While the B2B sales process requires human perspective more than ever, much of the legwork required before interaction with a potential lead can be automated using machine learning algorithms. Many of the company signals found in external data can help sales teams surface ideal leads, if they know what to look out for.
Within much of the traditional sales process today, particularly in cases involving B2B transactions which often require significant team buy-in, a human, emotive connection and element of trust remain essential. However we all know it’s often a long and arduous road to get to that final moment where the ink hits the proverbial paper and a sale is made.
For many, that road still involves a lot of up-front manual work, from prospecting, to cold calls, to emails, competitive research and eventually an understanding of what exactly will resonate with a particular customer’s needs. In today’s data-driven world, however, much of this can now be driven by artificial intelligence, freeing up the human to strengthen their relationships with customers and improve the consumer journey.
Intelligent sales technologies are being used by more and more of today’s top-performing sales teams. Artificial intelligence, sentiment analysis, next-step analysis and deep-learning can augment many of the more automated tasks involved in sales prospecting, lead generation, consumer engagement and more by surfacing leads based on signals identified within external data, and offering predictive, and prescriptive, analytics.
According to the recent Salesforce State of Sales Report, today only 21% of sales leaders in the UK are using artificial intelligence. However, they expect AI adoption to grow by 155% by 2020, meaning those that want to remain ahead of the competition will need a plan to integrate AI into their sales process today.
Raconteur’s Emma Woollacott writes that Salesforce UK vice president Max Roberts said, “Alongside creating efficiencies, AI can strengthen the sales pitch; thanks to the growing number of connected devices, customer data can be recorded and gathered from a range of sources.”
According to McKinsey, companies using digital tools and advanced analytics are seeing 2.3x the industry average revenue growth, 3% – 5% additional return on sales and an 8% higher total return to shareholders than the industry average.
The key here, however, is that these tools will be used to augment, rather than replace sales teams. Experts believe this technology and these analytics tools will help them to more easily discover prospects, engage further, personalize more sincerely and get to the deal faster, leaving them more time to determine how they can deliver a better and more personalized experience.
Signals help surface sales prospects
Signals gleaned from external data including news and social media, job listings, turnover and more can indicate when a lead might be ready for the sales team to approach. Events like growth in R&D, company restructuring, hiring acceleration, product recalls, and more can all be indicators of a firm’s readiness for particular products.
This type of AI-driven signal identification can be replicated across a number of industries looking to find leads that fit a particular criteria, from among a plethora of potential prospects. Leveraging algorithms to enhance this process will not only save the sales team time but ultimately surface a number of companies that would have been missed through other means.
Sales augmentation is just the start when it comes to ways we can use external data to make businesses more efficient and ultimately improve the customer experience on both sides. The online breadcrumbs companies leave behind offer immense possibility when it comes to enabling predictive insights that can inform business decisions across an organization.