The Enterprise Sales KPIs You Need to Track

It's officially Spooky Season, and what’s scarier than a cold call??

The answer: making the cold call, am I right??

Take a break from spooking (I mean cold calling) and check out today’s newsletter. We’ve got a list of the top enterprise sales KPIs you should be tracking, why a unified data strategy is the best approach for your company, and how marketers can use signals and AI to their advantage.?

Let’s get to it.?

The Enterprise Sales KPIs You Need to Track

For every enterprise sales leader who wants to rethink their team’s key performance indicators (KPIs), there’s a seemingly endless supply of best practices to choose from.?

The problem with best practices, though, is that they’re usually past practices. What may have worked well even a few years ago may not guarantee success today. This means that your enterprise sales KPIs should be continually reviewed and adjusted — the same as companies recalibrating their go-to-market efforts to meet ever-changing buyer behavior.

Here are the enterprise sales KPIs we recommend for 2024 and beyond.

1. ACV per Demo Rate

Annual Contract Value (ACV) per Demo Rate measures, in aggregate, how much value each meeting with a prospective customer represents. It also serves as a bellwether for the overall efficiency of your sales motion. If your reps are spending significant time and effort securing demos that translate into lower ACV, that’s a signal their time should be directed elsewhere. Here’s how to calculate it:?

ACV per Demo Rate = ACV won in a period?/ # of demos booked in same period

Many businesses focus on annual recurring revenue (ARR), but this poses potential pitfalls, such as a handful of larger accounts being overrepresented in the overall share of the ARR. Assessing performance with the AAR metric can be risky, because a few dominant accounts can hide an array of problems lurking in team performance — and expose them abruptly if a major account is lost.

2. Sales Cycle Length

Sales cycle length should strongly inform pipeline creation and broader goal-setting. Underestimating the length of time between creating and closing an opportunity can result in missed targets and lower revenue, not to mention demoralized reps.

Sales leaders can reduce their sales cycles by engaging prospects faster, automating their GTM motions, and removing friction from the contract process. However, according to sales consultant and best-selling author Anthony Iannarino, it’s important to strike the right balance between efficiency and giving deals the time they need to develop. “Right now, people are getting something wrong — they want to try to shorten the sales cycle,” Iannarino says. “When you’ve got uncertainty, if you try to speed things up, what you’re doing is taking away the time prospects need to have a conversation, to be confident and certain that what they’re doing is right, and that they’re going to be able to execute.”

3. Value per Additional Meeting

Given the length of the typical sales cycle, it’s important to contextualize metrics with the number of meetings it takes to actually secure a deal. More meetings typically means longer negotiations, which are ideally offset by higher contracts.?

ZoomInfo assess the value of each additional meeting by measuring its impact on closed deals, comparing opportunities that require two, three, or four meetings with those that only need one. This helps us identify which opportunities could benefit from an extra meeting, enabling our salespeople to use their time more effectively and increase the ACV.

4. Win/Paper Sent

This KPI captures how efficient (or not) late-stage negotiations have been. Imbalanced ratios can reveal potential problems in late-stage discussions.?

Win/Paper Sent Ratio:?# of closed-won deals / total # of contracts sent?

With elongated sales cycles, more stakeholders, and greater scrutiny, many factors that can impact later-stage negotiations are beyond a sales rep’s control. Identifying potential roadblocks is a vital first step in determining what reps and AMs can do to optimize their discussions with prospects and close deals faster.

Find the 3 remaining essential enterprise KPIs in this blog.

Signals & AI: How Today’s Top Marketers Find Buyers Faster

For today’s marketers, data is more than just numbers on a dashboard — it’s the key to unlocking new opportunities and staying ahead of the competition.?

But here’s the catch: many marketing teams are flooded with disconnected data points and siloed insights, struggling to understand what truly drives customer behavior. The key to breaking this cycle is to recognize and leverage high-quality buying signals — critical, timely indicators that can transform generic outreach into targeted, personalized interactions that drive results.?

When combined with AI, these signals become powerful tools that reach potential customers much earlier in their journey — giving go-to-market teams a massive advantage over competitors who rely solely on ideal customer profile matches, traditional intent data, or account-fit scores.?

To maximize effectiveness, marketers should map key signals to specific points in the sales funnel. By aligning signals with funnel stages, companies can better understand where a prospect or customer is in their journey.

Let’s consider a company that sells a SaaS platform for project management as an example.?

They might use the following signals:

• Website Behavior: Tracking visits to specific product pages or the “Request a Demo” page.
• Content Engagement: Monitoring downloads of whitepapers, engagement with case studies, or attendance at webinars.
• Intent Data: Purchasing signals from third-party sources that indicate the prospect is researching project management solutions.

They might map these signals to the sales funnel stage and trigger actions as follows:?

• Awareness Stage: If a signal indicates that a prospect is consuming a lot of top-of-funnel content (like blog posts or introductory videos), this could trigger an automated email campaign that offers further educational content.
• Consideration Stage: If a prospect starts engaging with product comparisons or case studies, this might trigger a notification to the sales team to reach out with a personalized offer or a demo.
• Decision Stage: If a prospect visits a pricing page multiple times or downloads a detailed product guide, this could trigger an accelerated sales outreach motion or a targeted ad campaign.

Instead of a one-size-fits-all marketing strategy, the company can deliver personalized messages and offers based on actual customer behavior. This alignment between marketing and sales ensures that efforts are focused on the right people at the right time, ultimately improving conversion rates and reducing wasted effort.

So what should you do to maximize the impact of your marketing strategy with signals and AI? We’ve got the steps you can take, along with our on-demand webinar covering all things signals, here.

How Unified Data Powers Go-to-Market AI Strategies

Business leaders have long known that fragmented data is a major obstacle to growth, consuming up to 20% of the average IT budget. But in the age of AI, disconnected data can cause havoc across a company’s entire go-to-market operation, leading to missed growth opportunities and wildly incorrect decision-making.?

What’s the solution? Integrating data from first-party, second-party, and third-party sources to create a seamless flow of information that fuels smarter, more targeted go-to-market strategies.?

Here’s what’s necessary to implement this practice at your company.?

A Unified Data Approach:?

“The more data you can feed AI, the more you give it context and enable it to make smarter decisions,” says Russell Levy, chief strategy officer at ZoomInfo. And conversely, if your first-, second-, and third-party data remain siloed, your AI-driven insights remain weaker.?

Having a unified data approach enables AI models to provide more accurate, context-rich insights, ultimately driving more effective go-to-market strategies.?

AI Models Grounded in Integrated Data:

AI models, particularly large language models (LLMs), can be prone to “hallucinations” — generating inaccurate or irrelevant information when they lack proper grounding in reliable data.?

By providing AI models with a robust foundation of integrated data, businesses can improve the precision and reliability of AI-generated insights, reducing the risk of errors and enhancing decision-making processes.

Leverage DaaS for Seamless Integration:

“As businesses invest in developing their own AI initiatives, they increasingly need access to the underlying data from not only internal, but external sources as well,” says Jake Graham, founder and CEO of Bobsled, a data sharing company.?

Sneh Kakileti, a product management VP at ZoomInfo, emphasizes that businesses are no longer running their operations solely within traditional CRMs. They inlist cloud-based platforms and data-sharing services to centralize their data and drive more sophisticated GTM strategies.

“We wanted to bring the value of our data asset to where customers actually ‘live’ and are making those data-driven decisions,” Kakileti says. This allows companies to seamlessly integrate various data sources into a unified system, making it easier to leverage AI tools and generate meaningful insights.

Read more about the benefits of a unified data strategy in this article.?

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