Use of algorithms in B2B sales operations

Implementing algorithms and machine learning models in B2B sales operations has fundamentally transformed how businesses approach customer targeting, lead generation, opportunity prioritisation, and sales process optimisation. AI-based solutions enhance efficiency and present opportunities for better customer insights and more precise and timely sales strategies. However, using algorithms is challenging: it requires careful planning and high-quality, comprehensive data.

This article explores the effects of algorithms from various perspectives and provides examples of their application.

Can sales teams trust AI?

One of the key challenges in using AI is the interpretability of algorithms and obtaining the approval of sales experts. For example, sales teams may hesitate to fully trust automated decisions, especially if the predictions made by algorithms are not easily explainable. This underscores the necessity of combining human expertise with data-driven decision-making and the need for accurate data and proper pre-processing of that sales data.

For example, Rezazadeh (2020) developed a solution in the Microsoft Azure environment using Gradient Boosting methods that predicted the realisation of B2B sales opportunities. This machine learning method builds predictive models step by step by learning from past mistakes. It begins with a weak model, usually a simple decision tree, and then incrementally adds new trees that correct the errors of the previous ones. This is done by minimizing the difference between the model’s predictions and actual values and adjusting for mistakes at each stage. Over multiple iterations, the model enhances its accuracy, making it highly effective for structured data analysis, such as customer segmentation and sales forecasting. Its optimised version, XGBoost, is widely utilised for business applications due to its speed and efficiency.

While Rezazadeh’s methods showed a significant improvement in forecasting accuracy over traditional manual estimates, their implementation necessitated ongoing human oversight and an understanding of the business context.

Another challenge is the quality of sales data in CRM and backend systems. Wisesa, Prasetyo & Sutrisno (2020) utilized machine learning methods for B2B sales forecasting and discovered that these methods significantly improved forecast accuracy compared to traditional approaches. The objective was to assess and compare the performance of various machine learning algorithms in accurately predicting future sales using sales data from 2016 to 2018, focusing on enhancing sales management, product strategies, and budgeting processes. The study highlights the importance of shifting from traditional forecasting methods to intelligent, data-driven techniques to manage the complexity and scale of sales data. This indicates that models that learn from data can provide substantial value to sales strategies, although their implementation necessitates careful data pre-processing and optimisation.

Targeting the right customers

The accuracy of customer targeting is also significant. Clustering algorithms are frequently employed in customer segmentation, however, selecting the right variables and interpreting the segments can be challenging. For this reason, it is crucial to test various combinations for optimal results.

For instance, Sheikh, Muneeb & Rizvi (2019) conducted a two-phase clustering analysis in the fintech sector, utilizing the LRFM (Length, Recency, Frequency, Monetary) model alongside K-means clustering, which enabled firms to identify distinct customer segments that could be targeted with tailored marketing strategies. This segmentation enhances customer engagement and optimises the allocation of marketing resources, leading to improved overall performance in B2B contexts.

The role of algorithms in lead generation and scoring has grown significantly, but their use also presents risks. A decade ago, D’Haen, Van den Poel & Thorleuchter (2015) developed a system that identified B2B leads through web crawling and text analysis. The algorithm identified potential customers based on online content by applying term frequency-inverse document frequency (TF-IDF) weighting and latent semantic analysis (LSA). This approach reduces the need for traditional manual lists and cold calling, yet the challenge remains in how effectively the algorithm can distinguish between truly potential leads and noise.

Today, transformer models like BERT (Bidirectional Encoder Representations from Transformers is a deep learning model created by Google), Topic analysis, and GPT can be utilised more efficiently to identify real customer potential. For example, aligning the target customer’s business strategy with the value created by the seller. Additionally, algorithms have transformed the scoring and prioritisation of leads, changing decisions that were once based on intuition into data-driven ones.

Engagement powered by machine learning

Additionally, neural networks have shown their capability to model complex dependencies in negotiation processes and pricing. A neural network is a computational model inspired by the human brain, designed to recognise patterns and relationships in data. It is widely used in tasks such as image recognition, natural language processing, and predictive analytics. Moosmayer, Schuppar & Siems (2013) explored how these models can forecast the outcomes of price negotiations in a B2B context. They found these models markedly more accurate than traditional linear regression models.

This underscores the algorithms’ ability to detect hidden patterns that humans might overlook. However, we must be cautious not to overinterpret algorithms and ensure that salespeople fully understand their proposed solutions.

In customer relationship management, machine learning models provide ways to identify customer churn risks and optimise customer value. Ullah, Ashraf & Khalid (2019) developed a model for predicting customer churn in the telecommunications industry, in which decision tree models achieved 88.63 % accuracy in identifying customer churn.

Similar methods can also be applied in B2B sales, where long-term customer relationships are critical. Algorithms can be used to identify customers at risk of switching to a competitor and react in time, for example, with targeted offers.

Looking from the point of view of sales operation management, an interesting example of the use of algorithms is sales personnel’s training and support systems. Luo et al. (2021) explored how AI-assisted coaching systems can improve salesperson performance. The study found that mid-level salespeople benefited the most from the feedback provided by AI, while the weakest and best salespeople needed more personalised coaching. Bottom-ranked agents suffered from information overload due to the AI’s comprehensive feedback, while top-ranked agents exhibited resistance due to a preference for human interpersonal communication.

The study emphasised the use of deep learning algorithms in AI systems, including Natural Language Understanding and Automatic Speech Recognition. These algorithms excelled at processing sales call data and generating actionable feedback. However, their effectiveness depended on the agents’ rankings and learning capabilities. The hybrid model, which combined the strengths of AI and humans, emerged as the most effective configuration.

The research of Luo et al. (2021) underscores the nuanced role of AI in workforce training, advocating for tailored AI designs and highlighting the complementary nature of human-AI collaboration in optimising performance outcomes. This suggests that algorithms can provide valuable support but cannot fully replace human guidance and contextual understanding.

Train sales teams to interpret algorithms

Using algorithms in B2B sales operations offers significant benefits, but challenges are associated with leveraging them. The effectiveness of algorithms largely depends on the quality of the data used, the correct application of the models, and the willingness of sales teams to use analytics as part of decision-making. Companies should ensure that sales teams receive the necessary training in interpreting algorithms and that they support the strategic goals of the business.

Ultimately, achieving the best outcome requires collaboration between humans and artificial intelligence, where machine learning models provide analytics and predictions, but human understanding and interaction skills remain at the center.

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