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Artificial Intelligence in Sales: 4 Steps to Use AI/ML Effectively

Artificial intelligence in sales seems like it's not worth the hype, but it's a big opportunity for companies. Learn why AI is crucial to competing in sales.

10 min read

Artificial intelligence (AI) in sales is the development and use of computer systems to assist companies in performing tasks that typically require a human intelligence to complete. Often AI is paired with machine learning (ML) which typically involves an algorithm that helps the computer system utilize data, continuously learn, and ultimately become smarter to better serve an organization's needs. AI/ML is a phrase that can spark excitement and interest in organizations, but often it seems like it's not worth all of the hype

In sales organizations, artificial intelligence is a relatively new idea and often carries the reputation that it's not useful unless you're a giant , but it has rapidly evolved over that last few years. As the sales environment increasingly grows more competitive, it's vital for organizations to adapt process and sales plans more effectively in order to stay ahead of the competition. This is where artificial intelligence in sales can give companies a big competitive advantage.

Without Data, AI/ML is Essentially Useless

Organizational leadership needs access to data to plan strategically. The same is true for artificial intelligence—AI/ML doesn't work effectively without access to data. AI/ML is designed to use data to consistently become more intelligent. To do this, it needs as much data as possible to identify patterns, trends, and other key findings.

Two great examples of this are Amazon's related item "customers also bought" and Netflix's tv series and movie suggestions. Believe it or not, both the purchase and viewing suggestions are gathered using AI/ML technology. By feeding in data of items that are typically purchased together, Amazon can suggest relevant items to improve their selling. Likewise, Netflix tracks viewers watching habits to suggest additional relevant content.

Without access to this data, Amazon and Netflix wouldn't be able to proactively offer suggestions for customers. Ultimately, it would be useless. The key to remember is you have a gold mine of information and all insight at your fingertips—it's your data. AI/ML merely allows you to utilize that data effectively for strategic, intelligent sales planning, execution, and analysis.

Gathering the Right Data

But when it comes to sales, it's important to remember that AI doesn't need to be a bottomless data pit. Rather, it needs to focus on the data that matters and will give you the insights you're looking for. AI for sales is what is known in scientific circles as “narrow AI,” meaning that it’s focused on a limited set of tasks and thus, built around a limited set of data associated with those tasks.

“General AI” would be a system like the HAL 9000 from 2001: a Space Odyssey, possessing an ability to build decisions based on an all-encompassing data set, much as a human might. That would require an enormous amount of data which itself would pose massive management challenges. Narrow AI simplifies the issue, drawing on data specifically associated with the tasks it’s in charge of.

For most sales organizations, AI/ML initially helps improve sales capacity, quota, territory, and compensation planning. It can later be used to help analyze performance, identify reps at risk for attrition, and improve sales forecasting accuracy.

Taking Advantage of Artificial Intelligence in Sales Organizations

Today's sales environment is growing at an unprecedented speed, and it's not going to slow down anytime soon. With goals growing more aggressive, companies must design the strongest sales plans they can. When AI combines with internal and third-party sales data, organizations can ensure they have the right amount of sales capacity resources, optimized sales territories, and incentives that are aligned with corporate goals.

For example, enterprises are using Xactly Insights, an advanced analytics offering based on more than 14 years of empirical incentive compensation data. In fact, organizations using Insights have seen more than 10% higher overall sales performance than non-Insights users. 

Insights uses AI to allow companies to visualize what best-in-class sales compensation looks like, benchmark where they stand against their peers, and gain predictive tips on everything from how to create world-class programs to how to increase sales rep retention

Other sets of data that need to be exposed to AI include the customer demographics of CRM, content usage data and training information stored within sales enablement, deal details and compensation and configuration information contained within CPQ, and pertinent data pulled from other sales-related applications.

Add in rich-third party data such as industry organization datasets, government economic indicators, and various inflationary measures for different geographies and a company (and its AI system) begins to have a rich, intelligence data set to learn, understand and make needed improvements from to improve rep engagement and revenue growth. This need for data places some important demands on any company:

1. End the Need for Manual Processes

The need for AI should spell the end of any manually executed sales management activities. Spreadsheets, pads and Post-it notes will no longer work, since the data they contain is impossible to expose to AI. They’ll have to be phased out.

2. Find the Best AI Software

Your sales operations, sales management and IT must determine the data needed to train and feed the AI solution. Sales ops and sales management must specify what applications generate that data, and IT will need to find software that replaces manual processes and integrates with AI.

3. Help AI Learn

Everyone involved in the sales process must keep an eye on the system. The suggestions AI makes today that help win deals may not work in the future, and if the data going back into the system are lacking, AI won’t be able to change as customers and markets change. Just as the nature, tactics and tricks of the sales profession have changed over the years, sales AI is also likely to change. It’s up to humans, who have gifts of innovation, empathy and intuition that can’t be programmed into AI, to make sure that their sales AI is delivering suggestions that continue to improve and adapt.

4. Develop an AI Strategy

Sales management, sales ops and IT must work together to develop an effective strategy to teach employees how to use AI properly. AI is a sales tool, not a replacement for sales talent. Salespeople must learn the best ways to use the insights it delivers in the context of a sales relationship. Buyers probably don’t want a fire hose-style stream of information, for example. If artificial intelligence in sales presents a salesperson with a wealth of content, the salesperson must know how to offer this to the customer in a way that makes sense for that particular deal.

Artificial intelligence in sales can become a crucial tool or it can become a gimmick that gets in the way as much as it helps out. The outcome will be determined by the data businesses collect, how they integrate it into AI, and how much attention is paid by sales ops, sales management and IT to keep AI on target. From there, the sky’s the limit.

Want to learn more about data-driven sales planning and the impact artificial intelligence in sales can have on performance? Download the white paper, "Optimizing Sales Planning with Data Intelligence."