The art of qualifying prospects has become as much science as art. Thanks to marketing automation from providers such as Marketo, Oracle, and Salesforce, business leaders have been able to automate the qualification of prospects. While far from perfect, users of marketing automation report that these tools’ lead-scoring capabilities (which measure the propensity of a lead to close) have enabled them to acquire the type of customers that are most likely to become repeat, loyal buyers.1 And, as study after study confirms, loyal customers buy more, are more profitable, and tend to refer others like them. All of this reduces selling costs and, more importantly, helps the business attract and retain the type of customers that are more likely to generate high lifetime value.
B2B Sales and the Human Touch
In many cases, marketers report that sales automation is able to bring purchases to a close with no human intervention. This is especially true in consumer sales. But, when it comes to B2B selling, longer, more complex sales cycles require relationship building from account executives that conduct in-person, individual sales calls tailored to meet the needs and buying practices of large enterprises. Moreover, these calls require support from expensive pre-sale resources.
AI sales assistants use analytics and natural language processing to silently record personal selling in the background. Applying artificial intelligence tools to the organization’s aggregate sales call activity (which is stored in the Cloud) helps individual account managers differentiate which of his or her prospects will likely go on to become loyal, and which will not — by recommending a next-best course of action. For example, Chorus.ai scores engagement based on how long prospects respond to a question, while also measuring how excited (or skeptical) they were about the question itself. For example, one company’s evaluation of its AI-analyzed calls revealed that the following questions were the most effective at sustaining positive engagement on an initial sales call:
- “Which processes work well with your onboarding approach?“
- “Do you mind sharing what doesn’t work well?“
- “What would you do if your CEO doubled your budget?“
The analysis also revealed that, while rapid-fire questions raised excitement, they also contributed to stressful conversations that hindered the productivity of those interactions.
How Do AI Sales Assistants Work?
While marketing automation streamlines sales workflows, it is dependent upon timely data entry from each individual salesperson. By enabling AI and analytics to extract insights from customer dialogue and responses, salespeople can improve the desired outcomes of their next-best actions without the burden of entering their own (and often bias) interpretations from a sales call. And, like any machine learning system, AI assistants get smarter and more reliable over time.
Currently, sales managers in organizations such as AdRoll are using insight from Chorus.ai technology to coach and train the global salesforce. Another B2B seller, FarmLogs (which helps farmers monitor and measure their crops), uses insight from the software to quickly provide coaching to the salesforce after analyzing daily sales calls. Alex Terry, CEO of Conversica, uses AI tools to provide his sales team with questions and account management behavior that is instrumental in developing trusted relationships. Marketing automation provider, Marketo, uses the technology to accelerate its own sales cycles.2