), per ramped rep (all of the preceding metrics), win / loss ratio, churn, CLTV (as you calculate it), etc. These are critical to Sales Operations, but for the most part lagging indicators. They do not, in other words, really arm Sales Ops with information they need to ensure solid sales performance quarters in advance, which is their core responsibility. If bookings are missed in a quarter, you’ll see that a rep fell short of quota, but you won’t (by paying attention to this group of metrics alone) understand whether this is because of a lack of Inquiries from Marketing, poor SDR qualification, or from simple failure to close SQOs. If you’ve used the learnings from steps #1-3 above to ensure a solid Sales Operations framework in place, you should know how these lagging indicators are going to turn out well in advance of close.
Hopefully, the above (along with the companion podcast) serves as a good framework for what matters in building a Sales Operations org. If you have any questions or are stuck on something, I’m also happy to help. Feel free to ping me on Twitter (@picnoulos).
This week Oleg Campbell, Founder and CEO of Reply.io, joins the Bowery Capital Startup Sales Podcast to discuss "Evolving Your Sales Tech Stack."