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. Poorly set targets often misfire, failing to deliver the expected benefits and demoralizing the sales force in the process. We see businesses in many sectors 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.
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.
Setting the Right Targets
As offerings proliferate and sales processes grow more complex, customer demand is becoming increasingly volatile and difficult to predict — and traditional top-down approaches to target setting may fall short.
Some companies are using innovative machine-learning techniques to predict customer behavior. To complement top-down data, they draw on territory- or account-level forecasts, including historical data from external and internal sources. The algorithms monitor and respond to factors that affect the reliability of their predictions, so they become more accurate the longer they are used.
At a global manufacturer, volatile demand made it hard to set sales targets. Sales managers struggled to discern the timing and degree of intervention that would improve front-line performance when monthly volumes were at risk. Without robust, granular sales data, forecasting was done manually, and it relied heavily on managers’ estimates. So, the company built a “data lake” using multiple internal sources and turned to machine learning to analyze sales patterns for each product at each reseller. The wealth of data points thus gained — for products, stock levels, prices, time of sale, and so on — allowed it to group resellers into similar clusters and develop a forecasting algorithm for each one, which was enhanced by manipulating inputs over time.
Results were impressive: The accuracy of forecasts improved by 80%. Having more-accurate forecasts enabled sales managers to adjust targets throughout the year to ensure that they were neither rewarding nor penalizing front-line and channel partners for market developments beyond their control.
Choosing the Right Frequency for Revision
How often should targets be reset? Revise them too frequently, and your administrative costs and communication challenges increase; revise them too slowly, and you may lose responsiveness to market changes and undermine reps’ engagement.
An industrial services company used machine-learning techniques to calculate the likelihood of churn for each customer; its algorithm correctly identified 60% of churners and 95% of non-churners. With the help of hand-picked reps, the company then designed a new target-setting model.
Under it, reps were paid a bonus for reaching out to customers identified as being at risk of churning. After experimenting with targets set at different intervals, using predictions from the algorithm along with feedback about customer behavior from the field, the company determined that the optimal frequency for revision was quarterly. By using the model to identify at-risk customers for reps to reach out to every quarter, it reduced its churn rate by 5%.
Making It Happen
Companies should examine whether the metrics they use to reward reps are aligned with their strategic objectives. They should set individual targets based not on past performance but on the potential of each rep’s customer portfolio. And they can boost reps’ motivation by setting and revising targets in line with customers’ purchasing cycles while conducting experiments to arrive at the optimal frequency.
Along the way, companies may struggle with imperfect data and with skepticism on the part of reps or management, or both. Success will probably require one experiment after another — testing target-setting models, revising sales targets, and using the new targets to optimize compensation plans. Data platforms and centralized data-management systems can provide a uniform source of reliable information to support real-time analytics. And bringing on talent such as data scientists and “translators” — intermediaries who explain business needs to technology specialists and explain technology aspects to business leaders—may also be key.
By taking the steps outlined above and persevering through iteration after iteration, organizations can become higher performers with larger sales, better margins, and a more motivated salesforce.