How artificial intelligence is sure to yield optimal sales goals and increase sales

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“How did they come up with such unrealistic sales goals?” “We’ll never achieve this sales goal.” When negative thoughts like these permeate through a sales team, they ultimately come true. By incorporating artificial intelligence, AI, into the sales goal-setting process, you’ll create goals to which your sales team will gravitate. 

With the traditional method of setting sales goals, leaders usually start with an annual revenue target and work backward, factoring in a few variables that will likely impact sales. Artificial intelligence can factor in more variables at scale than humanly possible, yielding more accurate goals. Not only can AI provide more accurate sales goals, but it can also provide direction on the actions the sales team should take to achieve those goals.  

How does AI help you set better sales goals? 

Machine learning (ML), a subset of AI, is trained to recognize patterns in data such as past sales, market conditions, customer behavior, sales representative characteristics, stock outages, and other variables that impact achieving sales goals. Throughout the selling process, it continues to learn, on its own, how to identify new patterns. This pattern recognition results in a model you can use to predict the optimal sales goal to achieve target revenues.  

AI can also yield rep-specific goals. You can put all your sales reps’ names into the system, and it will recommend a sales goal per rep based on their past and current sales performance and market conditions. Goals should be realistic and motivating. AI can help you achieve both. 

Below are three ways ML can help you achieve your sales goals:

1.   Revise Your Customer List at the Right Frequency

Over time environmental and industry changes can cause a shift in who your most valuable customers are. Machine learning systems revise predictions and recommendations about customers as real-time data reveals new patterns. Knowing the right frequency to adjust customers can be challenging. If done too frequently, sales reps often lose confidence in the selection process; but if done too late, there’s the potential of lost opportunities or wasted company spending. How much time and money has your team wasted calling on customers before realizing there would be no sale? When sales representatives constantly face a brick wall, motivation begins to dwindle. Despite knowing how critical it is to call on the right customers, many companies continue to target them blindly. With AI’s predictive capabilities, it can be your eyes into the future.

2.   Optimize Sales Representative Messaging

Some platforms use natural language processing (NLP) to analyze the messages the sales representatives deliver. NLP, a subset of AI, is the ability of computers to interpret and understand written and spoken language, including its intent. When analyzing sales calls, the NLP-based platform recognizes keywords and phrases spoken most frequently in the messages that result in a sale. Once it recognizes these relationships, it recommends words all sales reps should incorporate into their message for optimal sales results.   

3.   Capitalize on Customer Behavior 

 AI embedded systems use algorithms to classify or cluster customers into groups based on the types and timing of offers they responded to in the past. Some platforms reveal potential customer intent based on 1st Party, 3rd Party, known, and unknown data. The AI systems can recommend the best products, price, and time to communicate with your customers to help you achieve your sales goals.

AI is like a navigation system. You don’t have to follow its recommendations, but it usually gives you the best route to take so that you spend less time analyzing and more time driving revenue. 

For a complimentary consultation to determine if you can use artificial intelligence to improve your sales, contact Yolanda Royster at AI Business Partners. Click here to receive more articles like these from AI Business Partners delivered right to your inbox.