Is selling a science, or is it more human intuition and gut feelings?
In the past, it’s definitely been the latter. A company’s sales priorities have traditionally been to find and train the ideal sales person, then to do everything it can to retain its “sales superstars.”
But as customers have become more demanding, products and services more complex, and standards for profitability and growth more exacting, the industry is facing an urgent need to scale up through automation and not just rely on sales superstars.
Modern B2B sales require speed and science
Automation does help, but ordinary automation can only do so much. When you are talking about complex B2B sales, you need scientific exactitude. There are simply too many product and pricing combinations, and customers want answers and quotes quickly.
All of this means that organizations must turn sales into a science. Whether you call it artificial intelligence or augmented intelligence, it’s a science just the same.
That’s scary to many companies, because it means taking some decisions away from reps and managers. And because it also implies that you need a data scientist and some expensive business analysts, which is costly. Then there’s the risk your project will fail.
But data science and AI in B2B sales is has gotten a lot less scary due to recent developments:
1.) The industry is moving away from open-ended science projects, and more and more products are incorporating data science as an out-of-the-box product feature, taking the risk and cost out of the equation.
2.) The algorithms and predictive models are getting better. Continuous ensemble learning is used in many products, which means running multiple algorithms and taking the best of them. This increases accuracy.
Using these models and correlations, AI systems are able to prompt salespeople for the right pricing and configuration with increasing accuracy. This can boost overall sales, and has the most impact on sticky problems such as:
Pricing: A small 10% top-line discount can actually give away more than 50% of the bottom-line. This is very hard to figure out without data science.
Price optimization: What is the right price that will prompt the customer to purchase, but also bring in the most possible revenue for your company? This can only be achieved by algorithmic machine-learning models.
Compensation optimization: What commission plans bring in the most new sales, without costing the company too much?
Scientific selling solves these problems. And it brings great benefit not just to the “sales superstars,” who may be doing great without anyone’s help, but also to the “meaty middle” of the sales force – about 70 percent to 80 percent of your team. These folks can have their performance greatly improved with a bit of help.