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Sales targets are critical in helping businesses aim for growth and monitor progress. For salespeople, having sales targets helps manage expectations and often act as an incentive for additional financial compensation. Nonetheless, Andris Zoltners shares in his article published in the Harvard Business Review that allocating the right level of goals is just as important as having one in the first place. Businesses that set overly ambitious sales targets risk hurting seller morale and creating an unfair landscape between high and low performers. When sellers are faced with sales targets they view as unrealistic, the negative consequences can manifest themselves in the form of widespread underperformance, toxic cultures, and employee churn.
In the pursuit of creating “realistic” sales targets that challenge sellers but are also achievable, it is difficult for sales leaders to manage the variety of uncertainties involved. AI helps you maximize the productivity of your sellers by predicting the performance of each seller throughout the determined selling period. By learning from the historical performance of each seller, AI can take advantage of the high amount of factors to consider by finding patterns that likely contribute to seller performance. Sales leaders can use these predictions to intelligently validate the sales targets you plan to allocate for each seller. This helps ensure that the sales targets are challenging yet reasonable. For sellers that are forecasted to perform poorly, instead of giving negative reinforcement down the road in the form of lower compensation, which hurts both the seller and the business, you can set them up for success by providing additional enablement that can help them perform better than predicted.
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Retailers deliver better demand forecasting, marketing efficiency, transparency in the supply chain, and profitability through advanced AI applications and a myriad of AI use cases throughout the value chain: from store-level demand and out-of-stock (OOS) predictions to marketing channel modeling and customer LTV predictions. With the abundance of consumer data, changing consumption patterns, global supply chain shakeups, and increased pressure to drive better forecasting, retail businesses can no longer ignore the potential of AI in their industry.