Sales Analytics-Sales compensation is a critical lever in motivating a salesforce and driving growth in the business-to-business sector: Studies show that revising compensation in line with market trends can have a 50% greater impact on sales than advertisements have, for instance. A vital part of getting compensation right is setting the proper sales targets. Both academic research and our experience working with B2B companies in a variety of industries indicate that poorly set targets often misfire, failing to deliver the expected benefits and demoralizing the sales force in the process.
In fact, organizations often lose top sales talent because of target setting that penalizes success. One common misstep is using past performance as a yardstick. If a top performer overshoots her annual target by 20%, her next year’s target is set at 120% of the current year’s — while next year’s target for a rep who achieves just 90% of this year’s target remains unchanged. Not surprisingly, top performers find this unfair and often jump ship.
We see many businesses in many sectors similarly struggling to set ambitious but fair targets that will motivate salespeople to deliver organic growth. A few companies are finding solutions: They are using advanced analytics to identify the true drivers of business outcomes and are applying big data and machine learning to understand customer demand at an unprecedented level of accuracy and granularity. Armed with more reliable projections, they can establish more meaningful targets.
To set better targets, companies must answer three fundamental questions: How should we select our key performance indicators, or KPIs? How should we determine the right level for our targets? And how often should we set new ones?
Identifying the Right KPIs
Every company must wrestle with this question: Should it base commissions and bonuses on sales figures, profits, or some other metric? A poorly chosen metric can lead to poor results. When a chemicals producer used volume-based targets, its reps resorted to pitching low-margin products that required limited effort to sell rather than high-margin ones that required more effort but would have done more to boost profitability.
Big data and analytics can help identify the KPIs that are best aligned with business priorities and can help define granular metrics that can drive desired outcomes. A U.S. industrial services company was experiencing high customer churn — 20% — largely because reps had adopted aggressive selling tactics, such as bundling in elements customers hadn’t asked for. And once customers were signed up, reps didn’t stick around to ensure adequate onboarding. The offering was a monthly subscription service, meaning customers could cancel at any time — and thanks to the poor sales experience, many did.
Analytics showed that if customers stayed for six months, they usually stayed a full year. So, the company redesigned reps’ incentives around what we call a revenue persistency metric: the share of revenue that continues more than six months after a sale. This redirected reps from “hunting” to “farming”: improving the onboarding process and maintaining the customer relationship.