• Written by Muhammed Abdulla NC, CEO

A sales team that adopts AI expects to see a tangible impact on their performance, but in practice, the metrics that supposedly measure this impact often move for the wrong reasons, making it difficult to determine the true effect of the AI system. For instance, a shift supervisor checks the maintenance log to see if the new AI-powered predictive maintenance system has reduced downtime, only to find that the reduction is due to a change in the production schedule rather than the AI system itself.

Leading measures: forecast accuracy, win rate

When it comes to measuring the effectiveness of AI in sales, two leading measures that come to mind are forecast accuracy and win rate. A sales team that uses AI to analyse customer data and behaviour can potentially improve their forecast accuracy, which in turn can lead to better resource allocation and more effective sales strategies. For example, a category manager compares two suppliers using AI-driven analytics to determine which one is more likely to meet the sales team's needs.

Win rate is another important metric that can be influenced by AI. By analysing customer interactions and identifying patterns, AI can help sales teams tailor their pitches and improve their chances of closing deals. However, it's essential to remember that these metrics can be influenced by various factors, and it's crucial to isolate the impact of the AI system to get an accurate picture of its effectiveness.

Why the headline metric moves for unrelated reasons

Most people get wrong the idea that the headline metric, such as revenue growth, is a direct result of the AI system's impact. In reality, this metric can move for unrelated reasons, such as changes in the market, seasonal fluctuations, or even external factors like economic downturns. For instance, a sales team may see a surge in revenue growth due to a new product launch, which has nothing to do with the AI system.

This is where many organisations go wrong, as they attribute the change in the headline metric to the AI system without considering other factors. It's essential to dig deeper and analyse the underlying data to determine the true cause of the change. A thorough analysis can reveal that the AI system has had a minimal impact, and the change is due to other factors.

Baselining before rollout

Baselining is a critical step in measuring the effectiveness of AI in sales. Before rolling out the AI system, it's essential to establish a baseline for the key metrics, such as forecast accuracy and win rate. This baseline serves as a reference point to compare the performance of the sales team before and after the AI system's implementation.

By establishing a baseline, organisations can isolate the impact of the AI system and determine whether the changes in the metrics are due to the AI system or other factors. For example, a sales team can compare their forecast accuracy before and after the AI system's implementation to see if there's a significant improvement. If the improvement is substantial, it's likely that the AI system has had a positive impact.

What to record when nothing was found

Sometimes, despite the best efforts, the analysis may not reveal any significant impact of the AI system on the sales team's performance. In such cases, it's essential to record the findings and document the analysis process. This helps to identify areas where the AI system may not be effective and provides valuable insights for future improvements.

Recording the findings also helps to avoid repeating the same mistakes in the future. By documenting the analysis process, organisations can refine their approach and develop more effective strategies for measuring the impact of AI in sales. For instance, a sales team can use the findings to adjust their sales strategies or provide additional training to the sales staff.

What this does not do

This approach to measuring AI in sales does not provide a magic bullet for solving all sales-related problems. It's not a substitute for effective sales strategies or proper training of the sales staff. Instead, it provides a framework for evaluating the impact of AI on sales performance and identifying areas for improvement.

Organisations should be cautious not to rely solely on AI metrics and forget about other essential aspects of sales, such as building relationships, understanding customer needs, and developing effective sales strategies. By combining AI metrics with these essential aspects, organisations can develop a comprehensive approach to sales that drives real results.