Three Ways You Can Use ML And AI In Sales Process Optimization

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Co-founder and CEO at WINN.AI. Helping you close more deals by making your funnel faster, smarter and smoother.

Artificial intelligence (AI) and machine learning (ML) have been the tech revolution’s dynamic duo. From streamlining business operations to automating tedious tasks with optimal efficiency, saying that these breakthroughs are often crucial in the modern business world is an understatement.

According to recent research, 35% of companies are already using AI, and 42% are exploring its implementation in the future. When it comes to the myriad of industries and sectors that ML and AI have retooled, sales is at the forefront. From human-like chatbots to optimized CRM, progressive sales leaders are wise enough to scour for the most cutting-edge solutions to gain a competitive advantage in today’s cutthroat global economy.

In this article, I’ll dive into the top three ML and AI use cases in sales that have been making waves in the industry today.

1. Bridging The Gap Between Reps And Customers

Sales reps can only do so much. From juggling a plate full of admin tasks to closing deals, multitasking has always been that slippery slope that teams simply needed to navigate. Communicating with clients, in particular, has always been at the top of their mountain of tasks. From manually creating outreach messages to personalizing customer interactions, it seems that there are endless complex nuances that reps have to nail on a daily basis. What’s more, the likelihood of committing human errors grows as the number of tasks increases.

Salesforce analysts report that bridging the gap between reps and customers is ML and AI’s top use case, particularly when it comes to “understanding customer needs.” In fact, knowing the needs of a customer plays a crucial role in shaping every rep’s behaviors and activities during customer interactions, such as creating sales content, approaching prospects and delivering pitches.

AI technology can assist in customizing and automating customer interactions. Through the use of AI and ML, sales teams can set up pre-defined customization fields to send automated outreach messages. They can also improve the effectiveness of their outreach by conducting A/B tests and using AI-generated insights to gauge a prospect or lead’s interest level. Additionally, a rules engine can be utilized to modify pre-determined sequences or outreach campaigns based on specific criteria being met.

2. Automated Interaction Analysis

Automation is one of AI’s most popular buzzwords, and as advancements reach greater heights, this tech capability is now coupled with its ability to perform human-like deep analysis. Today, AI has the ability to analyze customer inquiries automatically, search through a database of information to find the relevant answer, provide automated summaries and display them on employees’ screens in real time while they’re still on calls.

Powered by conversational and generative AI, AI assistants can understand and capture prospects’ answers automatically and import the information to CRMs like Salesforce or HubSpot without disrupting workflows. These AI tools are designed to detect and interpret prospects’ questions and discreetly feed reps the information they need to keep the ball rolling. This helps reduce the manual work of sales teams, empowering them to focus on what they do best: sell.

Moreover, adaptive AI is one breakthrough that utilizes a sequence-based approach to analyze data, rather than simply collecting and processing it. This enables AI assistants to learn from new experiences while still working on previous predictions, all at a faster pace than humans due to their ability to receive large amounts of real-time feedback based on data.

3. Smarter Forecasting

ML and AI’s ability to reduce or remove guesswork through predictive analytics has been one of its most useful features. With the help of predictive analytics, businesses can examine their past and current sales to forecast outcomes, make data-driven decisions and, ultimately, help improve business performance. They can estimate outcomes by determining the factors that influence a particular result. This approach differs from diagnostic analytics, which only attempts to explain why something happened. Predictive modeling is frequently used in sales forecasting to aid in pipeline planning. This method employs statistical modeling, data mining techniques and ML to detect patterns in various datasets, highlighting potential opportunities and risks.

The growth of digital marketing has made it simpler for salespeople to access first-party and third-party customer data, allowing businesses to use predictive sales analysis for a variety of purposes, such as identifying quality leads, setting appropriate business goals, budgeting for the financial year, enhancing customer experiences and optimizing resources.

Keep in mind that sales teams are the backbone of an organization’s cash flow and revenue generation. To keep up with today’s hyper-competitive business landscape, it’s paramount for leaders to empower their sales departments with the most advanced tools and cutting-edge technologies to maximize their potential, and AI and ML should be considered as part of that toolkit.

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