Sales Technology: The Value of Artificial Intelligence and Machine Learning

Blog
Sep 23, 2021
3 min read
Discover how artificial intelligence and machine learning (AI/ML) sales technology can provide key insights to optimize your sales planning and performance.

​It’s hard to imagine daily life without technology. Whether it’s the computer at your desk, the cell phone in your hand, or the smart TV in your living, technology is everywhere. But it’s not limited to a physical device; it includes the array of intelligence capabilities it can perform in a matter of seconds.

Technology is constantly evolving, and artificial intelligence and machine learning (AI/ML) have grown in popularity and become a staple in everyday life—especially in the business world. 

When we search on Google, AI/ML intelligence helps autocomplete our search query and provides results based on our location and what others commonly search for. Industry titans like Netflix, YouTube, and Amazon all use AI/ML to recommend content or related products based on not only your behaviors but the behaviors of millions of other users.

The Use of AI/ML In Everyday Life

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From how we live to how to buy, artificial intelligence and machine learning play an increased role in decision making. In fact, 49 percent of consumers are willing to shop more frequently while 34 percent will spend more money, according to AI Trends.1 

However, It’s not just our home and personal lives that AI/ML impacts. 

AI/ML, when used correctly, can provide a huge competitive advantage for sales organizations. There are a lot of considerations that go into the sales plans that drive growth and performance in your organization, which is where artificial intelligence and machine learning become vital tools.

The Value of AI/ML with Sales Technology

Intelligent Revenue and performance monitoring tools have expanded in today's sales world. Before these advancements in technology, businesses relied heavily on incentive compensation to drive performance. But, as companies soon learned, it takes more than just optimized incentives to become a leading revenue organization.

This is where revenue operations (RevOps) comes in. RevOps is a collection of tools and process functions that automate and unite back- and front-office sales processes. It is implemented to improve sales execution and operational efficiency.

It helps you to align your sales team around a single source of truth and uses your data, along with AI/ML sales technology, to uncover strategic insights. You can then use those insights to inform decision-making and planning continuously throughout the entire fiscal year.

AI/MLcan be a game-changer when it comes to transforming a data-driven organization. But before you can take advantage of AI/ML sales technology, you need data to truly uncover the advantages of this technology. Without data, AI/ML is just a fancy algorithm. It won’t help you optimize planning and performance strategy. This technology needs data to learn from to provide insights.

Your data needs to be usable. That means you must compile your data from all available sources and undergo data cleanups to ensure your information is accurate and concise. Then you’re ready to apply AI/ML to your dataset to gain strategic insights.

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According to Gartner, “[Machine Learning] ML uses mathematical models to extract knowledge and patterns from data. Adoption of ML is increasing as organizations encounter exponential growth of data volumes and advancements in computing infrastructure.” However, only 14 percent of organizations surveyed leverage some form of AI in order to improve their processes.2

The problem may stem from not knowing where to get started with AI/ML sales technology. Below are a few ways companies can use artificial intelligence and machine learning to fuel sales performance and revenue growth.

How Xactly Applies Artificial Intelligence / Machine Learning (AI/ML) for Sales Organizations

Consider the following example: a top rep leaves your sales organization. What is the organizational impact in replacing him/her? On average, it costs between $115,000 - $150,000 to replace a sales rep (taking into account the acquisition costs, training, and lost territory sales). Not only that, but it takes a little over six months to hire someone and roughly a year for that new rep to be fully ramped.3

There are a number of ways AI/ML sales technology can help to not only plan in hiring but also be proactive in rep retention. 

  • Ramp: By analyzing historical data indicators, AI can provide recommendations on how to bring new members onto the team faster.
  • Annual Quota: This should be built around historical data, including prior year numbers, similar-sized companies, and the likelihood of attaining revenue based on current headcount. Does turnover impact this? AI can help provide a recommendation for this value.
  • Monthly Seasonality Setting: How much can you expect individual reps and an organization to attain? This can be driven by AI.
  • Sales Rep Attrition: Is someone at risk of leaving? Is that a top performer or a rep who isn’t performing but has no plans of leaving the organization? AI can help identify at-risk employees and give insight into how to best motivate them to stay or perform better.

All of these scenarios can be addressed by AI /ML alongside your automated sales processes. When you implement intelligent solutions, you gain a competitive advantage that identifies hidden opportunities, stimulates revenue growth, and improves sales planning. The smarter your sales organization is, the more successful your team will be.

To learn more about how to use an intelligent sales solution to gain data insights, reduce risks, and make strategic decisions in real-time, download the guide, “What is Sales Performance Management & How Can it Transform My Business?

Sources:

  1. AI Trends
  2. Gartner
  3. DePaul University 
  • Sales Performance Management
Author
Emily-Jahn
Emily Jahn
,
Content Marketing Manager

Emily Jahn is a Content Marketing Manager at Xactly. She earned a degree in advertising from The University of Colorado - Boulder and has experience in copywriting, social media, and digital marketing.