Explainable AI Maps the Hottest B2B Sales Leads Before Salespeople Dial

Every business-to-business sales team faces the same frustrating puzzle: a long list of customers, a short list of likely buyers, and no reliable way to tell them apart. A new study from researchers at the University of Turku in Finland, published in Applied Intelligence, offers a way through the fog. The team built a hybrid machine learning framework that combines three well-established techniques—SHAP explanations, self-organizing maps, and random forests—into a single pipeline that not only predicts which customers are likely to buy, but also explains why, groups them into meaningful segments, and weighs the decision to contact each one against its actual business cost.

The problem the researchers set out to solve is structural. B2B sales data is notoriously difficult to work with: datasets are small, purchase events are rare relative to non-purchases, and records are noisy and inconsistently kept. Only customers who were already contacted appear in the data, which introduces sampling bias, while competitive dynamics and internal buying processes remain invisible. Standard machine learning methods tend to struggle under these conditions, and even when they achieve good accuracy, their black-box nature makes sales teams reluctant to trust them. The Turku team, led by Tanja Vähämäki and Joona Mäntyvaara, deliberately designed their framework to be modular and interpretable rather than chasing maximum accuracy at any cost.

The framework works in three layers. In the first, a multi-output random forest regressor is trained on customer-level data to predict purchase probabilities for two focus products. In the second layer, SHAP values—derived from cooperative game theory, where each feature is treated as a player contributing to the model’s prediction—are computed to quantify exactly how much each input feature drives the forecast. These SHAP-derived importance scores are then used as feature weights inside a self-organizing map, an unsupervised neural technique invented by Teuvo Kohonen that projects high-dimensional customer data onto a two-dimensional grid while preserving topological relationships. In the third layer, the predicted probabilities are converted into contact decisions using thresholds optimized through ROC analysis and, crucially, through a cost-sensitive objective that reflects real business economics.

The embedding of SHAP values into the segmentation step is the study’s key methodological move. In most prior work, explainability tools are applied after the fact, merely to interpret an already-trained model. Here, the feature attributions actively shape the segmentation: customers are grouped on a map where the dimensions most predictive of purchase behavior are weighted most heavily. The result is what the authors call explainability-guided segmentation—customer groups that are not just statistically coherent but aligned with the actual drivers of buying. Because the self-organizing map preserves topology, neighboring nodes represent behaviorally similar groups, allowing sales teams to see gradual transitions between segments rather than arbitrary cluster boundaries.

The empirical evaluation used real B2B sales records from a telecommunications company: 3,007 customers described by 53 features, including historical purchases, relationship metrics, and firmographic data such as annual revenue and employee count. Training data came from the fourth quarter of 2021, with labels corresponding to realized purchases in the first quarter of 2024 for two focus products. The SHAP analysis revealed a strong self-effect—current ownership of a product was its own best predictor—alongside smaller but meaningful contributions from complementary products, suggesting that existing product relationships are the dominant determinant of future purchase likelihood.

The performance results are striking. Across all customers, the framework achieved an F1-score of 0.866 for Product 1 and 0.924 accuracy for Product 2, with area-under-the-curve values of 0.89 and 0.96 respectively. But the most impressive numbers emerged within the high-probability segments identified by the SHAP-weighted map: there, recall reached 100 percent and F1-scores climbed to 0.964 for Product 1 and 0.922 for Product 2. In heterogeneous segments, performance dropped substantially, confirming that the segmentation successfully isolates pockets of stable, actionable purchase signal from the surrounding noise. Five-fold cross-validation confirmed that these results were not artifacts of a favorable data split, and an ablation study showed that removing SHAP weighting degraded the quality of the high-probability segments.

The cost-sensitive decision layer may prove the most commercially consequential innovation. Rather than applying a single statistical cutoff, the framework optimizes the decision threshold separately for each customer size segment—small and medium enterprises, medium, and large accounts—using a cost matrix that combines direct sales revenue, cross-selling margin, strategic value, and the hourly cost of sales effort multiplied by interaction duration. The results were dramatic: for small customers the optimal threshold matched the statistical baseline, but for medium customers it fell from 0.57 to 0.17, and for large customers it collapsed to zero, meaning that contacting every large-account prospect maximizes expected profit because the potential returns outweigh even the cost of wasted calls. A sensitivity analysis raising sales costs by 20 percent left the optimal thresholds unchanged, indicating the policy is robust to moderate shifts in business assumptions.

The researchers also tested the framework’s modularity by swapping the random forest for XGBoost while keeping the rest of the pipeline intact. Segment-level performance was comparable with both ensemble learners, demonstrating that the benefits stem from the SHAP-guided segmentation logic rather than from any particular algorithm. This model-agnostic design matters for practitioners: companies can adopt the framework with whatever supervised learner suits their infrastructure, and future improvements in base models can be slotted in without redesigning the segmentation or decision layers.

The implications extend beyond the telecom case. Because the framework links predictions to financial decision criteria, it transforms sales targeting from intuition-driven guesswork into an economically grounded allocation problem. Sales managers can set different thresholds for different segments, prioritize cross-selling based on SHAP-identified complementary products, and replace subjective salesperson win probabilities with standardized probability estimates for pipeline forecasting. The topology-preserving map even supports expansion strategies, letting teams move from the highest-probability nodes to adjacent, behaviorally similar customers in a controlled way.

Limitations remain, and the authors are candid about them. Hyperparameters were chosen empirically rather than exhaustively optimized, the study covered only two products from a portfolio of 88, SHAP assumes a degree of feature independence that may not always hold, and the scarcity of public B2B datasets prevented extensive benchmarking against alternative method combinations. Future work, the team suggests, could incorporate time-series customer behavior and seasonal trends, integrate dynamic business health indicators, and validate the framework through A/B testing inside real customer relationship management workflows. Even so, the study demonstrates a compelling principle: when explainability is built into the architecture of a forecasting system rather than bolted on afterward, machine learning can deliver not just predictions that sales teams accept, but decisions they can defend to their balance sheets.

Subject of Research: Explainable machine learning for B2B sales opportunity forecasting using SHAP-guided segmentation and cost-sensitive prediction

Article Title: B2B opportunity forecasting with explainable segmentation and cost-sensitive prediction

Article References: Vähämäki, T., Mäntyvaara, J., Nevalainen, P., & Heikkonen, J. (2026). B2B opportunity forecasting with explainable segmentation and cost-sensitive prediction. Applied Intelligence, 56(15), Article 425. https://doi.org/10.1007/s10489-026-07401-z

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07401-z

Keywords: B2B sales forecasting, explainable AI, SHAP, self-organizing maps, random forest, customer segmentation, cost-sensitive prediction, machine learning, telecommunications, predictive analytics, sales targeting, Applied Intelligence