“Innovation distinguishes between a leader and a follower.”
Organizations are transforming their sales functions with artificial intelligence to stay ahead of the game. If you have not yet embraced the trend, you are missing a crucial competitive edge.
The emergence of vast amounts of data from multiple sources and platforms, generating new information every minute, has gifted companies with more consumer information than they’ve ever had before. Technology is getting smarter as it continues learning and optimizing recommendations. A study published in MIT Sloan Management Review reveals that “76% of early adopters are targeting higher sales growth with machine learning.”
AI and machine learning in sales: An explainer
Artificial intelligence is the broader concept of machines making decisions or performing process as a human would. Machine learning is an application of artificial intelligence that enables computer models to recognize shapes, designs, and patterns in existing data, allowing the machines to then learn for themselves how to take next action or make business predictions.
Each new piece of data received allows the machine to learn even more, update information, look for new patterns and continuously optimize recommendations. For example, every time Alexa doesn’t get the right command, or Netflix misses the movie recommendation, the model learns from this new data and alters its recognition process to adapt and respond better, or provide better suggestions the next time around.
Intelligent sales: From prospect to client, AI will be sales best friend
We are seeing a paradigm shift in sales from being reactive to proactive, and from instinct-driven to insight and data-driven. AI can guide the sales journey from identification to customer retention. Sales applications can pick up each and every signal, in the form of any action, by any customer, community, or partner, while machine learning can continuously improve actions, offers, and processes for your sales organization.
Organizations have large collections of data at their disposal regarding the purchasing and behavioral patterns of customers. Machine learning can identify these patterns to provide insights into customer behavior over time. It can generate models to correlate signals when a customer has made purchases and when they have not. These models can then recognize signals from future customers, identifying prospects as to where they might be in the sales cycle.
Machine learning can analyze data about leads that have already been converted to recognize the signals of what a converted lead may look like. Once the algorithm has been trained, lead scoring can be used to identify leads that will likely to be converted. In absence of categorized data, unsupervised learning allows the algorithm to identify patterns on its own. Lead scoring allows the sales team to make more sales and prevent wasting time on leads that would likely never convert.
Cultivating and Positioning
Thanks to machine learning, we are headed towards the dramatic transformation of consumer communication and customer experience. Multiple researches have suggested that “57% of the buying process is completed before a first interaction with sales.” AI sales bots can hone in on customer ‘intend’ signals and have proactive answers for initial queries about pricing, product features, or contract terms.
Machine learning will offer organizations the ability to compare historical sales efforts with prospects data (company size, stakeholders, solutions they want) and then connect the dots to better predict what solutions would be effective, who can influence the deal, what activities could increase the likelihood of closing the deal, how long will it take, and what to upsell or cross sell.
Opportunity scoring (model based on historic won/loss opportunities) will accelerate sales execution by aligning the sales team’s efforts on highly scored deals, improving win rates. By merging AI with virtual reality, sales can receive prospects virtually, tour a factory and view the manufacturing process, as well as give demonstrations and hold initial meetings with virtual sales reps. The outcomes will be more efficient and systematic because the discussion with prospects will be focused on areas that are most likely to be relevant to them.
AI can generate accurate revenue forecast predictions at a macro-level for sales managers by providing insights into sales trends segmented by sales organizations, sales reps, etc.. This can help optimize the 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 actions needed to improve sales.
Machines learning can guide managers with sales coaching, which is key to building a strong team. At the same time, AI can generate a training plan by analyzing the processes followed by star performers. AI can also help improve the productivity of sales reps by reducing time spent on mundane administrative tasks that can be automated, like sending personalized messages based on previous communications, social media responses, and CRM profile updates.
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, a model can recommend custom pricing to help sales reps sign the deal. It can provide guidance regarding discounts and commission by analyzing the success of various discounts previously used by the top reps and commission earned. In the near future, sophisticated AI applications will write quotes, and contracts could be generated and sent to the customers.
AI will make sales smarter
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.