Artificial Intelligence in B2B Sales

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Artificial Intelligence in B2B Sales

🔥 Various Artificial Intelligence (AI) building blocks may change the traditionally human-centered B2B sales process. These building blocks and the resulting AI applications generate several opportunities for value creation during the B2B sales process. Below I'll explain the value additions AI systems can make at various stages of the sales process. It's split up per stage of the sales process:

• Prospecting
• Pre-approach
• Approach
• Presentation
• Overcoming Objections
• Close
• Follow-up

This model applies to various B2B situations. It is extensively used in research and refers to the sales funnel.

• Prospecting : This task is also known as lead generation; the goal here is to identify high-quality leads. Prospect scoring or lead qualification is a challenging task that requires substantial human resources. AI can undertake activities that heavily relied on sales professionals and their support teams. Firstly, AI is well suited to analyze both structured and unstructured data in the segmentation of consumers and generate a list of prospects. For example, Natural language understanding can analyze text data, identify keywords, themes, or current interests from the text. Also, AI's ability to analyze huge amounts of structured data (e.g., web traffic and clicks) in combination with offline structured data (e.g., tenure and role in client organization) could create a well-rounded profile of the prospects. In addition to this, Machine Learning (ML) can be used to improve these algorithms without human interventions. AI can also analyze the precious data and determine what actual online and offline characteristics have the highest probability of resulting in conversions. Also, by understanding the text or speech of the consumer, AI can predict the probability that the prospects will purchase using natural language understanding. Dell uses AI-enabled analytics to identify types of prospect behaviour most likely to lead to the purchase of different products, thus helping its sales force pinpoint high-quality leads.
• Pre-approach and Approach : A prospect has been identified and positively evaluated as a quality lead, now it moves to the next two stages: Pre-approach and Approach. These stages mean acquiring more detailed information about the lead, including their needs, habits, preferences, and other relevant information and further making contact with them. While pre-approach is doing-your-homework, approach means developing a relationship to establish a sustainable rapport and building trust. Now, AI can enable the creation and delivery of highly tailored and personalized communication depending upon the sales lead's behaviour or profile. This is called targeted advertising. Al can also decide upon the content that is likely to appeal to the consumer. These tailored approaches will lead to higher engagement and conversions. ServiceMax, a provider of field-technician management software, uses ML to predict its site visitors. Showing prospects' recommendations reduced the bounce rate to its site by 70% and doubled the visitor time spent on the website. Another application is the service built by the sales platform firm 6sense. It provides visibility for B2B Marketing and Sales professionals into their buying journey of prospects and flags a window of opportunity, indicating when it is the most effective time for the salesperson to engage with the prospects.
• Presentation : Here, the selling firm will demonstrate the problem-solving capability of the offering and design the offer as per the potential client's needs. AI systems can augment the presentation task. Slide bots identify the main idea and the message of the sales presentations and subsequently recommend and execute the optimal layout and content. AI can be used to run a sentiment analysis of the presenter's words, voice, and tone and compare the same with desired norms, thus providing instantaneous feedback. Moreover, AI can also analyze the emotions of the audience to understand the sentiment or themes which can be useful to generate insights about concerns or objections from the customers.
• Overcoming Objections and Closing : At any given point in the sales process, the customer can object to one or more points made by the selling firm. AI changes the tasks involved in overcoming objections and enables salespeople to respond faster. For example, Startup Klue offers an AI-based service based on fundamental understanding and ML to curate competitive intelligence for B2B personal selling and sales management. This information is summarised in battlecards, which sales professionals can use to handle questions raised by the clients. Also, analyzing the historical data on pricing variables and other information about the customer leads to determining the best prices for different customers.
• Follow-up : This step involves two components: filling the current order and following up after the order is completed. In this case, AI can automate the workflows required for order processing and follow-up. AI can also automate some elements of the post-order service. Finally, AI can be used to analyze unstructured and structured customer data, which could be used to uncover new needs and to re-enter the funnel at stage one. Also, using ML, companies can find patterns in the past purchase and understand which products were often bought together and display the complementary product to the customer, thus increasing the overall size of the product and giving a good customer experience.

Source: P. Jeannette et al. (2020), "Collaborative Intelligence: How Human and Artificial Intelligence create value along the B2B sales funnel", 0007-6813, Kelley School of Business, Indiana University, Elsevier.