min(s) to read
/
April 16, 2024
We’ve all seen it. After a series of stakeholder meetings, product demos, and price negotiations, the hard-fought battle to win the deal is complete. But there’s only time to take a short breather, as the next clash,this time an internal one, is about to begin: determining fair compensation via sales crediting to all the parties involved in the deal. Oftentimes, sales operations and leadership are asked to step in to resolve disputes and disagreements on these sales credit allocations.
As sales processes grow more complex and team-based selling approaches become more prevalent, better crediting methods are needed. For this reason, companies are increasingly utilizing artificial intelligence (AI) to improve their sales crediting approaches. Sound complicated? It can be surprisingly straight forward with the right approach and perhaps some outside help, as we discuss below.
There are two primary methods for sales crediting, as defined below:
· Hierarchical crediting counts credit between the manager and sales rep, which ensures alignment.
· Collaborative crediting counts credit shared among the many sellers in a deal.
Credit is typically split through either a multi-count or shared split system. The multi-count system encourages collaboration but can put some strain on the goal-setting process; additionally, the potential for individuals to get more credit than otherwise would be typical can raise the cost of a sale, especially in plans with accelerators. The other option, shared split, more closely aligns to the corporate budget, but it amounts to a zero-sum game and requires thorough guidelines defining the rules of engagement.
New uses for AI are constantly emerging. At present, three areas where it can best be applied in the realm of sales crediting are in optimizing the crediting approach, powering sales crediting tools, and enabling continuous improvement.
AI can provide recommendations for the optimal distribution of credit among team members. By analyzing historical deals and client touch points, AI uses unsupervised learning techniques and pattern recognition to create deal “archetypes” to identify the different ways in which the sales force interacts with customers.
Once these archetypes are created, they can be compared to actual sales results. For example, if deals where a certain role is involved earlier in the process result in higher win rates, the organization can write its crediting guidelines to give more credit when said role is involved earlier and less credit when they come in later.
This data-driven approach ensures that credit is allocated based on objective criteria, enhancing transparency and minimizing potential disputes. It enables organizations to recognize individual performance and collaborative efforts, aligning with hierarchical and team-based crediting principles. In this way, it creates a win-win situation where sales credit is more fairly allocated for contributions to winning, and employees are incentivized to spend more of their time doing activities that have been empirically shown to result in more wins for the company.
AI-driven crediting systems can dynamically adapt to changing scenarios, providing real-time insights and ensuring that credit allocation remains aligned with sales objectives. This approach encompasses hierarchical crediting, where the performance rolls up from subordinates to supervisors, and collaborative crediting, where multiple sellers deserve credit for the sale.
Organizations can leverage AI-powered tools designed to facilitate seamless crediting processes. These tools can integrate with customer relationship management (CRM) systems and other tools to automate data collection, analysis, and credit allocation, reducing administrative burden and increasing efficiency.
Sales teams can access intuitive dashboards that provide visibility into their individual and team performance, fostering a sense of transparency and motivation. These AI-powered tools cater to the needs of both hierarchical and collaborative crediting systems.
That being said, organizations need to be wary of using “blackbox” models to explain to salespeople why they were credited what they were. AI decisions can seem arbitrary if the model isn’t well understood and well-communicated.
AI analytics provide an opportunity for continuous improvement in sales crediting practices. As discussed above, AI algorithms can identify patterns and correlations that improve sales. Data capture over time and changed seller behaviors may create opportunities to refine or change the crediting guidelines, again using AI as a feedback loop.
AI-powered analytics give sales leaders actionable insights to optimize their sales crediting strategies, reinforce what works with sellers, and drive long-term performance, including reviewing downstream margins to determine what sales credit allocations should be for successful deals.
By harnessing the power of AI, sales leaders can navigate the complexities of team-centric selling, ensure fair credit allocation, and drive optimal performance. Organizations can create efficient and transparent crediting processes that align with hierarchical and team-based crediting principles by leveraging AI for data analysis, rule-based allocation, and recommendation systems.
Tom Hill is a Partner with RevenueShift and can be reached at tom.hill@revenueshift.com.
Kevin Andres is the Lead Data Scientist with RevenueShift and can be reached at kevin.andres@revenueshift.com.
