Support of AI-based software solutions in the B2B sales process
An excerpt from Tamara Mayer’s bachelor thesis with a status quo analysis and recommendations for action.
Tamara Mayer is a graduate of the Landshut University of Applied Sciences and has dealt with the topic of artificial intelligence in B2B sales in her bachelor’s thesis. In the context of this thesis, she conducted several expert interviews – among others with the managing director of Qymatix Solutions GmbH Lucas Pedretti.
In the following excerpt of her work, Tamara Mayer gives concrete recommendations to companies who want to optimize their sales processes with AI.
You can find out how best to proceed and what is essential when implementing AI-based software here:
Extract from Chapter 5.2 “Recommendations for action.”
“In the context of this paper, interviews were conducted with Lucas Pedretti, co-founder and managing director of Qymatix Solutions GmbH, and Livia Rainsberger, managing director of WISSENCE, an expert in management consulting with a focus on digitization and optimization of sales organizations.”
From the expert interview with L. Rainsberger about recommendations for the use of KI
“Companies need Artificial Intelligence to stay competitive. AI-based software increases sales productivity by automatizing repetitive tasks. This software allows salespeople to focus on the relevant functions that effectively drive sales. Besides, employees receive recommendations for action and additional information from various software programs that they can use in their customer relationships.”
A company that uses AI-based software in sales will not only remain competitive but will also gain a competitive advantage over companies that do not yet integrate artificial intelligence.
“Companies should, therefore, consider using intelligent software as soon as possible. However, they should apply AI software to align with their goals and strategies.”
Consequently, companies should not start directly with the introduction but should inform themselves in advance about the procedure. Some companies, for example, discover excellent software support from a vendor and want to implement it quickly and without thinking, without even knowing whether this software also has advantages for their process. For this reason, the sales process itself should be examined in detail beforehand.
Companies should ask themselves in which steps of the customer journey the use of artificial intelligence makes sense, or whether certain stages should remain human-driven. This analysis is especially true when considering the customer’s perspective. Some potential customers in B2B, for example, inform themselves before making contact. Once ambiguities arise, they expect a human to solve their problem and not a chatbot.
Only after a managing director has sufficiently considered these beforementioned aspects can a holistic approach to the software requirements take place. After a manager has listed these first general provisions, she can ultimately search or develop an AI-based software.
Companies should, therefore, develop first a strategy for implementation before they can begin with the vendor selection. By following this approach, companies can optimize their time plans.
To support planning and implementation, companies can also use specialized consultants or implementation support to achieve the optimal combination of man and machine. Neither of the two factors mentioned should take precedence in a sales process. “
From the expert interview with L. Pedretti about the requirements for the use of KI
“Before the implementation itself, however, there are also conditions of the various software supports, which are presented for illustration purposes by Qymatix Solutions GmbH.
For one thing, companies should already have a list of potential customers and several hundred existing customers, since the AI needs enough data to learn from it. Similarly, enough sales data must also be available; for example, the company should carry out at least 5.000 transactions per year. This minimum amount of data ensures enough data complexity and variety.
Furthermore, the company must be prepared to adapt its own sales processes to the respective AI software. Specific recommendations for action require different responses than in the past.
If a company fulfil these prerequisites, it can start with the implementation. Due to the very nature of machine learning, this can only be done iteratively, i.e. in several attempts, since something is improved after each phase until the software is perfectly adapted. At Qymatix, this takes place in at least three stages.
In the first phase, called modelling, we and our algorithms check data volume and quality, together with a draft on recommended sales processes. This phase is also known as “Proof-of-value”. Once the users and business owners are familiar with the results, a technical implementation follows. The company can then test the adjusted software in the final phase.
“Since increasingly more companies are adopting AI technology for sales, waiting is a high-risk bet.”
Besides, being a first-adopter can bring an enormous competitive advantage.
In summary, we can say that for the introduction of artificial intelligence in sales, like any other technology, companies should assess their main challenges, find the right software and plan accordingly.
Also, AI software brings with them requirements that the company needs to check before their introduction. This preparation for AI-based software may also include training sales staff on the upcoming transformation.”
There is little we can add from our side. In this section of the thesis, Tamara Meyer sums up fundamental aspects that companies should consider when introducing AI.
We particularly like the fact that it not only deals with the software itself but also with the steps before the implementation of AI in sales. If a company does not adequately plan the introduction of an AI-based software, often high follow-up costs arise, and execution gets delayed.
For the successful adoption of AI-based software, companies should engage their sales teams early on. A lack of preparation often means that the best software remains unused.
This idea is the point we try to convey with many of our blog posts: the technical implementation of the best AI software alone is not enough to be successful. Users and managers should understand how to use the tool in their specific context successfully.
The effort pays off in any case: AI-optimized processes are necessary to remain competitive in the future. And those who start earlier gain a competitive advantage!
We want to thank Tamara for this excerpt from her thesis and wish her a successful start into her working life!
Do you have any further questions on Predictive Analytics? We are happy to help!