Consider two sales reps who are compensated very differently.

One of them is compensated in a traditional manner, using outcome measures such as sales volume, growth, market share, etc. But the availabilities of these metrics lag by weeks (even months at times), and they are never quite sure about performance in the moment. But they’re confident that most of the sales will come even if they do not put in the effort—so they think sometimes skipping a few customer calls may be okay when the sale is all but certain.

The other rep is paid for completing a set of activities each week. They know what needs to get done by the end of the week and are always confident what the incentive pay will be. However, they also know that no one is monitoring these activities, and they may not place as much importance on quality as the organization might prefer.

Neither of these situations is ideal. Yet for decades, sales organizations have chosen to lean heavily on outcome metrics to drive performance. The assumption is simple: the more calls, emails, meetings and demos a salesperson conducted, the higher their chance of making sales, so why measure activity when you can measure the output of that activity? This approach, while undoubtedly effective in measuring performance and paying incentives, has long had shortcomings that have held organizations back from designing an effective incentive plan.

However, with the recent shifts in how employees wish to be compensated and advances in artificial intelligence (AI), the landscape for sales performance metrics is shifting dramatically. Today, organizations have access to sophisticated AI tools that can assess the quality of sales activities with greater precision and objectivity. These new capabilities provide a far more nuanced and effective way to measure and reward sales performance, moving beyond merely counting activities to focus on the impact and substance of sales interactions. This, in turn, enables the design of human-centered incentives that can be personalized to individual efforts that are likely to be unique and a challenge to measure objectively. This is especially important for situations where sales results may not be immediately visible, or where the outcomes of certain activities (such as effective communication) are indirectly tied to sales.

Let’s explore the limitations of traditional activity-based performance metrics and highlight how ZS is transforming the way sales teams can be evaluated and rewarded using AI.

Historically, activity-based metrics have been the default standard for measuring sales effectiveness—particularly for nonsales field roles. The most common metrics include the number of calls made, emails sent, meetings scheduled and deals closed. While these metrics are easy to track and report, they fail to account for the most crucial factors that influence sales success. These include the quality of the interactions between salespeople and potential customers or the salesperson’s ability to understand and respond to customer needs.
 

There are several issues with relying on activity metrics as a sole indicator of sales success:

1. Quantity over quality: Salespeople can “game” activity-based metrics by focusing on quantity over quality. For instance, they might schedule a high number of meetings or send numerous emails, but these activities may not necessarily move the sales process forward or provide real value to customers. In this scenario, the salesperson might appear to be performing well, even though they are not achieving meaningful results.
2. Lack of context: Activity metrics often fail to account for the context in which the activity occurs. For example, a salesperson might have fewer calls but might have a deeper, more insightful conversation with a key decision-maker that drives the deal forward. Activity-based metrics would not recognize the significance of that conversation, as it does not reflect the number of calls made.
3. Neglecting soft skills: Sales success isn’t just about making calls or sending emails—it’s about how those interactions are executed. A salesperson’s communication skills, emotional intelligence and ability to build rapport with clients are crucial in shaping the sales process. Unfortunately, traditional activity metrics do not capture these “soft” factors, which can have a profound impact on closing deals and customer satisfaction.
4. Short-term focus: Many activity-based metrics are designed to measure short-term performance. While this can be valuable for tracking daily or weekly outputs, it does little to capture long-term sales success, which often relies on relationship building, trust and a deep understanding of customer needs.

Recent advancements in AI have opened new possibilities for measuring sales performance more holistically. By leveraging AI tools, organizations can move beyond counting activities or measuring effectiveness through observed behaviors and begin assessing the quality of those activities in ways that were previously impossible.
 

AI-powered tools can analyze conversations between salespeople and customers in real time, offering insights into the tone, sentiment and engagement level of each interaction. For instance, AI can detect whether a salesperson is listening actively, asking open-ended questions and responding empathetically, all of which are crucial for building trust and rapport with customers. AI can also flag instances where a salesperson might be overly aggressive or dismissive, helping managers identify areas for improvement.
 

AI can also analyze emotional cues in sales conversations, offering insights into how well a salesperson reads and responds to the emotional state of the customer. For example, AI can evaluate whether a salesperson is attuned to signs of frustration or hesitation and whether they adjust their approach accordingly. Emotional intelligence is an essential skill for sales professionals, and AI can help quantify and reward this aspect of performance in a way that traditional metrics can’t. Training and development focused on this aspect of selling can be the key differentiator that can help organizations transition their reps from being the messenger to the mainstay at a customer’s office.
 

AI can also track a salesperson’s participation in internal initiatives, such as training programs, skills development and other self-improvement activities. Sales performance isn’t just about how well a salesperson performs today, but how much they are investing in improving their abilities for the future. AI can measure the completion of practice initiatives, such as role-playing exercises or training modules, and factor this into performance evaluations. This promotes a culture of continuous improvement and rewards salespeople who are committed to personal growth.

The evolution of sales effectiveness tools powered by AI is revolutionizing how organizations assess and reward performance. Here are three things you can do now to ready your field effectiveness and compensation programs:
 

Use AI to measure quality along with quantity: ZS’s next generation of effectiveness tools can assess language use and tone as well as body language in both real-time and practice settings. AI-powered training platforms such as ZS’s Virtual Rep Trainer and Coach.AI can help objectively evaluate learning at scale and create new data on skills levels and behaviors. We can also gather customer feedback on the actual experience of the interaction with the rep. This treasure trove of data can be integrated into the information CONTEXTstream and used for a wide range of use cases ranging from incentives to improved targeting of customers.
 

Develop a deeper understanding of your field teams: New methodologies such as conjoint surveys combined with focus groups can help understand what truly drives your field team to perform better. Some may be motivated by outcome-based rewards, whereas others may need intermediate nudges based on the quality of their activities. Instead of a one-size-fits-all approach, organizations should now transition toward personalized motivation journeys for individual personas that exist in the team.
 

Design incentives that tap into human motivations: Informed by what drives field teams to great performance, we can leverage AI to measure the controllable metrics, that is, the effectiveness of their interactions in addition to their sales results. This move toward a more human-centered approach to incentives will help field personnel feel compensated for both their efforts as well as results.
 

Management by objectives can initially include the effort-oriented metrics as a measure of performance. Eventually, we envision incentive plans will have AI-driven quality components that reps are measured on directly. This will be even more valuable for field roles, such as key account managers and customer success representatives, where sales outcomes may not be directly attributable to the team or may lag significantly from the activity.
 

At ZS, we combine deep expertise in incentives with AI-powered sales force effectiveness at scale—which will be key to unlocking this new era of performance measurement, enabling a more effective and fair system for both employees and their organizations. The future of performance measurement will continue to be focused on outcomes. But we envision a future where we can reward employees based on the deeper dynamics of customer relationships and their ability to build those relationships in ways that create long-term value.