Hype or real benefit?

Technical report by Michael Märtin

Artificial intelligence in B2B sales

Now that digitalization has found its way into most companies, the topic of artificial intelligence (AI) is currently on everyone's lips. It is undeniably hype, but it is nevertheless worth taking a look behind the scenes. A particularly lively discussion has arisen around the use of AI applications in business customer sales. But what does the term AI actually mean? Where is artificial intelligence already being used successfully today? And how does sales in particular benefit from intelligent applications?

We are hardly aware that artificial intelligence is already permeating many areas of our everyday lives: Anyone who uses voice assistants such as Siri or Alexa, posts a picture on social networks or follows series recommendations on streaming services inevitably comes into contact with AI. Until now, companies have mainly used intelligent technologies to automate their production and logistics processes. While it seems relatively logical to rely on the intelligence of machines here, the focus is now increasingly shifting to disciplines where the use of AI was not even conceivable until recently - such as B2B sales, which in many companies is a domain of interpersonal decision-making: the salesperson with the best personal network and the right gut feeling is often considered the key factor for a successful closing rate. This could now change with the help of AI.

AI - the most important definitions

In simple terms, artificial intelligence means that computers are supposed to solve tasks that would require the use of human intelligence if employees were to try to solve them themselves. The concept is not fundamentally new. Scientifically discussed as early as the 1950s and continuously researched since the 1960s, AI scenarios failed to make a breakthrough in the business world for a long time. Only the fundamental changes of the last ten years, such as the ability to store huge amounts of data in the cloud at low cost and process it with exponentially increased computing power, have led to rapidly growing interest in AI applications. The terms machine learning, deep learning and predictive analytics are used particularly frequently.

• Machinelearning comprises algorithms that derive behavioral patterns from data. Instead of following programmed rules, the computer is given a specific goal: It learns to derive its own result from the input of a great deal of sample data.
• Deep learning is a sub-area of machine learning and uses algorithms to simulate the brain's neural network - with the aim of learning about a specific subject area and solving corresponding tasks. The artificial neural networks extend over many very deep levels ("deep") and use large volumes of data.
• Predictive analytics uses historical data to predict future events. Very large volumes of data from the past are analyzed and used together with many other factors to make predictions about future developments.

The concept of predictive analytics in particular already has numerous applications for companies' sales departments.

Predictions and recommendations for action in sales

Planning and forecasting make up a large part of a salesperson's day-to-day work: How will customers behave and which sales measures can be successful accordingly? What is the probability of closing open leads? What turnover can result from this in the next financial year? AI is far superior to humans when it comes to solving such ubiquitous problems. When it comes to such questions, it can make the most of its great strength: analyzing huge amounts of data. By automatically and quickly analyzing a wide range of data from various sources (historical and current data, internal and external data, company-specific and macroeconomic data, etc.), predictive analytics tools relieve sales staff of the time-consuming and often tedious task of forecasting. Forecasting specific future deal probabilities and sales is faster and, above all, much more accurate with AI support. After all, the forecast is created independently of personal assessments and on an incomparably higher-quality database.

More time for value-adding tasks

In addition, supplementary AI applications estimate the needs and willingness to buy of new and existing customers. For this purpose, the system automatically creates a scoring for new inquiries, i.e. it uses its self-learning properties to evaluate the probability of closing a deal, the duration of the sales process, the quality of the leads and much more. By prioritizing leads according to their probability of closing, sales employees can focus on the most promising inquiries and respond more quickly. Modern AI solutions therefore go far beyond predictions: they provide sales staff with specific recommendations. In addition to more accurate forecasts and improved decision-making, significant time savings are a key benefit of using AI: sales employees can invest the time gained directly in maintaining customer relationships.

Challenges and unanswered questions

Although the numerous advantages offered by AI are certainly convincing, there are reservations about the intelligent helper tools. Many sales employees fear that they could be replaced by AI. This concern usually arises from a lack of knowledge about the technology or the fear of having to share their own hard-earned expertise with colleagues. This can be remedied if management communicates the basic principles and potential of intelligent systems in an understandable way. It should create transparent structures and demonstrate that the opportunities offered by AI lie in expanding one's own skills and tasks. However, the biggest obstacle on the way to AI-supported sales is the use of a data base that is too small and of poor quality. Artificial systems cannot make precise predictions and useful recommendations for action based on a small amount of historical data. The introduction of an AI-based solution must therefore be accompanied by a new corporate culture of data collection and maintenance. It is also essential that the systems are continuously monitored and corrected. After all, AI applications also make mistakes. In contrast to conventional IT systems, however, AI solutions learn through continuous corrections and can therefore create increasingly reliable forecasts. There are also still many questions from a legal perspective: if leads from AI are wrongly classified as unpromising and sales are lost as a result, who is responsible? The manufacturer of the AI system? Or the sales employee? This will require careful consideration.

Concrete steps for B2B sales

The benefits of AI stand and fall with the data used. Companies are therefore well advised to invest in a CRM solution that creates a solid and clean database by integrating a wide variety of data sources and maintaining them accordingly. Ideally, the system should already include technologies for intelligent sales support. Established providers such as SAP (with "Leonardo"), SugarCRM (with "Hint") or SalesForce (with "Einstein") have already expanded their CRM range with useful and easy-to-use AI tools that fulfill the tasks described above. Strengthened in this way, the CRM system can be transformed from a simple directory of customer and sales data into the linchpin of AI-supported sales. At the same time, it is advisable to carefully introduce employees to AI-optimized sales processes with great potential. Only when sales teams understand how AI works can they accept the solutions as transparent and trustworthy support. A lively exchange between management, sales and IT is advantageous if sales modifies its processes step by step on the basis of AI applications.

AI as valuable support for sales

AI applications can increase efficiency and productivity in B2B sales enormously: sales decisions based on intelligent algorithms are far superior to gut decisions. There is already a whole range of tools, for example for predictive analytics, which relieve employees of time-consuming, recurring tasks and also strengthen customer loyalty. AI is currently finding its way into B2B sales primarily via the well-known software providers and their CRM solutions enhanced with AI technologies. The development is still in its infancy, but will progress very quickly. Despite its undeniable advantages, AI is no substitute for intuition or human foresight. All the AI approaches shown here are to be classified as "weak", i.e. limited to the respective application. Many decisions in sales require foresight, an intellectual capacity for abstraction and an empathic component. Artificial intelligence cannot glean all of this from data. Sales employees should use their experience and expertise to make critical business decisions, but allow AI to support them. This combination of experience and algorithms opens up a new definition of sales management.