How Will You Measure Your Sales Team’s Performance in 2021?

By: Gbenga Ige 

As sales leaders look back on 2020, many aren’t sure which metrics mined from terabytes of sales data are needed to understand how to plan for, manage, and track their sales teams’ performance in 2021. And the big question on everyone’s minds is, “what data do I need to anchor the 2021 plan on?” Settle on an approach that involves looking back on the sales season desired and set up a system of metrics —behavioral, leading and outcome—that govern how your team executes.

Sales leaders are all inundated with a significant amount of data. The ability to draw insights from data (and act on these insights) is a key capability that separates winners from losers.

Working with sales leaders across a wide range of industries at SBI, we see a significant number of metrics being collected and analyzed. However, what matters is the actions that the insights drive from the metrics. The metrics can help determine the right levers to pull for increased performance across sales teams. For example, if channel account managers have 52% selling/customer engagement time or if your return on marketing spend dropped 5% in 2020, what exactly does that translate to as an insight? What actions will you take? How will that data drive your business decisions in 2021?

A system of metrics—behavioral, leading, and outcome-based—provides the right platform to influence how the sales team executes in a positive way.

For leaders in the planning phase, here is some perspective on how top software leaders build agility into their planning processes and a dynamic revenue planning tool to help guide you through the process.

Choosing Metrics and Adopting a Framework 

Developing a data-based sales strategy starts with adopting a framework (and there are many frameworks out there). The framework should be based on a deep understanding of sales’ historical performance and help identify key products/solutions and customer segments/markets. This provides knowledge of where the sales teams’ strength lies and could inform where to focus in the coming year.

An alternate approach is to project yourself to year-end and “look back” on the year you want to have. This approach requires the sales leader to fast forward to the end of the fiscal period and answer the following questions:

  • What metrics captured correlated with my sales team performance in FY21? 
  • What products “popped” in FY21? 
  • Was customer segment performance the same as last year? 
  • How different was my deal split across new logo sales and current customer sales? 

In most organizations, data to answer the questions above is not difficult to get. However, the right questions to ask are not always that simple and are often not as discreet. In such situations, business leaders develop derived metrics that are specific to not just the market and business but to the client’s context. For example, for a cybersecurity software provider, that key metric could be the percentage conversion of marketing qualified leads (MQL) after a security breach. For a SaaS provider, the derived metric could be the number of senior business leaders (customers) in the sales funnel who publicly committed to digital transformations.

Translating Data Into Action

Ultimately, an effective data-based sales strategy should shape the culture within a high-performing sales team. Data insights and metrics captured should drive the right actions by sales reps in their bid to meet their quotas and, by implication, get compensated accordingly.

Breaking down sales metrics into the behavioral, leading, and outcome buckets is a useful tactic that sales leaders can use to manage their teams.

Behavioral metrics help measure how well the sales organization “does the right things” to guarantee their success. They highlight how well the organization adheres to habits that drive success. An example is the % of deals registered in Salesforce vs. Excel spreadsheets. This would apply to an organization that has invested in sophisticated sales tools that have limited adoption within the sales force. Another example could be the average lead response time. This could apply in an organization that converts fewer leads than competitors/benchmarks or just wants to improve the response time to customer inquiries.

In my experience running transformations and working with sales leaders, this category of metrics is the least reported and likely has a stronger correlation to outcomes.

Leading indicators are the metrics that help show what could happen in the future. I consider these as a measure of what happens early in the sale cycle. Pipeline is the classic metric here; however, more action-based metrics that “help lay the groundwork” for the sales teams’ success also squarely fall in here. Examples are the number of calls made and employee satisfaction scores (as a precursor to retention metrics).

Outcome/Performance indicators are the metrics that share how well the team has performed. As described above, these are the indicators that sales leaders want to look at and see how well their teams have done at the end of the year. While bookings (and revenue) are the easiest and most straight-forward, win-rate, number of customers and average deal size are also good measures.

The technology to use and how to create visibility to the metrics (dashboards, monthly/weekly achievement prizes, gamification, etc.) to the sales team are also important factors to consider. We know that the era of virtual selling is upon us, so these methodologies should take the remote work environment into account. Metric systems that suck Sales Operations capacity (to compute) might not be the best investment of your sales resources. The tradeoff between precision and accuracy that comes with normal measurement systems are also at play here.

Building the right data-based framework to drive decision making and influence what the sales team does on a day-to-day basis can be the most significant driver to your sales teams’ performance this year. Ultimately, as leaders adapt their strategy based on what the metrics say, planning should be dynamic.

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