By Swati Sinha
Ginni Rometty, the CEO of IBM, once said: “Some people call this ‘artificial intelligence,’ but the reality is this technology will enhance us. So, instead of artificial intelligence, I think we’ll augment our intelligence.”
The era of augmented intelligence (AI) has been a gift for sales teams. And, if you’re not currently leveraging the vast amounts of data generated every minute from multiple sources and platforms for enhanced customer engagement, you are losing to your competitors: Gartner has predicted that global business value derived from AI will reach $3.9 trillion in 2022. And a study published in MIT Sloan Management Review reveals that 76 percent of early adopters are targeting higher sales growth with machine learning.
Augmented Intelligence Makes Sales Smarter
Early adopters are using AI to complement – not replace – their sales teams. There are many fantastic benefits of running a sales organization enhanced by augmented intelligence, including the following.
Benefit #1: More time selling. A recent article by McKinsey Global Institute states that 45 percent of time spent on sales-related activities can be cut down using AI. So, sales reps can spend more time selling and closing instead of completing routine, time-consuming jobs.
Benefit #2: Creating synergies. A major point of contention between sales and marketing is seamless continuation in customer engagement and lack of lead conversion. With the help of AI, marketing and sales won’t miss on strong leads and opportunities.
Benefit #3: Customer loyalty. By having better customer intelligence, sales reps can build long-lasting relationships with customers.
Benefit #4: Lower costs. By automating routine tasks and intelligent forecasting, organizations can optimize resource allocation, lower costs, and shorten the sales cycle.
Transform Your Sales Organization
Your sales organization directly impacts revenue and profit, and machine learning will help transform a sales organization from being reactive to proactive – and from intuitive to prescriptive. It can guide the sales journey from identification to customer retention.
Four Key Areas Where AI Can Be a Big Boost
How does this work? Let’s look at the four key areas where AI can make a significant impact for sales teams.
Area #1: Prospecting and Sales Leads
First, massive digital and social data on customers provides collective insights that can be used to identify prospects and strong leads. AI can also provide insights for upcoming customer meetings – and schedule them, too. Following up with cold leads can be discouraging and a waste of time for a sales rep, and this process can be customized and automated with AI.
Area #2: Customer Cultivation and Acquisition
Marketing has already seen the success of personalized messaging versus generic. Similarly, conversations between sales reps and prospects will improve if focused on areas that are most likely to be relevant to them.
Most sales conversations take place via email or phone. Natural Language Processing (NLP) can guide sales rep conversations based on customer information and honest signals. Over time, machine learning can assess, via feedback loops, what is working and what is not and can accordingly guide the rep further. Machines can also generate training plans based on the activities of other star sales reps.
Timely deal offers are key to the success of any deal, and an AI-guided sales rep will have all the information needed to close sales. Based on past sales data, custom pricing can be recommended to help win deals. Machine learning can provide guidance regarding discounts and commissions by analyzing the success of previous discounts that worked. All this information can then be used to generate proposals and contracts (with confidence rating) and systems can initially ask sales rep to review the proposal/contract – which can improve and be automated over time, based on feedback.
Machine learning can also recognize the signals of what a converted lead or opportunity looks like and flag it for the sales to focus on them and not spend time spent on deals that would likely never convert.
Area #3: Customer Retention
Depending on the industry, the cost of acquiring a customer can be 5-25 percent higher than retaining them – and increasing competition will further increase the cost. Identifying signals from customers before they churn – and taking proactive steps to retain them – will increase the lifetime value of customers.
Area #4: Sales Operations
Machine learning can help sales operations improve in the following ways.
- Sales training – Machine learning can guide managers with sales coaching, a key to building strong teams. At the same time, AI can generate a personalized training plan by analyzing all the actions taken by sales reps (such as written and phone communication follow-ups) and compare them with the processes followed by star performers. It can then provide guidance on corrective measures.
- Sales reporting – Sales managers can view team performance (such as deals missed or quota met) in real time and take prescriptive actions to keep reps on track.
- Sales forecasting – AI can forecast revenue at a macro level for sales managers by providing insights into sales trends, segmented by sales organizations, sales reps, etc. This can help optimize resource allocation to build healthy pipeline, analyze team performance, and be cost-effective.
Conclusion: Why Sales and AI Are Best Friends
AI can’t replace the value of human interaction when it comes to building relationships with customers, but it can make them smarter and more productive through guided selling and automating the operational job, allowing sales reps to focus on their primary job: delivering value to customers and building loyalty that leads to organic revenue growth.
Swati Sinha is a senior director of global solution marketing at SAP for Sales Cloud. A seasoned enterprise software professional with varied experience in product marketing, product management, and engineering, she has worked with organizations both large and small. She is a technologist at heart and empathetic by nature, which gives her the ability to understand customer needs and tell a story about how technology can solve their business problem. She has an MBA and a master’s degree in computer applications. In her free time she likes to connect with her community and support charities.