We all know that AI can help you improvise your buyer’s journey, and in today’s market, AI is no longer an option – it’s a necessity to remain competitive.
Early adopters of AI are using it as an assistant or augmented intelligence to complement – not replace – their sales team, as it helps them make more agile decisions.
An intelligent sales organization: Worth its weight in CX gold
There are many fantastic benefits of running an intelligent sales organization, some of which include:
More time selling: A recent article by McKinsey Global Institute states that 40% 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.
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.
Customer loyalty: By having better customer intelligence, sales reps can build long-lasting relationships with customers.
Lower costs: By automating routine task and intelligent forecasting, organizations can optimize resource allocation, lower costs, and shorten the sales cycle.
Analyze your current state
Sales organizations spend a majority of their time in routines and tasks which need manual intervention. Tasks of sales organizations can be broadly divided into the following categories:
Human interaction: These activities require EQ. Sales people must walk the buying journey with the customer, carefully understanding the needs, while building trust and loyalty.
Routine: These tasks are repetitive, and are impossible for humans to scale, like sending emails to cold leads, weekly sales reports, etc.
Time-consuming: These tasks require a lot of time and information to produce results, like quotation and contract generation, lead and opportunity prioritization, etc.
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.
Customer identification: Digital and social transformation has created massive data on customer behaviors, and these collective insights can be used to identify future prospects and strong leads. Automatic scripts or emails can be crafted using past interactions and other customer insights from various sources, like social media, etc. 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.
Customer cultivation and acquisition: Marketing has already seen the success of personalized messaging versus generic. Similarly, conversations of sales reps and prospects will be improved if focused on areas that are most likely to be relevant to them.
A majority of 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 offers are the key to success of any deal, and an AI guided sales rep will have all the information needed to close sales. From generating accurate pricing to discounts, processes that takes tens of thousands of hours can be automated with machine learning.
Based on past sales data, custom pricing can be recommended to help win deals. ML can provide guidance regarding discounts and commissions by analyzing the success of previous discounts that worked. All of this information can then be used to generate proposals/contracts, with confidence rating, systems can initially ask sales rep to review the proposal/contract, which can improve over time based on feedback.
Machine learning can recognize the signals of what a converted lead or opportunity looks like. Once the algorithm has been trained, lead/opportunity scoring can create a priority list of leads/opportunities to focus on. In absence of categorized data, unsupervised learning allows the algorithm to identify patterns on its own. Lead/opportunity scoring allows the sales team to make more sales by reducing time spent on deals that would likely never convert.
Customer retention: Depending on the industry, the cost of acquiring a customer can be between 5-25% 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 lifetime values of the customer.
Sales operations: Machine learning can help to improvise sales operations.
- Sales training– Machines 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, like written and phone communication follow-ups, etc., 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 in real-time, like deals missed, quota met, etc., 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. With prescriptive insight, managers can gain perspective into the underlying reasons for sales trends, as well as take actions needed to improve sales.
Source: The Future of Commerce