The limits of AI in the B2B sector

The status quo in B2B marketing and sales

There is a general interest in the B2B sector in not missing out on this development. However, intelligent systems are much less widespread in the B2B sector than they are in the B2C area. Only 16% of B2B companies, for example, are already using predictive analytics or plan to implement intelligent forecasting tools in their sales. And 18% of B2B suppliers currently see no concrete applications for AI in their organization. However, experts forecast that 75% of B2B companies will use AI in some form by 2025 in order to optimize at least one of their primary sales processes.

The potential: What can AI really do?

The use of AI applications in marketing and sales is still in its early stages. But if they are used correctly, intelligent systems are already capable of providing support in the following areas:

• Optimizing the efficiency of marketing and sales: AI can be used to automate monotonous and repetitive sales tasks, such as contacting and qualifying leads or answering customer questions. AI can classify data faster and in larger volumes, and use this information to derive forecasts. Used correctly, around 50% of administrative tasks could be automated and carried out by the system. This would free up sales staff and allow them to focus on productive, value-creating and customer-centered activities.
• Improving decision-making quality: The discussion of AI frequently revolves mainly around the issue of automation. Doing so neglects the potential of AI in terms of supporting decision-making and in direct customer interactions. For example, AI can make sales staff aware of lucrative sales opportunities or create profiles using simulations and modeling in order to better forecast customer behavior. This expanded information base can help sales managers make more precise decisions.
• Enhancing the customer experience: In order to keep pace in a hard-fought and international market environment, companies must be able to meet customers’ increased expectations. Chatbots (conversational user interfaces) can be used to answer questions from customers around the world and around the clock in real time and individually. From the perspective of customers, AI-based algorithms enable more intuitive, more reliable and more useful interaction with the supplier that can be tailored to their specific needs.

The reality: What are the limitations of AI?

Because of the specific features of the B2B sector, implementing AI for B2B companies may prove challenging. The following limitations must be taken into account before the introduction of AI:

• Deficient data volumes and quality: In order to be able to make reliable statements, AI models need large amounts of data. For example, to qualify especially promising leads, information is taken from the data pool of existing customers. But of course the number of customers a B2B company has is much smaller than that of a B2C supplier. As a result, the characteristics of a customer or an individual transaction are often much more case-specific, making it difficult to identify clear patterns in the data pool. This has an impact on the precision of the resulting forecasts and recommended actions.
• More difficult access to data: One of the basic requirements for AI is the use of data, but access to such data is not always guaranteed in the B2B sector. Business customers rarely authorize suppliers to use data points (e.g. usage data) for modeling for other customers and to optimize their systems. If suppliers have information, it is usually fragmented and in isolated information systems that are used by individual departments for different purposes. Against this background, in order to even consider using AI, media gaps need to be overcome, interfaces secured, and data from individual points of interaction with the customer aggregated centrally.
• Insufficient seller competence: Although the use of intelligent systems can improve the quality of marketing and sales work, they will not replace marketing and sales managers. However, they do present entirely new challenges for the sales function. More precisely, for the sales team’s analytical capabilities and data affinity, because they must be able to correctly interpret the AI-generated insights and apply them in a targeted manner or augment them with subsequent analyses. Thus, while forecasts of customer behavior can be helpful, they cannot replace a holistic view of the buying center in the B2B sector. AI has so far been unable to simulate this unit, which is comprised of numerous individuals, making it necessary for sales employees to analyze it manually.

Definition of AI

The term artificial intelligence (AI) comes from IT and refers to the ability of machines or computers to imitate the cognitive functions of the human brain – for example independent learning and solving problems. AI is capable of identifying statistical correlations in large volumes of data, performing repetitive tasks, and deriving recommendations for further action in certain situations. Potential areas of application for AI in marketing and sales include the automated qualification of leads, the creation of precise sales forecasts, the identification of cross- and upselling potential on the basis of customer behavior, and the dynamic establishment of prices on the basis of individual willingness to pay, etc.

Sources:
Artificial intelligence: potentials and application in B2B sales. (2019). LEAD Innovation Management.
Faggella, D. (2018). Applying Artificial Intelligence in B2B and B2C – What’s the Difference? Emerj.
Stamford, C. (2021). Press Release. Gartner.
Syam, N. and Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: machine learning and artificial intelligence in sales research and practice. 69, 135-146. Industrial Marketing Management.