The more successful an organisation is in designing sales compensation that drives only those activities that boost sales without taking too long or consuming unnecessary resources, the more likely it is to create more effective sales organisations and better business results. Data analytics can help find the optimal set of activities that drive sales, and reward only these activities.
1. Identifying High-Impact Activities
Once you know which activities drive sales success, the next step is to implement a robust compensation plan that encourages the reps to engage in those particular behaviours. Identifying high-impact activities involves looking at your data to see which actions are directly connected to successful sales results. For example, Company XYZ found that its sales reps needed to follow up with a prospect within 48 hours of the initial meeting to close 30 per cent more deals. Given that the normal sales cycle was a week, this was an activity that warranted attention. Data analysis helped XYZ distil a single high-impact activity that the company could focus on with additional compensation to drive the sales force to complete more of them.
2. Setting Clear and Measurable Goals
It also means reminding ourselves that the best and most valuable information is hard data. The data-driven approach is likewise better and more valuable because companies can set tangible goals. Consider ABC Corp, who examined its historical sales data in order to produce realistic targets for its salespeople. By looking at new market trends for more limited-run products and analysing how they fit within their previous sales numbers, ABC Corp could set progressive, selffocused goals and realistic benchmarks to achieve those goals, all within the broader context of the strategy (e.g., maintaining current market share while launching new products, thus requiring additional sales in order to penetrate new markets). The clear answers this provided helped clarify the direction in which they were guiding the sales force, thereby increasing their productivity and efficiency.
3. Designing a Balanced Compensation Structure
Balancing your compensation mix is key. DEF Ltd, for instance, incentivised sales reps not only with base salary but also commissions and bonuses. With analytics, DEF Ltd learnt that front-loaded commissions for new customer acquisition meant a spike in clients obtained. At the same time, they launched bonus programmes for productivity-related targets, such as reducing the sales cycle time by 20 per cent. This balanced approach motivates sales reps to sell more and do it more productively.
4. Continuous Monitoring and Adjustments
As much as regular monitoring is important, it should be continuous. Data analytics can come in really handy in tracking pay for performance on an ongoing basis. For example, GHI Inc, reviews data on performance of the entire sales team on a monthly basis to see if the compensation scheme is working or not. If enough employees in one product line are required to sell more products than their counterparts in another product line, then GHI Inc can tweak the scheme to result in more productivity by applying additional bonuses or rewards. In the case of GHI Inc, when it found that sales were stalling for the underperforming product lines, it increased bonuses. And the stall reversed almost immediately.
5. Encouraging a Data-Driven Culture
It also helps the sales team to develop a data culture. The data analytics training provided by JKL Co. to the sales staff enables the salespeople to monitor their activities and identify their personal development areas. This institutional culture change has allowed the sales team to better track their performance and contribute to the company strategic development.
To conclude, one needs to formulate compensation plans that help to drive sales, grow productivity and improve efficiency, as these efforts might have high impact on the performance of a company. In order to do so, the first step is to identify the high impact activities, the second one is to set behaviour-related goals, the third is to design the optimal compensation structure, and the fourth and final one is to monitor performance along the way. Keeping these steps in mind, organisations will be in a much better position to motivate sales teams to drive more sales at higher quality levels that better serve customers and maximise the firm’s profits. Case studies of XYZ, ABC Corp., DEF Ltd., GHI Inc. and JKL Co. show that leveraging data analytics can lead to more efficient and effective sales operations.
