An effective sales incentive strategy is critical for fostering competitiveness and profitability in today’s fast-paced business landscape. An incentive plan not only inspires a sales team, giving them powerful incentive to drive results, but it also forces their efforts to align with that of the organisation. The ability to leverage modern data analytics and artificial intelligence (AI) to refine sales incentive strategy is a new and powerful way for organisations to engage and reward their top performers. This article demonstrates how using historical sales data and artificial intelligence can lead to better sales incentive planning, and ultimately better business results.
The Foundation of a Successful Sales Incentive Strategy
A sales incentive strategy offers a structured approach to focusing one or more sales teams towards and even beyond their targets through a mixture of rewards and recognition. It generally involves activation through financial reinforcement linked to reaching or exceeding targets, although non-financial incentives – such as career and personal growth and public recognition – can also be used. The goal is to primacy – ensuring that the sales force focuses its use of time and energy on the achievement of strategic goals, which in turn is vital to greater growth and profitability.
Utilizing Past Sales Performance Data
When we look at historical sales performance data, we’re actually looking at an incredibly powerful source of information for understanding what kind of incentives would be effective for a particular set of incentive recipients, in a given situation. If conducted properly, using sales performance history across different sales incentives scenarios can help to refine sales incentives. Here’s just one example of how organisations can use that data:
1. Identifying Successful Sales Patterns
Sales managers can learn from analysing past sales data what kinds of actions are most strongly correlated with success (for example, how phone calls are commonly made). By testing which incentives lead to those kinds of actions, then rewarding such actions in the future, a company can come closer to replicating past successes.
For instance, a consumer electronics company realised that holiday sales spiked each year when sales teams recommended bundle deals. Armed with this information, they created an incentive programme to incentivise reps for pitching bundled products around critical holiday periods – and seasonal sales jumped 30 per cent as a result.
2. Profiling Top Performers
For example, if you know what the top performers actually do to sell, not just what they think they do, you can create an incentive programme that will help the entire sales team emulate those behaviours.
For example, an industrial B2B software company found that all its superstar reps repeatedly won business with small and medium-sized enterprises (SMEs) by providing tailored service and prices. The company developed a bonus scheme that incentivised reps to imitate how these models worked – leading to a 40 per cent increase in SME sales.
3. Segmenting the Sales Force
Sales teams are likely to have individuals with varying levels of experience, knowledge and motivation. Segmenting the sales force enables the creation of incentive budgets aligned to the behaviours required of each group, and so based on the performance data we would stratify the team into performance-based segments. Within each segment we would then be able to identify those factors most suited to motivate that group.
For example, a new sales team at a pharmaceutical company was segmented to include new hires, mid-level reps and senior veterans. They offered unique incentive programs for each group including additional training programmes for new hires, performance-linked bonuses to mid-level reps, and market expansion rewards to senior veterans.
Integrating AI for Enhanced Incentive Strategies
Many aspects of business operations are being transformed today by Artificial Intelligence (AI), and one area in particular that’s changing is sales performance management. Here’s how AI can be leveraged to improve sales incentive strategies:
1. Predictive Analytics for Future Trends
Armed with prominent market-share data, AI can forecast sales trends and outcomes far in the future – allowing firms to change their incentive schemes in advance of market fluctuations. AI-powered predictive analytics can enable firms to predict which products and services will likely be in demand, which sales methods will be most successful, and where opportunities or risks may arise.
Case study: An enterprise retailer employed an AI system to detect an escalating interest of consumers in environmentally friendly products. Before this trend was widely trending in search engines, and even before the internal social-media analytics showed an uptick in posts about resource-harvesting products, the AI system alerted company executives to raise commissions for company employees marketing green products, allowing the company a first mover advantage to capture a fast-growing product market segment.
2. Personalizing Incentive Programs
By applying AI, individual incentive plans can be designed to cater to sales reps’ own performance and customised preferences. For example, AI can help tailor programmes to match an incentive component that best meets a rep’s distinct psychological triggers concerning compensation, such as access to training or progression to managerial roles, and recognize individuals’ unique career aspirations and motivations.
For instance, a telecommunications customer created an AI tool to understand its sales representatives’ desires and career aspirations. General approaches included giving some reps a large bonus so they could make more money and others access to mentors as a way to accelerate their careers. Both strategies were accomplished within their personalised incentive packages. This leads to increased happiness and, ultimately, greater sales team performance.
Frequently Asked Questions (FAQs)
1. How can AI assist in designing an effective sales incentive strategy?
AI can help to craft more effectual sales incentive plans by generating insights from historical sales results into plans with successful patterns, and providing current and forward-looking insights into incentive plan design and targeting. It can enhance the design of individual performance incentive plans and facilitate real-time adjustments to the plans as market conditions change and incentive plans have an impact on the sales force dynamics.
2. What types of data are most crucial for developing a sales incentive strategy?
Important information to inform a sales incentive programme include historical sales performance, demographics of customers and buyer behaviour, emerging market trends, and performance metrics for individual sales reps. Collectively this information provides insight into what drives sales performance, and how to best motivate a sales team.
3. How frequently should an organization review and adjust its sales incentive strategy?
This means that going forward, an organisation’s sales incentive strategy should be revisited and reconfigured periodically, which is most likely going to become quarterly versus annual. And it can be done on the spot, not in advance – dynamically, with AI. ‘In the next several years, the sophistication of [AI’s] training in human behaviour is going to be able to predict incentives and let organisations adjust harder than they normally could,’ he says.
4. What are the dangers of not utilising data and AI in sales incentive strategies?
Without using data and AI, a company risks incentive plans that are decoupled from market trends; less effective in motivating its sales force; and unoptimised in terms of profitability, thereby missing out on sales, and decreasing competitive edge.
5. How can smaller organizations with limited resources implement data-driven and AI-enhanced incentive strategies?
Smaller entities can progressively grow by learning from low-cost data analytics tools by employing big data analytics from the first set of sales data gathered, and work their way up slowly. Gradually, AI solutions will be internalised once they have developed their capacity over the years, with an initial focus on very simple data analytics and development in cooperation with AI service providers. In addition to being a source of competitive advantage, increasingly including historical sales performance data and AI into sales incentive-strategy design has become a fundamental requirement given the dynamic and changing environment. This is because, if successful sales patterns can be identified and re-engineered, if high performers can be profiled, if necessary pieces of the sales system can be segmented, and if AI is used to anticipate customer behaviour and design personalised incentives to nudge that behaviour, lottery-type incentive plans can be designed to achieve durable sales and profits. While technology continues its advancement, the ability to use AI to optimise sales incentive-strategy design will only expand as an option to capture the greatest sales impact.
