How Big Data Improves Sales Management and Predicts Performance

Opinion

Enterprises worth their salt typically use a set of standard key performance indicators (KPIs) to evaluate progress, but there are limits to the effectiveness of these measures in improving or predicting future performance. According to Harvard Business Review, the top KPIs in sales include average annual quota and quota attainment average. The former describes the dollar value of the quota, averaged across quarters, and the latter describes the percentage of time quotas were achieved across an organization or team.

While these KPIs do provide a backward-looking snapshot of a salesperson’s performance, they do nothing to help the salesperson or manager understand the everyday behaviors that are responsible for the outcomes. But what if managers could understand which daily activities are correlated with successful outcomes, intervene earlier for employees who are falling behind, and encourage employees to adopt best practices to improve individual sales performance?

That’s where people analytics comes in. It’s the missing link between real-time work behaviors and quarter-end lagging indicators such as KPI metrics. Ultimately, predicting outcomes and improving productivity allows companies to course-correct before quarterly results come in.

People analytics, an emerging big data technology, draws on aggregated, anonymized data from email, calendar, and other company-specified datasets, to help employees and executives understand how time is invested, and if it’s paying off with increased sales. Put simply, the data helps managers recognize why some employees are not meeting their KPIs and how to best coach them towards improvement. In the absence of these data, managers cannot provide fact-based coaching toward practices that bolster sales within their own organizations.

Imagine, for example, that an underperforming salesperson misses his quota target for the quarter. What options does a manager have? She can coach him based on her observations or experience, or she can reprimand or dismiss him. The latter options aren’t particularly enticing, especially given the high price of attrition.

But there are limits to observations and experience in coaching employees. Without actual data around how the salesperson spends his time or communicates with clients, managers are left to inference, self-reporting, and past experience that might not translate to the employee’s current circumstances. Equipped with real-time, company-generated data, managers can coach employees around specific behaviors that are proven to work within their own organization.

Here’s how people analytics metrics can be paired with standard KPIs to drive sales and improve coaching.

According to data garnered through people analytics, salespeople who consistently meet their quota spend 25 percent more time with customers than underperforming salespeople. This in and of itself isn’t particularly earth-shattering. We assume that when salespeople invest quality time building a relationship with and understanding their customers, it pays off. But by looking at the data over time, we see an interesting trend around when top salespeople invest that time.

High performers invest most of their time with customers at the beginning of the quarter and taper communications by the end, as their internal communications ramp up. Investing time at the start of the quarter allows them to build foundational trust and understanding and facilitate necessary internal communications after that customer rapport is established. This trend was repeated across industries and geographies. Also, customers who received significant attention from a salesperson at the beginning of the quarter spent more overall.

By coaching employees to spend as much time with customers as top performers do, particularly at the beginning of the quarter, managers can train underperforming salespeople on a specific, measurable behavior that is highly correlated with increasing quota size. Managers can also review their company-specific data around this behavior and share it with employees to boost performance on KPIs. Not only does this help managers provide direction, but it also empowers salespeople with a specific action item that has proven its worth within the organization. No longer relegated to deciphering general, ambiguous recommendations from managers, salespeople receive a tangible suggestion that’s based on their peers’ success.

Employees’ effectiveness at internal networking is unexpectedly also correlated with routinely meeting or exceeding quotas. People analytics measures how efficiently a company’s employees build and maintain internal networks, by gathering data on the frequency, duration and intimacy of their interactions (five or fewer people is deemed an “intimate” interaction). Employees who exceed sales quotas tend to have 20 percent larger internal networks and spend 20 percent more time with senior-level management.

This was one of the most interesting correlations we found because it has nothing to do with client interaction or face-time. The significance of the data is twofold. Strong internal networks make it easier to secure a sale and get it approved. And having access to senior-level management means more mentoring and better modeling from experienced salespeople.

Bolstered by this understanding, managers can help underperforming salespeople forge relationships with senior-level management and support employees in growing their internal networks. Managers can also use the data to pair top internal networkers with mid-internal networkers, effectively bridging the gap in proficiency. The data can also help employees prioritize internal networking. Far from mere socializing, internal networking helps salespeople forge connections that are key in making sales happen.

Because the correlation between analytics metrics and sales KPIs are so strong, people analytics data can predict whether a salesperson will miss his or her quota for the quarter, beginning only one month into the quarter. Internal network size, time spent with customers, and the time spent with managers are highly predictive of quota attainment. Moreover, the data can actually help to predict how much money customers will spend based on how much time a salesperson spends with the customer and when during the quarter those interactions take place.

All of this can provide managers an early warning sign that a salesperson might be off track for their quota, and an opportunity to correct-course or at least warn senior executives and shareholders of the lackluster quarterly results ahead.

Anyone who’s taken a basic statistics class understands that correlation does not equal causation. But correlation can illuminate fascinating relationships between behaviors and outcomes. While the data are promising, we should proceed with prudent optimism. People analytics isn’t a cure-all for poor salespeople, and there’s likely a confluence of behaviors that lead to success in sales.

What people analytics can do, however, is provide companies with unprecedented insight about what successful salespeople are doing within their organization that effectively leads to high performance. We can use the data as a basis to coach employees and conduct A/B testing around which behaviors seem to have a causal relationship with high performance, versus an associated relationship. People analytics data provide extraordinary visibility into what successful people do and how others can replicate those behaviors.

Ryan Fuller is co-founder and CEO of VoloMetrix. Follow @VoloMetrix

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