Sales

Artificial Intelligence in Sales: Revolutionizing Sales for 2022

by
Craig Wisneski
,
March 19, 2022

What if we told you that artificial intelligence could help your sales teams close more deals and boost customer retention? Sounds too good to be true? It’s not—in fact, the majority of sales and marketing teams are already seeing these benefits.

Why the frenzy? Simply put, AI can take sales reps out of the mundane data entry and analysis tasks, and free them up to focus on more high-value activities like relationship building and strategy. Additionally, machine learning can help sales teams identify patterns and trends in customer data that would be otherwise difficult to spot.

This article explores how AI is currently being used in sales, and how you can implement it in your own organization to achieve better results.

How is AI used in sales?

At a high level, AI analyzes large data sets to identify patterns and correlations that humans would be unable to detect. The purpose of this report is to explore how AI is being used in sales organizations and the benefits this technology brings to the table.

Salespeople are constantly trying to identify new leads, qualify them, and close deals. In order to keep up with the competition, it is crucial for companies to adopt technologies that help their sales teams operate more efficiently. According to Semrush, 40% of marketing and sales departments prioritize AI technology and machine learning for their success, which is why the likes of Gartner recommend sales managers use artificial intelligence tools.

Let's dive into a few key use-cases of AI in sales enablement, sales forecasting, and more.

Optimize your conversions

By analyzing data about past customer behavior, sales leaders can create models that predict which prospects are most likely to convert. This information can then be used to prioritize leads and increase the chances of converting them into customers. In other words, intelligent lead scoring can qualify leads, most commonly in B2B sales, to speed up your sales cycle and ensure that your sales activities are focused on productive opportunities.

Sales teams are getting overwhelmed with the amount of data they need to analyze to identify leads that have the highest potential for conversion. Even with the help of predictive analytics, deducing which customers are most likely to convert is still a daunting task. However, artificial intelligence can be used to automate this process and make it more accurate.

This process is crucial for sales and marketing alignment as well, as marketing teams need to hand off high-quality leads to sales representatives in order to ensure that they are working with the best prospects. After all, research shows that well-aligned sales and marketing teams drive more than 200% revenue growth from marketing.

In addition to predictive analytics, AI can also be used to optimize website design and content. By analyzing data about how customers interact with websites, businesses can identify areas that need improvement. For example, if it is observed that many customers leave a website without making a purchase, the business could redesign the website to make it more user-friendly.

Improve your sales pipelines

The use of artificial intelligence can optimize conversions by predicting and optimizing all stages of the funnel, from lead ranking to post-purchase engagement. By using AI, businesses can improve their chances of converting leads into customers and retaining customers over the long term.

One way that AI can be used for conversion optimization is through the use of predictive analytics. Predictive analytics uses data mining and machine learning algorithms to analyze past data in order to identify patterns and trends that can be used to predict future outcomes. This information can then be used to optimize business processes and increase conversions.

For example, a business could use predictive analytics to determine the best time to reach out to a prospect based on their past behavior, typically stored in a CRM (Customer Relationship Management) tool. If a customer has visited a company's website but not made a purchase, the business could use predictive analytics to determine the best time to reach out to them with a special offer or coupon. 

To give another example, predictive analytics can be used to determine which of a company's products are most likely to be purchased by a particular customer. This information can then be used to create customized product offers that are more likely to result in a sale. 

AI can also be used to improve the overall customer experience. By using AI, businesses can personalize the customer experience based on their past behavior. This can include tailor-made product recommendations (i.e. upselling), suggestions for complementary products (i.e. cross-selling), and even personalized support. 

The use of AI can also help businesses to identify and fix any problems that may occur during the sales funnel. For instance, if there is a spike in the number of refunds being issued, AI can be used to determine the cause of the problem and fix it.

Overall, the use of AI can help businesses to optimize the sales funnel and increase their chances of making a sale.

Reduce churn

Churn is the silent SaaS killer, and sales teams around the world are feeling the pain. In this section, we explain how artificial intelligence can be used to reduce churn for sales teams.

Churn is the number of customers who discontinue use of a product or service. AI can be used to identify and target high-churn users with personalized offers or messages that encourage them to remain engaged with the product or service. McKinsey reports that “analytics-driven approach to base management can help telecom companies reduce churn by as much as 15%,” which means that using AI for churn reduction can be the difference between profitability and bankruptcy. 

More precisely, sales teams can simply connect a historical dataset of customers to Akkio (containing both those who have churned and those who have not), and select the churn column to build a predictive model. This model can then be deployed on new customers in real-time to identify those who are most likely to churn and head off the problem before it becomes too costly.

Suggest next actions

Sales reps can easily face decision fatigue when it comes to the massive volume of leads they need to follow up on. This is only compounded by the fact that much of the selling process today still relies on human judgment and interaction. Artificial intelligence can help reduce the burden of decision fatigue for sales reps and suggest the next best steps to take for each lead.

For example, AI can recommend the best time to call a prospect, based on past behavior and preferences, as well as when to send them an email or an offer, or it could recommend where to contact a customer, from social media platforms like LinkedIn to email or a phone call. Beyond lead management, AI can also be used to provide sales reps with real-time insights about their customers and prospects. This can help them identify opportunities and understand customer needs in order to personalize their interactions and close more deals.

Even specific actions, like target account selection and lead prioritization, can be automated using AI. This would allow sales reps to focus on pursuing the most promising leads, while leaving the majority of the follow-up work to machines. In this way, AI can help reduce decision fatigue and increase sales productivity.

Pricing optimization

Too high a price, and goods or services may go unsold; too low a price, and the company’s profits may be squeezed. Navigating this Goldilocks zone is essential for sales teams, but it can be difficult to determine the ideal price.Fortunately, artificial intelligence can help sales teams optimize pricing through predictive analytics and data-driven decision-making.

AI can do this by analyzing purchasing patterns, historical prices, and competitor behavior to identify the right price point for each product or service. Additionally, AI can help sales teams stay ahead of any changes in the market that could impact pricing decisions.

For instance, suppose you're selling a high-ticket software product. Using AI, you can price the product at a point that maximizes your profits while still allowing it to sell. If demand for the product begins to drop, AI can help you adjust your prices to reflect the new market conditions.

Similarly, if you're selling a lower-priced item, AI can help you find the sweet spot where you make enough money to remain profitable while still undercutting your competitors.

In either case, AI can help sales teams avoid pricing decisions that may have negative consequences down the road. By using AI, sales teams can pricing with confidence, knowing that they've made the optimal decision for their business.

Sales support message prioritization 

If you've ever handled a support ticket from a customer, you know that some inquiries are more time-consuming and complex than others. In some cases, it might make sense to divert support staff away from low-priority tickets in order to address more pressing issues. Sales AI tools, like chatbots, can handle these repetitive tickets with basic natural language understanding, freeing up sales professionals to focus on more important work. Bots are used by other departments as well, such as for automated credit approval and AI assistants that predict appointment cancellations.

Thanks to AI, sales teams can now use predictive analytics to identify and prioritize the most pressing messages. By analyzing past customer interactions and feedback, AI can help identify the most common or serious issues that need attention. This approach allows sales teams to focus on the issues that are likely to have the biggest impact on customer satisfaction and success. This not only saves time and improves customer satisfaction, but it also frees up support staff to handle more complex tasks.

Sales teams that use AI to prioritize messages can benefit from improved customer satisfaction and increased efficiency. By addressing the most pressing issues first, sales teams can provide better customer service and improve their bottom line.

How to apply AI to your sales process?

Historically, to implement AI in your sales process, you’d need a team of data engineers and data scientists. Data engineers are responsible for managing the flow of data between systems and preparing it for analysis. Data scientists analyze data to find patterns and insights that can be used to improve sales performance.

These teams would then clean and prepare the data. This includes removing noise and outliers, formatting the data, and creating labels. They’d then need to build and iteratively tune the models.

Further, in order to use AI, you’d need access to powerful computers and large servers. This can be in the form of a data center or on the cloud. When choosing a data center or cloud provider, you’d need to consider factors such as price, performance, and security.

The modern AI approach

Nowadays, you no longer need to build a team of data engineers and data scientists to implement AI in your sales process. With Akkio, you can leverage AI in clicks, and build and deploy models without any code. Akkio automates the entire modeling process, from data preparation to deployment.

To get started with Akkio, you first need to import your data into the platform. Once your data is loaded, Akkio will automatically detect and prepare the data for modeling.

Next, you simply select the column you'd like to predict, whether it's churn, conversion, deal size, time-to-close, or any other sales KPI you're interested in. Akkio will then automatically find the best models for the task at hand.

You can then choose to deploy the model in minutes. You can deploy in any setting, including through Akkio's direct integrations with tools like Snowflake, Salesforce, Hubspot, and Google Sheets. Our Zapier integration opens up thousands of additional applications that you can use with Akkio. Further, more technically-inclined teams can use our API to deploy models in any environment they choose.

So, if you're looking for an easy way to implement AI in your sales process, look no further than Akkio. With Akkio, you can get started in minutes and see results in short order.

Conclusion

More and more businesses are turning to artificial intelligence to power their sales efforts. AI-powered sales tools can automate routine tasks, provide insights that would otherwise be unavailable, and suggest strategies based on customer data.

Akkio makes it easy for businesses to use AI in their sales processes with no coding required. Through the use of Akkio, businesses can quickly and easily build custom machine learning models to help them better understand their customers and sales data. With Akkio's free trial, businesses can get started using AI in their sales processes today.

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